认知无线电网络主用户定位与位置信息应用研究
|
摘要
由于受到电磁波传播特性等限制,目前能够用来进行无线通信的频谱资源非常有限。但随着无线通信应用大量增加,特别是近几年来无线互联网应用的爆发式增长,对无线频谱资源提出了大量的需求。而当前普遍采用固定分配制度,各国的可用无线电频段几乎已经分配殆尽。认知无线电技术能够在避免对授权用户干扰的同时,利用授权用户频谱进行传输。认知无线电技术的提出,为解决应用增长和资源有限的供需矛盾问题,提供了一种有效的解决途径。本论文针对认知无线电网络中的主用户定位和位置信息应用问题,围绕主用户位置信息的获取、利用以及主用户定位误差对从用户网络性能的影响进行了深入的研究。
首先,在研究传感器网络定位算法的基础上,根据认知无线电网络的特点,研究了基于接收信号强度的主用户定位算法和节点选择算法。在主用户发射信号强度已知情形下,提出了一种无需迭代的定位算法,并对此算法线性化过程中的模型误差与接收信号强度之间的关系进行了分析。考虑定位算法的几何结构性能,基于估计理论,分析了节点分布的几何结构与估计性能下界之间的关系,进而,基于模型误差和几何结构分析,提出了一种节点分区选择算法。该算法不但能够在降低模型误差的前提下避免较差的几何结构,提高定位的精确性。而且通过节点选择降低了计算的规模,减少了定位算法的计算量,从而降低了节点能量消耗,提高了网络生存时间。
其次,针对一类在无线传感器网络定位中广泛使用的,基于多次最小二乘的定位算法的模型误差问题,分析了模型误差与接收信号强度的关系。同时考虑估计性能下界与节点选择数量之间的关系,提出了一种节点选择算法,可以在模型精确性和几何结构性能上获得一个良好的折中,提高了原算法的定位精度,并降低了原算法计算复杂度。
进一步研究了基于位置信息并区分信道特征的频谱分配算法。针对认知无线电网络可利用频谱资源间的频率差别较大,导致频率成为影响用户间干扰关系主要因素的问题,提出了根据频率进行信道分类的标准。这种标准更适用于认知无线电网络“频谱稀缺”的环境。在资源分配优化问题中,利用主用户位置信息进行了空间重用,有效地提高了频谱利用效率。
最后,研究了主用户定位误差对认知无线电网络性能的影响。考虑一个基于OFDMA技术的认知无线电网络的下行通信场景中,主用户定位误差对资源分配算法以及从用户网络性能的影响。建立了包含主用户位置不确定性的最优资源分配鲁棒优化模型,并将其转换为一个普通优化问题进行求解。基于功率控制的“注水原理”,研究了主用户定位误差对从用户网络性能不同影响程度的条件,以及对从用户网络性能影响的上界。提出了一种可行的判断定位误差对从用户网络影响程度的方法。同时也证明了之前提出的主用户定位和节点选择算法适合在基于位置信息的资源分配问题中应用。
The available spectrums are very limited due to the character of electromagnetic wave itself. But along with the increasing applications of wireless communication, especially with the sharp increasing applications on the wireless internet in recent years, large amount of wireless spectrum resource is required. Because of the fixed allocation rule which is currently wide used, the available radio spectrum has been assigned almost completely in every country. Cognitive radio technology can utilize the licensed users’frequency for transmission and avoid interferences on licensed users. This technology provides an effective solution to solve the problem between the increasing wireless applications and the limited resource. In this thesis, we have studied the location information collection and utilization including the primary user localization algorithm, location-based spectrum allocation algorithm, and the impacts of the localization error on the secondary network performance.
First, motivated by the sensor network localization algorithms, considering the requirements of localization in cognitive radio networks, we have studied the received signal strength based localization algorithm and the node selection algorithm. A localization algorithm without requirement of iteration is proposed under the condition that the transmitting signal strength is known. The relationship between model error brought in the process of linearization and received signal strength is analyzed. Based on estimation theory, the relationship between the lower bound of estimation performance and geometry structure is analyzed with the consideration of the geometry structure performance in localization. Then, based on the model error and geometry analysis, a node selection algorithm is proposed to reduce the error and avoid the poor geometry. Thus, the localization accuracy is improved. On the other hand, the proposed algorithm greatly reduces the computational scale and the computation amount is largely reduced. Therefore, node energy consumption is reduced and the network lifetime is prolonged.
Second, we have studied the localization problem for a class of localization algorithms widely used in wireless sensor networks without primary user’s emission signal strength. The model error is analyzed. Taking into account the relationship between the lower bound of the geometry structure and the estimated lower bound, the relationship between the model error and the geometry structure is studied. A node selection algorithm is proposed to achieve a good compromise between the model accuracy and the geometry performance. Thereby, the localization accuracy is improved and the complexity of the algorithm is reduced.
Third, we have studied the location-based spectrum allocation for heterogeneous channels. Considering the distinction between the available spectrums and the difference on the radius of transmission and interference between the spectrum frequencies may be large, a more applicable standard for the "spectrum scarcity" character in cognitive radio networks, which classify channels according to the channel frequency, is proposed. In resource allocation problem, taking into account the spatial reuse, we proposed a location based method and it can improve the efficiency of spectrum utilization.
Finally, the impacts of the location error on the cognitive radio network performance have been studied. By considering the downlink transmission in an OFDMA based cognitive radio network, we analyze the performance changes of resource allocation and secondary user networks when there exists the location error on the primary use. We focus on the effects of localization error for interference radius. Based on the "water filling" theory in power allocation problems, the upper bound on the impacts of localization error on secondary users are derived under the condition that location error interval is known. The conditions are proposed for the assessment of effects on the secondary uses networks. A feasible method to judge the impact degree of location error is given based on our proposed conditions. It is also proved that our localization and node selection method are applicable in spectrum spatial reusing.
