Cognitive communications have emerged as a promising solution to enhance, adapt, and invent new tools and capabilities that transcend conventional wireless networks. Deep learning (DL) is critical in enabling essential features of cognitive systems because of its fast prediction performance, adaptive behavior, and model-free structure. These features are especially significant for multi-antenna wireless communications systems, which generate and handle massive data. Multiple antennas may provide multiplexing, diversity, or antenna gains that, respectively, improve the capacity, bit error rate, or the signal-to-interference-plus-noise ratio. In practice, multi-antenna cognitive communications encounter challenges in terms of data complexity and diversity, hardware complexity, and wireless channel dynamics. The DL-based solutions tackle these problems at the various stages of communications processing such as channel estimation, hybrid beamforming, user localization, and sparse array design. There are research opportunities to address significant design challenges arising from insufficient data coverage, learning model complexity, and data transmission overheads. This article provides synopses of various DL-based methods to impart cognitive behavior to multi-antenna wireless communications.
翻译:认知通信已成为超越常规无线网络的加强、适应和发明新工具和能力的有希望的解决方案。深层次学习(DL)对于使认知系统的基本特征具备能力至关重要,因为其快速预测性能、适应性行为和无模式结构。这些特征对于生成和处理大量数据的多线无线通信系统来说尤为重要。多天线可以提供多路、多样性或天线增益,分别提高能力、位误率或信号对干涉加噪音比率。在实践中,多线认知通信在数据复杂性和多样性、硬件复杂性和无线频道动态方面都遇到挑战。基于DL的解决方案在通信处理的不同阶段,如频道估计、混合光束、用户本地化和稀少的阵列设计,解决这些问题。有研究机会应对数据覆盖面不足、学习模型复杂程度和数据传输管理等重大设计挑战。这篇文章提供了基于DL的各种方法的合成方法,向多线无线通信传授认知行为。