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Sensing time and power optimization in MIMO cognitive radio networks

机译:MIMO认知无线电网络中的传感时间和功率优化

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摘要

In this paper, we investigate the sensing-throughput tradeoff in multi-antenna cognitive radio (CR) systems. Specifically, we optimize the sensing threshold, sensing time, and transmit power of a multi-input multi-output (MIMO) CR system for maximization of the opportunistic system throughput under transmit power and probability of false alarm and detection constraints. To this end, we propose a new transmission protocol which allows the CR user to simultaneously perform data transmission and spectrum sensing on different spatial subchannels. We formulate a non-convex optimization problem for the optimal choice of the sensing threshold, sensing times, and transmit powers in the different spatial subchannels of MIMO CR systems. Since finding the global optimal solution entails a very high complexity, we develop an efficient iterative algorithm that is based on the concept of alternating optimization and solves only convex subproblems in each iteration. Simulation results show that the developed algorithm closely approaches the global optimal performance and achieves significant performance gains compared to baseline schemes employing equal powers or equal sensing times in all subcannels.
机译:在本文中,我们研究了多天线认知无线电(CR)系统中的传感吞吐量权衡。具体来说,我们优化了多输入多输出(MIMO)CR系统的检测阈值,检测时间和发送功率,以在发送功率以及错误警报和检测约束的可能性下最大化机会系统吞吐量。为此,我们提出了一种新的传输协议,该协议允许CR用户在不同的空间子信道上同时执行数据传输和频谱感测。我们为MIMO CR系统的不同空间子信道中的感应阈值,感应时间和发射功率的最佳选择制定了一个非凸优化问题。由于找到全局最优解需要非常高的复杂性,因此我们开发了一种高效的迭代算法,该算法基于交替优化的概念,并且每次迭代仅解决凸子问题。仿真结果表明,与在所有子通道中采用相等功率或相等感测时间的基线方案相比,所开发的算法紧密接近全局最佳性能,并获得了显着的性能提升。

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