蜂窝网中基于功率和时延折衷的包调度策略研究
|
摘要
近年来,随着无线数据业务(移动Internet,无线多媒体等)需求的日益增长,一般认为全IP架构将成为下一代无线网络的主流架构,即各种业务将在数据包的层面上获得统一。数据包的调度是有线网络中一项较为成熟的技术,但将其用于无线网络却要有挑战得多——无线信道的时变性以及功率的受限性是其中两个最为主要的因素。本论文的主要目标是研究并设计能有效提高系统功率效率的包调度策略,具体而言,重点研究不同通信环境下(单用户与多用户;平坦衰落与频率选择性衰落;理想信道信息与非理想信道信息等)链路时延与功率的折衷关系,进而设计在保障服务质量(Quality of Services, QoS)(时延)的前提下使发射功率最小的次优或简化策略。
论文首先讨论了单用户平坦衰落信道下的包调度策略以及功率时延关系。该调度问题可用马尔可夫决策过程(Markov Decision Process, MDP)建模,通过对MDP的分析我们发现:在最优策略下,功率和时延具有凸函数的关系,仿真结果也证实了这一结论。最优策略的求解可通过动态规划得到,但复杂度非常高,因此我们给出了一种简化策略,在该策略中发送速率是当前队列长度与信道状态的一个简单函数,算法复杂度得以大大降低,仿真结果表明其性能较最优策略仅有较少的下降。另外我们还利用Lyapunov稳定性定理证明了该简化策略可以保证队列的稳定性。其次,结合实际环境我们分别分析了在缓冲区为有限长并采用QAM调制情形下的调度策略以及部分信道信息对调度策略性能的影响。
在平坦衰落信道中,利用时间分集效应是包调度策略用来降低功率的一个重要手段,而在频率选择性衰落信道中,调度器除了可以利用时间分集效应外,还可以利用频域上的分集以获取进一步的性能增益。我们分析并提出了OFDM系统下的最优和次优包调度策略,对于最优策略,我们给出了两种求优模型:一种是基于无限长时间的平均代价准则MDP模型,另一种是基于稳态概率的线性规划(Linear Programming, LP)模型。在业务到达过程以及队列状态都具有Markov性的条件下,可以证明这两种模型是等价的,因此它们具有相同的最优解,但这两种模型的复杂度都随状态数呈指数增长,随着子载波数量的增加,其求解是异常复杂的。基于此我们提出了两种简化策略:子时隙算法和MLQHR+OFDM算法。子时隙算法将时频二维空间的求优转化为时间一维空间的求优,其复杂度仅随子载波数线性增长。MLQHR+OFDM算法则是借鉴平坦衰落信道下的简化策略,将发送速率限定为子载波信道状态和队列状态的一个简单函数,使复杂度大大降低。仿真结果表明:相比最优策略而言,子时隙算法在功率效率方面只有2%的降低,而MLQHR+OFDM算法虽然功率损耗增加大约10%,但考虑到其低复杂度的优点,我们认为它还是具有一定实用的价值。
论文最后讨论了多址信道和广播信道下的多用户调度策略。在这两种信道模型中,系统功率和发射速率都具有凸函数的关系,因而调度策略仍然可以利用时间分集效应降低功率,另外,还可以利用多用户分集进一步提高系统性能。我们具体分析了两用户的情况,证明了系统功率和时延具有凸函数的关系,仿真结果也支持这一结果。与单用户情形类似,我们也分别给出了这两种信道模型下的简化调度策略,仿真结果显示:与最优策略相比,简化算法功率损耗增加大约15%,但复杂度却大大降低。另外我们讨论了多址信道的功率区问题,证明了对于给定的时延,都对应着一个功率区,该功率区的控制面边界上的每个点都对应于一个功率凸组合问题的最优解,最后我们还给出了求功率区的具体算法。
With the increasing demand for high-data-rate services, All-IP architecture has been widely regarded as the mainstream one in the next-generation wireless network, i.e., all services are based on packets. The scheduling of packets is a relatively mature technique in the wireline networks, while in the wireless networks it is much more challenging, mainly due to the severity and time-varying nature of the wireless channel as well as the limited power in the mobile terminals. In this dissertation we aim at studying and devising high power-efficiency packet scheduling strategies, specifically, we explore the optimal tradeoff between the queuing delay and power in various communication scenarios (single user and multi-user; flat fading and frequency-selective fading; perfect CSI vs. imperfect one, etc.), and further design the suboptimal strategies to minimize the transmission power under the constraint of QoS (delay) guarantee.
We first address the power-delay tradeoff properties and scheduling algorithms in the single-user flat-fading-channel scenario. Under mild conditions, the scheduling problem can be formulated as a MDP (Markov Decision Process), through the analysis of which we can find that the average transmission power is a non-increasing, strictly convex function of the average delay. As the complexity of the optimal solution which can be yielded by the dynamic programming method is very high, we propose a very simple scheme in which the scheduled rate is a logarithm function of the current queue length and channel state. Simulation results show that the performance loss of our suboptimal algorithm is very low. Furthermore, we prove the capability of our algorithm to keep the queue length bounded by utilizing the Lyapunov stability theorem. In addition, we take some practical issues into consideration by analyzing the scheduling algorithm under the condition of finite buffer and QAM modulation mode as well as the impact of the partial CSI over the scheduling performance.
In the flat fading channel, temporal diversity is an important means of decreasing transmission power, while in the frequency-selective channels, we can further exploit the spectral diversity in addition to the temporal one. We analyze the optimal scheduling strategy and propose suboptimal ones. In specific, we present two optimization models for the optimum strategy: one is based on the average-cost MDP based on infinite time horizon, the other one is formulated as a Linear Programming (LP) problem based on the“steady-state”probability. It can be proved that under the Markov-chain assumption of both the arrival process and the fading process the two seemingly different models are equivalent in essence. However, the complexity of the two models is both exponential with the states. To reduce the complexity, we design two suboptimal algorithms: the sub-slotting algorithm and the MLQHR+OFDM algorithm. For the former by transforming a two-dimensional optimization problem to one-dimensional one, we can reduce the complexity to just linear with the sub-carrier numbers; as for the latter, it is the extension of the simplified algorithm for the flat-fading scenario and is more useful in the frequency-selective channel due to its very low implementation complexity .Numerical results indicate that the sub-slotting algorithm only incurs as low as 2% performance loss compared with the optimum algorithm, while the performance degradation of ALQHR+OFDM algorithm is somewhat bigger.
In the last we discuss the multi-user scheduling strategies in the Multiple-Access Channel and Broadcast Channel, respectively. In specific, we analyze the two-user scenario and prove the same convexity property of power versus delay as in the single-user case. What’s different is that in the multi-user environment an additional form of diversity can be utilized. Similarly as the single-user scenario, we also present the simple algorithms for the above two channels. Compared with the optimum scheduling algorithms, our proposed algorithms exhibit about 15 percent loss in the power efficiency, while their complexities are much lower than the optimum one. In addition we analyze the power region in the MAC channel. We prove that for any given delay, there’s a unique power region in whose dominant boundary every point corresponds to the optimal solution to a convex power combination optimization problem. In addition, we present the detailed algorithm for computing the power region.
