基于OFDM的多准则下认知无线电资源分配问题的研究
|
摘要
资源分配问题是确保通信系统正常运行且有效利用系统资源的关键技术之一。然而,在面对近些年出现的为解决频谱资源日益紧张与现实频谱利用率过低之间矛盾的认知无线电这一全新技术时,由于认知系统的动态多变特性,使传统原有的资源分配算法难以满足这一新技术的需要。如何在认知系统中合理与高效地分配系统资源已成为当前无线通信领域里研究的热点课题,同时该项技术也必将为无线通信技术的下一步发展提供新的机遇与挑战。
当前,针对认知无线电系统这一全新领域的资源分配算法主要包括有基于经典凸优化、博弈论、图着色、协作方式及智能优化等理论的相关算法。这些算法虽然在一定程度上满足了认知系统在某一时刻的优化目标,但从算法对认知系统动态特性的适应性上来看,却并非是最佳的系统描述与求解方法。为适应认知无线电这一新技术的相关特性,本论文采用了多种准则从不同角度对该问题进行讨论、分析和研究,并提出了相应的系统模型与解决方案。
论文的前两章阐述了本文的研究意义和背景,介绍了目前国内外对认知无线电资源分配问题的研究现状,并详细分析了现存算法的一些主要不足。简要介绍了认知无线电基本概念与OFDM的基本原理以及采用OFDM技术作为认知无线电系统实现的技术手段的诸多优点,给出认知系统下资源分配问题所具有的特点。
为适应认知系统网络环境多变的特性,同时兼顾系统中各用户间的公平性,本论文在第3章提出了基于公平度门限的认知无线电系统资源分配算法。由于传统通信系统中多要求各用户间具有严格的比例公平性,这在一方面降低了系统的整体容量,另一方面又因运算复杂而减少了有效传输时间。相对于传统通信系统,认知系统具有伺机通信的特点,即当主用户暂时未使用该频段时,认知系统可以暂时接入系统进行数据传输,而当主用户重新占用该频段时,其必须归还原有频段的使用权。这就容易造成认知系统资源分配策略计算还未完成,主用户就已经回到原来频段的现象。过于复杂的资源分配算法占用了认知用户宝贵的传输时间,使认知系统的性能大大下降。因此,对于认知系统来说,能够获得较多的传输机会比用户间精确的公平要重要得多。在权衡了用户间公平性与算法复杂度之间的关系后,本文提出了公平度门限的概念,算法在牺牲了部分公平性的同时,获得了更高的系统容量,且在系统功率分配阶段采用了粒子群优化方法,大大加快了算法的收敛速度,使之更加适应认知系统的动态特点。仿真结果表明,本章所提算法在保证用户间一定公平性的前提下,有效地提高了系统的整体性能,且算法具有较快的收敛速度。
论文第4章根据认知系统受主用户活动强度影响显著的特点,提出了一种基于主用户活动行为,同时综合考虑主用户中断概率约束的资源分配算法。在认知系统中,认知用户必须实时监测主用户对频谱的占用情况,从而做出相应的资源分配策略。然而,当主用户活动较为频繁时,认知用户所做出的反应往往跟不上主用户的这种快速变化,因此需要从一种新的角度来刻画主用户的活动情况,从而做出更加适合认知系统特点的资源分配算法。在本章所提算法中,将主用户在各频段上的活动情况通过相应的活动概率来加以定义,然后从统计学的角度给出认知用户在各频段上由于主用户活动所造成的速率损失,从而得到该频段认知用户实际能够获得的速率。同时,为确保主用户的传输质量,论文中引入了主用户数据传输时的中断概率作为该优化问题的限制条件进行求解。该算法在综合考虑了主用户的活动强度对认知系统的影响的同时,又通过引入信息中断概率来保证主用户的QoS要求。通过计算机仿真实验结果,可以看出该算法无论从系统容量和对网络的适应性上都较之前文献中提出的算法有了较大的提升。
在网络环境快速多变的认知无线电系统中,稳定与可靠的传输显得更为重要。论文在第5章中通过引入经济学中的组合投资理论,将认知环境下的资源分配问题类比为证券市场中的最佳投资选择问题,从而获得了在方差最小化意义下稳定的数据传输方案,同时以系统用户间干扰作为整个优化问题的约束条件,以限制认知系统对主用户的有害干扰。该所提出的算法中,将待分配的功率看作证券市场中的“总资产”,各子载波上的信道容量看作功率分配的“收益”。因此该问题的优化目标即在给定系统期望速率的前提下,使系统偏离期望速率的波动达到最小。这就与证券投资市场中的“风险最小化”问题从其数学本质上来看是同一个问题。因此,我们利用经济学中的组合投资理论,将其应用到认知系统的资源分配问题中来,同时引入用户间干扰门限作为约束条件以保证主用户的通信质量。仿真结果表明,该算法在保证系统稳定传输的前提下,较文献中前人提出的算法在主用户保护上更加有效,更符合认知系统自身的特点。
为使认知系统在整个传输过程中获得均值意义下的最大传输速率,论文在第6章中利用动态规划的思想对认知系统的资源分配问题进行了分析与研究,并通过严格的数学推导得出最终的动态规划框架下的迭代表达式。从认知系统以时隙作为单元进行数据通信的角度来看,它刚好符合动态规划理论中在各阶段做出决策的特点,且认知系统动态变化的特点也可通过动态规划中状态间的转移关系较好的进行刻画,故可以采用动态规划的方法对认知系统的资源分配问题进行求解。该算法首先通过离散时间马尔科夫链模型对主用户在各子载波上的占用情况进行描述,然后给出各种网络状态之间的相互转移关系,同时为保证主用户的传输质量,引入主用户速率损失模型作为约束,最后给出了具体的迭代数学表达式用以算法的最终实现。仿真结果表明,本章所提算法在使认知系统整个传输过程中的平均速率达到最大化的同时,有效地保证了主用户的传输速率要求,为认知环境下的资源分配问题提供了一种新的方便、快速与实用化的解决方案。
最后,第7章对论文进行了总结,并展望了下一步的研究工作。
Resource allocation is one of the key technologies to guarantee communication system to work normally as well as utilize the system resource efficiently. However, an entirely new technique, i.e. cognitive radio which is supposed to resolve the contradiction between the scarcity of wireless frequency spectrum and low utilization of the existed spectrum, has hardly satisfied the needs of this technique due to its fast dynamic feature with former resource allocation algorithms. How to reasonably and efficiently allocate resource in the cognitive radio system is becoming a new hot research spot in the research area of wireless communication. At the same time, this technique also can surely provide many opportunities and challenges for the future development of communication technologies.
Recently, the existing cognitive radio resource allocation algorithms are mainly based on classical convex optimization theory, game theory, graph coloring theory, cooperative manner, as well as intelligent optimization theory etc. Although these algorithms can partially satisfy some optimal objectives in one timeslot, they are not the most suitable methods to describe system physical feature to this new problem from the point of view of cognitive radio dynamic feature. In order to fit the new characters of this technology, this paper tries to use multi-norms to discuss, analyze and study this problem from different profiles, and gives a corresponding system model and some solutions.
The research significance and background, the current status on cognitive radio resource allocation research both at home and aboard are presented in the first two chapters. And the analysis of the main weakness of the existing algorithms and basic concept of cognitive radio under OFDM principle are discussed briefly. Then the merits of implementing cognitive radio system by OFDM technology are expounded. At last, the features of cognitive radio resource allocation problem are given.
In order to adapt the fast changing feature of network environment and consider the fairness among users in cognitive radio system, chapter 3 proposes a fairness threshold based cognitive radio resource allocation algorithm. On the one hand, traditional communication system often demands a strict proportional fairness that may cause the whole system capacity drop. And on the other hand, the complexity of traditional algorithms may also lead to the reduction of system available transmission time. Comparing to the traditional wireless communications, cognitive radio has an opportunistic transmission feature. This makes that the cognitive user can use the licensed spectrum bands during the time when primary user does not use it. However cognitive user must release the bands immediately when primary user reoccupies them. It may easily bring the problem that the resource allocation algorithm has not been calculated and the primary users have already come back to the bands. Moreover, more complex algorithms take more precious transmission time, so that the system performance heavily degrades. Therefore, it is clear that much more transmission opportunities are more important than the extreme strict fairness among different users in cognitive radio system. By considering the trade-off between system fairness and algorithm complexity, we propose a fairness threshold concept that obtains more capacity by sacrificing partial fairness in the system. At power allocation stage, we introduce the particle optimization method to resolve this problem. It can largely promote algorithm convergence speed. Simulation results demonstrate that the proposed algorithm efficiently improves whole system performance under a certain fairness condition, meanwhile obtain a fast convergence speed.
Due to cognitive radio system is often significantly affected by primary user’s activity, we propose a primary user activity based and primary user transmission outage probability constrained resource allocation algorithm in chapter 4. In a cognitive radio networks, secondary user must watch the spectrum situation timely in order to find out the optimal resource allocation strategy. However, when primary user’s activity is too frequent, secondary user often can not catch up this rapid change. In consideration of this problem, in order to obtain a more appropriate resource allocation algorithm for cognitive radio networks, a new description model for primary user’s activity is needed. Here, we construct a model for primary user’s activity on each frequency spectrum by corresponding activity probability. To obtain the real data rate of secondary user, the data rate loss of each spectrum from a statistics view is calculated. Meanwhile, an outage probability concept as one constraint of this optimization problem is introduced in order to guarantee primary user’s transmission quality. In the computer simulation results, it can be seen that the proposed algorithm efficiently improves the performance both in system capacity and network adaptation compare to the former method.
In a fast changing cognitive network environment, it is considered that the stable and reliable transmission is more important. In chapter 5, we introduce the portfolio selection theory in economy to formulize cognitive radio resource allocation problem into a best investment selection problem. And the stable transmission schemes in a minimum-variance sense are given. At the same time, to limit harm interference from the cognitive user, mutual interference concept is introduced as a constraint of this problem. In this algorithm we look the system total power and channel capacity on each subcarrier as total security assets and return of each security respectively. Therefore, our optimal objective is to minimize the variance of system data rate under a given expected transmission rate. From the mathematics, it is the same problem with“risk variance minimization problem”in security market. Hence, we can use portfolio selection theory to deal with resource allocation problem in a cognitive radio system. Meanwhile, communication quality for primary user by mutual interference threshold is efficiently protected. Simulation results show that the propose algorithm can provide more efficient protection to primary user compare to the former scheme at the same stable transmission rate. This improvement makes the proposed algorithm more adaptable for cognitive radio system.
In order to maximize a long-term average rate during the whole transmission process in cognitive radio system, a dynamic programming method to analyze the resource allocation problem is proposed, and a mathematic iterative expression is derived in chapter 6. Since in the cognitive radio transmission the data is transmitted in each time slot, it is just correspond with the character of dynamic programming that the decisions are made in each stage. At the same time, the dynamic changing feature of cognitive radio can also be well described by transitional relationship among different states in a dynamic programming framework. Therefore, it is a valuable attempt to deeply discuss the cognitive radio resource allocation problem with dynamic programming method. This method can model primary user’s occupation on each subcarrier by discrete-time Markov chain. And it gives the transition probabilities among different system states. Meanwhile, a primary rate loss model to guarantee primary user’s transmission quality is introduced. Finally, a specific dynamic programming iterative expression is given. In the given computer simulations, it shows that the proposed algorithm not only maximizes the long-term average rate in the whole transmission process but also efficiently guarantee primary user’s request data rate. In addition, it also provides a convenient, fast and practical method for cognitive radio resource allocation.
