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Joint Relay Scheduling, Channel Access, and Power Allocation for Green Cognitive Radio Communications

机译:绿色认知无线电通信的联合中继调度,信道访问和功率分配

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摘要

The capacity of cognitive radio (CR) systems can be enhanced significantly by deploying relay nodes to exploit the spatial diversity. However, the inevitable imperfect sensing in CR has vital effects on the policy of relay selection, channel access, and power allocation that play pivotal roles in the system capacity. The increase in transmission power can improve the system capacity, but results in high energy consumption, which incurs the increase of carbon emission and network operational cost. Most of the existing schemes for CR systems have not jointly considered the imperfect sensing scenario and the tradeoff between the system capacity and energy consumption. To fill in this gap, this paper proposes an energy-aware centralized relay selection scheme that takes into account the relay selection, channel access, and power allocation jointly in CR with imperfect sensing. Specifically, the CR system is formulated as a partially observable Markov decision process (POMDP) to achieve the goal of balancing the system capacity and energy consumption as well as maximizing the system reward. The optimal policy for relay selection, channel access, and power allocation is then derived by virtue of a dynamic programming approach. A dimension reduction strategy is further applied to reduce its high computation complexity. Extensive simulation experiments and results are presented and analysed to demonstrate the significant performance improvement compared to the existing schemes. The performance results show that the received reward increases more than 50% and the network lifetime increases more than 35%, but the system capacity is reduced less than 6% only.
机译:通过部署中继节点来利用空间分集,可以显着增强认知无线电(CR)系统的容量。但是,CR中不可避免的不完善的传感对中继选择,信道访问和功率分配策略起着至关重要的作用,这些策略在系统容量中起着至关重要的作用。传输功率的增加可以提高系统容量,但会导致高能耗,从而导致碳排放量和网络运营成本的增加。现有的大多数CR系统方案都没有共同考虑不完善的传感方案以及系统容量和能耗之间的权衡。为了填补这一空白,本文提出了一种能量感知的集中式中继选择方案,该方案将具有不完善感测的CR中的中继选择,信道访问和功率分配共同考虑在内。具体而言,CR系统被公式化为部分可观察的马尔可夫决策过程(POMDP),以实现平衡系统容量和能耗以及最大化系统回报的目标。然后借助动态编程方法得出用于中继选择,信道访问和功率分配的最佳策略。进一步应用降维策略来降低其高计算复杂度。进行了广泛的仿真实验和结果并进行了分析,以证明与现有方案相比性能有了显着提高。性能结果表明,收到的奖励增加了50%以上,网络寿命增加了35%以上,但是系统容量减少的幅度仅不到6%。

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