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Low-Complexity MIMO Detection Based on Reinforcement Learning With One-Bit ADCs
IEEE Transactions on Vehicular Technology ( IF 6.8 ) Pub Date : 2021-07-26 , DOI: 10.1109/tvt.2021.3099228
Tae-kyoung Kim , Yo-Seb Jeon , Moonsik Min

This paper proposes a low-complexity reinforcement learning detection (RLD) algorithm for multi-input multi-output systems with one-bit analog-to-digital converters. The proposed algorithm exploits pairs of quantized received signals and detected symbols as training examples to train the likelihood function (LF) of the system. A major challenge in optimizing the RLD algorithm is to determine the optimal policy that decides whether to exploit the training examples based on their reliabilities. Determining the optimal policy inherently involves huge complexities in reflecting all possible transitions among candidate symbols. Thus, we simplify the optimal policy by considering only the most probable candidates among all possible decisions to reduce this complexity. Another major challenge in applying the RLD algorithm is that it requires high computational complexity to produce soft information for detection. Thus, we define new branch and path metrics derived from the LF and then remove the candidate symbols whose path metrics are smaller than a pre-defined value to alleviate the complexity. Moreover, we analyze the complexity of the proposed algorithm by deriving the expected number of surviving candidates. Simulation results show that the proposed algorithm provides a better performance-complexity tradeoff than the conventional RLD algorithm.

中文翻译:

基于 1 位 ADC 的强化学习的低复杂度 MIMO 检测

本文提出了一种低复杂度的强化学习检测 (RLD) 算法,用于具有一位模数转换器的多输入多输出系统。所提出的算法利用成对的量化接收信号和检测符号作为训练示例来训练系统的似然函数(LF)。优化 RLD 算法的一个主要挑战是确定最佳策略,根据它们的可靠性来决定是否利用训练示例。确定最佳策略本质上涉及反映候选符号之间所有可能转换的巨大复杂性。因此,我们通过仅考虑所有可能决策中最可能的候选者来简化最优策略,以降低这种复杂性。应用 RLD 算法的另一个主要挑战是它需要很高的计算复杂度来产生用于检测的软信息。因此,我们定义从 LF 派生的新分支和路径度量,然后删除路径度量小于预定义值的候选符号以减轻复杂性。此外,我们通过推导幸存候选者的预期数量来分析所提出算法的复杂性。仿真结果表明,所提出的算法比传统的 RLD 算法提供了更好的性能-复杂度权衡。我们通过推导存活候选者的预期数量来分析所提出算法的复杂性。仿真结果表明,所提出的算法比传统的 RLD 算法提供了更好的性能-复杂度权衡。我们通过推导存活候选者的预期数量来分析所提出算法的复杂性。仿真结果表明,所提出的算法比传统的 RLD 算法提供了更好的性能-复杂度权衡。
更新日期:2021-09-21
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