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Enhanced Receive Spatial Modulation Based on Power Allocation

机译:基于功率分配的增强型接收空间调制

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In this paper, we investigate the benefits of power allocation (PA) for multiple-input multiple-output (MIMO) receive spatial modulation (RSM) with both a total transmit power constraint (TTPC) and a per-antenna power constraint (PAPC). First, we derive optimal PA closed-form solutions that maximize the minimum distance d(min) between the received signal points for (N-t x 2)-element RSM with arbitrary phase-shift keying schemes (where N-t is the number of transmit antennas) subject to a TTPC. Based on the derived solutions and the error vector reduction (EVR) method, we propose a low-complexity iterative algorithm to identify PA parameters for high numbers of receive antennas (N-r >= 2). Specifically, the EVR-based PA (EVR-PA) algorithm resembles its traditional exhaustive-search-based counterpart, but only exploits the receive distances of a few dominant error vectors to iteratively optimize the PA matrix. Then, a more strict yet practical PAPC is considered for PA in RSM-MIMO systems, and a well-designed approximate convex optimization (ACO)-based iterative PA algorithm is proposed. Compared to EVR-PA, the ACO-based PA (ACO-PA) algorithm first formulates the PA problems with the PAPC in RSM into constrained quadratic program problems and then utilizes the powerful augmented Lagrangian multiplier to find their optimal solutions. Our simulation results show that the proposed EVR-PA- and ACO-PA-aided RSM schemes outperform the equal-power-allocated RSM- and PA-aided spatial multiplexing schemes.
机译:在本文中,我们研究了具有总发射功率约束(TTPC)和每个天线功率约束(PAPC)的多输入多输出(MIMO)接收空间调制(RSM)的功率分配(PA)的好处。首先,我们推导出最佳的PA闭式解,该解以任意相移键控方案(其中Nt是发射天线的数量)使(Nt x 2)元素RSM的接收信号点之间的最小距离d(min)最大。受TTPC约束。基于导出的解决方案和误差矢量减少(EVR)方法,我们提出了一种低复杂度的迭代算法来识别大量接收天线(N-r> = 2)的PA参数。具体来说,基于EVR​​的功率放大器(EVR-PA)算法类似于其传统的基于穷举搜索的功率放大器,但是仅利用一些主要误差向量的接收距离来迭代优化功率放大器矩阵。然后,针对RSM-MIMO系统中的功率放大器考虑了更为严格而又实用的功率放大器,提出了一种设计良好的基于​​近似凸优化(ACO)的迭代功率放大器算法。与EVR-PA相比,基于ACO的PA(ACO-PA)算法首先将RSM中PAPC中的PA问题公式化为约束的二次程序问题,然后利用功能强大的增强拉格朗日乘子找到最佳解决方案。我们的仿真结果表明,提出的EVR-PA和ACO-PA辅助的RSM方案优于等功率分配的RSM和PA的空间复用方案。

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