论文首先讨论了单用户平坦衰落信道下的包调度策略以及功率时延关系。该调度问题可用马尔可夫决策过程(Markov Decision Process, MDP)建模,通过对MDP的分析我们发现:在最优策略下,功率和时延具有凸函数的关系,仿真结果也证实了这一结论。最优策略的求解可通过动态规划得到,但复杂度非常高,因此我们给出了一种简化策略,在该策略中发送速率是当前队列长度与信道状态的一个简单函数,算法复杂度得以大大降低,仿真结果表明其性能较最优策略仅有较少的下降。另外我们还利用Lyapunov稳定性定理证明了该简化策略可以保证队列的稳定性。其次,结合实际环境我们分别分析了在缓冲区为有限长并采用QAM调制情形下的调度策略以及部分信道信息对调度策略性能的影响。
在平坦衰落信道中,利用时间分集效应是包调度策略用来降低功率的一个重要手段,而在频率选择性衰落信道中,调度器除了可以利用时间分集效应外,还可以利用频域上的分集以获取进一步的性能增益。我们分析并提出了OFDM系统下的最优和次优包调度策略,对于最优策略,我们给出了两种求优模型:一种是基于无限长时间的平均代价准则MDP模型,另一种是基于稳态概率的线性规划(Linear Programming, LP)模型。在业务到达过程以及队列状态都具有Markov性的条件下,可以证明这两种模型是等价的,因此它们具有相同的最优解,但这两种模型的复杂度都随状态数呈指数增长,随着子载波数量的增加,其求解是异常复杂的。基于此我们提出了两种简化策略:子时隙算法和MLQHR+OFDM算法。子时隙算法将时频二维空间的求优转化为时间一维空间的求优,其复杂度仅随子载波数线性增长。MLQHR+OFDM算法则是借鉴平坦衰落信道下的简化策略,将发送速率限定为子载波信道状态和队列状态的一个简单函数,使复杂度大大降低。仿真结果表明:相比最优策略而言,子时隙算法在功率效率方面只有2%的降低,而MLQHR+OFDM算法虽然功率损耗增加大约10%,但考虑到其低复杂度的优点,我们认为它还是具有一定实用的价值。
论文最后讨论了多址信道和广播信道下的多用户调度策略。在这两种信道模型中,系统功率和发射速率都具有凸函数的关系,因而调度策略仍然可以利用时间分集效应降低功率,另外,还可以利用多用户分集进一步提高系统性能。我们具体分析了两用户的情况,证明了系统功率和时延具有凸函数的关系,仿真结果也支持这一结果。与单用户情形类似,我们也分别给出了这两种信道模型下的简化调度策略,仿真结果显示:与最优策略相比,简化算法功率损耗增加大约15%,但复杂度却大大降低。另外我们讨论了多址信道的功率区问题,证明了对于给定的时延,都对应着一个功率区,该功率区的控制面边界上的每个点都对应于一个功率凸组合问题的最优解,最后我们还给出了求功率区的具体算法。
With the increasing demand for high-data-rate services, All-IP architecture has been widely regarded as the mainstream one in the next-generation wireless network, i.e., all services are based on packets. The scheduling of packets is a relatively mature technique in the wireline networks, while in the wireless networks it is much more challenging, mainly due to the severity and time-varying nature of the wireless channel as well as the limited power in the mobile terminals. In this dissertation we aim at studying and devising high power-efficiency packet scheduling strategies, specifically, we explore the optimal tradeoff between the queuing delay and power in various communication scenarios (single user and multi-user; flat fading and frequency-selective fading; perfect CSI vs. imperfect one, etc.), and further design the suboptimal strategies to minimize the transmission power under the constraint of QoS (delay) guarantee.
We first address the power-delay tradeoff properties and scheduling algorithms in the single-user flat-fading-channel scenario. Under mild conditions, the scheduling problem can be formulated as a MDP (Markov Decision Process), through the analysis of which we can find that the average transmission power is a non-increasing, strictly convex function of the average delay. As the complexity of the optimal solution which can be yielded by the dynamic programming method is very high, we propose a very simple scheme in which the scheduled rate is a logarithm function of the current queue length and channel state. Simulation results show that the performance loss of our suboptimal algorithm is very low. Furthermore, we prove the capability of our algorithm to keep the queue length bounded by utilizing the Lyapunov stability theorem. In addition, we take some practical issues into consideration by analyzing the scheduling algorithm under the condition of finite buffer and QAM modulation mode as well as the impact of the partial CSI over the scheduling performance.
In the flat fading channel, temporal diversity is an important means of decreasing transmission power, while in the frequency-selective channels, we can further exploit the spectral diversity in addition to the temporal one. We analyze the optimal scheduling strategy and propose suboptimal ones. In specific, we present two optimization models for the optimum strategy: one is based on the average-cost MDP based on infinite time horizon, the other one is formulated as a Linear Programming (LP) problem based on the“steady-state”probability. It can be proved that under the Markov-chain assumption of both the arrival process and the fading process the two seemingly different models are equivalent in essence. However, the complexity of the two models is both exponential with the states. To reduce the complexity, we design two suboptimal algorithms: the sub-slotting algorithm and the MLQHR+OFDM algorithm. For the former by transforming a two-dimensional optimization problem to one-dimensional one, we can reduce the complexity to just linear with the sub-carrier numbers; as for the latter, it is the extension of the simplified algorithm for the flat-fading scenario and is more useful in the frequency-selective channel due to its very low implementation complexity .Numerical results indicate that the sub-slotting algorithm only incurs as low as 2% performance loss compared with the optimum algorithm, while the performance degradation of ALQHR+OFDM algorithm is somewhat bigger.
In the last we discuss the multi-user scheduling strategies in the Multiple-Access Channel and Broadcast Channel, respectively. In specific, we analyze the two-user scenario and prove the same convexity property of power versus delay as in the single-user case. What’s different is that in the multi-user environment an additional form of diversity can be utilized. Similarly as the single-user scenario, we also present the simple algorithms for the above two channels. Compared with the optimum scheduling algorithms, our proposed algorithms exhibit about 15 percent loss in the power efficiency, while their complexities are much lower than the optimum one. In addition we analyze the power region in the MAC channel. We prove that for any given delay, there’s a unique power region in whose dominant boundary every point corresponds to the optimal solution to a convex power combination optimization problem. In addition, we present the detailed algorithm for computing the power region.