首先,在研究传感器网络定位算法的基础上,根据认知无线电网络的特点,研究了基于接收信号强度的主用户定位算法和节点选择算法。在主用户发射信号强度已知情形下,提出了一种无需迭代的定位算法,并对此算法线性化过程中的模型误差与接收信号强度之间的关系进行了分析。考虑定位算法的几何结构性能,基于估计理论,分析了节点分布的几何结构与估计性能下界之间的关系,进而,基于模型误差和几何结构分析,提出了一种节点分区选择算法。该算法不但能够在降低模型误差的前提下避免较差的几何结构,提高定位的精确性。而且通过节点选择降低了计算的规模,减少了定位算法的计算量,从而降低了节点能量消耗,提高了网络生存时间。
其次,针对一类在无线传感器网络定位中广泛使用的,基于多次最小二乘的定位算法的模型误差问题,分析了模型误差与接收信号强度的关系。同时考虑估计性能下界与节点选择数量之间的关系,提出了一种节点选择算法,可以在模型精确性和几何结构性能上获得一个良好的折中,提高了原算法的定位精度,并降低了原算法计算复杂度。
进一步研究了基于位置信息并区分信道特征的频谱分配算法。针对认知无线电网络可利用频谱资源间的频率差别较大,导致频率成为影响用户间干扰关系主要因素的问题,提出了根据频率进行信道分类的标准。这种标准更适用于认知无线电网络“频谱稀缺”的环境。在资源分配优化问题中,利用主用户位置信息进行了空间重用,有效地提高了频谱利用效率。
最后,研究了主用户定位误差对认知无线电网络性能的影响。考虑一个基于OFDMA技术的认知无线电网络的下行通信场景中,主用户定位误差对资源分配算法以及从用户网络性能的影响。建立了包含主用户位置不确定性的最优资源分配鲁棒优化模型,并将其转换为一个普通优化问题进行求解。基于功率控制的“注水原理”,研究了主用户定位误差对从用户网络性能不同影响程度的条件,以及对从用户网络性能影响的上界。提出了一种可行的判断定位误差对从用户网络影响程度的方法。同时也证明了之前提出的主用户定位和节点选择算法适合在基于位置信息的资源分配问题中应用。
The available spectrums are very limited due to the character of electromagnetic wave itself. But along with the increasing applications of wireless communication, especially with the sharp increasing applications on the wireless internet in recent years, large amount of wireless spectrum resource is required. Because of the fixed allocation rule which is currently wide used, the available radio spectrum has been assigned almost completely in every country. Cognitive radio technology can utilize the licensed users’frequency for transmission and avoid interferences on licensed users. This technology provides an effective solution to solve the problem between the increasing wireless applications and the limited resource. In this thesis, we have studied the location information collection and utilization including the primary user localization algorithm, location-based spectrum allocation algorithm, and the impacts of the localization error on the secondary network performance.
First, motivated by the sensor network localization algorithms, considering the requirements of localization in cognitive radio networks, we have studied the received signal strength based localization algorithm and the node selection algorithm. A localization algorithm without requirement of iteration is proposed under the condition that the transmitting signal strength is known. The relationship between model error brought in the process of linearization and received signal strength is analyzed. Based on estimation theory, the relationship between the lower bound of estimation performance and geometry structure is analyzed with the consideration of the geometry structure performance in localization. Then, based on the model error and geometry analysis, a node selection algorithm is proposed to reduce the error and avoid the poor geometry. Thus, the localization accuracy is improved. On the other hand, the proposed algorithm greatly reduces the computational scale and the computation amount is largely reduced. Therefore, node energy consumption is reduced and the network lifetime is prolonged.
Second, we have studied the localization problem for a class of localization algorithms widely used in wireless sensor networks without primary user’s emission signal strength. The model error is analyzed. Taking into account the relationship between the lower bound of the geometry structure and the estimated lower bound, the relationship between the model error and the geometry structure is studied. A node selection algorithm is proposed to achieve a good compromise between the model accuracy and the geometry performance. Thereby, the localization accuracy is improved and the complexity of the algorithm is reduced.
Third, we have studied the location-based spectrum allocation for heterogeneous channels. Considering the distinction between the available spectrums and the difference on the radius of transmission and interference between the spectrum frequencies may be large, a more applicable standard for the "spectrum scarcity" character in cognitive radio networks, which classify channels according to the channel frequency, is proposed. In resource allocation problem, taking into account the spatial reuse, we proposed a location based method and it can improve the efficiency of spectrum utilization.
Finally, the impacts of the location error on the cognitive radio network performance have been studied. By considering the downlink transmission in an OFDMA based cognitive radio network, we analyze the performance changes of resource allocation and secondary user networks when there exists the location error on the primary use. We focus on the effects of localization error for interference radius. Based on the "water filling" theory in power allocation problems, the upper bound on the impacts of localization error on secondary users are derived under the condition that location error interval is known. The conditions are proposed for the assessment of effects on the secondary uses networks. A feasible method to judge the impact degree of location error is given based on our proposed conditions. It is also proved that our localization and node selection method are applicable in spectrum spatial reusing.
引文
1谢显中,感知无线电技术及其应用,北京:电子工业出版社,2008.
2 http://www.ntia.doc.gov/osmhome/allochrt.pdf.
3 Mitola J., Cognitive radio: An integrated agent architecture for software defined radio. [dissertation]. Doctor of Technology, Royal Inst. Technol. (KTH), Stockholm, Sweden, 2000.
4 Mitola J., Software radios: Survey, critical evaluation and future directions. IEEE Aerospace and Electronic Systems Magazine, 2002, 8(4):25-36.
5 Haykin, S., Cognitive radio: brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications, 2005, 23(2):201-220.
6 Curescu C. and S. Nadjm-Tehrani, Facilitating opportunities for flexible, efficient, and reliable spectrum use employing cognitive radio technologies. The FCC Notice of Proposed Rulemaking and Order: 03.108.
7 Ian F. Akyildiz, Won-Yeol Lee, Mehmet C. Vuran and Shantidev Mohanty, NeXt generation dynamic spectrum access cognitive radio wireless networks: a survey. Computer networks, 2006, 50(13):2127-2159.
8 IEEE 802.22 Working Group on Wireless Regional Area Networks. http://www.ieee802.org/22/.
9 Cordeiro, C., Challapali, K., Birru, D., Sai Shankar, N., IEEE 802.22: the first worldwide wireless standard based on cognitive radios. First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005, 328-337.
10 Robert W. Brodersen, Adam Wolisz, Danijela Cabric, et al., Corvus: a cognitive radio approach for usage of virtual unlicensed spectrum. Berkeley Wireless Research Center (BWRC) White paper, 2004.
11 Celebi H., Guvenc I., Gezici S. and Arslan, H., Cognitive-Radio Systems for Spectrum, Location, and Environmental Awareness. IEEE Antennas and Propagation Magazine, 2010, 52(4):41-61.
12 Kim S., H. Jeon and J. Ma. Robust Localization with Unknown Transmission Power for Cognitive Radio. IEEE Military Communications Conference, MILCOM 2007,1-6.
13 Zhiyao Ma, Ben Letaief K., Wei Chen and Zhigang Cao, A semi range-based iterative localizationalgorithm for cognitive radio networks. IEEE Transactions on Vehicular Technology, 2010, 59(2):704-717.
14 Celebi H. and H. Arslan, Utilization of location information in cognitive wireless networks. IEEE Wireless Communications, 2007, 14(4):6-13.
15 Haewoon N., M.B. Ghorbel and M. Alouini. Location-based resource allocation for OFDMA cognitive radio systems. 2010 Proceedings of the Fifth International Conference on Cognitive Radio Oriented Wireless Networks & Communications (CROWNCOM), 2010,1-5.
16 Kandeepan S., Reisenfeld S., Aysal T.C., et al. Bayesian tracking in cooperative localization for cognitive radio networks. IEEE 69th Vehicular Technology Conference, VTC Spring, 2009,1-5.
17 Yucek T. and H. Arslan, A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Communications Surveys & Tutorials, 2009,11(1):116-130.