At last, chapter 7 concludes the whole paper and forecasts the next step research work.
当前,针对认知无线电系统这一全新领域的资源分配算法主要包括有基于经典凸优化、博弈论、图着色、协作方式及智能优化等理论的相关算法。这些算法虽然在一定程度上满足了认知系统在某一时刻的优化目标,但从算法对认知系统动态特性的适应性上来看,却并非是最佳的系统描述与求解方法。为适应认知无线电这一新技术的相关特性,本论文采用了多种准则从不同角度对该问题进行讨论、分析和研究,并提出了相应的系统模型与解决方案。
论文的前两章阐述了本文的研究意义和背景,介绍了目前国内外对认知无线电资源分配问题的研究现状,并详细分析了现存算法的一些主要不足。简要介绍了认知无线电基本概念与OFDM的基本原理以及采用OFDM技术作为认知无线电系统实现的技术手段的诸多优点,给出认知系统下资源分配问题所具有的特点。
为适应认知系统网络环境多变的特性,同时兼顾系统中各用户间的公平性,本论文在第3章提出了基于公平度门限的认知无线电系统资源分配算法。由于传统通信系统中多要求各用户间具有严格的比例公平性,这在一方面降低了系统的整体容量,另一方面又因运算复杂而减少了有效传输时间。相对于传统通信系统,认知系统具有伺机通信的特点,即当主用户暂时未使用该频段时,认知系统可以暂时接入系统进行数据传输,而当主用户重新占用该频段时,其必须归还原有频段的使用权。这就容易造成认知系统资源分配策略计算还未完成,主用户就已经回到原来频段的现象。过于复杂的资源分配算法占用了认知用户宝贵的传输时间,使认知系统的性能大大下降。因此,对于认知系统来说,能够获得较多的传输机会比用户间精确的公平要重要得多。在权衡了用户间公平性与算法复杂度之间的关系后,本文提出了公平度门限的概念,算法在牺牲了部分公平性的同时,获得了更高的系统容量,且在系统功率分配阶段采用了粒子群优化方法,大大加快了算法的收敛速度,使之更加适应认知系统的动态特点。仿真结果表明,本章所提算法在保证用户间一定公平性的前提下,有效地提高了系统的整体性能,且算法具有较快的收敛速度。
论文第4章根据认知系统受主用户活动强度影响显著的特点,提出了一种基于主用户活动行为,同时综合考虑主用户中断概率约束的资源分配算法。在认知系统中,认知用户必须实时监测主用户对频谱的占用情况,从而做出相应的资源分配策略。然而,当主用户活动较为频繁时,认知用户所做出的反应往往跟不上主用户的这种快速变化,因此需要从一种新的角度来刻画主用户的活动情况,从而做出更加适合认知系统特点的资源分配算法。在本章所提算法中,将主用户在各频段上的活动情况通过相应的活动概率来加以定义,然后从统计学的角度给出认知用户在各频段上由于主用户活动所造成的速率损失,从而得到该频段认知用户实际能够获得的速率。同时,为确保主用户的传输质量,论文中引入了主用户数据传输时的中断概率作为该优化问题的限制条件进行求解。该算法在综合考虑了主用户的活动强度对认知系统的影响的同时,又通过引入信息中断概率来保证主用户的QoS要求。通过计算机仿真实验结果,可以看出该算法无论从系统容量和对网络的适应性上都较之前文献中提出的算法有了较大的提升。
在网络环境快速多变的认知无线电系统中,稳定与可靠的传输显得更为重要。论文在第5章中通过引入经济学中的组合投资理论,将认知环境下的资源分配问题类比为证券市场中的最佳投资选择问题,从而获得了在方差最小化意义下稳定的数据传输方案,同时以系统用户间干扰作为整个优化问题的约束条件,以限制认知系统对主用户的有害干扰。该所提出的算法中,将待分配的功率看作证券市场中的“总资产”,各子载波上的信道容量看作功率分配的“收益”。因此该问题的优化目标即在给定系统期望速率的前提下,使系统偏离期望速率的波动达到最小。这就与证券投资市场中的“风险最小化”问题从其数学本质上来看是同一个问题。因此,我们利用经济学中的组合投资理论,将其应用到认知系统的资源分配问题中来,同时引入用户间干扰门限作为约束条件以保证主用户的通信质量。仿真结果表明,该算法在保证系统稳定传输的前提下,较文献中前人提出的算法在主用户保护上更加有效,更符合认知系统自身的特点。
为使认知系统在整个传输过程中获得均值意义下的最大传输速率,论文在第6章中利用动态规划的思想对认知系统的资源分配问题进行了分析与研究,并通过严格的数学推导得出最终的动态规划框架下的迭代表达式。从认知系统以时隙作为单元进行数据通信的角度来看,它刚好符合动态规划理论中在各阶段做出决策的特点,且认知系统动态变化的特点也可通过动态规划中状态间的转移关系较好的进行刻画,故可以采用动态规划的方法对认知系统的资源分配问题进行求解。该算法首先通过离散时间马尔科夫链模型对主用户在各子载波上的占用情况进行描述,然后给出各种网络状态之间的相互转移关系,同时为保证主用户的传输质量,引入主用户速率损失模型作为约束,最后给出了具体的迭代数学表达式用以算法的最终实现。仿真结果表明,本章所提算法在使认知系统整个传输过程中的平均速率达到最大化的同时,有效地保证了主用户的传输速率要求,为认知环境下的资源分配问题提供了一种新的方便、快速与实用化的解决方案。
最后,第7章对论文进行了总结,并展望了下一步的研究工作。
Resource allocation is one of the key technologies to guarantee communication system to work normally as well as utilize the system resource efficiently. However, an entirely new technique, i.e. cognitive radio which is supposed to resolve the contradiction between the scarcity of wireless frequency spectrum and low utilization of the existed spectrum, has hardly satisfied the needs of this technique due to its fast dynamic feature with former resource allocation algorithms. How to reasonably and efficiently allocate resource in the cognitive radio system is becoming a new hot research spot in the research area of wireless communication. At the same time, this technique also can surely provide many opportunities and challenges for the future development of communication technologies.
Recently, the existing cognitive radio resource allocation algorithms are mainly based on classical convex optimization theory, game theory, graph coloring theory, cooperative manner, as well as intelligent optimization theory etc. Although these algorithms can partially satisfy some optimal objectives in one timeslot, they are not the most suitable methods to describe system physical feature to this new problem from the point of view of cognitive radio dynamic feature. In order to fit the new characters of this technology, this paper tries to use multi-norms to discuss, analyze and study this problem from different profiles, and gives a corresponding system model and some solutions.
The research significance and background, the current status on cognitive radio resource allocation research both at home and aboard are presented in the first two chapters. And the analysis of the main weakness of the existing algorithms and basic concept of cognitive radio under OFDM principle are discussed briefly. Then the merits of implementing cognitive radio system by OFDM technology are expounded. At last, the features of cognitive radio resource allocation problem are given.
In order to adapt the fast changing feature of network environment and consider the fairness among users in cognitive radio system, chapter 3 proposes a fairness threshold based cognitive radio resource allocation algorithm. On the one hand, traditional communication system often demands a strict proportional fairness that may cause the whole system capacity drop. And on the other hand, the complexity of traditional algorithms may also lead to the reduction of system available transmission time. Comparing to the traditional wireless communications, cognitive radio has an opportunistic transmission feature. This makes that the cognitive user can use the licensed spectrum bands during the time when primary user does not use it. However cognitive user must release the bands immediately when primary user reoccupies them. It may easily bring the problem that the resource allocation algorithm has not been calculated and the primary users have already come back to the bands. Moreover, more complex algorithms take more precious transmission time, so that the system performance heavily degrades. Therefore, it is clear that much more transmission opportunities are more important than the extreme strict fairness among different users in cognitive radio system. By considering the trade-off between system fairness and algorithm complexity, we propose a fairness threshold concept that obtains more capacity by sacrificing partial fairness in the system. At power allocation stage, we introduce the particle optimization method to resolve this problem. It can largely promote algorithm convergence speed. Simulation results demonstrate that the proposed algorithm efficiently improves whole system performance under a certain fairness condition, meanwhile obtain a fast convergence speed.
Due to cognitive radio system is often significantly affected by primary user’s activity, we propose a primary user activity based and primary user transmission outage probability constrained resource allocation algorithm in chapter 4. In a cognitive radio networks, secondary user must watch the spectrum situation timely in order to find out the optimal resource allocation strategy. However, when primary user’s activity is too frequent, secondary user often can not catch up this rapid change. In consideration of this problem, in order to obtain a more appropriate resource allocation algorithm for cognitive radio networks, a new description model for primary user’s activity is needed. Here, we construct a model for primary user’s activity on each frequency spectrum by corresponding activity probability. To obtain the real data rate of secondary user, the data rate loss of each spectrum from a statistics view is calculated. Meanwhile, an outage probability concept as one constraint of this optimization problem is introduced in order to guarantee primary user’s transmission quality. In the computer simulation results, it can be seen that the proposed algorithm efficiently improves the performance both in system capacity and network adaptation compare to the former method.
In a fast changing cognitive network environment, it is considered that the stable and reliable transmission is more important. In chapter 5, we introduce the portfolio selection theory in economy to formulize cognitive radio resource allocation problem into a best investment selection problem. And the stable transmission schemes in a minimum-variance sense are given. At the same time, to limit harm interference from the cognitive user, mutual interference concept is introduced as a constraint of this problem. In this algorithm we look the system total power and channel capacity on each subcarrier as total security assets and return of each security respectively. Therefore, our optimal objective is to minimize the variance of system data rate under a given expected transmission rate. From the mathematics, it is the same problem with“risk variance minimization problem”in security market. Hence, we can use portfolio selection theory to deal with resource allocation problem in a cognitive radio system. Meanwhile, communication quality for primary user by mutual interference threshold is efficiently protected. Simulation results show that the propose algorithm can provide more efficient protection to primary user compare to the former scheme at the same stable transmission rate. This improvement makes the proposed algorithm more adaptable for cognitive radio system.
In order to maximize a long-term average rate during the whole transmission process in cognitive radio system, a dynamic programming method to analyze the resource allocation problem is proposed, and a mathematic iterative expression is derived in chapter 6. Since in the cognitive radio transmission the data is transmitted in each time slot, it is just correspond with the character of dynamic programming that the decisions are made in each stage. At the same time, the dynamic changing feature of cognitive radio can also be well described by transitional relationship among different states in a dynamic programming framework. Therefore, it is a valuable attempt to deeply discuss the cognitive radio resource allocation problem with dynamic programming method. This method can model primary user’s occupation on each subcarrier by discrete-time Markov chain. And it gives the transition probabilities among different system states. Meanwhile, a primary rate loss model to guarantee primary user’s transmission quality is introduced. Finally, a specific dynamic programming iterative expression is given. In the given computer simulations, it shows that the proposed algorithm not only maximizes the long-term average rate in the whole transmission process but also efficiently guarantee primary user’s request data rate. In addition, it also provides a convenient, fast and practical method for cognitive radio resource allocation.
