In this article, we propose an orthogonal frequency-division multiplexing system supported by the compressed sensing assisted index modulation, termed as (OFDM-CSIM), applied to millimeter-wave (mmWave) communications. In the OFDM-CSIM mmWave system, information is conveyed not only by the classic constellation symbols but also by the on/off status of subcarriers, where the size of constellation symbols and the number of active subcarriers can be beneficially configured for maximizing the system’s throughput. We conceive a machine learning (ML) assisted adaptive OFDM-CSIM mmWave system, which simultaneously benefits from the OFDM with index modulation (IM), compressed sensing (CS) and the hybrid beamforming techniques. Specifically, a ML-assisted link adaptation scheme is designed based on the $k$ -nearest neighbors ( $k$ -NN) algorithm with the objective to maximize the system’s throughput. Our studies show that the proposed ML-assisted link adaptation is capable of providing higher throughput than the conventional threshold-based link adaptation when different antenna structures are considered. Furthermore, the achievable data rates of four types of antenna arrays, including uniform linear array (ULA), uniform rectangular planar array (URPA), uniform circle planar array (UCPA) and uniform cylindrical array (UCYA), are investigated and compared over mmWave channels. The simulation results show that the UCYA achieves the highest data rate among these antenna arrays.

中文翻译：

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毫米波通信的机器学习辅助自适应索引调制

在本文中，我们提出了一种被称为（OFDM-CSIM）的压缩感知辅助索引调制支持的正交频分多路复用系统，该系统适用于毫米波（mmWave）通信。在OFDM-CSIM mmWave系统中，信息不仅通过经典星座图符号进行传输，还通过子载波的开/关状态进行传输，其中可以有利地配置星座图符号的大小和活动子载波的数量，以最大程度地提高系统的吞吐量。我们构想了一种机器学习（ML）辅助的自适应OFDM-CSIM mmWave系统，该系统同时受益于具有索引调制（IM），压缩感测（CS）和混合波束成形技术的OFDM。具体而言，基于以下内容设计了ML辅助链路自适应方案： $ k $ -最近的邻居（ $ k $ -NN）算法，目的是最大化系统的吞吐量。我们的研究表明，当考虑不同的天线结构时，与传统的基于阈值的链路自适应相比，所提出的ML辅助链路自适应能够提供更高的吞吐量。此外，研究了四种类型的天线阵列可实现的数据速率，包括均匀线性阵列（ULA），均匀矩形平面阵列（URPA），均匀圆形平面阵列（UCPA）和均匀圆柱阵列（UCYA），并在mmWave上进行了比较。渠道。仿真结果表明，UCYA实现了这些天线阵列中最高的数据速率。