18 Urkowitz H., Energy detection of unknown deterministic signals. Proceedings of the IEEE, 2005,55(4):523-531.
19 Van Trees H.L., Detection, estimation, and modulation theory. Wiley-Interscience, 2001.
20 Oner M. and F. Jondral. Cyclostationarity based air interface recognition for software radio systems. IEEE Radio and Wireless Conference, 2004, 263-266.
21 Ganesan, G. and Y. Li. Cooperative spectrum sensing in cognitive radio networks. IEEE Transactions on Wireless Communications, 2005, 8(10):5166-5175.
22 Aysal T.C., S. Kandeepan and R. Piesiewicz. Cooperative spectrum sensing with noisy hard decision transmissions. IEEE International Conference on Communications, ICC '09, 2009,1-5.
23 Uchiyama H., Umebayashi K., Fujii T., et al., Study on soft decision based cooperative sensing for cognitive radio networks. IEICE Transactions on Communications, 2008, 91(1):95-101.
24 Uchiyama H., Umebayashi K., Kamiya, Y., et al. Study on cooperative sensing in cognitive radio based ad-hoc network. IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC, 2007,1-5.
25 Ruiliang C., P. Jung-Min and J.H. Reed, Defense against primary user emulation attacks in cognitive radio networks. IEEE Journal on Selected Areas in Communications, 2008, 26(1):25-37.
26 Pengbo Si, Hong Ji, Yu F.R. and Leung V.C.M., Optimal cooperative internetwork spectrum sharing for cognitive radio systems with spectrum pooling. IEEE Transactions on Vehicular Technology, 2010, 59(4):1760-1768.
27 Xin Kang, Rui Zhang, Ying-Chang Liang and Garg H.K., Optimal power allocation strategies for fading cognitive radio channels with primary user outage constraint. IEEE Journal on Selected Areas in Communications, 2011, 29(2):374-383.
28 Ngo D.T., C. Tellambura and H.H. Nguyen, Resource allocation for OFDMA-based cognitive radio multicast networks with primary user activity consideration. IEEE Transactions on Vehicular Technology, 2010, 59(4):1668-1679.
29 Lan Z., L. Ying-Chang and X. Yan, Joint Beamforming and Power Allocation for Multiple Access Channels in Cognitive Radio Networks. IEEE Journal on Selected Areas in Communications,2008, 26(1):38-51.
30 Hou Y.T., S. Yi and H.D. Sherali, Spectrum sharing for multi-hop networking with cognitive radios. IEEE Journal on Selected Areas in Communications, 2008, 26(1):146-155.
31 Chen D., Q. Zhang and W. Jia. Aggregation aware spectrum assignment in cognitive ad-hoc networks.
3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications, CrownCom, 2008,1-6.
32 Xia Z. and H. Zheng. TRUST: A general framework for truthful double spectrum auctions. IEEE INFOCOM, 2009, 999-1007.
33 Peng C., H. Zheng and B.Y. Zhao, Utilization and fairness in spectrum assignment for opportunistic spectrum access. Mobile Networks and Applications, 2006, 11(4):555-576.
34 Jun X., M. Maode and L. Choi Look. AOA cooperative position localization. IEEE Global Telecommunications Conference, 2008. IEEE GLOBECOM, 2008,1-5.
35 Boushaba M., A. Hafid and A. Benslimane, High accuracy localization method using AoA in sensor networks. Computer networks, 2009, 53(18):3076-3088.
36 Rong P. and M.L. Sichitiu. Angle of arrival localization for wireless sensor networks. 3rd Annual IEEE Communications Society on Sensor and Ad Hoc Communications and Networks, 2006, 374-38.
37 Fette B.A., Cognitive radio technology. Newnes, 2006.
38 Duval O., Punchihewa A., Gagnon F., et al. Blind multi-sources detection and localization for cognitive radio. IEEE Global Telecommunications Conference, IEEE GLOBECOM, 2008,1-5.
39 Wan L. and P. Yao, TOA based localization error geometry distribution in sensor networks. Dianguang yu Kongzhi(Electronics, Optics & Control), 2010, 17(4): 41-44.
40 Guvenc I. and C.C. Chong, A survey on TOA based wireless localization and NLOS mitigationtechniques. IEEE Communications Surveys & Tutorials, 2009, 11(3):107-124.
41 Mogi T. and T. Ohtsuki. TOA localization using RSS weight with path loss exponents estimation in NLOS environments. 4th Asia-Pacific Conference on Communications, APCC, 2008,1-5.
42 Bocquet M., C. Loyez and A. Benlarbi-Delai, Using enhanced-TDOA measurement for indoor positioning. IEEE Microwave and Wireless Components Letters, 2005,15(10):612-614.
43 Ho K. and W. Xu, An accurate algebraic solution for moving source location using TDOA and FDOA measurements. IEEE Transactions on Signal Processing, 2004, 52(9):2453-2463.
44 Watters J.M., L. Strawczynski and D.G. Steer, Combining GPS with TOA/TDOA of cellular signals to locate terminal. 1999, United States Patents, No. 5982324.
45 DesJardins G.A., TDOA/FDOA technique for locating a transmitter. 1996, United States Patents, No. 5570099.
46 Yarkan S. and H. Arslan, Exploiting location awareness toward improved wireless system design in cognitive radio. IEEE Communications Magazine,2008, 46(1):128-136.
47 Celebi H. and H. Arslan, Cognitive positioning systems. IEEE Transactions on Wireless Communications, 2007, 6(12):4475-4483.
48 Celebi H. and H. Arslan, Adaptive Positioning systems for cognitive Radios. 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2007, 78-84.
49 Dardari D. Karisan, Y. Gezici, S. D'Amico and A. Mengali U., Performance Limits on ranging with cognitive radio. IEEE International Conference on Communications Workshops, ICC Workshops, 2009, 1-5.
50 Mao G. and B. Fidan, Localization algorithms and strategies for wireless sensor networks. 2009, Information Science Reference-Imprint of: IGI Publishing.
51 Boukerche A., Oliveira H.A.B., Nakamura E.F. and Loureiro A.A.F., Localization systems for wireless sensor networks. IEEE Wireless Communications, 2007, 14(6):6-12.
52 Ash J. and L. Potter. Sensor network localization via received signal strength measurements with directional antennas. in Proceedings of the 2004 Allerton Conference on Communication, Control, and Computing 2004, 1861-1870.
53 Ouyang R.W., A.K.S. Wong and C.T. Lea, Received signal strength-based wireless localization via semidefinite programming: Noncooperative and cooperative schemes. IEEE Transactions on Vehicular Technology, 2010, 59(3):1307-1318.
54 Nelson J.K., M.U. Hazen and M.R. Gupta. Global Optimization for Multiple Transmitter Localization. IEEE Military Communications Conference, 2006. MILCOM 2006.1-7.
55 Yuan Zhang, Lichun Bao, Welling, M. and Shih-Hsien Yang. Base station localization in search of empty spectrum spaces in cognitive radio networks. 5th International Conference on Mobile Ad-hoc and Sensor Networks, 2009, 94-101.