At last, chapter 7 concludes the whole paper and forecasts the next step research work.
引文
[1] MARCUS M, BURTLE J, FRANCA B, et al.Federal Communications Commission Spectrum Policy Task Force [OL]. Report of the Unlicensed Devices and Experimental Licenses Working Group, No. 02-135, November 2002. http://transition.fcc.gov/sptf/files/SEWGFinalReport_1.pdf.
[2] MITOLA J. Cognitive radio for flexible mobile multimedia communications [C]. In Proceedings of Mobile Multimedia Communications, San Diego, CA, USA, November 15-17, 1999: 3-10.
[3] BOYD S and VANDENBERGHE L. Convex Optimizaiton [M]. New York, Cambridge University Press, 2004.
[4] CHENG P, ZHANG Z, CHEN H H, et al. Optimal distributed joint frequency, rate and power allocation in cognitive OFDMA systems [J]. IET Communication, 2008, 2(6): 815-826.
[5] YANG B, FENG G, SHEN Y, et al. Channel-aware access for cognitive radio networks [J]. IEEE Transactions on Vehicle Technology, 2009, 58(7): 3726-3737.
[6] CHO H, ANDREWS J G. Upper bound on the capacity of cognitive radio without cooperation [J]. IEEE Transactions on Wireless Communication, 2009 8(9): 4380-4385.
[7] WANG W, PENG T, WANG W B. Optimal power control under interference temperature constraints in cognitive radio network [C]. 2007 IEEE Wireless Communications and Networking Conference (WCNC 2007): Mar 2007, Kowloon, Hong Kong. IEEE, 2007: 116-120.
[8] LE L B, HOSSAIN E. Resource allocation for spectrum underlay in cognitive radio networks [J]. IEEE Transactions on Wireless Communications, 2008, 7(12): 5306-5315.
[9] LI H Y, GAI Y B, HE Z Q, et al. Optimal power control game algorithm for cognitive radio networks with multiple interference temperature limits [C]. 2008 IEEE Vehicular Technology Conference(VTC 2008-Spring): May 11-14, 2008, Singapore. IEEE, 2008: 1554-1558.
[10] ZHANG R. On peak versus average interference power constraints for protecting primary users in cognitive radio networks [J]. IEEE Transactions on Wireless Communications, 2009, 8(4): 2112-2120.
[11] WANG W, WANG W B, LU Q X, et al. Geometry-based optimal power control of fading multiple access channels for maximum sum-rate in cognitive radio networks [J]. IEEE Transactions on Wireless Communication, 2010, 9(6): 1843-1848.
[12] CUMANAN K, KRISHNA R, XIONG Z, et al. Multiuser spatial multiplexing techniques with constraints on interference temperature for cognitive radio networks [J]. IET Signal Processing, 2010, 4(6): 666-672.
[13] ZHANG R, LIANG Y C, CUI S. Dynamic resource allocation in cognitive radio networks [J]. IEEE Signal Processing Magazine, 2010, 27(3): 102-114.
[14] KIM D I, LE L B, HOSSAIN E. Joint rate and power allocation for cognitive radios in dynamic spectrum access environment [J]. IEEE Transactions on Wireless Communication, 2008, 7(12): 5517-5527.
[15] MUSAVIAN L, A?SSA S. Outage-constrained capacity of spectrum-sharing channels in fading environments [J]. IET Communications, 2008, 2(6): 724-732.
[16] HAN Y, PANDHARIPANDE A, TING S H. Cooperative decode-and-forward relaying for secondary spectrum access [J]. IEEE Transactions on Wireless Communication, 2009, 8(10): 4945-4950.
[17] PRASAD N, WANG X. Outage minimization and rate allocation for the multiuser gaussianinterference channels with successive group decoding [J]. IEEE Transactions on Information Theory, 2009, 55(12): 5540-5557.
[18] ZOU Y, YAO Y D, ZHENG B. Outage probability analysis of cognitive transmissions: Impact of spectrum sensing overhead[J]. IEEE Transactions on Wireless Communication, 2010, 9(8): 2676-2688.
[19] DING H, GE J, DA COSTA D B, et al. Energy-efficient and low-complexity schemes for uplink cognitive cellular networks [J]. IEEE Communication Letters, 2010, 14(12): 1101-1103.
[20] HUANG K, ZHANG R. Cooperative feedback for multiantenna cognitive radio networks [J] IEEE Transactions on Signal Processing, 2011, 59(2): 747-758.
[21] KANG X, ZHANG R, LIANG Y C, et al. Optimal power allocation strategies for fading cognitive radio channels with primary user outage constraint [J]. IEEE Journal on Selected Areas in Communications, 2011, 29(2): 374-383.
[22] ZHANG Y, LEUNG C. Resource allocation in an OFDM-based cognitive radio system [J]. IEEE Transactions on Wireless Communications, 2010, 9(10): 1536-1276.
[23] MITRAN P, LE L B, ROSENBERG C. Queue-aware resource allocation for downlink OFDMA cognitive radio networks [J]. IEEE Transactions on Wireless Communication, 2010, 9(10): 1536-1276.
[24] LIM H, SEOL D, IM G. Resource allocation for mitigating the effect of sensing errors in cognitive radio networks [J]. IEEE Communications Letters, 2010, 14(12): 1119-1121.
[25] CUMANAN K, KRISHNA R, MUSAVIAN L, et al. Joint beamforming and user maximization techniques for cognitive radio networks based on branch and bound method [J]. IEEE Transactions on Wireless Communications, 2010, 9(10): 3082-3092.
[26] QU X, LI M, ZHANG J F, et al. Downlink joint subchannel and power allocation in cognitive OFDM systems with QoS guarantee for primary users [C]. 2010 2nd International Conference on Future Computer and Communication (ICFCC): May 21-24, 2010, Wuhan, CHN. IEEE, 2010: V3 706-711.
[27] CANBERK B, AKYILDIZ I F, OKTUG S. Primary user activity modeling using first-difference filter clustering and correlation in cognitive radio networks [J]. IEEE/ACM Transactions on Networking, 2011, 19(1): 170-183.
[28] CHAI C C, CHEW Y H. Power control for cognitive radios in Nakagami fading channels with outage probability requirement [C]. 2010 IEEE Global Telecommunications Conference (GLOBECOM 2010): Dec 6-10, 2010, Miami, FL, USA. IEEE, 2010: 1-5.
[29] SUN D W, ZHENG B Y. A novel sub-carrier and power joint allocation algorithm for multi-user cognitive OFDM [C]. 10th International Conference on Signal Processing (ICSP 2010), Beijing, CHN, October 2010: 1458-1462.
[30] MWANGOKA J W, BEN LETAIEF K, CAO Z G. Robust end-to-end QoS maintenance in non contiguous OFDM based cognitive radios [C]. IEEE International Conference on Communications, Beijing, China, May 2008: 2905-2909.
[31] [8] WEISS T, HILLENBRAND J, KROHN A, et al. Mutual interference in OFDM-based spectrum pooling systems [C]. 59th Vehicular Technology Conference. Milan, Italy, May 2004: 1873-1877.
[32] ZHOU X, LI G Y, LI D, et al. Probabilistic resource allocation for opportunistic spectrum access [J]. IEEE Transactions on Wireless Communications, 2010, 9(9): 2870-2879.
[33] NEEL J, BUEHRER R M, REED J H, et al. Game theoretic analysis of a network of cognitive radios [C]. Midwest Symposium on Ciruits and Systems (MWSCAS 2002), Oklahoma, OK,August. 2002: 409-412(Vol. 3).
[34] NEEL J, REED J H, GILLES R P. The role of game theory in the analysis of software radio networks [C]. SDR’02 Technical Conference & Thirty-first SDR Meeting, California, CA, November 2002.
[35] NIE N, COMANICIU C. Adaptive channel allocation spectrum etiquette for cognitive radio networks [J]. Mobile Networks and Applications, 2006, 11(6): 779-797.
[36] THOMAS R W, KOMALI R S, Mackenzie A B. Joint power and channel minimization in topology control: A cognitive network approach [C]. IEEE International Conference on Communications (ICC’07), Glasgow, UK, June 2007: 6538-6543.
[37] GIUPPONI L, IBARS C. Distributed cooperation in cognitive radio networks: Overlay versus underlay paradigm [C]. IEEE 69th Vehicular Technology Conference (VTC’09-Spring), Barcelona, ESP, 26-29 April 2009: 1-6.
[38] ZHANG J, ZHANG Q. Stackelberg game for utility-based cooperative cognitive radio networks [C]. Proceedings of the tenth ACM international symposium on Mobile ad hoc networking and computing (MobiHoc’09), New York, NY, May 2009: 23-31.
[39] HALLDóRSSON M M, HALPERN J Y, LI L E, et al. On spectrum sharing games [J]. Distributed Computing, 2010, 22(4): 235-248.
[40] WANG F, KRUNZ M, CUI S. Price-based spectrum management in cognitive radio networks [J]. IEEE Journal of Selected Topics in Signal Processing, 2008, 2(1): 74-87.
[41] WU Y, WANG B, LIU K R, et al. Repeated open spectrum sharing game with cheat-proof strategies [J]. IEEE Transactions on Wireless Communications, 2009, 8(4): 1922-1933.
[42] SCHAAR M V D, FU F. Spectrum access games and strategic learning in cognitive radio networks for delay-critical applications [J]. Proceedings of the IEEE, 2009, 97(4): 720-740.
[43] ATTAR A, NAKHAI M R, AGHVAMI A H. Cognitive radio game for secondary spectrum access problem [J]. IEEE Transactions on Wireless Communications, 2009, 8(4): 2121-2131.
[44] SAAD W, HAN Z, DEBBAH M, et al. Coalitional games for distributed collaborative spectrum sensing in cognitive radio networks [C]. 2009 Proceedings IEEE INFOCOM, Rio de Janeiro, BR, April 2009: 2114-2122.
[45] HUANG J W, KRISHNAMURTHY V. Transmission control in cognitive radio systems with latency constraints as a switching control dynamic game [C]. 47th IEEE Conference on Decision and Control (CDC’08), Cancun, MEX, Dec. 2008: 3823-3828.
[46] FU F, SCHAAR M V D. Learning to compete for resources in wireless stochastic games [J]. IEEE Transactions on Vehicular Technology, 2009, 58(4): 1904-1919.
[47] TAN X Z, LIU Y T, XU G S. Dynamic spectrum allocation in cognitive radio: Auction and Equilibrium[C]. International Forum on Information Technology and Applications (IFITA 2009), Chengdu, CHN, May 2009: 554-558.
[48] LIEN S Y, LIN Y Y, CHEN K C. Cognitive and game-theoretical radio resource management for autonomous femtocells with QoS guarantees [J]. IEEE Transactions on Wireless Communications, 2011, 10(7): 2196-2206.
