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arXiv 2008.06993cs.ITmath.IT
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不完美信道状态信息下具有优化资源分配的Box-RLS译码器的渐近性能

Asymptotic Performance of Box-RLS Decoders under Imperfect CSI with Optimized Resource Allocation

Ayed M. Alrashdi, Abla Kammoun, Ali H. Muqaibel, Tareq Y. Al-Naffouri

AI总结:

本文针对大规模MIMO系统在不完美信道状态信息下的符号检测问题,利用CGMT推导了无约束RLS和盒约束RLS译码器的MSE与SER渐近表达式,并在此基础上优化导频与数据的功率分配及训练符号数量。

摘要:

本文研究大规模多输入多输出(MIMO)无线通信系统中的符号检测问题。我们考虑在硬阈值处理之前采用两种正则化最小二乘(RLS)译码器的变体,即无约束RLS和带盒约束的RLS。对于所有方案,我们重点评估当信道使用线性最小均方误差(LMMSE)估计器进行估计时,在大规模MIMO系统上传输的M进制脉冲幅度调制(M-PAM)符号的均方误差(MSE)和符号错误概率(SEP)。在这种情况下,信道估计误差是高斯分布的,这使得可以利用凸高斯极小极大定理(CGMT)在系统维数和相干时长以相同速率增长时推导MSE和SER的渐近近似。随后利用所得表达式,在总发射能量约束下推导导频与数据之间的最优功率分配。此外,我们为所有方案推导了有效吞吐量的渐近近似,并利用该近似联合优化训练符号的数量及其相关功率。数值结果验证了理论结果的准确性。

英文摘要:

This paper considers the problem of symbol detection in massive multiple-input multiple-output (MIMO) wireless communication systems. We consider hard-thresholding preceeded by two variants of the regularized least squares (RLS) decoder; namely the unconstrained RLS and the RLS with box constraint. For all schemes, we focus on the evaluation of the mean squared error (MSE) and the symbol error probability (SEP) for M-ary pulse amplitude modulation (M-PAM) symbols transmitted over a massive MIMO system when the channel is estimated using linear minimum mean squared error (LMMSE) estimator. Under such circumstances, the channel estimation error is Gaussian which allows for the use of the convex Gaussian min-max theorem (CGMT) to derive asymptotic approximations for the MSE and SER when the system dimensions and the coherence duration grow large with the same pace. The obtained expressions are then leveraged to derive the optimal power distribution between pilot and data under a total transmit energy constraint. In addition, we derive an asymptotic approximation of the goodput for all schemes which is then used to jointly optimize the number of training symbols and their associated power. Numerical results are presented to support the accuracy of the theoretical results.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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