56 Zhiyao Ma, Ben Letaief K.,Wei Chen and Zhigang Cao. A semi range-based iterative localization algorithm for cognitive radio networks. IEEE Wireless Communications and Networking Conference, WCNC, 2009.1-5.
57马志垚,陈巍,曹志刚,认知无线电网络中基于检测概率的主用户定位算法.北京邮电大学学报, 2009, 32(2):14-19.
58 Hong Li, Ma Junfei, Xu Fangmin, et al. Optimization of collaborative spectrum sensing for cognitive radio. IEEE International Conference on Networking Sensing and Control, 2008, 1730-1733.
59 Wang Zaili, Feng Zhiyong, Song Jingqun, et al. A practical semi range-based localization algorithm for cognitive radio. IEEE 71st Vehicular Technology Conference (VTC 2010-Spring), 2010,1-5.
60 Adrian N. Bishop, Bar?? Fidan, Kutluy?l Do?an?ay, et al., Exploiting geometry for improved hybrid AOA/TDOA-based localization. Signal Processing, 2008, 88(7):1775-1791.
61 Guolin S. and J. van de Beek. Simple distributed interference source localization for radio environment mapping. IEEE Wireless day, IFIP. 2010, 1-5.
62 Song Liu, Yingying Chen, Trappe W. Greenstein and L.J. Non-interactive localization of cognitive radios based on dynamic signal strength mapping. Sixth International Conference on Wireless On-Demand Network Systems and Services, WONS, 2009, 85-92.
63 Fazel K., S. Kaiser and E. Corporation, Multi-carrier and spread spectrum systems: from OFDM and MC-CDMA to LTE and WiMAX. Wiley Chichester, 2008.
64 Srikanth S., Kumaran V., Manikandan C. and Murugesapandian., Orthogonal Frequency Division Multiple Access: is it the multiple access system of the future? White paper, http://www. au-kbc. org/comm/comm_resource. htm.
65 Weiss T. and F.K. Jondral, Spectrum pooling: an innovative strategy for the enhancement of spectrum efficiency. IEEE Communications Magazine, 2005, 42(3):8-14.
66 Tao Peng, Wei Wang, Qianxi Lu and Wenbo Wang. Subcarrier allocation based on water-fillinglevel in OFDMA-based cognitive radio networks. International Conference on Wireless Communications, Networking and Mobile Computing, 2007, 196-199.
67 Tao Peng, Wei Wang, Qianxi Lu and Wenbo Wang. Asymptotic performance of uplink resource allocation for OFDMA-based cognitive radio networks. 11th IEEE Singapore International Conference on Communication Systems, ICCS, 2008, 1509-1513.
68 Qianxi Lu, Wei Wang, Tao Peng and Wenbo Wang. Efficient multiuser water-filling algorithm under interference temperature constraints in OFDMA-based cognitive radio networks. International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications, 2007, 174-177.
69 Peng Cheng, Zhaoyang Zhang, Hui Huang and Peiliang Qiu. A distributed algorithm for optimal resource allocation in cognitive OFDMA systems. IEEE International Conference on Communications, ICC '08. 2008, 4718-4723.
70 Xiangwei Z., Z. Zhaoyang and C. Peng. An interference avoidance scheme for OFDM cognitive radio systems. International Conference on Communication Technology, ICCT '06. 2006, 1-5.
71 Rui Z. Optimal power control over fading cognitive radio channel by exploiting primary user CSI. IEEE Global Telecommunications Conference, IEEE GLOBECOM, 2008, 1-5.
72 Weiss T., Hillenbrand J., Krohn A. and Jondral F.K. Mutual interference in OFDM-based spectrum pooling systems. IEEE 59th Vehicular Technology Conference, VTC 2004-Spring. 2004,1873-1877.
73赵成仕,认知无线网络中无线资源管理的研究.[博士学位论文],北京邮电大学,2010.
74 Ngo D.T., C. Tellambura, and H.H. Nguyen. Resource allocation for OFDM-based cognitive radio multicast networks. Wireless Communications and Networking Conference, 2009, WCNC 2009, 1-6.
75 Zhao C., M. Zou and K. Kwak, Mutual interference considered power allocation in OFDM-based cognitive networks: The single SU case. Computer Communications, 2009, 32(18): 1965-1974.
76 Zhao C., M. Zou and K. Kwak, Mutual interference considered power allocation in OFDM-based cognitive networks: the multiple SUs case. Annals of Telecommunications, 2010, 65(7):341-351.
77马月槐,徐友云,蔡跃明,一种基于OFDM的多用户认知无线电系统联合资源分配算法.电路与系统学报, 2010,15(6):82-89.
78王鹏,钟晓峰,肖立民,周世东,王京,基于OFDM的认知无线电系统中最优功率分配.清华大学学报(自然科学版), 2009, 49(8):1144-1147.
79 Nisar M.D., W. Utschick and T. Hindelang, Maximally robust 2-D channel estimation for OFDM systems. IEEE Transactions on Signal Processing, 2010, 58(6):3163-3172.
80 Biguesh M. and A.B. Gershman, Training-based MIMO channel estimation: a study of estimator tradeoffs and optimal training signals. IEEE Transactions on Signal Processing, 2006, 54(3): 884-893.
81 Bjornson E. and B. Ottersten, A framework for training-based estimation in arbitrarily correlated Rician MIMO channels with Rician disturbance. IEEE Transactions on Signal Processing, 2010, 58(3):1807-1820.
82 Tulino A.M., A. Lozano and S. Verd, Impact of antenna correlation on the capacity of multiantenna channels. IEEE Transactions on Information Theory, 2005, 51(7):2491-2509.
83 Love D.J., Heath R.W., Lau V.K.N., et al., An overview of limited feedback in wireless communication systems. IEEE Journal on Selected Areas in Communications, 2008, 26(8): 1341-1365.
84 Ganesan G. and Y. Li. Agility improvement through cooperative diversity in cognitive radio. IEEE Global Telecommunications Conference, GLOBECOM '05, 2005, 2505-2509.
85 Sahai A., N. Hoven and R. Tandra. Some fundamental limits on cognitive radio. Allerton Conference on Communication Control and Computing, 2004, 1-11.
86 Patwari N., Ash J.N., Kyperountas S., et al., Locating the nodes: cooperative localization in wireless sensor networks. IEEE Signal Processing Magazine, 2005, 22(4):54-69.
87 Ian F. Akyildiz, Won-Yeol Lee, Mehmet C. Vuran and Shantidev Mohanty, A survey on spectrum management in cognitive radio networks. IEEE Communications Magazine,2008, 46(4):40-48.
88 Sheng, X. and Y.H. Hu, Maximum likelihood multiple-source localization using acoustic energy measurements with wireless sensor networks. IEEE Transactions on Signal Processing, 2004, 53(1):44-53.
89 Kitakoga N. and T. Ohtsuki. Distributed EM algorithms for acoustic source localization in sensor networks. IEEE 64th Vehicular Technology Conference, VTC-2006 Fall. 2006, 1-5.