[49] YANG C G, LI J D, TIAN Z. Optimal power control for cognitive radio networks under coupled interference constraints: A cooperative game-theoretic perspective [J]. IEEE Transactions on Vehicular Technology, 2010, 59(4): 1696-1706.
[50] WANG B, WU Y L, LIU K J R, et al. An anti-jamming stochastic game for cognitive radio networks [J]. IEEE Journal on Selected Areas in Communications, 2011, 29(4): 877-889.
[51] HUANG J, KRISHNAMURTHY V. Transmission control in cognitive radio as a Markoviandynamic game: Structural result on randomized threshold policies [J]. IEEE Transactions on Communications, 2010, 58(1): 301-310.
[52] KO C H, WEI H Y. Game theoretical resource allocation for Inter-BS coexistence in IEEE 802.22 [J]. IEEE Transactions on Vehicular Technology, 2010, 59(4): 1729-1744.
[53] SHEN T. Power control game based on new pricing function for cognitive radio [C]. International Conference on Computer Science and Service System (CSSS), Najing CHN, June 2011: 2408-2411.
[54] LI D P, XU Y Y, WANG X B, et al. Coalitional game theoretic approach for secondary spectrum access in cooperative cognitive radio networks [J]. IEEE Transactions on Wireless Communications, 2011, 10(3): 844-856.
[55] SCUTARI G, PALOMAR D P. MIMO: Cognitive radio: A game theoretical approach [J]. IEEE Transactions on Signal Processing, 2010, 58(2): 761-780.
[56] LI Y B, WANG L F, L Y. An improved game-theoretic spectrum sharing algorithm in cognitive radio networks[C]. 3rd International Conference on Computer Research and Development (ICCRD), Shanghai, CHN, March 2011: 499-503.
[57]廖鼎,杨震.认知无线电中基于无限次重复博弈的功率控制算法[J].南京邮电大学学报(自然科学版),2009, 29(5): 72-75.
[58]黄丽亚,刘臣,王锁萍.改进的认知无线电频谱共享博弈模型[J].通信学报, 2010, 31(2): 136-140.
[59] BUZZI S, SATURNINO D. A game-theoretic approach to energy-efficient power control and receiver design in cognitive CDMA wireless networks [J]. IEEE Journal of Selected Topics in Signal Processing, 2011, 5(1): 137-150.
[60] XIAO Y, BI G A, NIYATO D. Game theoretic analysis for spectrum sharing with multi-hop relaying [J]. IEEE Transactions on Wireless Communications, 2011, 10(5): 1527-1537.
[61] ALAYESH M, GHANI N. Game-theoretic approach for primary-secondary user power control under fast flat fading channels [J]. IEEE Communications Letters, 2011, 15(5): 491-493.
[62] WANG J H, SCUTARI G, PALOMAR D P. Robust MIMO cognitive radio via game theory [J]. IEEE Transactions on Signal Processing, 2011, 59(3): 1183-1201.
[63]罗丽平,邱焕新,张广驰,等.具有约束条件的认知无线电网络最优频谱价格函数[J].电子学报, 2011, 39(3): 562-566.
[64] BELMEGA E V, DJEUMOU B, LASAULCE S. Resource allocation games in interference relay channels [C]. International Conference on Game Theory for Networks (GameNets 2009), Istanbul, Turkey, May 2009: 575-584.
[65] LIN Y, ZHU Q, CAI L. An Improved channel allocation algorithm based on list-coloring [C]. 6th International Conference on Wireless Communications Networking and Mobile Computing (WiCOM’10), Chengdu, CHN, Sep. 2010: 1-4.
[66] PENG C, ZHENG H, ZHAO B Y. Utilization and fairness in spectrum assignment for opportunistic spectrum access [J]. Mobile Networks and Applications, 2006, 11(4): 555-576.
[67] LIU Y, XU G, TAN X. A novel spectrum allocation mechanism based on graph coloring and bidding theory [C]. International Conference on Computational Intelligence and Natural Computing (CINC’09), Wuhan, CHN, Jun. 2009: 155-158.
[68] WANG J, HUANG Y, JIANG H. Improved algorithm of spectrum allocation based on graph coloring model in cognitive radio [C]. WRI International Conference on Communications and Mobile Computing (CMC’09), Yunnan, CHN, Jan. 2009: 353-357.
[69]陈劼,李少谦,廖楚林.认知无线电网络中基于需求的频谱资源分配算法研究[J].计算机应用, 2008, 28(9): 2188-2194.
[70] ZHANG G, FENG S. Subcarrier allocation algorithms based on graph-coloring in cognitive radio [C]. 3rd IEEE International Conference Computer Science and Information Technology (ICCSIT’10), Chengdu, CHN, Jul. 2010: 535-540.
[71] ZHANG Q, JIA J C, ZHANG J. Cooperative relay to improve diversity in cognitive radio networks [J]. IEEE Communications Magazine, 2009, 47(2): 111-117.
[72] Ganesan G, Li Y. Agility improvement through cooperative diversity in cognitive radio [C]. Global Telecommunications Conference, St. Louis, Missouri, USA, December 2005: 2505-2509.
[73] ZOU Y L, ZHU J, ZHENG B Y, et al. An adaptive cooperation diversity scheme with best-relay selection in cognitive radio networks. IEEE Transactions on Signal Processing, 2010, 58(10): 5438-5445.
[74] XU H, LI B C. Efficient resource allocation with flexible channel cooperation in OFDMA cognitive radio networks [C]. 2010 Proceedings IEEE INFOCOM, San Diego, California, USA, March 2010: 1-9.
[75] DING L, MELODIA T, BATALAMA S N, et al. Distributed routing, relay selection, and spectrum allocation in cognitive radio and cooperative Ad Hoc networks [C]. 7th Annual IEEE Communications Society Conference on Sensor Mesh and Ad Hoc Communications and Networks, Boston, Massachusetts, June 2010: 1-9.
[76] TADROUS J, SULTAN A, NAFIE M. Admission and power control for spectrum sharing cognitive radio networks [J]. IEEE Transactions on Wireless Communications, 2011, 10(6): 1945-1955.
[77] QIAO X Y, TAN Z H, XU S Y, et al. Combined power allocation in cognitive radio-based relay-assisted netwoks [C]. IEEE International conference on Communications Workshops (ICC 2010), Capetown, South Africa, May 2010: 1-5.
[78] FELICE M D, CHOWDHURY K R, BONONI L. Analyzing the potential of cooperative cognitive radio technology on inter-vehicle communication [C]. 2010 IFIP Wireless Days, Venice, Italy, October 2010: 1-6.
[79] GATSIS N, MARQUES A G, GIANNAKIS G B. Power control for cooperative dynamic spectrum access networks with diverse QoS constraints [J]. IEEE Transactions on Communications, 2010, 58(3): 933-944.
[80] SOHAIB K, CHOI Y, HAN Y. Outage improvement in cognitive relay networks by using a generalized regional mode [C]. 72nd Vehicular Technology Conference (VTC 2010-Fall),Ottawa, ON, Canada,Sep 2010: 1-5.
[81] YANG W, BAN D S, LIANG W F, et al. A Genetic Algorithm for joint resource allocation in cooperative cognitive radio networks [C]. 7th International Wireless Communication and Mobile Computing Conference (IWCMC 2011), Istanbul, Turkey, July 2011: 167-172.
[82] NGO D T, LE-NGOC T. Distributed resource allocation for cognitive radio networks with spectrum sharing constraints [J]. IEEE Transactions on Vehicular Technology, 2011, 60(7): 3436-3449.
[83] RONDEAU T W, LE B, RIESER C J, et al. Cognitive radios with genetic algorithms: Intelligent control of software defined radios [C]. SDR Forum Technical Conference (SDR’04), Los Angeles, CA, Jan. 2004: C3-C8.
[84] XU S, ZHANG Q, LIN W. PSO-based OFDM adaptive power and bit allocation for multiusercognitive radio system [C]. 5th International Conference Wireless Communications, Networking and Mobile Computing (WiCOM’09), Beijing, CHN, 2009: 1-4.
[85] ZHENG S, LUO C, YANG X. Cooperative spectrum sensing using particle swarm optimization [J]. Electronics Letters, 2010, 46(22): 1525-1526.
[86] EL-KHAMY S E, ABOUL-DAHAB M A, and ATTIA M M. A hybrid of particle swarm optimization and genetic algorithm for multicarrier cognitive radio [C]. 26th National Radio Science Conference (NRSC’09), New Cairo, EGY, Mar. 2009: 1-7.
[87] ZHANG B W, HU K, ZHU Y. Spectrum allocation in cognitive radio networks using swarm intelligence [C]. 2nd International Conference Communication Software and Networks (ICCSN’10), Singapore, SGP, February 2010: 8-12.
[88] LORENZO P D, BARBAROSSA S. Distributed resource allocation in cognitive radio systems based on social foraging swarms [C]. 11th IEEE International Conference Signal Processing Advances in Wireless Communications (SPAWC’10), Marrakech, MA, June 2010: 1-5.
[89] UDGATA S K, KUMAR K P, SABAT S L. Swarm intelligence based resource allocation algorithm for cognitive radio network [C]. 1st International Conference Parallel Distributed and Grid Computing (PDGC’10), Solan, IND, October 2010: 324-329.
[90] ZHAO Z, PENG Z, ZHENG S, et al. Cognitive radio spectrum allocation using evolutionary algorithms [J]. IEEE Transactions on Wireless Communications, 2009, 8(9): 4421-4425.
[91] WAHEED M, CAI A. Evolutionary algorithms for radio resource management in cognitive radio network [C]. 28th IEEE International Conference Performance Computing and Communications (IPCCC’09), Scottsdale, AZ, December 2009: 431-436.
[92] YAU K L A, KOMISARCZUK P, Teal P D. Achieving context awareness and intelligence in distributed cognitive radio networks: A payoff propagation approach [C]. IEEE Workshops of International Conference on Advanced Information Networking and Applications (WAINA), Biopolis, Singapore, March 2011: 210-215.
[93] ASHRAFINIA S, PAREEK U, NAEEM M, et al. Biogeography-based optimization for joint relay assignment and power allocation in cognitive radio systems [C]. IEEE Symposium on Swarm Intelligence (SIS), Paris, FRA, April 2011, 1-8.
[94] PAREEK U, LEE D C. Resource allocation in bidirectional cooperative cognitive radio networks using swarm intelligence [C]. IEEE Symposium on Swarm Intelligence (SIS), Paris, FRA, April 2011: 1-7.
[95] XU D, LI Y, WU C, et al. A learner based on neural network for cognitive radio [C]. 12th IEEE International Conference on Communication Technology (ICCT), Nanjing, CHN, November 2010: 893-896.
[96] HE A, KYUNG K B, NEWMAN T R, et al. A Survey of Artificial Intelligence for cognitive radios [J]. IEEE Transactions on Vehicular Technology, 2010, 59(4): 1578-1592.
[97] XUE F, QU D M, ZHU G X, et al. Smart channel switching in cognitive radio networks [C]. 2nd International Congress on Image and Signal Processing, Tianjin, CHN, October 2009: 1-5.