引文
[1]曹淑敏.中国移动通信发展趋势与战略研究.无线电技术与信息,2006,(11):6-10
[2]王京.第三代移动通信技术的发展.通讯世界, 2002, 8(12): 59-60
[3]张平.第四代移动通信系统的关键技术及分析.电信科学, 2002, 18(8): 30-34
[4]尤肖虎. FuTURE B3G研究开发及关键技术进展.移动通信, 2006, 30(6): 18-22
[5]王重钢,隆克平,龚向阳等.分组交换网络中队列调度算法的研究及其展.电子学报,2001, 29(4): 553-559
[6] Shimonishi H., Yoshida M.. An improvement of weighted round robin cell scheduling in ATM networks. In: Proceedings of the 1997 IEEE Global Telecommunications Conference. Phoenix, AZ, USA : 1997. 1119-1123
[7] Shreedhar M., Varghese G.. Efficient fair queueing using deficit round robin. IEEE/ ACM Transactions on Networking, 1996, 4(3): 375-385
[8] Altintas O., Atsumi Y., Yoshida T.. Urgency-based Round Robin: A new scheduling discipline for multiservice packet switching networks. IEICE Transactions on Communications, 1998, E81-B(11): 2013-2022
[9] Parekb A. K., Gallagerr G.. A generalized processor sharing approach to flow control in tegrated services networks: the single-node case. IEEE/ACM Trans. on Networking, 1993, 1(3): 344-357
[10] Bennett R., Zhang H.. WF2Q: Worst case fair weighted fair queueing. In: Proceedings - IEEE INFOCOM. San Francisco, CA, USA: 1996. 120-128
[11] Bennett J. C. R., Zhang H.. Hierarchical packet fair queueing algorithms. IEEE/ACM Trans. on Networking, 1997, 5(5): 675-689
[12] Golestani S. J.. A self-clocked fair queueing scheme for broad band applications. In: Proc IEEE INFOCOM. Toronto, Ont, Can: 1994. 636-645
[13] Chiussi F. M., Francini A.. Minimum delay self-clocked fair queueing algorithm for packet-switched networks. In: Proc IEEE INFOCOM. San Francisco, CA, USA: 1998. 1112-1121
[14] Goyal P., Vin H. M.. Start time fair queueing: A scheduling algorithm for integrated services packets witching networks. IEEE/ACM Trans. on Networking, 1997, 5(5): 690-703
[15] Varma A., Stiliadis D.. Hardware implementation of fair queueing algorithms for asynchronous transfer mode networks. IEEE Communications Magazine, 1997,35(12): 54- 67
[16] Stiliadis D., Varma A.. Efficient fair queueing algorithms for packet-switched networks. IEEE/ACM Trans. on Networking, 1998, 6(2): 175-185
[17] Stiliadis D., Varma A.. A general methodology for designing efficient traffic scheduling and shaping algorithm. In: Proc IEEE INFOCOM. Kobe, Jpn: 1997. 326-335
[18] Suri H., Varghese G., Chandranmenon G.. Leap forward virtual clock: a new fair queueing scheme with guaranteed delays and throughput fairness. In: Proc IEEE INFOCOM. Kobe, Jpn: 1997, 557-565
[19] Gurin R., Peris V.. Quality-of-service in packet networks basic mechanisms and directions. Computer Networks, 1999, 31(3): 169-179
[20] Zhang H., Ferrari D.. Rate-controled service discipines. Journal of high speed networks, 1995, 3 (4): 389-412
[21] Cruz L.. Quality of service guarantees in virtual circuits witched networks. IEEE Journal Selected Areas in Communications, 1995, 13( 6): 1048-1056
[22] Sanriowan H., Cruz L.. Scheduling for quality of services guarantees via service curves. In: Proc IEEE INFOCOM. Las Vegas, NV, USA : 1995. 512-520
[23] Sanriowan .H., Cruz R. L., Polyzos G. C.. SCED: A generalized scheduling policy for guaranteeing quality-of-service. IEEE/ACM Trans. on Networking, 1999, 7(5): 669-684
[24] Stoica I., Zhang H., Ng T. S. E.. Hierarchical fair service curve algorithm for link-sharing, real-time and priority services. IEEE/ACM Trans. On Networking, 2000, 8(2): 185-199
[25] Cao Y. X., Li V. O. K.. Scheduling algorithms in broad band wireless networks. Proceedings of the IEEE,2001, 89(1): 76-87
[26]朱光喜,梅朝晖.飞速发展的宽带移动IP技术.移动通信,2003, 3(27): 38-43
[27] Bhagwat P., Krishna A., Tripathi S.. Enhancing throughput over wireless LAN's using channel state dependent packet scheduling. In: Proc IEEE INFOCOM. San Francisco,CA ,USA. 1996. 1133-1140
[28] Floyd S., Jacobson V.. Link-sharing and resource management models for packet networks. IEEE/ACM Trans. Networking, 1995, 3(4): 365-386
[29] Lu S., Bharghavan V.. Fair scheduling in wireless packet networks. IEEE/ACM Trans. Networking, 1999, 7(4): 473-489
[30] Ng T. S. E., Stoica I., Zhang H.. Packet fair queueing algorithms for wireless networks with location-dependent errors. In: Proc IEEE INFOCOM. San Francisco,CA ,USA: 1998. 1103-1111
[31] Wang K. C., Chin Y. L.. A fair scheduling algorithm with adaptive compensation in wireless networks. In: Conf Rec IEEE Global Telecommun Conf. San Antonio, TX, USA: 2001. 3543-3547
[32] Ramanathan P., grawal A. P.. Adapting packet fair queuing algorithms to wireless networks. In: Proc.of ACM MOBIGOM. Dallas, TX,USA: 1998. 1-9
[33] Ramakrishna S., Holtzman J.. A scheme for throughput maximization in a dual-class CDMA system. IEEE Journal on Selected Areas in Communications, 1998, 6(6): 830-844.
[34] Jalali A., Padovani R., Pankaj R.. Data throughput of CDMA-HDR a high efficiency-high data rate personal communication wireless system. In: IEEE Vehicular Technology Conference. Tokyo, Japan: 2000. 1854-1858
[35] Chandrakasan A., Brodersen R.. Minimizing Power Consumption in Digital CMOS Circuits. Proceedings of the IEEE, 1995, 83(4): 498-523
[36] Larson L.. Radio Frequency Integrated Circuit Technology for Low-power Wireless Communications. IEEE Personal Communications, 1998, 5(3): 11-19
[37] Wieselthier J., Nguyen G., Ephremides A.. Resource-Limited Energy-Efficient Wireless Multicast of Session Traffic. In: Proc Hawaii Int Conf Syst Sci. Maui, HI, USA: 2001. 3460-3469
[38] Lorch J., Smith A.. Software Strategies for Portable Computer Energy Management. IEEE Personal Communication, 1998, 5(3): 60-73
[39] Goldsmith A. J., Varaiya P.. Capacity of Fading Channels with Channel Side Information. IEEE Transaction on Information Theory, 1997, 43(6): 1986-1992
[40] Bigieri E., Caire G.., Taricco G.. Coding and Modulation under Power Constraints. IEEE Personal Communications, 1998, 5(3): 32-39
[41] Sung C. W., Wong W. S.. Power control and rate management for wireless multimedia CDMA systems. IEEE Transactions on Communications, 2001, 49(7): 1215-1226
[42] Knopp R., Humblet P. A.. Information capacity and power control in single cell multiuser communications. In: Proceedings of International Conference on Communications. Seattle, USA: 1995. 331-335
[43] Bettesh I., Shamai S.. A Low delay algorithm for the multiple access channel with Rayleigh fading. In: PIMRC. Boston, MA, USA: 1998. 1367-1372
[44] Qiu X. X., Chuang J.. Link adaptation in wireless data networks for throughput maximization under retransmissions. In: Proceedings of International Conference on Communications. Vancouver, Canada: 1999. 1272-1277
[45] Chockalingam A., Zorzi M.. Energy Efficiency of Media Access Protocols for Mobile Data Networks. IEEE Trans Communications, 1998, 46(11): 1418-1421
[46] Zorzi M., Rao R. R.. Energy Constrained Error Control for Wireless Channels. IEEE Personal Communications, 1997, 4(6): 27-33
[47] Woesner H., Ebert J., Schlager M., et al. Power-Saving Mechanisms in Emerging StAndards for Wireless LANs: the MAC Level Perspective. IEEE Personal Communications, 1998, 5(3): 40-48
[48] Agrawal P.. Energy Efficient Protocols for Wireless Systems. In: Proc. Of IEEE Inte rnational Symposium on Personal, Indoor and Mobile Radio Communications. New York, USA: 1998. 564-569
[49] Nakano K., Olariu. S.. Energy-Efficient Initialization Protocols for Radio Networks with no Collision Detecrion. IEEE Transactions on Parallel and Distributed Systems, 2000, 11(2): 544-557