90 Meng W., W. Xiao and L. Xie, An efficient EM algorithm for energy-based multisourcelocalization in wireless sensor networks. IEEE Transactions on Instrumentation and Measurement, 2011, 60(3):1017-1027.
91 Ho K. and M. Sun, An accurate algebraic closed-form solution for energy-based source localization. IEEE Transactions on Audio, Speech, and Language Processing, 2007, 15(8): 2542-2550.
92 Blatt D. and A. Hero, Energy-based sensor network source localization via projection onto convex sets. IEEE Transactions on Signal Processing, 2006, 54(9):3614-3619.
93 Bishop A.N., Fidan B., Anderson B.D.O., et al. Optimality analysis of sensor-target geometries in passive localization: Part 1-Bearing-only localization. 3rd International Conference on Intelligent Sensors, Sensor Networks and Information, ISSNIP , 2007, 7-12.
94 Bishop A.N., Fidan B., Anderson, B.D.O., et al., Optimality analysis of sensor-target geometries in passive localization: Part 2-Time-of-arrival based localization. 3rd International Conference on Intelligent Sensors, Sensor Networks and Information, ISSNIP, 2007, 13-18.
95 Adrian N. Bishop, Bari(?) Fidan, Brian D.O. Anderson, et al., Optimality analysis of sensor-target localization geometries. Automatica, 2010, 46(3):479-492.
96 Bishop A.N. and P. Jensfelt. An optimality analysis of sensor-target geometries for signal strength based localization. 2010: 5th International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 2009, 7-10.
97 Cheung K.W., So H.C., Ma W.-K. and Chan, Y.T. Received signal strength based mobile positioning via constrained weighted least squares. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03), 2003, 6-10.
98 Sayed A.H., Fundamentals of adaptive filtering. Wiley-IEEE Press, 2003.
99 Joshi S. and S. Boyd, Sensor selection via convex optimization. IEEE Transactions on Signal Processing, 2009, 57(2):451-462.
100 Hanbiao Wang, Pottie G., Kung Yao and Estrin, D.. Entropy-based sensor selection heuristic for target localization. Proceeding IPSN '04 Proceedings of the 3rd international symposium on Information processing in sensor networks, 2004, 36-45.
101 Wang H., K. Yao and D. Estrin, Information-theoretic approaches for sensor selection and placement in sensor networks for target localization and tracking. Journal of communications andnetworks, 2005, 7(4):438-449.
102 Ho K., X. Lu and L. Kovavisaruch, Source localization using TDOA and FDOA measurements in the presence of receiver location errors: Analysis and solution. IEEE Transactions on Signal Processing, 2007, 55(2):684-696.
103 Chan Y. and K. Ho, A simple and efficient estimator for hyperbolic location. IEEE Transactions on Signal Processing, 1994, 42(8):1905-1915.
104 Miao, M. and D.H.K. Tsang. Impact of channel heterogeneity on spectrum sharing in cognitive radio networks. IEEE International Conference on Communications, ICC '08. 2008, 2377-2382.
105 Hou Y.T., S. Yi and H.D. Sherali. Optimal spectrum sharing for multi-hop software defined radio networks. 26th IEEE International Conference on Computer Communications, 2007, 1-9.
106 Hou Y.T., Y. Shi and H.D. Sherali, Spectrum sharing for multi-hop networking with cognitive radios. IEEE Journal on Selected Areas in Communications, 2008, 26(1):146-155.
107 Liu Y., K. Contractor and Y. Kang. Path Loss For Short Range Telemetry. Springer 4th International Workshop on Wearable and Implantable Body Sensor Networks, 2007, 13:70-74.
108 Ben-Tal A. and Nemirovski A., Robust optimization methodology and applications, Mathematical Programming, 2002, 92(3):453-480.
109 Bertsimas D. and Sim M., The price of robustness, Operations research, 2004, 52(1):35-53.
110 Yang K., Wu Y., Huang J., et al., Distributed robust optimization for communication networks. The 27th IEEE Conference on Computer Communications, 2008, 1157-1165.
111李志鹏等, WCDMA基频讯号处理与系统设计实务.沧海书局,2007.
112 Peng Wang, Ming Zhao, Limin Xiao, et al. Power allocation in OFDM-based cognitive radio systems. IEEE Global Telecommunications Conference, GLOBECOM '07. 2007, 4061 - 4065.
113 Zhao Q. and B.M. Sadler, A survey of dynamic spectrum access. IEEE Signal Processing Magazine, 2007, 24(3):79-89.
114 Cheng Q. and B. Kollimarla. Joint channel and power allocation based on user satisfaction for cognitive radio. 43rd Annual Conference on Information Sciences and Systems, 2009, 579-584.
115 Munz G., S. Pfletschinger and J. Speidel. An efficient waterfilling algorithm for multiple access OFDM. IEEE Conference in Global Telecommunications, GLOBECOM '02. 2002, 681-685.
116 Tse D. and P. Viswanath, Fundamentals of wireless communication. Cambridge Univ Press, 2005.
117 Chandrasekhar V., J. Andrews and A. Gatherer, Femtocell networks: a survey. IEEE Communications Magazine, 2008, 46(9):59-67.
118 Jie Z., Femtocells: technologies and deployment. Wiley Online Library, 2010.
119 Peng Wang, Xiaofeng Zhong, Limin Xiao, et al. A general power allocation algorithm for ofdm-based cognitive radio systems: IEEE International Conference on Communications Workshops, ICC Workshops, 2009, 1-5.
2 http://www.ntia.doc.gov/osmhome/allochrt.pdf.
3 Mitola J., Cognitive radio: An integrated agent architecture for software defined radio. [dissertation]. Doctor of Technology, Royal Inst. Technol. (KTH), Stockholm, Sweden, 2000.
4 Mitola J., Software radios: Survey, critical evaluation and future directions. IEEE Aerospace and Electronic Systems Magazine, 2002, 8(4):25-36.
5 Haykin, S., Cognitive radio: brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications, 2005, 23(2):201-220.
6 Curescu C. and S. Nadjm-Tehrani, Facilitating opportunities for flexible, efficient, and reliable spectrum use employing cognitive radio technologies. The FCC Notice of Proposed Rulemaking and Order: 03.108.
7 Ian F. Akyildiz, Won-Yeol Lee, Mehmet C. Vuran and Shantidev Mohanty, NeXt generation dynamic spectrum access cognitive radio wireless networks: a survey. Computer networks, 2006, 50(13):2127-2159.
8 IEEE 802.22 Working Group on Wireless Regional Area Networks. http://www.ieee802.org/22/.
9 Cordeiro, C., Challapali, K., Birru, D., Sai Shankar, N., IEEE 802.22: the first worldwide wireless standard based on cognitive radios. First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005, 328-337.
10 Robert W. Brodersen, Adam Wolisz, Danijela Cabric, et al., Corvus: a cognitive radio approach for usage of virtual unlicensed spectrum. Berkeley Wireless Research Center (BWRC) White paper, 2004.