[98] SI P B, ZHANG Y H, YANG R Z, et al. Resource allocation policy of primary users in proactively-optimization cognitive radio networks [C]. 3rd International Conference on Computational Intelligence, Communication systems and Networks (CICSyN), Bali, Indonesia, July 2011: 343-347.
[99] UNNIKRISHNAN J, VEERAVALLI V V. Algorithms for dynamic spectrum access with learning for cognitive radio [J]. IEEE Transactions on Signal Processing, 2010, 58(2): 750-760.
[100] ZHU L, MAO H Q. A new random number generate algorithm for cognitive radio networks [C].International Conference on Computational Intelligence and Software Engineering (CiSE), Wuhan, CHN, December 2010: 1-5.
[101] ZHI X Y, XU S, HE Z Q, et al. The application of fuzzy neural network in cognitive radio network [C]. 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC), Zhengzhou, CHN, August 2011: 7310-7316.
[102] TAN H F, LI Y Z, CHEN Y M, et al. A novel access network selection scheme using Q-learning algorithm for cognitive terminal [C]. 5th International ICST Conference on Communication and Networking, Beijing, CHN, August 2010: 1-5.
[103] ALSARHAN A, AGARWAL A. Resource adaptations for revenue optimization in cognitive mesh network using reinforcement learning [C]. IEEE GLOBECOM Workshops, Miami, Florida, USA, December 2010: 1108-1112.
[104] CHEN J, WEN C. A novel cognitive radio adaptation for wireless multicarrier systems [J]. IEEE Communications Letters, 2010, 14(7): 629-631.
[105] Mitola J. Cognitive radio: An integrated Agent Architecture for software defined radio [D]. Royal Institute of Technology (KTH), Stockholm, Sweden, 2000.
[106] HAYKIN S. Cognitive radio: brain-empowered wireless communications [J]. IEEE Journal on Selected Areas in Communications, 2005, 23(2): 201-220.
[107] Facilitating opportunities for flexible, efficient and reliable spectrum use employing cognitive radio technologies [OL]. Federal Communications Commission, NPRM & Order, ET Docket No. 03-108, FCC 03-322, 2003. http://www.cs.ucdavis.edu/~liu/289I/Material/FCC-03-322A1.pdf
[108] WYGLINSKI A M, NEKOVEE M, HOU Y T. Cognitive Radio Communications and Networks: Principles and Practice [M]. Amsterdam, Boston, London, et al. Elsevier, December 2009.
[109] ENGELS M. Wireless OFDM systems: how to make them work [M]. New York, Boston, Dordrecht, et al. Kluwer Academic Publishers, 2002.
[110] HUANG S G, HWANG C H. Improvement of active interference cancellation: avoidance technique for OFDM cognitive radio [J]. IEEE Transactions on Wireless Communications, 2009, 8(12): 5928-5937.
[111]张然然,刘元安,林晓峰,等.认知无线电下行链路中的OFDMA资源分配算法[J].电子学报, 2010, 38(3): 632-637.
[112] ZHANG Y H, LEUNG C. A Distributed Algorithm for resource allocation in OFDM cognitive radio systems [J]. IEEE Transactions on Vehicular Technology, 2011, 60(2): 546-554.
[113] HASAN Z, BANSAL G, HOSSAIN E, et al. Energy-efficient power allocation in OFDM-based cognitive radio systems: A risk-return model [J]. IEEE Transactions on Wireless Communications, 2009, 8(12): 6078-6088.
[114] CHOWDHURY K R, AKYLDIZ I F. OFDM-based common control channel design for cognitive radio Ad Hoc networks [J]. IEEE Transactions on Mobile Computing, 2011, 10(2): 228-238.
[115] ZHANG Y H, LEUNG C. Resource allocation for non-real-time services in OFDM-based cognitive radio systems [J]. IEEE Communications Letters, 13(1): 16-18.
[116] BHARADIA D, BANSAL G, KALIGINEEDI P, et al. Relay and power allocation schemes for OFDM-based cognitive radio systems [J]. IEEE Transactions on Wireless Communications, 2011, 10(9): 2812-2817.
[117] WANG S W, HUANG F J, ZHOU Z H. Fast power allocation algorithm for cognitive radio networks [J]. IEEE Communications Letters, 2011, 15(8): 845-847.
[118] BANSAL G, HOSSAIN M J, BHARGAVA V K. Adaptive power loading for OFDM-based cognitive radio systems with statistical interference constraint [J]. IEEE Transactions on WirelessCommunications, 2011, 10(9): 2786-2791.
[119] KO J, KIM C. Communication method between spectrum heterogeneous secondary users in OFDM-based cognitive radio [J]. IET Electronics Letters, 2011, 47(14): 827-829.
[120] SHAHRAKI H S, MOHAMED-POUR K. Power allocation in multiple-input multiple-output orthogonal frequency division multiplexing-based cognitive radio networks [J]. IET Communications, 2011, 5(3): 362-370.
[121] LACATUS C, AKOPIAN D, YADDANAPUDI P, et al. Flexible Spectrum and power allocation for OFDM-unlicensed wireless systems [J]. IEEE Systems Journal, 2009, 3(2): 254-264.
[122] MA Y, KIM D I, WU Z Q. Optimization of OFDMA-based cellular cognitive radio networks [J]. IEEE Transactions on Communications, 2010, 58(8): 2265-2276.
[123] ALMALFOUH S M, STUBER G L. Interference-aware radio resource allocation in OFDMA-based cognitive radio networks [J]. IEEE Transactions on Vehicular Technology, 2011, 60(4): 1699-1713.
[124] CHOI K W, HOSSAIN E, KIM D I. Downlink subchannel and power allocation in multi-cell OFDMA cognitive radio networks [J]. IEEE Transactions on Wireless Communications, 2011, 10(7): 2259-2271.
[125] MARQUES A G, WANG X, GINNAKIS G B. Dynamic resource management for cognitive radios using limited-rate feedback [J]. IEEE Transactions on Signal Processing, 2009, 57(9): 3651-3666.
[126] Mahmoud H A, Yücek T, Arslan H. OFDM for cognitive radio: merits and challenges [J]. IEEE Wireless Communications Magazine, 2009, 16(2): 6-15.
[127] JAIN R, HAWE W, CHIU D. A Quantitative measure of fairness and discrimination for resource allocation in Shared Computer Systems[R]. Technical Report, Digital Equipment Corporation, DEC-TR-301, September 26, 1984. http://www.cs.wustl.edu/~jain/papers/ftp/fairness.pdf.
[128] KENNEDY J, EBERHART R C, SHI Y. Swarm Intelligence [M]. San Francisco, CA: Morgan Kaufman, 2001.
[129] CHUNG S T, GOLDSMITH A J. Degrees of freedom in adaptive modulation: a unified view[J]. IEEE Transactions on Communications, 2001, 49(9): 1561-1571.
[130] DEB K. An efficient constraint handling method for genetic algorithms[J]. Computer Methods in Applied Mechanics and Engineering, 2000, 186(2-4): 311-338.
[131] SHEN Z, ANDREWS J G, EVANS B L. Optimal power allocation in multiuser OFDM systems [C]. In Proceedings of Global Telecommunications Conference, San Francisco, CA, December 2003: 1-5.
[132] MA Y. Rate maximization for downlink OFDMA with proportional fairness [J]. IEEE Transactions on Vehicular Technology, 2008, 57(5): 3267-3274.
[133] WANG W, LIU T J, WANG T T, et al. Primary user activity based channel allocation in cognitive radio networks [C]. 2010 IEEE 72nd Vehicular Technology Conference (VTC 2010-Fall): Sep 6-9, 2010, Ottawa, ON, Canada. IEEE, 2010: 1-5.
[134] CHEN C H, WANG C L. Power allocation for OFDM-based cognitive radio systems under primary user activity [C]. 2010 IEEE 71st Vehicular Technology Conference (VTC 2010-Spring): May 16-19, Taipei, Taiwan. IEEE, 2010: 1-5.
[135] NGO D T, TELLAMBURA C, NGUYEN H H. Resource allocation for OFDMA-based cognitive radio multicast networks with primary user activity consideration [J]. IEEE Transactions on Vehicular Technology, 2010, 59(4): 1668-1679.
[136] HASAN Z, HOSSAIN E, DESPINS C, et al. Power allocation for cognitive radios based onprimary user activity in an OFDM system [C]. 2008 IEEE Global Telecommunications Conference (GLOBECOM 2008), New Orleans, LA, USA, Nov 30– Dec 4, 2008: 1-6.
[137] OZAROW L H, SHAMAI S, WYNER A D. Information theoretic considerations for cellular mobile radio [J]. IEEE Transactions on Vehicular Technology, 1994, 43(2): 359-378
[138] BERTSEKAS D P. Nonlinear Programming, 2nd ed[M]. Boston, MA: Athena Scientific Press, 1999.
[139] BOYD S, VANDENBERGHE L. Convex Optimization[M]. Cambridge, UK: Cambridge University Press, 2004
[140] MARKOWITZ H. Portfolio Selection [J]. The Journal of Finance, 1952, 7(1): 77-91.
[141] MWANGOKA J W, BEN LETAIEF K, CAO Z G. Statistical resource allocation for multi-band cognitive radio systems [J]. Physical Communication, 2009, 2(1-2): 116-126.
[142] AGRESTI A. Categorical Data Analysis. 2nd ed[M]. US: Wiley Press, 2002.
[143] WYSOCKI T, JAMALIPOUR A. Mean-Variance Based QoS Management in Cognitive Radio[J]. IEEE Transactions on Wireless Communications, 2010, 9(10): 3281-3289.
[144] FU A C, Modiano E, Tsitsiklis J N. Optimal energy allocation and admission control for communications satellites [J]. IEEE/ACM Transactions on Networking, 2003, 11(3): 488-500.
[145] FU A, MODIANO E, TSITSIKLIS J N. Optimal transmission scheduling over a fading channel with energy and deadline constraints[J]. IEEE transactions on Wireless Communications, 2006, 5(3): 630-641.
[146] BETTESH I, SHAMAI S. Optimal power and rate control for minimal average delay: The single-user case [J]. IEEE Transactions on Information Theory, 2006, 52(9): 4115-4141.
[147] ZHANG F, CHANSON S T. Improving communication energy efficiency in wireless networks powered by renewable energy sources [J]. IEEE Transactions on Vehicular Technology, 2005, 54(6): 2125-2136.
[148] BERTSEKAS D P. Dynamic Programming and Optimal Control [M]. Athena Scientific Press, 2006.
[149] Bellman R E. Dynamic Programming [M]. [S. l.]: Princeton University Press, June 1957.
[150] GAO L, WU P, CUI S G. Power and rate control with dynamic programming for cognitive radios [C]. 2007 Global Telecommunications Conference (GLOBECOM 2007), Washington, DC, USA, Nov 26– 30, 2007: 1699-1703.
[151] KANG X, GARG H K, LIANG Y C, et al. Optimal power allocation for OFDM-based cognitive radio with new primary transmission protection criteria [J]. IEEE Transactions on Wireless Communications, 2010, 9(6): 2066-2075.