[50] Meng T., Hung A., Esern E., et al. Low-Power Signal Processing System Design for Wireless Applications. IEEE Personal Communications, 1998, 5(3): 20-233
[51] Horn U., Girod B., Belzer B.. Scalable Video Coding for Multimedia Applications and Robust Transmission over Wireless Channels. In: Proc. of the 7th Workshop on Packet Video. Brisbane, Australia: 1996. 43-48
[52] Zhang Q., Zhu W., Ji Zu., et al. A Power Optimized Joint Source Channel Coding for Scalable Video Streaming over Wireless Channel. In: Proc. of IEEE International Symposium on Circuits and Systems. Sydney, Australia: 2001. 137-147
[53] Appadwedula S., Goel M., Jones D. L., et al. Efficient Wireless Image Transmission under a Total Power Constraint. In: Proc. of IEEE Signal Processing Society, Second Workshop on Multimedia Signal Processing. Redondo Beach, CA, USA: 1998. 573-578
[54] Shakkottai S., Rappaport T. S., Karlesson P. C.. Cross-layer Design for Wireless Networks. IEEE Communications Magazine, 2003, 41(10): 74-80
[55] Cameiro G., Ruela J., Ricardo M.. Cross-layer Design in 4G Wireless Terminals. IEEE Wireless Communications, 2004, 11(2): 7-13
[56] Bhagwat P., Bhattacharya P., Krishna A., et al. Using Channel State Dependent Packet Scheduling to Improve TCP Throughput over Wireless LANs. Wireless Networks, 1997, 3(1): 91-102
[57] Telatar I. E., Gallager R.. Combining Queueing Theory with Information Theory for Multiaccess. IEEE Journal Selected Areas in Communications, 1995, 13(6): 963-969
[58] Yeh .E.. An Inter-Layer View of Multiaccess Communications. In: IEEE International Symposium on Information Theory-Proceedings. Lausanne, Switzerland: 2002.112
[59] Yeh E.. Delay-optimal rate allocation in multiaccess communications: a cross-layer view. In: Proceedings of IEEE Workshop on Multimedia Signal Processing. St.Thomas, VI, USA : 2002. 404-407
[60] Yeh E. M., Cohen A. S.. A fundamental cross-layer approach to uplink resource allocation. In: Proc IEEE MilCommun Conf Monterey, CA, United States: 2003. 699-704
[61] Yeh E. M., Cohen A. S.. Throughput and delay optimal resource allocation in multiaccess fading channels. In: IEEE Int Symp Inf Theor Proc. Yokohama, Japan: 2003. 245-245.
[62] Yeh E. M.. Delay optimal multiaccess communication for general packet length distributions. In: IEEE Int Symp Inf Theor Proc. Chicago, IL, United States: 2004. 247
[63] Yeh, E. M.. Cohen A. S.. Throughput optimal power and rate control for queued multiaccess and broadcast communications. In: IEEE Int Symp Inf Theor Proc. Chicago, IL, United States: 2004. 112
[64] Boche H., Wiczanowski M.. Queueing theoretic optimal scheduling for multiple input multiple output multiple access channel. In: Proceedings of the 3rd IEEE International Symposium on Signal Processing and Information Technology. Darmstadt, Germany: 2003. 576-579
[65] Boche H., Wiczanowski M.. Stability region of arrival rates and optimal scheduling for MIMO-MAC- a cross-layer approach. In: 2004 International Zurich Seminar on Communications. Zurich, Switzerland: 2004. 18-21
[66] Boche H., Jorswieck E. A.. Channel aware scheduling for multiple antenna multiple access channels. In: Conf Rec Asilomar Conf Signals Syst Comput. Pacific Grove, CA, United States: 2003. 992-996
[67] Boche H., Wiczanowski M.. Optimal scheduling for high speed uplink packet access - a cross-layer approach. In: IEEE Veh Technol Conf. Milan, Italy: 2004. 2575-2579
[68] Neely M. J., Modiano E., Rohrs C. E.. Power allocation and routing in multibeam satellites with time-varying channels. Networking, IEEE/ACM Transactions on, 2003, 11(1): 138 -152
[69] ] Neely M. J.. Energy optimal control for time-varying wireless networks. Information Theory, IEEE Transactions on, 2006, 52(7): 2915-2934.
[70] Neely M. J., Modiano E., Rohrs C. E.. Dynamic power allocation and routing fortime-varying wireless networks. Selected Areas in Communications, IEEE Journal on, 2005, 23(1): 89-103
[71] Berry R. A., Gallager R. G.. Communication over fading channels with delay constraints. IEEE Trans. Inf. Theory, 2002, 48(5): 1135-1149
[72] Berry R. A.. Order optimal energy efficient transmission policies in the small delay regime Information Theory. In: IEEE Int Symp Inf Theor Proc. Chicago, IL, United States: 2004. 45
[73] Negi R., Goel S.. An information-theoretic approach to queuing in wireless channels with large delay bounds. In: GLOBECOM IEEE Global Telecommun. Conf. Dallas, TX, United States: 2004. 116-22
[74] Goyal M., Kumar A., Sharma V.. Power constrained and delay optimal policies for scheduling transmission over a fading channel. In: Proc IEEE INFOCOM. San Francisco, CA, United States: 2003. 311-320
[75] Qi Q., Milstein L. B., Vaman D. R.. Distributed Power and Scheduling Management for Mobile Ad Hoc Networks with Delay Constraints. In: MILCOM. Washington, DC, USA: 2006. 1-7
[76] Ergen S. C., Varaiya P.. PEDAMACS: power efficient and delay aware medium access protocol for sensor networks. Mobile Computing, IEEE Transactions on, 2006, 5(7): 920-930
[77] Rajan D., Sabharwal A., Aazhang B.. Delay and rate constrained transmission policies over wireless channels. In: Proc. Global Telecommunication. San Antonio, TX , United States: 2001. 806-810
[78] Collins B. E., Cruz R. L.. Transmission policies for time varying channels with average delay constraints. In: Proc. Allerton Conf. Communication, Control, Compute. 1999. 709-717
[79] Rajan D., Sabharwal A., Aazhang B.. Transmission policies for bursty traffic sources on wireless channels. In: Proc. Conf. Inf. Sci., Syst. Baltimore, MD, USA: 2001. 429-433
[80] Djonin D. V., Karmokar A. K., Bhargava V. K.. Optimal and suboptimal scheduling over time varying flat fading channels. IEEE Transactions on Wireless Communications, 2006, 5(2): 906-910
[81] Peng L. X., Zhu G. X., Lu X. F., et al. Delay constrained throughput maximization over fading channels. In: 6th International Conference on ITS Telecommunications Proceedings. Chengdu, China: 2006. 61-64
[82] Karmokar A. K., Djonin D. V., Bhargava V. K.. Delay-aware Power Adaptation for Incremental Redundancy Hybrid ARQ over Fading Channels with Memory. In:Proceedings of International Conference on Communications. Istanbul, Turkey: 2006. 4315-4320
[83] Rajan D., Sabharwal A., Aazhang B.. Delay-bounded packet scheduling of bursty traffic over wireless channels. Information Theory, IEEE Transactions on, 2004, 50(1): 125-144
[84] Hoang A. T., Motani M.. Buffer and channel adaptive transmission over fading channels with imperfect channel state information. In: Wireless Communications and Networking Conference. Atlanta, GA, United States: 2004. 1891-1896
[85] Hoang A. T., Motani M.. Buffer and channel adaptive modulation for transmission over fading channels. In: Proceedings of International Conference on Communications. Anchorage, AK, United States: 2003. 2748-2752
[86] Wang H., Mandayam N. B.. Opportunistic file transfer over a fading channel under energy and delay constraints. IEEE Trans Commun, 2005, 53(4): 632-644
[87] Wang H., Mandayam N. B.. Opportunistic packet scheduling with imperfect channel knowledge. In: IEEE Vehicular Technology Conference. Los Angeles, CA, United States: 2004. 2635-2639
[88] Karmokar A. K., Djonin D. V., Bhargava V. K.. Delay constrained rate and power adaptation over correlated fading channels. In: IEEE GLOBECOM. Dallas, TX, United States: 2004. 3448-3453
[89]路正南,张怀胜,运筹学. (第1版).合肥:中国科技大学出版社, 2004年
[90]彭烈新,朱光喜,蔡德钧.无线信道中基于时延约束下的一种调度策略.电子与信息学报(已录用).