11 Celebi H., Guvenc I., Gezici S. and Arslan, H., Cognitive-Radio Systems for Spectrum, Location, and Environmental Awareness. IEEE Antennas and Propagation Magazine, 2010, 52(4):41-61.
12 Kim S., H. Jeon and J. Ma. Robust Localization with Unknown Transmission Power for Cognitive Radio. IEEE Military Communications Conference, MILCOM 2007,1-6.
13 Zhiyao Ma, Ben Letaief K., Wei Chen and Zhigang Cao, A semi range-based iterative localizationalgorithm for cognitive radio networks. IEEE Transactions on Vehicular Technology, 2010, 59(2):704-717.
14 Celebi H. and H. Arslan, Utilization of location information in cognitive wireless networks. IEEE Wireless Communications, 2007, 14(4):6-13.
15 Haewoon N., M.B. Ghorbel and M. Alouini. Location-based resource allocation for OFDMA cognitive radio systems. 2010 Proceedings of the Fifth International Conference on Cognitive Radio Oriented Wireless Networks & Communications (CROWNCOM), 2010,1-5.
16 Kandeepan S., Reisenfeld S., Aysal T.C., et al. Bayesian tracking in cooperative localization for cognitive radio networks. IEEE 69th Vehicular Technology Conference, VTC Spring, 2009,1-5.
17 Yucek T. and H. Arslan, A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Communications Surveys & Tutorials, 2009,11(1):116-130.
18 Urkowitz H., Energy detection of unknown deterministic signals. Proceedings of the IEEE, 2005,55(4):523-531.
19 Van Trees H.L., Detection, estimation, and modulation theory. Wiley-Interscience, 2001.
20 Oner M. and F. Jondral. Cyclostationarity based air interface recognition for software radio systems. IEEE Radio and Wireless Conference, 2004, 263-266.
21 Ganesan, G. and Y. Li. Cooperative spectrum sensing in cognitive radio networks. IEEE Transactions on Wireless Communications, 2005, 8(10):5166-5175.
22 Aysal T.C., S. Kandeepan and R. Piesiewicz. Cooperative spectrum sensing with noisy hard decision transmissions. IEEE International Conference on Communications, ICC '09, 2009,1-5.
23 Uchiyama H., Umebayashi K., Fujii T., et al., Study on soft decision based cooperative sensing for cognitive radio networks. IEICE Transactions on Communications, 2008, 91(1):95-101.
24 Uchiyama H., Umebayashi K., Kamiya, Y., et al. Study on cooperative sensing in cognitive radio based ad-hoc network. IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC, 2007,1-5.
25 Ruiliang C., P. Jung-Min and J.H. Reed, Defense against primary user emulation attacks in cognitive radio networks. IEEE Journal on Selected Areas in Communications, 2008, 26(1):25-37.
26 Pengbo Si, Hong Ji, Yu F.R. and Leung V.C.M., Optimal cooperative internetwork spectrum sharing for cognitive radio systems with spectrum pooling. IEEE Transactions on Vehicular Technology, 2010, 59(4):1760-1768.
27 Xin Kang, Rui Zhang, Ying-Chang Liang and Garg H.K., Optimal power allocation strategies for fading cognitive radio channels with primary user outage constraint. IEEE Journal on Selected Areas in Communications, 2011, 29(2):374-383.
28 Ngo D.T., C. Tellambura and H.H. Nguyen, Resource allocation for OFDMA-based cognitive radio multicast networks with primary user activity consideration. IEEE Transactions on Vehicular Technology, 2010, 59(4):1668-1679.
29 Lan Z., L. Ying-Chang and X. Yan, Joint Beamforming and Power Allocation for Multiple Access Channels in Cognitive Radio Networks. IEEE Journal on Selected Areas in Communications,2008, 26(1):38-51.
30 Hou Y.T., S. Yi and H.D. Sherali, Spectrum sharing for multi-hop networking with cognitive radios. IEEE Journal on Selected Areas in Communications, 2008, 26(1):146-155.
31 Chen D., Q. Zhang and W. Jia. Aggregation aware spectrum assignment in cognitive ad-hoc networks.
3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications, CrownCom, 2008,1-6.
32 Xia Z. and H. Zheng. TRUST: A general framework for truthful double spectrum auctions. IEEE INFOCOM, 2009, 999-1007.
33 Peng C., H. Zheng and B.Y. Zhao, Utilization and fairness in spectrum assignment for opportunistic spectrum access. Mobile Networks and Applications, 2006, 11(4):555-576.
34 Jun X., M. Maode and L. Choi Look. AOA cooperative position localization. IEEE Global Telecommunications Conference, 2008. IEEE GLOBECOM, 2008,1-5.
35 Boushaba M., A. Hafid and A. Benslimane, High accuracy localization method using AoA in sensor networks. Computer networks, 2009, 53(18):3076-3088.
36 Rong P. and M.L. Sichitiu. Angle of arrival localization for wireless sensor networks. 3rd Annual IEEE Communications Society on Sensor and Ad Hoc Communications and Networks, 2006, 374-38.
37 Fette B.A., Cognitive radio technology. Newnes, 2006.
38 Duval O., Punchihewa A., Gagnon F., et al. Blind multi-sources detection and localization for cognitive radio. IEEE Global Telecommunications Conference, IEEE GLOBECOM, 2008,1-5.
39 Wan L. and P. Yao, TOA based localization error geometry distribution in sensor networks. Dianguang yu Kongzhi(Electronics, Optics & Control), 2010, 17(4): 41-44.
40 Guvenc I. and C.C. Chong, A survey on TOA based wireless localization and NLOS mitigationtechniques. IEEE Communications Surveys & Tutorials, 2009, 11(3):107-124.
41 Mogi T. and T. Ohtsuki. TOA localization using RSS weight with path loss exponents estimation in NLOS environments. 4th Asia-Pacific Conference on Communications, APCC, 2008,1-5.
42 Bocquet M., C. Loyez and A. Benlarbi-Delai, Using enhanced-TDOA measurement for indoor positioning. IEEE Microwave and Wireless Components Letters, 2005,15(10):612-614.
43 Ho K. and W. Xu, An accurate algebraic solution for moving source location using TDOA and FDOA measurements. IEEE Transactions on Signal Processing, 2004, 52(9):2453-2463.
44 Watters J.M., L. Strawczynski and D.G. Steer, Combining GPS with TOA/TDOA of cellular signals to locate terminal. 1999, United States Patents, No. 5982324.
45 DesJardins G.A., TDOA/FDOA technique for locating a transmitter. 1996, United States Patents, No. 5570099.
46 Yarkan S. and H. Arslan, Exploiting location awareness toward improved wireless system design in cognitive radio. IEEE Communications Magazine,2008, 46(1):128-136.
47 Celebi H. and H. Arslan, Cognitive positioning systems. IEEE Transactions on Wireless Communications, 2007, 6(12):4475-4483.
48 Celebi H. and H. Arslan, Adaptive Positioning systems for cognitive Radios. 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2007, 78-84.
