认知无线电系统中信道状态转移概率估计和接入策略

Channel State Transition Probabilities Estimation and Access for Cognitive Radio Systems

摘要: 在认知无线电系统的设计中，经常将首要用户的信道状态用马尔可夫模型描述，并依此设计次要用户的信道接入策略。为了更好地设计次要用户的频谱接入策略，我们需要计算出首要用户马尔可夫模型的具体参数。在本文中，基于离散马尔可夫模型，我们提出了一个对首要用户的信道转移概率进行估计的算法。该算法采用了最大似然准则来估计信道状态转移概率。另外，我们运用中心极限定理来分析算法的精确度和观测采样点数目之间的关系。在此基础上，我们给出了一个次要用户的频谱接入策略：在给定冲突限制的条件下，以最大化有效传输吞吐量为目标，求解最优传输时间。仿真结果证明了我们所提出的状态转移概率估计算法具有较强的精确性；同时，提出的频谱接入策略能够使有效传输吞吐量获得最大。

Abstract: In Cognitive Radio (CR) systems, secondary user’s (SU) spectrum access scheme is always designed when primary user’s (PU) channel state is formulated as a Markov model. To better design the spectrum access scheme for SUs in CR systems, we should figure out the detailed parameters for PUs Markov channel model. In this paper, we propose an estimation algorithm for PU’s channel state transition probabilities based on a discrete-time Markov process. Maximum likelihood method was adopted to obtain the channel state transition probabilities. Besides, Central Limit Theorem is utilized to build the relationship between estimation precision and the number of converging observation samples. Furthermore, we also propose a SU’s spectrum access scheme based on an optimal transmission time, which maximizes the transmission throughput with respect to a given collision constrain. Simulation results demonstrate the precision of the proposed estimation algorithm and the efficiency of the proposed spectrum access scheme.