[91] Wang H., Mandayam N. B.. A simple packet-transmission scheme for wireless data over fading channels. Communications, IEEE Transactions on, 2004, 52(7): 1055-1059.
[92] Zhang Y. J., Letaief K. B.. Adaptive resource allocation and scheduling for multiuser packet-based OFDM networks. In: IEEE International Conference on Communications. Paris, France: 2004. 2949-2953.
[93] Hossain J., Djonin D. V., Bhargava V. K.. Delay limited optimal and suboptimal power and bit loading algorithms for OFDM systems over correlated fading channels. In: Global Telecommunications Conference, IEEE. St. Louis, MO, United States: 2005. 2787-2792
[94] Jakes W. C.. Microwave Mobile Communications. New Jersey: IEEE Press, , 1993.
[95] Wang H. S., Moayeri N.. Finite-state markov channel- a useful model for radio communication channels. IEEE Transactions on Vehicular Technology, 1995, 44(1):473-479
[96] Viterbi A.. Principles of Digital Communication and Coding. McGraw-Hill, 1979.
[97] Wong K. K.. Stochastic power allocation using causal channel information for delay-limited communications. IEEE Communications Letters, 2006, 10(11): 748-750
[98] Berry R.. Power and Delay Trade-off in Fading Channels. [PhD thesis]. Cambridge, MA: Massachusetts Institute of Technology, June 2000.
[99] Ma D. J., Makowski A. M., Shwartz A.. Estimation and Optimal Control for Constrained Markov Chains. In: IEEE Conf. on Decision and Control. Athens, Greece: 1986. 994-999
[100] Kumar P. R., Meyn S. P.. Duality and linear programs for stability and performance analysis of queueing networks and scheduling policies. In: Proc IEEE Conf Decis Control. Lake Buena Vista, FL, USA: 1996. 4-17
[101] Foschini G. J., Salz J.. Digital communications over fading radio channels. In: Conference Record - International Conference on Communications. Boston, MA, USA: 1983. 429-456
[102] Misra S., Swami A., Tong L.. Optimal training over the Gauss-Markov fading channel: A cutoff rate analysis. In: ICASSP IEEE Int Conf Acoust Speech Signal Process Proc. Montreal, Que, Canada: 2004. 809-812.
[103] Jakes W.. Microwave Mobile Communications. New York: Wiley, 1974.
[104] Czylwik A.. Adaptive OFDM for wideband radio channels. In: Conf Rec IEEE Global Telecommun Conf. London, UK: 1994. 713-718
[105] Keller T., Hanzo L.. Adaptive multicarrier modulation: A convenient framework for time-frequency processing in wireless communications. Proceedings of the IEEE, 2000, 88(5): 611-642
[106] Bingham J. A. C.. Multicarrier Modulation for Data Transmission: An Idea Whose Time Has Come. IEEE Communications Magazine, 1990, 28(5): 5-14
[107] Fischer R. F. H., Huber J. B.. A new loading algorithm for discrete multitone transmission. In: Proc. Globecom. London, U.K.: 1996. 724-728
[108] Hughes-Hartogs D.. Emsemble modem structure for imperfect transmission media. U.S. patents Nos. 4,679,227(July 1987), 4,731,816(March 1988) and 4,833,706(May 1989).
[109] Chow P. S., Cioffi J. M., Bingham J. A. C.. A practical discrete multitone transceiver loading algorithm for data transmission over spectrally shaped channels. IEEE Trans. Commun., 1995, 43(2): 772-775
[110] Yeh E. M., Cohen. A. S.. A fundamental cross-layer approach to uplink resourceallocation. In: IEEE MILCOM. Monterey, CA, United States: 2003. 699-704
[111] Puterman M. L.. Markov Decision Processes: Discrete Stochastic Dynamic Programming. New York: John Wiley & Sons, 1994.
[112] Altman E.. Constrained Markov Decision Processes: Stochastic Modeling. London: Chapman and Hall/CRC, 1999.
[113]刘次华.随机过程(第二版).武汉:华中科技大学出版社, 2000. 108.
[114] Biglieri E., Proakis J., Shamai S.. Fading channels: information-theoretic and communications aspects. Information Theory, IEEE Transactions on,1998, 44(6): 2619-2692
[115] Andrews M., Kumaran K., Ramanan K., et al. Providing quality of service over a shared wireless link. IEEE Communications Magazine, 2001, 39(4): 150-154
[116] Cove T. M., Thomas J. A.. Elements of Information Theory. New York: Wiley, 1991.