49 Dardari D. Karisan, Y. Gezici, S. D'Amico and A. Mengali U., Performance Limits on ranging with cognitive radio. IEEE International Conference on Communications Workshops, ICC Workshops, 2009, 1-5.
50 Mao G. and B. Fidan, Localization algorithms and strategies for wireless sensor networks. 2009, Information Science Reference-Imprint of: IGI Publishing.
51 Boukerche A., Oliveira H.A.B., Nakamura E.F. and Loureiro A.A.F., Localization systems for wireless sensor networks. IEEE Wireless Communications, 2007, 14(6):6-12.
52 Ash J. and L. Potter. Sensor network localization via received signal strength measurements with directional antennas. in Proceedings of the 2004 Allerton Conference on Communication, Control, and Computing 2004, 1861-1870.
53 Ouyang R.W., A.K.S. Wong and C.T. Lea, Received signal strength-based wireless localization via semidefinite programming: Noncooperative and cooperative schemes. IEEE Transactions on Vehicular Technology, 2010, 59(3):1307-1318.
54 Nelson J.K., M.U. Hazen and M.R. Gupta. Global Optimization for Multiple Transmitter Localization. IEEE Military Communications Conference, 2006. MILCOM 2006.1-7.
55 Yuan Zhang, Lichun Bao, Welling, M. and Shih-Hsien Yang. Base station localization in search of empty spectrum spaces in cognitive radio networks. 5th International Conference on Mobile Ad-hoc and Sensor Networks, 2009, 94-101.
56 Zhiyao Ma, Ben Letaief K.,Wei Chen and Zhigang Cao. A semi range-based iterative localization algorithm for cognitive radio networks. IEEE Wireless Communications and Networking Conference, WCNC, 2009.1-5.
57马志垚,陈巍,曹志刚,认知无线电网络中基于检测概率的主用户定位算法.北京邮电大学学报, 2009, 32(2):14-19.
58 Hong Li, Ma Junfei, Xu Fangmin, et al. Optimization of collaborative spectrum sensing for cognitive radio. IEEE International Conference on Networking Sensing and Control, 2008, 1730-1733.
59 Wang Zaili, Feng Zhiyong, Song Jingqun, et al. A practical semi range-based localization algorithm for cognitive radio. IEEE 71st Vehicular Technology Conference (VTC 2010-Spring), 2010,1-5.
60 Adrian N. Bishop, Bar?? Fidan, Kutluy?l Do?an?ay, et al., Exploiting geometry for improved hybrid AOA/TDOA-based localization. Signal Processing, 2008, 88(7):1775-1791.
61 Guolin S. and J. van de Beek. Simple distributed interference source localization for radio environment mapping. IEEE Wireless day, IFIP. 2010, 1-5.
62 Song Liu, Yingying Chen, Trappe W. Greenstein and L.J. Non-interactive localization of cognitive radios based on dynamic signal strength mapping. Sixth International Conference on Wireless On-Demand Network Systems and Services, WONS, 2009, 85-92.
63 Fazel K., S. Kaiser and E. Corporation, Multi-carrier and spread spectrum systems: from OFDM and MC-CDMA to LTE and WiMAX. Wiley Chichester, 2008.
64 Srikanth S., Kumaran V., Manikandan C. and Murugesapandian., Orthogonal Frequency Division Multiple Access: is it the multiple access system of the future? White paper, http://www. au-kbc. org/comm/comm_resource. htm.
65 Weiss T. and F.K. Jondral, Spectrum pooling: an innovative strategy for the enhancement of spectrum efficiency. IEEE Communications Magazine, 2005, 42(3):8-14.
66 Tao Peng, Wei Wang, Qianxi Lu and Wenbo Wang. Subcarrier allocation based on water-fillinglevel in OFDMA-based cognitive radio networks. International Conference on Wireless Communications, Networking and Mobile Computing, 2007, 196-199.
67 Tao Peng, Wei Wang, Qianxi Lu and Wenbo Wang. Asymptotic performance of uplink resource allocation for OFDMA-based cognitive radio networks. 11th IEEE Singapore International Conference on Communication Systems, ICCS, 2008, 1509-1513.
68 Qianxi Lu, Wei Wang, Tao Peng and Wenbo Wang. Efficient multiuser water-filling algorithm under interference temperature constraints in OFDMA-based cognitive radio networks. International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications, 2007, 174-177.
69 Peng Cheng, Zhaoyang Zhang, Hui Huang and Peiliang Qiu. A distributed algorithm for optimal resource allocation in cognitive OFDMA systems. IEEE International Conference on Communications, ICC '08. 2008, 4718-4723.
70 Xiangwei Z., Z. Zhaoyang and C. Peng. An interference avoidance scheme for OFDM cognitive radio systems. International Conference on Communication Technology, ICCT '06. 2006, 1-5.
71 Rui Z. Optimal power control over fading cognitive radio channel by exploiting primary user CSI. IEEE Global Telecommunications Conference, IEEE GLOBECOM, 2008, 1-5.
72 Weiss T., Hillenbrand J., Krohn A. and Jondral F.K. Mutual interference in OFDM-based spectrum pooling systems. IEEE 59th Vehicular Technology Conference, VTC 2004-Spring. 2004,1873-1877.
73赵成仕,认知无线网络中无线资源管理的研究.[博士学位论文],北京邮电大学,2010.
74 Ngo D.T., C. Tellambura, and H.H. Nguyen. Resource allocation for OFDM-based cognitive radio multicast networks. Wireless Communications and Networking Conference, 2009, WCNC 2009, 1-6.
75 Zhao C., M. Zou and K. Kwak, Mutual interference considered power allocation in OFDM-based cognitive networks: The single SU case. Computer Communications, 2009, 32(18): 1965-1974.
76 Zhao C., M. Zou and K. Kwak, Mutual interference considered power allocation in OFDM-based cognitive networks: the multiple SUs case. Annals of Telecommunications, 2010, 65(7):341-351.
77马月槐,徐友云,蔡跃明,一种基于OFDM的多用户认知无线电系统联合资源分配算法.电路与系统学报, 2010,15(6):82-89.
78王鹏,钟晓峰,肖立民,周世东,王京,基于OFDM的认知无线电系统中最优功率分配.清华大学学报(自然科学版), 2009, 49(8):1144-1147.
79 Nisar M.D., W. Utschick and T. Hindelang, Maximally robust 2-D channel estimation for OFDM systems. IEEE Transactions on Signal Processing, 2010, 58(6):3163-3172.
80 Biguesh M. and A.B. Gershman, Training-based MIMO channel estimation: a study of estimator tradeoffs and optimal training signals. IEEE Transactions on Signal Processing, 2006, 54(3): 884-893.
81 Bjornson E. and B. Ottersten, A framework for training-based estimation in arbitrarily correlated Rician MIMO channels with Rician disturbance. IEEE Transactions on Signal Processing, 2010, 58(3):1807-1820.