[152] SRINIVASA S, JAFAR S A. Soft sensing and optimal power control for cognitive radio [J]. IEEE Transactions on Wireless Communications, 2010, 9(12): 3638-3649.
[153] GAO L, CUI S G. Multi-band power and rate control for cognitive radios with energy constraints: A dynamic programming approach [C]. 2008 IEEE International Conference on Communications (ICC 2008), Beijing, CHN, May 19-23, 2008: 3563-3567.
[2] MITOLA J. Cognitive radio for flexible mobile multimedia communications [C]. In Proceedings of Mobile Multimedia Communications, San Diego, CA, USA, November 15-17, 1999: 3-10.
[3] BOYD S and VANDENBERGHE L. Convex Optimizaiton [M]. New York, Cambridge University Press, 2004.
[4] CHENG P, ZHANG Z, CHEN H H, et al. Optimal distributed joint frequency, rate and power allocation in cognitive OFDMA systems [J]. IET Communication, 2008, 2(6): 815-826.
[5] YANG B, FENG G, SHEN Y, et al. Channel-aware access for cognitive radio networks [J]. IEEE Transactions on Vehicle Technology, 2009, 58(7): 3726-3737.
[6] CHO H, ANDREWS J G. Upper bound on the capacity of cognitive radio without cooperation [J]. IEEE Transactions on Wireless Communication, 2009 8(9): 4380-4385.
[7] WANG W, PENG T, WANG W B. Optimal power control under interference temperature constraints in cognitive radio network [C]. 2007 IEEE Wireless Communications and Networking Conference (WCNC 2007): Mar 2007, Kowloon, Hong Kong. IEEE, 2007: 116-120.
[8] LE L B, HOSSAIN E. Resource allocation for spectrum underlay in cognitive radio networks [J]. IEEE Transactions on Wireless Communications, 2008, 7(12): 5306-5315.
[9] LI H Y, GAI Y B, HE Z Q, et al. Optimal power control game algorithm for cognitive radio networks with multiple interference temperature limits [C]. 2008 IEEE Vehicular Technology Conference(VTC 2008-Spring): May 11-14, 2008, Singapore. IEEE, 2008: 1554-1558.
[10] ZHANG R. On peak versus average interference power constraints for protecting primary users in cognitive radio networks [J]. IEEE Transactions on Wireless Communications, 2009, 8(4): 2112-2120.
[11] WANG W, WANG W B, LU Q X, et al. Geometry-based optimal power control of fading multiple access channels for maximum sum-rate in cognitive radio networks [J]. IEEE Transactions on Wireless Communication, 2010, 9(6): 1843-1848.
[12] CUMANAN K, KRISHNA R, XIONG Z, et al. Multiuser spatial multiplexing techniques with constraints on interference temperature for cognitive radio networks [J]. IET Signal Processing, 2010, 4(6): 666-672.
[13] ZHANG R, LIANG Y C, CUI S. Dynamic resource allocation in cognitive radio networks [J]. IEEE Signal Processing Magazine, 2010, 27(3): 102-114.
[14] KIM D I, LE L B, HOSSAIN E. Joint rate and power allocation for cognitive radios in dynamic spectrum access environment [J]. IEEE Transactions on Wireless Communication, 2008, 7(12): 5517-5527.
[15] MUSAVIAN L, A?SSA S. Outage-constrained capacity of spectrum-sharing channels in fading environments [J]. IET Communications, 2008, 2(6): 724-732.
[16] HAN Y, PANDHARIPANDE A, TING S H. Cooperative decode-and-forward relaying for secondary spectrum access [J]. IEEE Transactions on Wireless Communication, 2009, 8(10): 4945-4950.
[17] PRASAD N, WANG X. Outage minimization and rate allocation for the multiuser gaussianinterference channels with successive group decoding [J]. IEEE Transactions on Information Theory, 2009, 55(12): 5540-5557.
[18] ZOU Y, YAO Y D, ZHENG B. Outage probability analysis of cognitive transmissions: Impact of spectrum sensing overhead[J]. IEEE Transactions on Wireless Communication, 2010, 9(8): 2676-2688.
[19] DING H, GE J, DA COSTA D B, et al. Energy-efficient and low-complexity schemes for uplink cognitive cellular networks [J]. IEEE Communication Letters, 2010, 14(12): 1101-1103.
[20] HUANG K, ZHANG R. Cooperative feedback for multiantenna cognitive radio networks [J] IEEE Transactions on Signal Processing, 2011, 59(2): 747-758.
[21] KANG X, ZHANG R, LIANG Y C, et al. Optimal power allocation strategies for fading cognitive radio channels with primary user outage constraint [J]. IEEE Journal on Selected Areas in Communications, 2011, 29(2): 374-383.
[22] ZHANG Y, LEUNG C. Resource allocation in an OFDM-based cognitive radio system [J]. IEEE Transactions on Wireless Communications, 2010, 9(10): 1536-1276.
[23] MITRAN P, LE L B, ROSENBERG C. Queue-aware resource allocation for downlink OFDMA cognitive radio networks [J]. IEEE Transactions on Wireless Communication, 2010, 9(10): 1536-1276.
[24] LIM H, SEOL D, IM G. Resource allocation for mitigating the effect of sensing errors in cognitive radio networks [J]. IEEE Communications Letters, 2010, 14(12): 1119-1121.
[25] CUMANAN K, KRISHNA R, MUSAVIAN L, et al. Joint beamforming and user maximization techniques for cognitive radio networks based on branch and bound method [J]. IEEE Transactions on Wireless Communications, 2010, 9(10): 3082-3092.
[26] QU X, LI M, ZHANG J F, et al. Downlink joint subchannel and power allocation in cognitive OFDM systems with QoS guarantee for primary users [C]. 2010 2nd International Conference on Future Computer and Communication (ICFCC): May 21-24, 2010, Wuhan, CHN. IEEE, 2010: V3 706-711.
[27] CANBERK B, AKYILDIZ I F, OKTUG S. Primary user activity modeling using first-difference filter clustering and correlation in cognitive radio networks [J]. IEEE/ACM Transactions on Networking, 2011, 19(1): 170-183.
[28] CHAI C C, CHEW Y H. Power control for cognitive radios in Nakagami fading channels with outage probability requirement [C]. 2010 IEEE Global Telecommunications Conference (GLOBECOM 2010): Dec 6-10, 2010, Miami, FL, USA. IEEE, 2010: 1-5.
[29] SUN D W, ZHENG B Y. A novel sub-carrier and power joint allocation algorithm for multi-user cognitive OFDM [C]. 10th International Conference on Signal Processing (ICSP 2010), Beijing, CHN, October 2010: 1458-1462.
[30] MWANGOKA J W, BEN LETAIEF K, CAO Z G. Robust end-to-end QoS maintenance in non contiguous OFDM based cognitive radios [C]. IEEE International Conference on Communications, Beijing, China, May 2008: 2905-2909.
[31] [8] WEISS T, HILLENBRAND J, KROHN A, et al. Mutual interference in OFDM-based spectrum pooling systems [C]. 59th Vehicular Technology Conference. Milan, Italy, May 2004: 1873-1877.
[32] ZHOU X, LI G Y, LI D, et al. Probabilistic resource allocation for opportunistic spectrum access [J]. IEEE Transactions on Wireless Communications, 2010, 9(9): 2870-2879.
[33] NEEL J, BUEHRER R M, REED J H, et al. Game theoretic analysis of a network of cognitive radios [C]. Midwest Symposium on Ciruits and Systems (MWSCAS 2002), Oklahoma, OK,August. 2002: 409-412(Vol. 3).
[34] NEEL J, REED J H, GILLES R P. The role of game theory in the analysis of software radio networks [C]. SDR’02 Technical Conference & Thirty-first SDR Meeting, California, CA, November 2002.
[35] NIE N, COMANICIU C. Adaptive channel allocation spectrum etiquette for cognitive radio networks [J]. Mobile Networks and Applications, 2006, 11(6): 779-797.
[36] THOMAS R W, KOMALI R S, Mackenzie A B. Joint power and channel minimization in topology control: A cognitive network approach [C]. IEEE International Conference on Communications (ICC’07), Glasgow, UK, June 2007: 6538-6543.
[37] GIUPPONI L, IBARS C. Distributed cooperation in cognitive radio networks: Overlay versus underlay paradigm [C]. IEEE 69th Vehicular Technology Conference (VTC’09-Spring), Barcelona, ESP, 26-29 April 2009: 1-6.
[38] ZHANG J, ZHANG Q. Stackelberg game for utility-based cooperative cognitive radio networks [C]. Proceedings of the tenth ACM international symposium on Mobile ad hoc networking and computing (MobiHoc’09), New York, NY, May 2009: 23-31.
[39] HALLDóRSSON M M, HALPERN J Y, LI L E, et al. On spectrum sharing games [J]. Distributed Computing, 2010, 22(4): 235-248.
[40] WANG F, KRUNZ M, CUI S. Price-based spectrum management in cognitive radio networks [J]. IEEE Journal of Selected Topics in Signal Processing, 2008, 2(1): 74-87.
[41] WU Y, WANG B, LIU K R, et al. Repeated open spectrum sharing game with cheat-proof strategies [J]. IEEE Transactions on Wireless Communications, 2009, 8(4): 1922-1933.
[42] SCHAAR M V D, FU F. Spectrum access games and strategic learning in cognitive radio networks for delay-critical applications [J]. Proceedings of the IEEE, 2009, 97(4): 720-740.
[43] ATTAR A, NAKHAI M R, AGHVAMI A H. Cognitive radio game for secondary spectrum access problem [J]. IEEE Transactions on Wireless Communications, 2009, 8(4): 2121-2131.
[44] SAAD W, HAN Z, DEBBAH M, et al. Coalitional games for distributed collaborative spectrum sensing in cognitive radio networks [C]. 2009 Proceedings IEEE INFOCOM, Rio de Janeiro, BR, April 2009: 2114-2122.
[45] HUANG J W, KRISHNAMURTHY V. Transmission control in cognitive radio systems with latency constraints as a switching control dynamic game [C]. 47th IEEE Conference on Decision and Control (CDC’08), Cancun, MEX, Dec. 2008: 3823-3828.
[46] FU F, SCHAAR M V D. Learning to compete for resources in wireless stochastic games [J]. IEEE Transactions on Vehicular Technology, 2009, 58(4): 1904-1919.
[47] TAN X Z, LIU Y T, XU G S. Dynamic spectrum allocation in cognitive radio: Auction and Equilibrium[C]. International Forum on Information Technology and Applications (IFITA 2009), Chengdu, CHN, May 2009: 554-558.
[48] LIEN S Y, LIN Y Y, CHEN K C. Cognitive and game-theoretical radio resource management for autonomous femtocells with QoS guarantees [J]. IEEE Transactions on Wireless Communications, 2011, 10(7): 2196-2206.
[49] YANG C G, LI J D, TIAN Z. Optimal power control for cognitive radio networks under coupled interference constraints: A cooperative game-theoretic perspective [J]. IEEE Transactions on Vehicular Technology, 2010, 59(4): 1696-1706.
[50] WANG B, WU Y L, LIU K J R, et al. An anti-jamming stochastic game for cognitive radio networks [J]. IEEE Journal on Selected Areas in Communications, 2011, 29(4): 877-889.