[117] Zheng L., Thomas J. A.. Elements of Information Theory: A Fundamental Tradeoff in Multiple. In: IEEE ITST. Lausanne, Switzerland: 2002. 476-476
[118] Uthansakul P., Bialkowski M. E., Bialkowski K.. Modeling of wideband MIMO channel for indoor environments assuming ray clusters contributing to power delay profiles. In: IEEE Antennas and Propagation Society International Symposium. Albuquerque, NM, USA: 2006. 317-320
[2]王京.第三代移动通信技术的发展.通讯世界, 2002, 8(12): 59-60
[3]张平.第四代移动通信系统的关键技术及分析.电信科学, 2002, 18(8): 30-34
[4]尤肖虎. FuTURE B3G研究开发及关键技术进展.移动通信, 2006, 30(6): 18-22
[5]王重钢,隆克平,龚向阳等.分组交换网络中队列调度算法的研究及其展.电子学报,2001, 29(4): 553-559
[6] Shimonishi H., Yoshida M.. An improvement of weighted round robin cell scheduling in ATM networks. In: Proceedings of the 1997 IEEE Global Telecommunications Conference. Phoenix, AZ, USA : 1997. 1119-1123
[7] Shreedhar M., Varghese G.. Efficient fair queueing using deficit round robin. IEEE/ ACM Transactions on Networking, 1996, 4(3): 375-385
[8] Altintas O., Atsumi Y., Yoshida T.. Urgency-based Round Robin: A new scheduling discipline for multiservice packet switching networks. IEICE Transactions on Communications, 1998, E81-B(11): 2013-2022
[9] Parekb A. K., Gallagerr G.. A generalized processor sharing approach to flow control in tegrated services networks: the single-node case. IEEE/ACM Trans. on Networking, 1993, 1(3): 344-357
[10] Bennett R., Zhang H.. WF2Q: Worst case fair weighted fair queueing. In: Proceedings - IEEE INFOCOM. San Francisco, CA, USA: 1996. 120-128
[11] Bennett J. C. R., Zhang H.. Hierarchical packet fair queueing algorithms. IEEE/ACM Trans. on Networking, 1997, 5(5): 675-689
[12] Golestani S. J.. A self-clocked fair queueing scheme for broad band applications. In: Proc IEEE INFOCOM. Toronto, Ont, Can: 1994. 636-645
[13] Chiussi F. M., Francini A.. Minimum delay self-clocked fair queueing algorithm for packet-switched networks. In: Proc IEEE INFOCOM. San Francisco, CA, USA: 1998. 1112-1121
[14] Goyal P., Vin H. M.. Start time fair queueing: A scheduling algorithm for integrated services packets witching networks. IEEE/ACM Trans. on Networking, 1997, 5(5): 690-703
[15] Varma A., Stiliadis D.. Hardware implementation of fair queueing algorithms for asynchronous transfer mode networks. IEEE Communications Magazine, 1997,35(12): 54- 67
[16] Stiliadis D., Varma A.. Efficient fair queueing algorithms for packet-switched networks. IEEE/ACM Trans. on Networking, 1998, 6(2): 175-185
[17] Stiliadis D., Varma A.. A general methodology for designing efficient traffic scheduling and shaping algorithm. In: Proc IEEE INFOCOM. Kobe, Jpn: 1997. 326-335
[18] Suri H., Varghese G., Chandranmenon G.. Leap forward virtual clock: a new fair queueing scheme with guaranteed delays and throughput fairness. In: Proc IEEE INFOCOM. Kobe, Jpn: 1997, 557-565
[19] Gurin R., Peris V.. Quality-of-service in packet networks basic mechanisms and directions. Computer Networks, 1999, 31(3): 169-179
[20] Zhang H., Ferrari D.. Rate-controled service discipines. Journal of high speed networks, 1995, 3 (4): 389-412
[21] Cruz L.. Quality of service guarantees in virtual circuits witched networks. IEEE Journal Selected Areas in Communications, 1995, 13( 6): 1048-1056
[22] Sanriowan H., Cruz L.. Scheduling for quality of services guarantees via service curves. In: Proc IEEE INFOCOM. Las Vegas, NV, USA : 1995. 512-520
[23] Sanriowan .H., Cruz R. L., Polyzos G. C.. SCED: A generalized scheduling policy for guaranteeing quality-of-service. IEEE/ACM Trans. on Networking, 1999, 7(5): 669-684
[24] Stoica I., Zhang H., Ng T. S. E.. Hierarchical fair service curve algorithm for link-sharing, real-time and priority services. IEEE/ACM Trans. On Networking, 2000, 8(2): 185-199
[25] Cao Y. X., Li V. O. K.. Scheduling algorithms in broad band wireless networks. Proceedings of the IEEE,2001, 89(1): 76-87
[26]朱光喜,梅朝晖.飞速发展的宽带移动IP技术.移动通信,2003, 3(27): 38-43
[27] Bhagwat P., Krishna A., Tripathi S.. Enhancing throughput over wireless LAN's using channel state dependent packet scheduling. In: Proc IEEE INFOCOM. San Francisco,CA ,USA. 1996. 1133-1140
[28] Floyd S., Jacobson V.. Link-sharing and resource management models for packet networks. IEEE/ACM Trans. Networking, 1995, 3(4): 365-386
[29] Lu S., Bharghavan V.. Fair scheduling in wireless packet networks. IEEE/ACM Trans. Networking, 1999, 7(4): 473-489
[30] Ng T. S. E., Stoica I., Zhang H.. Packet fair queueing algorithms for wireless networks with location-dependent errors. In: Proc IEEE INFOCOM. San Francisco,CA ,USA: 1998. 1103-1111
[31] Wang K. C., Chin Y. L.. A fair scheduling algorithm with adaptive compensation in wireless networks. In: Conf Rec IEEE Global Telecommun Conf. San Antonio, TX, USA: 2001. 3543-3547
[32] Ramanathan P., grawal A. P.. Adapting packet fair queuing algorithms to wireless networks. In: Proc.of ACM MOBIGOM. Dallas, TX,USA: 1998. 1-9
[33] Ramakrishna S., Holtzman J.. A scheme for throughput maximization in a dual-class CDMA system. IEEE Journal on Selected Areas in Communications, 1998, 6(6): 830-844.
