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机会式频谱接入优化问题的研究 被引量：1

Study on opportunistic spectrum access optimization problem
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摘要 研究机会式频谱接入技术中探测与接入策略的优化问题.首先,以与原问题等价的信度马尔可夫决策过程为基本模型,基于性能势的核心概念,从性能灵敏度的角度出发,分析不同策略下系统的性能差异,给出了优化探测与接入策略的迭代算法;然后,通过分析系统的样本路径,结合该问题中连续状态空间可集结的特点,进一步讨论了策略迭代算法的基于样本路径的具体实现.两个仿真示例验证了算法的有效性. The sense and access optimization problem in opportunistic spectrum access technology is considered.Based on the belief Markov decision process model,which is equivalent to the original partially observable Markov decision process,the performance differences between two different policies are investigated from a sensitivity-based view with the help of the performance potential.Then the policy iteration algorithm is designed.By analyzing the sample path of the system,the sample-path based policy iteration algorithm is developed.Two examples are provided to illustrate the effectiveness of the algorithm.
作者 黄永皓 陈曦
机构地区 清华大学智能技术与系统国家重点实验室 清华大学智能与网络化系统研究中心
出处 《控制与决策》 EI CSCD 北大核心 2010年第6期857-861,866,共6页 Control and Decision
基金 国家自然科学基金项目(60574064 60736027)
关键词 机会式频谱接入技术 信度马尔可夫决策过程 性能势 策略迭代 Opportunistic spectrum access Belief Markov decision process Performance potential Policy iteration
分类号 TP13 [自动化与计算机技术—控制理论与控制工程]
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参考文献10

  • 1Zhao Q, Sadler B M. A survey of dynamic spectrum access[J]. IEEE Signal Processing Magazine, 2007, 24(3): 79-89.
  • 2Zhao Q, Tong L, Swami A, et al. Decentralized cognitive MAC for opportunistic spectrum access in ad hoc networks: A POMDP framework[C]. Proc of 1st IEEE Int Symposium on New Frontiers in Dynamic Spectrum Access Networks. Baltimore: IEEE Press, 2005: 589-600.
  • 3Zhao Q, Krishnamachari B, Liu K Q. Low-complexity approaches to spectrum opportunity tracking[C]. Proc of 2nd Int Conf on Cognitive Radio Oriented Wireless Networks and Communications. Orlando: IEEE Press, 2007: 27-35.
  • 4Zhao Q, Krishnamachari.B. Structure and optimality of myopic sensing for opportunistic spectrum access[C]. Proc of IEEE Int Conf on Communications 2007. Glasgow: IEEE Press, 2007: 6476-6481.
  • 5Zhao Q, Krishnamachari B, Liu K Q. On myopic sensing for multi-channel opportunistic access: Structure, optimality, and performance[J]. IEEE Trans on Wireless Communications, 2008, 7(12): 5431-5440.
  • 6Javidi T, Krishnamachari B, Zhao Q, et al. Optimality of myopic sensing in multi-channel opportunistic access[C]. Proc of IEEE int Conf on Communications 2008. Beijing: IEEE Press, 2008: 2107-2112.
  • 7Zhao Q, Geirhofer S, Tong L, et al. Opportunistic spectrum access via periodic channel sensing[J]. IEEE Trans on Signal Processing, 2008, 56(2): 785-796.
  • 8Cao X R, Guo X P. Partially observable Markov decision processes with reward information: Basic ideas and models[J]. IEEE Trans on Automatic Control, 2007, 52(4):677-681.
  • 9Cao X R. Stochastic learning and optimization: A sensitivity-based approach[M]. New York: Springer, 2007.
  • 10Sutton R S, Barto A G. Reinforcement learning: An introduction[M]. Cambridge: The MIT Press, 1998.

同被引文献9

  • 1Zhao Q, Sadler B M. A survey of dynamic spectrum access[J]. IEEE Signal Processing Magazine, 2007, 24(3): 79-89.
  • 2Zhao Q, Tong L, Swami A, et al. Decentralized cognitive MAC for opportunistic spectrum access in ad hoe networks: A POMDP framework[C]. Proc of 1st IEEE Int Symposium on New Frontiers in Dynamic Spectrum Access Networks. Baltimore: IEEE Press, 2005: 589-600.
  • 3Zhao Q, Krishnamachari B, Liu K Q. Low-complexity approaches to spectrum opportunity tracking[C]. Proc of2nd Int Conf Cognitive Radio Oriented Wireless Networks and Communications. Orlando: IEEE Press, 2007: 27-35.
  • 4Zhao Q, Krishnamachari B, Liu K Q. On myopic sensing for multi-channel opportunistic access: Structure, optimality and performance[J]. IEEE Trans on Wireless Communications, 2008, 7(12): 5431-5440.
  • 5Ahmad S H, Liu M, Javidi T, et al. Optimality of myopic sensing in multi-channel opportunistic access[J]. IEEE Trans on Information Theory, 2009, 55(9): 4040-4050.
  • 6Zhao Q, Geirhofer S, Tong L, et al. Opportunistic spectrum access via periodic channel sensing[J]. IEEE Trans on Signal Processing, 2008, 56(2): 785-796.
  • 7Cao X R. Basic ideas for event-based optimization of Markov systems[J]. Discrete Event Dynamic Systems, 2005, 15(2): 169-197.
  • 8Cao X R. Stochastic learning and optimization: A sensitivity-based approach[M]. New York: Springer, 2007: 387-446.
  • 9Sutton R S, Barto A G. Reinforcement learning: An introduction[M]. Cambridge: The MIT Press, 1998: 122.

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控制与决策
2010年 第6期

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