82 Tulino A.M., A. Lozano and S. Verd, Impact of antenna correlation on the capacity of multiantenna channels. IEEE Transactions on Information Theory, 2005, 51(7):2491-2509.
83 Love D.J., Heath R.W., Lau V.K.N., et al., An overview of limited feedback in wireless communication systems. IEEE Journal on Selected Areas in Communications, 2008, 26(8): 1341-1365.
84 Ganesan G. and Y. Li. Agility improvement through cooperative diversity in cognitive radio. IEEE Global Telecommunications Conference, GLOBECOM '05, 2005, 2505-2509.
85 Sahai A., N. Hoven and R. Tandra. Some fundamental limits on cognitive radio. Allerton Conference on Communication Control and Computing, 2004, 1-11.
86 Patwari N., Ash J.N., Kyperountas S., et al., Locating the nodes: cooperative localization in wireless sensor networks. IEEE Signal Processing Magazine, 2005, 22(4):54-69.
87 Ian F. Akyildiz, Won-Yeol Lee, Mehmet C. Vuran and Shantidev Mohanty, A survey on spectrum management in cognitive radio networks. IEEE Communications Magazine,2008, 46(4):40-48.
88 Sheng, X. and Y.H. Hu, Maximum likelihood multiple-source localization using acoustic energy measurements with wireless sensor networks. IEEE Transactions on Signal Processing, 2004, 53(1):44-53.
89 Kitakoga N. and T. Ohtsuki. Distributed EM algorithms for acoustic source localization in sensor networks. IEEE 64th Vehicular Technology Conference, VTC-2006 Fall. 2006, 1-5.
90 Meng W., W. Xiao and L. Xie, An efficient EM algorithm for energy-based multisourcelocalization in wireless sensor networks. IEEE Transactions on Instrumentation and Measurement, 2011, 60(3):1017-1027.
91 Ho K. and M. Sun, An accurate algebraic closed-form solution for energy-based source localization. IEEE Transactions on Audio, Speech, and Language Processing, 2007, 15(8): 2542-2550.
92 Blatt D. and A. Hero, Energy-based sensor network source localization via projection onto convex sets. IEEE Transactions on Signal Processing, 2006, 54(9):3614-3619.
93 Bishop A.N., Fidan B., Anderson B.D.O., et al. Optimality analysis of sensor-target geometries in passive localization: Part 1-Bearing-only localization. 3rd International Conference on Intelligent Sensors, Sensor Networks and Information, ISSNIP , 2007, 7-12.
94 Bishop A.N., Fidan B., Anderson, B.D.O., et al., Optimality analysis of sensor-target geometries in passive localization: Part 2-Time-of-arrival based localization. 3rd International Conference on Intelligent Sensors, Sensor Networks and Information, ISSNIP, 2007, 13-18.
95 Adrian N. Bishop, Bari(?) Fidan, Brian D.O. Anderson, et al., Optimality analysis of sensor-target localization geometries. Automatica, 2010, 46(3):479-492.
96 Bishop A.N. and P. Jensfelt. An optimality analysis of sensor-target geometries for signal strength based localization. 2010: 5th International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 2009, 7-10.
97 Cheung K.W., So H.C., Ma W.-K. and Chan, Y.T. Received signal strength based mobile positioning via constrained weighted least squares. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03), 2003, 6-10.
98 Sayed A.H., Fundamentals of adaptive filtering. Wiley-IEEE Press, 2003.
99 Joshi S. and S. Boyd, Sensor selection via convex optimization. IEEE Transactions on Signal Processing, 2009, 57(2):451-462.
100 Hanbiao Wang, Pottie G., Kung Yao and Estrin, D.. Entropy-based sensor selection heuristic for target localization. Proceeding IPSN '04 Proceedings of the 3rd international symposium on Information processing in sensor networks, 2004, 36-45.
101 Wang H., K. Yao and D. Estrin, Information-theoretic approaches for sensor selection and placement in sensor networks for target localization and tracking. Journal of communications andnetworks, 2005, 7(4):438-449.
102 Ho K., X. Lu and L. Kovavisaruch, Source localization using TDOA and FDOA measurements in the presence of receiver location errors: Analysis and solution. IEEE Transactions on Signal Processing, 2007, 55(2):684-696.
103 Chan Y. and K. Ho, A simple and efficient estimator for hyperbolic location. IEEE Transactions on Signal Processing, 1994, 42(8):1905-1915.
104 Miao, M. and D.H.K. Tsang. Impact of channel heterogeneity on spectrum sharing in cognitive radio networks. IEEE International Conference on Communications, ICC '08. 2008, 2377-2382.
105 Hou Y.T., S. Yi and H.D. Sherali. Optimal spectrum sharing for multi-hop software defined radio networks. 26th IEEE International Conference on Computer Communications, 2007, 1-9.
106 Hou Y.T., Y. Shi and H.D. Sherali, Spectrum sharing for multi-hop networking with cognitive radios. IEEE Journal on Selected Areas in Communications, 2008, 26(1):146-155.
107 Liu Y., K. Contractor and Y. Kang. Path Loss For Short Range Telemetry. Springer 4th International Workshop on Wearable and Implantable Body Sensor Networks, 2007, 13:70-74.
108 Ben-Tal A. and Nemirovski A., Robust optimization methodology and applications, Mathematical Programming, 2002, 92(3):453-480.
109 Bertsimas D. and Sim M., The price of robustness, Operations research, 2004, 52(1):35-53.
110 Yang K., Wu Y., Huang J., et al., Distributed robust optimization for communication networks. The 27th IEEE Conference on Computer Communications, 2008, 1157-1165.
111李志鹏等, WCDMA基频讯号处理与系统设计实务.沧海书局,2007.
112 Peng Wang, Ming Zhao, Limin Xiao, et al. Power allocation in OFDM-based cognitive radio systems. IEEE Global Telecommunications Conference, GLOBECOM '07. 2007, 4061 - 4065.
113 Zhao Q. and B.M. Sadler, A survey of dynamic spectrum access. IEEE Signal Processing Magazine, 2007, 24(3):79-89.
114 Cheng Q. and B. Kollimarla. Joint channel and power allocation based on user satisfaction for cognitive radio. 43rd Annual Conference on Information Sciences and Systems, 2009, 579-584.
115 Munz G., S. Pfletschinger and J. Speidel. An efficient waterfilling algorithm for multiple access OFDM. IEEE Conference in Global Telecommunications, GLOBECOM '02. 2002, 681-685.
116 Tse D. and P. Viswanath, Fundamentals of wireless communication. Cambridge Univ Press, 2005.
117 Chandrasekhar V., J. Andrews and A. Gatherer, Femtocell networks: a survey. IEEE Communications Magazine, 2008, 46(9):59-67.
118 Jie Z., Femtocells: technologies and deployment. Wiley Online Library, 2010.
119 Peng Wang, Xiaofeng Zhong, Limin Xiao, et al. A general power allocation algorithm for ofdm-based cognitive radio systems: IEEE International Conference on Communications Workshops, ICC Workshops, 2009, 1-5.