[34] Jalali A., Padovani R., Pankaj R.. Data throughput of CDMA-HDR a high efficiency-high data rate personal communication wireless system. In: IEEE Vehicular Technology Conference. Tokyo, Japan: 2000. 1854-1858
[35] Chandrakasan A., Brodersen R.. Minimizing Power Consumption in Digital CMOS Circuits. Proceedings of the IEEE, 1995, 83(4): 498-523
[36] Larson L.. Radio Frequency Integrated Circuit Technology for Low-power Wireless Communications. IEEE Personal Communications, 1998, 5(3): 11-19
[37] Wieselthier J., Nguyen G., Ephremides A.. Resource-Limited Energy-Efficient Wireless Multicast of Session Traffic. In: Proc Hawaii Int Conf Syst Sci. Maui, HI, USA: 2001. 3460-3469
[38] Lorch J., Smith A.. Software Strategies for Portable Computer Energy Management. IEEE Personal Communication, 1998, 5(3): 60-73
[39] Goldsmith A. J., Varaiya P.. Capacity of Fading Channels with Channel Side Information. IEEE Transaction on Information Theory, 1997, 43(6): 1986-1992
[40] Bigieri E., Caire G.., Taricco G.. Coding and Modulation under Power Constraints. IEEE Personal Communications, 1998, 5(3): 32-39
[41] Sung C. W., Wong W. S.. Power control and rate management for wireless multimedia CDMA systems. IEEE Transactions on Communications, 2001, 49(7): 1215-1226
[42] Knopp R., Humblet P. A.. Information capacity and power control in single cell multiuser communications. In: Proceedings of International Conference on Communications. Seattle, USA: 1995. 331-335
[43] Bettesh I., Shamai S.. A Low delay algorithm for the multiple access channel with Rayleigh fading. In: PIMRC. Boston, MA, USA: 1998. 1367-1372
[44] Qiu X. X., Chuang J.. Link adaptation in wireless data networks for throughput maximization under retransmissions. In: Proceedings of International Conference on Communications. Vancouver, Canada: 1999. 1272-1277
[45] Chockalingam A., Zorzi M.. Energy Efficiency of Media Access Protocols for Mobile Data Networks. IEEE Trans Communications, 1998, 46(11): 1418-1421
[46] Zorzi M., Rao R. R.. Energy Constrained Error Control for Wireless Channels. IEEE Personal Communications, 1997, 4(6): 27-33
[47] Woesner H., Ebert J., Schlager M., et al. Power-Saving Mechanisms in Emerging StAndards for Wireless LANs: the MAC Level Perspective. IEEE Personal Communications, 1998, 5(3): 40-48
[48] Agrawal P.. Energy Efficient Protocols for Wireless Systems. In: Proc. Of IEEE Inte rnational Symposium on Personal, Indoor and Mobile Radio Communications. New York, USA: 1998. 564-569
[49] Nakano K., Olariu. S.. Energy-Efficient Initialization Protocols for Radio Networks with no Collision Detecrion. IEEE Transactions on Parallel and Distributed Systems, 2000, 11(2): 544-557
[50] Meng T., Hung A., Esern E., et al. Low-Power Signal Processing System Design for Wireless Applications. IEEE Personal Communications, 1998, 5(3): 20-233
[51] Horn U., Girod B., Belzer B.. Scalable Video Coding for Multimedia Applications and Robust Transmission over Wireless Channels. In: Proc. of the 7th Workshop on Packet Video. Brisbane, Australia: 1996. 43-48
[52] Zhang Q., Zhu W., Ji Zu., et al. A Power Optimized Joint Source Channel Coding for Scalable Video Streaming over Wireless Channel. In: Proc. of IEEE International Symposium on Circuits and Systems. Sydney, Australia: 2001. 137-147
[53] Appadwedula S., Goel M., Jones D. L., et al. Efficient Wireless Image Transmission under a Total Power Constraint. In: Proc. of IEEE Signal Processing Society, Second Workshop on Multimedia Signal Processing. Redondo Beach, CA, USA: 1998. 573-578
[54] Shakkottai S., Rappaport T. S., Karlesson P. C.. Cross-layer Design for Wireless Networks. IEEE Communications Magazine, 2003, 41(10): 74-80
[55] Cameiro G., Ruela J., Ricardo M.. Cross-layer Design in 4G Wireless Terminals. IEEE Wireless Communications, 2004, 11(2): 7-13
[56] Bhagwat P., Bhattacharya P., Krishna A., et al. Using Channel State Dependent Packet Scheduling to Improve TCP Throughput over Wireless LANs. Wireless Networks, 1997, 3(1): 91-102
[57] Telatar I. E., Gallager R.. Combining Queueing Theory with Information Theory for Multiaccess. IEEE Journal Selected Areas in Communications, 1995, 13(6): 963-969
[58] Yeh .E.. An Inter-Layer View of Multiaccess Communications. In: IEEE International Symposium on Information Theory-Proceedings. Lausanne, Switzerland: 2002.112
[59] Yeh E.. Delay-optimal rate allocation in multiaccess communications: a cross-layer view. In: Proceedings of IEEE Workshop on Multimedia Signal Processing. St.Thomas, VI, USA : 2002. 404-407
[60] Yeh E. M., Cohen A. S.. A fundamental cross-layer approach to uplink resource allocation. In: Proc IEEE MilCommun Conf Monterey, CA, United States: 2003. 699-704
[61] Yeh E. M., Cohen A. S.. Throughput and delay optimal resource allocation in multiaccess fading channels. In: IEEE Int Symp Inf Theor Proc. Yokohama, Japan: 2003. 245-245.
[62] Yeh E. M.. Delay optimal multiaccess communication for general packet length distributions. In: IEEE Int Symp Inf Theor Proc. Chicago, IL, United States: 2004. 247
[63] Yeh, E. M.. Cohen A. S.. Throughput optimal power and rate control for queued multiaccess and broadcast communications. In: IEEE Int Symp Inf Theor Proc. Chicago, IL, United States: 2004. 112
[64] Boche H., Wiczanowski M.. Queueing theoretic optimal scheduling for multiple input multiple output multiple access channel. In: Proceedings of the 3rd IEEE International Symposium on Signal Processing and Information Technology. Darmstadt, Germany: 2003. 576-579
[65] Boche H., Wiczanowski M.. Stability region of arrival rates and optimal scheduling for MIMO-MAC- a cross-layer approach. In: 2004 International Zurich Seminar on Communications. Zurich, Switzerland: 2004. 18-21
[66] Boche H., Jorswieck E. A.. Channel aware scheduling for multiple antenna multiple access channels. In: Conf Rec Asilomar Conf Signals Syst Comput. Pacific Grove, CA, United States: 2003. 992-996
[67] Boche H., Wiczanowski M.. Optimal scheduling for high speed uplink packet access - a cross-layer approach. In: IEEE Veh Technol Conf. Milan, Italy: 2004. 2575-2579
[68] Neely M. J., Modiano E., Rohrs C. E.. Power allocation and routing in multibeam satellites with time-varying channels. Networking, IEEE/ACM Transactions on, 2003, 11(1): 138 -152
[69] ] Neely M. J.. Energy optimal control for time-varying wireless networks. Information Theory, IEEE Transactions on, 2006, 52(7): 2915-2934.