[51] HUANG J, KRISHNAMURTHY V. Transmission control in cognitive radio as a Markoviandynamic game: Structural result on randomized threshold policies [J]. IEEE Transactions on Communications, 2010, 58(1): 301-310.
[52] KO C H, WEI H Y. Game theoretical resource allocation for Inter-BS coexistence in IEEE 802.22 [J]. IEEE Transactions on Vehicular Technology, 2010, 59(4): 1729-1744.
[53] SHEN T. Power control game based on new pricing function for cognitive radio [C]. International Conference on Computer Science and Service System (CSSS), Najing CHN, June 2011: 2408-2411.
[54] LI D P, XU Y Y, WANG X B, et al. Coalitional game theoretic approach for secondary spectrum access in cooperative cognitive radio networks [J]. IEEE Transactions on Wireless Communications, 2011, 10(3): 844-856.
[55] SCUTARI G, PALOMAR D P. MIMO: Cognitive radio: A game theoretical approach [J]. IEEE Transactions on Signal Processing, 2010, 58(2): 761-780.
[56] LI Y B, WANG L F, L Y. An improved game-theoretic spectrum sharing algorithm in cognitive radio networks[C]. 3rd International Conference on Computer Research and Development (ICCRD), Shanghai, CHN, March 2011: 499-503.
[57]廖鼎,杨震.认知无线电中基于无限次重复博弈的功率控制算法[J].南京邮电大学学报(自然科学版),2009, 29(5): 72-75.
[58]黄丽亚,刘臣,王锁萍.改进的认知无线电频谱共享博弈模型[J].通信学报, 2010, 31(2): 136-140.
[59] BUZZI S, SATURNINO D. A game-theoretic approach to energy-efficient power control and receiver design in cognitive CDMA wireless networks [J]. IEEE Journal of Selected Topics in Signal Processing, 2011, 5(1): 137-150.
[60] XIAO Y, BI G A, NIYATO D. Game theoretic analysis for spectrum sharing with multi-hop relaying [J]. IEEE Transactions on Wireless Communications, 2011, 10(5): 1527-1537.
[61] ALAYESH M, GHANI N. Game-theoretic approach for primary-secondary user power control under fast flat fading channels [J]. IEEE Communications Letters, 2011, 15(5): 491-493.
[62] WANG J H, SCUTARI G, PALOMAR D P. Robust MIMO cognitive radio via game theory [J]. IEEE Transactions on Signal Processing, 2011, 59(3): 1183-1201.
[63]罗丽平,邱焕新,张广驰,等.具有约束条件的认知无线电网络最优频谱价格函数[J].电子学报, 2011, 39(3): 562-566.
[64] BELMEGA E V, DJEUMOU B, LASAULCE S. Resource allocation games in interference relay channels [C]. International Conference on Game Theory for Networks (GameNets 2009), Istanbul, Turkey, May 2009: 575-584.
[65] LIN Y, ZHU Q, CAI L. An Improved channel allocation algorithm based on list-coloring [C]. 6th International Conference on Wireless Communications Networking and Mobile Computing (WiCOM’10), Chengdu, CHN, Sep. 2010: 1-4.
[66] PENG C, ZHENG H, ZHAO B Y. Utilization and fairness in spectrum assignment for opportunistic spectrum access [J]. Mobile Networks and Applications, 2006, 11(4): 555-576.
[67] LIU Y, XU G, TAN X. A novel spectrum allocation mechanism based on graph coloring and bidding theory [C]. International Conference on Computational Intelligence and Natural Computing (CINC’09), Wuhan, CHN, Jun. 2009: 155-158.
[68] WANG J, HUANG Y, JIANG H. Improved algorithm of spectrum allocation based on graph coloring model in cognitive radio [C]. WRI International Conference on Communications and Mobile Computing (CMC’09), Yunnan, CHN, Jan. 2009: 353-357.
[69]陈劼,李少谦,廖楚林.认知无线电网络中基于需求的频谱资源分配算法研究[J].计算机应用, 2008, 28(9): 2188-2194.
[70] ZHANG G, FENG S. Subcarrier allocation algorithms based on graph-coloring in cognitive radio [C]. 3rd IEEE International Conference Computer Science and Information Technology (ICCSIT’10), Chengdu, CHN, Jul. 2010: 535-540.
[71] ZHANG Q, JIA J C, ZHANG J. Cooperative relay to improve diversity in cognitive radio networks [J]. IEEE Communications Magazine, 2009, 47(2): 111-117.
[72] Ganesan G, Li Y. Agility improvement through cooperative diversity in cognitive radio [C]. Global Telecommunications Conference, St. Louis, Missouri, USA, December 2005: 2505-2509.
[73] ZOU Y L, ZHU J, ZHENG B Y, et al. An adaptive cooperation diversity scheme with best-relay selection in cognitive radio networks. IEEE Transactions on Signal Processing, 2010, 58(10): 5438-5445.
[74] XU H, LI B C. Efficient resource allocation with flexible channel cooperation in OFDMA cognitive radio networks [C]. 2010 Proceedings IEEE INFOCOM, San Diego, California, USA, March 2010: 1-9.
[75] DING L, MELODIA T, BATALAMA S N, et al. Distributed routing, relay selection, and spectrum allocation in cognitive radio and cooperative Ad Hoc networks [C]. 7th Annual IEEE Communications Society Conference on Sensor Mesh and Ad Hoc Communications and Networks, Boston, Massachusetts, June 2010: 1-9.
[76] TADROUS J, SULTAN A, NAFIE M. Admission and power control for spectrum sharing cognitive radio networks [J]. IEEE Transactions on Wireless Communications, 2011, 10(6): 1945-1955.
[77] QIAO X Y, TAN Z H, XU S Y, et al. Combined power allocation in cognitive radio-based relay-assisted netwoks [C]. IEEE International conference on Communications Workshops (ICC 2010), Capetown, South Africa, May 2010: 1-5.
[78] FELICE M D, CHOWDHURY K R, BONONI L. Analyzing the potential of cooperative cognitive radio technology on inter-vehicle communication [C]. 2010 IFIP Wireless Days, Venice, Italy, October 2010: 1-6.
[79] GATSIS N, MARQUES A G, GIANNAKIS G B. Power control for cooperative dynamic spectrum access networks with diverse QoS constraints [J]. IEEE Transactions on Communications, 2010, 58(3): 933-944.
[80] SOHAIB K, CHOI Y, HAN Y. Outage improvement in cognitive relay networks by using a generalized regional mode [C]. 72nd Vehicular Technology Conference (VTC 2010-Fall),Ottawa, ON, Canada,Sep 2010: 1-5.
[81] YANG W, BAN D S, LIANG W F, et al. A Genetic Algorithm for joint resource allocation in cooperative cognitive radio networks [C]. 7th International Wireless Communication and Mobile Computing Conference (IWCMC 2011), Istanbul, Turkey, July 2011: 167-172.
[82] NGO D T, LE-NGOC T. Distributed resource allocation for cognitive radio networks with spectrum sharing constraints [J]. IEEE Transactions on Vehicular Technology, 2011, 60(7): 3436-3449.
[83] RONDEAU T W, LE B, RIESER C J, et al. Cognitive radios with genetic algorithms: Intelligent control of software defined radios [C]. SDR Forum Technical Conference (SDR’04), Los Angeles, CA, Jan. 2004: C3-C8.
[84] XU S, ZHANG Q, LIN W. PSO-based OFDM adaptive power and bit allocation for multiusercognitive radio system [C]. 5th International Conference Wireless Communications, Networking and Mobile Computing (WiCOM’09), Beijing, CHN, 2009: 1-4.
[85] ZHENG S, LUO C, YANG X. Cooperative spectrum sensing using particle swarm optimization [J]. Electronics Letters, 2010, 46(22): 1525-1526.
[86] EL-KHAMY S E, ABOUL-DAHAB M A, and ATTIA M M. A hybrid of particle swarm optimization and genetic algorithm for multicarrier cognitive radio [C]. 26th National Radio Science Conference (NRSC’09), New Cairo, EGY, Mar. 2009: 1-7.
[87] ZHANG B W, HU K, ZHU Y. Spectrum allocation in cognitive radio networks using swarm intelligence [C]. 2nd International Conference Communication Software and Networks (ICCSN’10), Singapore, SGP, February 2010: 8-12.
[88] LORENZO P D, BARBAROSSA S. Distributed resource allocation in cognitive radio systems based on social foraging swarms [C]. 11th IEEE International Conference Signal Processing Advances in Wireless Communications (SPAWC’10), Marrakech, MA, June 2010: 1-5.
[89] UDGATA S K, KUMAR K P, SABAT S L. Swarm intelligence based resource allocation algorithm for cognitive radio network [C]. 1st International Conference Parallel Distributed and Grid Computing (PDGC’10), Solan, IND, October 2010: 324-329.
[90] ZHAO Z, PENG Z, ZHENG S, et al. Cognitive radio spectrum allocation using evolutionary algorithms [J]. IEEE Transactions on Wireless Communications, 2009, 8(9): 4421-4425.
[91] WAHEED M, CAI A. Evolutionary algorithms for radio resource management in cognitive radio network [C]. 28th IEEE International Conference Performance Computing and Communications (IPCCC’09), Scottsdale, AZ, December 2009: 431-436.
[92] YAU K L A, KOMISARCZUK P, Teal P D. Achieving context awareness and intelligence in distributed cognitive radio networks: A payoff propagation approach [C]. IEEE Workshops of International Conference on Advanced Information Networking and Applications (WAINA), Biopolis, Singapore, March 2011: 210-215.
[93] ASHRAFINIA S, PAREEK U, NAEEM M, et al. Biogeography-based optimization for joint relay assignment and power allocation in cognitive radio systems [C]. IEEE Symposium on Swarm Intelligence (SIS), Paris, FRA, April 2011, 1-8.
[94] PAREEK U, LEE D C. Resource allocation in bidirectional cooperative cognitive radio networks using swarm intelligence [C]. IEEE Symposium on Swarm Intelligence (SIS), Paris, FRA, April 2011: 1-7.
[95] XU D, LI Y, WU C, et al. A learner based on neural network for cognitive radio [C]. 12th IEEE International Conference on Communication Technology (ICCT), Nanjing, CHN, November 2010: 893-896.
[96] HE A, KYUNG K B, NEWMAN T R, et al. A Survey of Artificial Intelligence for cognitive radios [J]. IEEE Transactions on Vehicular Technology, 2010, 59(4): 1578-1592.
[97] XUE F, QU D M, ZHU G X, et al. Smart channel switching in cognitive radio networks [C]. 2nd International Congress on Image and Signal Processing, Tianjin, CHN, October 2009: 1-5.
[98] SI P B, ZHANG Y H, YANG R Z, et al. Resource allocation policy of primary users in proactively-optimization cognitive radio networks [C]. 3rd International Conference on Computational Intelligence, Communication systems and Networks (CICSyN), Bali, Indonesia, July 2011: 343-347.
[99] UNNIKRISHNAN J, VEERAVALLI V V. Algorithms for dynamic spectrum access with learning for cognitive radio [J]. IEEE Transactions on Signal Processing, 2010, 58(2): 750-760.
[100] ZHU L, MAO H Q. A new random number generate algorithm for cognitive radio networks [C].International Conference on Computational Intelligence and Software Engineering (CiSE), Wuhan, CHN, December 2010: 1-5.
[101] ZHI X Y, XU S, HE Z Q, et al. The application of fuzzy neural network in cognitive radio network [C]. 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC), Zhengzhou, CHN, August 2011: 7310-7316.
[102] TAN H F, LI Y Z, CHEN Y M, et al. A novel access network selection scheme using Q-learning algorithm for cognitive terminal [C]. 5th International ICST Conference on Communication and Networking, Beijing, CHN, August 2010: 1-5.
[103] ALSARHAN A, AGARWAL A. Resource adaptations for revenue optimization in cognitive mesh network using reinforcement learning [C]. IEEE GLOBECOM Workshops, Miami, Florida, USA, December 2010: 1108-1112.
[104] CHEN J, WEN C. A novel cognitive radio adaptation for wireless multicarrier systems [J]. IEEE Communications Letters, 2010, 14(7): 629-631.
[105] Mitola J. Cognitive radio: An integrated Agent Architecture for software defined radio [D]. Royal Institute of Technology (KTH), Stockholm, Sweden, 2000.
[106] HAYKIN S. Cognitive radio: brain-empowered wireless communications [J]. IEEE Journal on Selected Areas in Communications, 2005, 23(2): 201-220.
[107] Facilitating opportunities for flexible, efficient and reliable spectrum use employing cognitive radio technologies [OL]. Federal Communications Commission, NPRM & Order, ET Docket No. 03-108, FCC 03-322, 2003. http://www.cs.ucdavis.edu/~liu/289I/Material/FCC-03-322A1.pdf
[108] WYGLINSKI A M, NEKOVEE M, HOU Y T. Cognitive Radio Communications and Networks: Principles and Practice [M]. Amsterdam, Boston, London, et al. Elsevier, December 2009.
[109] ENGELS M. Wireless OFDM systems: how to make them work [M]. New York, Boston, Dordrecht, et al. Kluwer Academic Publishers, 2002.
[110] HUANG S G, HWANG C H. Improvement of active interference cancellation: avoidance technique for OFDM cognitive radio [J]. IEEE Transactions on Wireless Communications, 2009, 8(12): 5928-5937.
[111]张然然,刘元安,林晓峰,等.认知无线电下行链路中的OFDMA资源分配算法[J].电子学报, 2010, 38(3): 632-637.
[112] ZHANG Y H, LEUNG C. A Distributed Algorithm for resource allocation in OFDM cognitive radio systems [J]. IEEE Transactions on Vehicular Technology, 2011, 60(2): 546-554.
[113] HASAN Z, BANSAL G, HOSSAIN E, et al. Energy-efficient power allocation in OFDM-based cognitive radio systems: A risk-return model [J]. IEEE Transactions on Wireless Communications, 2009, 8(12): 6078-6088.
[114] CHOWDHURY K R, AKYLDIZ I F. OFDM-based common control channel design for cognitive radio Ad Hoc networks [J]. IEEE Transactions on Mobile Computing, 2011, 10(2): 228-238.
[115] ZHANG Y H, LEUNG C. Resource allocation for non-real-time services in OFDM-based cognitive radio systems [J]. IEEE Communications Letters, 13(1): 16-18.
[116] BHARADIA D, BANSAL G, KALIGINEEDI P, et al. Relay and power allocation schemes for OFDM-based cognitive radio systems [J]. IEEE Transactions on Wireless Communications, 2011, 10(9): 2812-2817.
[117] WANG S W, HUANG F J, ZHOU Z H. Fast power allocation algorithm for cognitive radio networks [J]. IEEE Communications Letters, 2011, 15(8): 845-847.
[118] BANSAL G, HOSSAIN M J, BHARGAVA V K. Adaptive power loading for OFDM-based cognitive radio systems with statistical interference constraint [J]. IEEE Transactions on WirelessCommunications, 2011, 10(9): 2786-2791.
[119] KO J, KIM C. Communication method between spectrum heterogeneous secondary users in OFDM-based cognitive radio [J]. IET Electronics Letters, 2011, 47(14): 827-829.
[120] SHAHRAKI H S, MOHAMED-POUR K. Power allocation in multiple-input multiple-output orthogonal frequency division multiplexing-based cognitive radio networks [J]. IET Communications, 2011, 5(3): 362-370.
[121] LACATUS C, AKOPIAN D, YADDANAPUDI P, et al. Flexible Spectrum and power allocation for OFDM-unlicensed wireless systems [J]. IEEE Systems Journal, 2009, 3(2): 254-264.
[122] MA Y, KIM D I, WU Z Q. Optimization of OFDMA-based cellular cognitive radio networks [J]. IEEE Transactions on Communications, 2010, 58(8): 2265-2276.
[123] ALMALFOUH S M, STUBER G L. Interference-aware radio resource allocation in OFDMA-based cognitive radio networks [J]. IEEE Transactions on Vehicular Technology, 2011, 60(4): 1699-1713.
[124] CHOI K W, HOSSAIN E, KIM D I. Downlink subchannel and power allocation in multi-cell OFDMA cognitive radio networks [J]. IEEE Transactions on Wireless Communications, 2011, 10(7): 2259-2271.
[125] MARQUES A G, WANG X, GINNAKIS G B. Dynamic resource management for cognitive radios using limited-rate feedback [J]. IEEE Transactions on Signal Processing, 2009, 57(9): 3651-3666.
[126] Mahmoud H A, Yücek T, Arslan H. OFDM for cognitive radio: merits and challenges [J]. IEEE Wireless Communications Magazine, 2009, 16(2): 6-15.
[127] JAIN R, HAWE W, CHIU D. A Quantitative measure of fairness and discrimination for resource allocation in Shared Computer Systems[R]. Technical Report, Digital Equipment Corporation, DEC-TR-301, September 26, 1984. http://www.cs.wustl.edu/~jain/papers/ftp/fairness.pdf.
[128] KENNEDY J, EBERHART R C, SHI Y. Swarm Intelligence [M]. San Francisco, CA: Morgan Kaufman, 2001.
[129] CHUNG S T, GOLDSMITH A J. Degrees of freedom in adaptive modulation: a unified view[J]. IEEE Transactions on Communications, 2001, 49(9): 1561-1571.
[130] DEB K. An efficient constraint handling method for genetic algorithms[J]. Computer Methods in Applied Mechanics and Engineering, 2000, 186(2-4): 311-338.
[131] SHEN Z, ANDREWS J G, EVANS B L. Optimal power allocation in multiuser OFDM systems [C]. In Proceedings of Global Telecommunications Conference, San Francisco, CA, December 2003: 1-5.
[132] MA Y. Rate maximization for downlink OFDMA with proportional fairness [J]. IEEE Transactions on Vehicular Technology, 2008, 57(5): 3267-3274.
[133] WANG W, LIU T J, WANG T T, et al. Primary user activity based channel allocation in cognitive radio networks [C]. 2010 IEEE 72nd Vehicular Technology Conference (VTC 2010-Fall): Sep 6-9, 2010, Ottawa, ON, Canada. IEEE, 2010: 1-5.
[134] CHEN C H, WANG C L. Power allocation for OFDM-based cognitive radio systems under primary user activity [C]. 2010 IEEE 71st Vehicular Technology Conference (VTC 2010-Spring): May 16-19, Taipei, Taiwan. IEEE, 2010: 1-5.
[135] NGO D T, TELLAMBURA C, NGUYEN H H. Resource allocation for OFDMA-based cognitive radio multicast networks with primary user activity consideration [J]. IEEE Transactions on Vehicular Technology, 2010, 59(4): 1668-1679.
[136] HASAN Z, HOSSAIN E, DESPINS C, et al. Power allocation for cognitive radios based onprimary user activity in an OFDM system [C]. 2008 IEEE Global Telecommunications Conference (GLOBECOM 2008), New Orleans, LA, USA, Nov 30– Dec 4, 2008: 1-6.
[137] OZAROW L H, SHAMAI S, WYNER A D. Information theoretic considerations for cellular mobile radio [J]. IEEE Transactions on Vehicular Technology, 1994, 43(2): 359-378
[138] BERTSEKAS D P. Nonlinear Programming, 2nd ed[M]. Boston, MA: Athena Scientific Press, 1999.
[139] BOYD S, VANDENBERGHE L. Convex Optimization[M]. Cambridge, UK: Cambridge University Press, 2004
[140] MARKOWITZ H. Portfolio Selection [J]. The Journal of Finance, 1952, 7(1): 77-91.
[141] MWANGOKA J W, BEN LETAIEF K, CAO Z G. Statistical resource allocation for multi-band cognitive radio systems [J]. Physical Communication, 2009, 2(1-2): 116-126.
[142] AGRESTI A. Categorical Data Analysis. 2nd ed[M]. US: Wiley Press, 2002.
[143] WYSOCKI T, JAMALIPOUR A. Mean-Variance Based QoS Management in Cognitive Radio[J]. IEEE Transactions on Wireless Communications, 2010, 9(10): 3281-3289.
[144] FU A C, Modiano E, Tsitsiklis J N. Optimal energy allocation and admission control for communications satellites [J]. IEEE/ACM Transactions on Networking, 2003, 11(3): 488-500.
[145] FU A, MODIANO E, TSITSIKLIS J N. Optimal transmission scheduling over a fading channel with energy and deadline constraints[J]. IEEE transactions on Wireless Communications, 2006, 5(3): 630-641.
[146] BETTESH I, SHAMAI S. Optimal power and rate control for minimal average delay: The single-user case [J]. IEEE Transactions on Information Theory, 2006, 52(9): 4115-4141.
[147] ZHANG F, CHANSON S T. Improving communication energy efficiency in wireless networks powered by renewable energy sources [J]. IEEE Transactions on Vehicular Technology, 2005, 54(6): 2125-2136.
[148] BERTSEKAS D P. Dynamic Programming and Optimal Control [M]. Athena Scientific Press, 2006.
[149] Bellman R E. Dynamic Programming [M]. [S. l.]: Princeton University Press, June 1957.
[150] GAO L, WU P, CUI S G. Power and rate control with dynamic programming for cognitive radios [C]. 2007 Global Telecommunications Conference (GLOBECOM 2007), Washington, DC, USA, Nov 26– 30, 2007: 1699-1703.
[151] KANG X, GARG H K, LIANG Y C, et al. Optimal power allocation for OFDM-based cognitive radio with new primary transmission protection criteria [J]. IEEE Transactions on Wireless Communications, 2010, 9(6): 2066-2075.
[152] SRINIVASA S, JAFAR S A. Soft sensing and optimal power control for cognitive radio [J]. IEEE Transactions on Wireless Communications, 2010, 9(12): 3638-3649.
[153] GAO L, CUI S G. Multi-band power and rate control for cognitive radios with energy constraints: A dynamic programming approach [C]. 2008 IEEE International Conference on Communications (ICC 2008), Beijing, CHN, May 19-23, 2008: 3563-3567.