[70] Neely M. J., Modiano E., Rohrs C. E.. Dynamic power allocation and routing fortime-varying wireless networks. Selected Areas in Communications, IEEE Journal on, 2005, 23(1): 89-103
[71] Berry R. A., Gallager R. G.. Communication over fading channels with delay constraints. IEEE Trans. Inf. Theory, 2002, 48(5): 1135-1149
[72] Berry R. A.. Order optimal energy efficient transmission policies in the small delay regime Information Theory. In: IEEE Int Symp Inf Theor Proc. Chicago, IL, United States: 2004. 45
[73] Negi R., Goel S.. An information-theoretic approach to queuing in wireless channels with large delay bounds. In: GLOBECOM IEEE Global Telecommun. Conf. Dallas, TX, United States: 2004. 116-22
[74] Goyal M., Kumar A., Sharma V.. Power constrained and delay optimal policies for scheduling transmission over a fading channel. In: Proc IEEE INFOCOM. San Francisco, CA, United States: 2003. 311-320
[75] Qi Q., Milstein L. B., Vaman D. R.. Distributed Power and Scheduling Management for Mobile Ad Hoc Networks with Delay Constraints. In: MILCOM. Washington, DC, USA: 2006. 1-7
[76] Ergen S. C., Varaiya P.. PEDAMACS: power efficient and delay aware medium access protocol for sensor networks. Mobile Computing, IEEE Transactions on, 2006, 5(7): 920-930
[77] Rajan D., Sabharwal A., Aazhang B.. Delay and rate constrained transmission policies over wireless channels. In: Proc. Global Telecommunication. San Antonio, TX , United States: 2001. 806-810
[78] Collins B. E., Cruz R. L.. Transmission policies for time varying channels with average delay constraints. In: Proc. Allerton Conf. Communication, Control, Compute. 1999. 709-717
[79] Rajan D., Sabharwal A., Aazhang B.. Transmission policies for bursty traffic sources on wireless channels. In: Proc. Conf. Inf. Sci., Syst. Baltimore, MD, USA: 2001. 429-433
[80] Djonin D. V., Karmokar A. K., Bhargava V. K.. Optimal and suboptimal scheduling over time varying flat fading channels. IEEE Transactions on Wireless Communications, 2006, 5(2): 906-910
[81] Peng L. X., Zhu G. X., Lu X. F., et al. Delay constrained throughput maximization over fading channels. In: 6th International Conference on ITS Telecommunications Proceedings. Chengdu, China: 2006. 61-64
[82] Karmokar A. K., Djonin D. V., Bhargava V. K.. Delay-aware Power Adaptation for Incremental Redundancy Hybrid ARQ over Fading Channels with Memory. In:Proceedings of International Conference on Communications. Istanbul, Turkey: 2006. 4315-4320
[83] Rajan D., Sabharwal A., Aazhang B.. Delay-bounded packet scheduling of bursty traffic over wireless channels. Information Theory, IEEE Transactions on, 2004, 50(1): 125-144
[84] Hoang A. T., Motani M.. Buffer and channel adaptive transmission over fading channels with imperfect channel state information. In: Wireless Communications and Networking Conference. Atlanta, GA, United States: 2004. 1891-1896
[85] Hoang A. T., Motani M.. Buffer and channel adaptive modulation for transmission over fading channels. In: Proceedings of International Conference on Communications. Anchorage, AK, United States: 2003. 2748-2752
[86] Wang H., Mandayam N. B.. Opportunistic file transfer over a fading channel under energy and delay constraints. IEEE Trans Commun, 2005, 53(4): 632-644
[87] Wang H., Mandayam N. B.. Opportunistic packet scheduling with imperfect channel knowledge. In: IEEE Vehicular Technology Conference. Los Angeles, CA, United States: 2004. 2635-2639
[88] Karmokar A. K., Djonin D. V., Bhargava V. K.. Delay constrained rate and power adaptation over correlated fading channels. In: IEEE GLOBECOM. Dallas, TX, United States: 2004. 3448-3453
[89]路正南,张怀胜,运筹学. (第1版).合肥:中国科技大学出版社, 2004年
[90]彭烈新,朱光喜,蔡德钧.无线信道中基于时延约束下的一种调度策略.电子与信息学报(已录用).
[91] Wang H., Mandayam N. B.. A simple packet-transmission scheme for wireless data over fading channels. Communications, IEEE Transactions on, 2004, 52(7): 1055-1059.
[92] Zhang Y. J., Letaief K. B.. Adaptive resource allocation and scheduling for multiuser packet-based OFDM networks. In: IEEE International Conference on Communications. Paris, France: 2004. 2949-2953.
[93] Hossain J., Djonin D. V., Bhargava V. K.. Delay limited optimal and suboptimal power and bit loading algorithms for OFDM systems over correlated fading channels. In: Global Telecommunications Conference, IEEE. St. Louis, MO, United States: 2005. 2787-2792
[94] Jakes W. C.. Microwave Mobile Communications. New Jersey: IEEE Press, , 1993.
[95] Wang H. S., Moayeri N.. Finite-state markov channel- a useful model for radio communication channels. IEEE Transactions on Vehicular Technology, 1995, 44(1):473-479
[96] Viterbi A.. Principles of Digital Communication and Coding. McGraw-Hill, 1979.
[97] Wong K. K.. Stochastic power allocation using causal channel information for delay-limited communications. IEEE Communications Letters, 2006, 10(11): 748-750
[98] Berry R.. Power and Delay Trade-off in Fading Channels. [PhD thesis]. Cambridge, MA: Massachusetts Institute of Technology, June 2000.
[99] Ma D. J., Makowski A. M., Shwartz A.. Estimation and Optimal Control for Constrained Markov Chains. In: IEEE Conf. on Decision and Control. Athens, Greece: 1986. 994-999
[100] Kumar P. R., Meyn S. P.. Duality and linear programs for stability and performance analysis of queueing networks and scheduling policies. In: Proc IEEE Conf Decis Control. Lake Buena Vista, FL, USA: 1996. 4-17
[101] Foschini G. J., Salz J.. Digital communications over fading radio channels. In: Conference Record - International Conference on Communications. Boston, MA, USA: 1983. 429-456
[102] Misra S., Swami A., Tong L.. Optimal training over the Gauss-Markov fading channel: A cutoff rate analysis. In: ICASSP IEEE Int Conf Acoust Speech Signal Process Proc. Montreal, Que, Canada: 2004. 809-812.
[103] Jakes W.. Microwave Mobile Communications. New York: Wiley, 1974.
[104] Czylwik A.. Adaptive OFDM for wideband radio channels. In: Conf Rec IEEE Global Telecommun Conf. London, UK: 1994. 713-718
[105] Keller T., Hanzo L.. Adaptive multicarrier modulation: A convenient framework for time-frequency processing in wireless communications. Proceedings of the IEEE, 2000, 88(5): 611-642
[106] Bingham J. A. C.. Multicarrier Modulation for Data Transmission: An Idea Whose Time Has Come. IEEE Communications Magazine, 1990, 28(5): 5-14
[107] Fischer R. F. H., Huber J. B.. A new loading algorithm for discrete multitone transmission. In: Proc. Globecom. London, U.K.: 1996. 724-728
[108] Hughes-Hartogs D.. Emsemble modem structure for imperfect transmission media. U.S. patents Nos. 4,679,227(July 1987), 4,731,816(March 1988) and 4,833,706(May 1989).
[109] Chow P. S., Cioffi J. M., Bingham J. A. C.. A practical discrete multitone transceiver loading algorithm for data transmission over spectrally shaped channels. IEEE Trans. Commun., 1995, 43(2): 772-775
[110] Yeh E. M., Cohen. A. S.. A fundamental cross-layer approach to uplink resourceallocation. In: IEEE MILCOM. Monterey, CA, United States: 2003. 699-704
[111] Puterman M. L.. Markov Decision Processes: Discrete Stochastic Dynamic Programming. New York: John Wiley & Sons, 1994.
[112] Altman E.. Constrained Markov Decision Processes: Stochastic Modeling. London: Chapman and Hall/CRC, 1999.
[113]刘次华.随机过程(第二版).武汉:华中科技大学出版社, 2000. 108.
[114] Biglieri E., Proakis J., Shamai S.. Fading channels: information-theoretic and communications aspects. Information Theory, IEEE Transactions on,1998, 44(6): 2619-2692
[115] Andrews M., Kumaran K., Ramanan K., et al. Providing quality of service over a shared wireless link. IEEE Communications Magazine, 2001, 39(4): 150-154
[116] Cove T. M., Thomas J. A.. Elements of Information Theory. New York: Wiley, 1991.
[117] Zheng L., Thomas J. A.. Elements of Information Theory: A Fundamental Tradeoff in Multiple. In: IEEE ITST. Lausanne, Switzerland: 2002. 476-476
[118] Uthansakul P., Bialkowski M. E., Bialkowski K.. Modeling of wideband MIMO channel for indoor environments assuming ray clusters contributing to power delay profiles. In: IEEE Antennas and Propagation Society International Symposium. Albuquerque, NM, USA: 2006. 317-320