Next-generation wireless networks are getting significant attention because they promise 10-factor enhancement in mobile broadband along with the potential to enable new heterogeneous services. Services include massive machine type communications desired for Industrial 4.0 along with ultra-reliable low latency services for remote healthcare and vehicular communications. In this article, we present the design of intelligent and reconfigurable physical layer (PHY) to bring these services to reality. First, we design and implement the reconfigurable PHY via a hardware-software co-design approach on system-on-chip consisting of the ARM processor and field-programmable gate array (FPGA). The reconfigurable PHY is then made intelligent by augmenting it with online machine learning (OML) based decision-making algorithm. Such PHY can learn the environment (for example, wireless channel) and dynamically adapt the transceivers' configuration (i.e., modulation scheme, word-length) and select the wireless channel on-the-fly. Since the environment is unknown and changes with time, we make the OML architecture reconfigurable to enable dynamic switch between various OML algorithms on-the-fly. We have demonstrated the functional correctness of the proposed architecture for different environments and word-lengths. The detailed throughput, latency, and complexity analysis validate the feasibility and importance of the proposed intelligent and reconfigurable PHY in next-generation networks.

中文翻译：

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迈向智能可重构无线物理层 (PHY)


下一代无线网络正受到广泛关注，因为它们有望在移动宽带方面实现 10 倍的增强，并具有支持新异构服务的潜力。服务包括工业 4.0 所需的大规模机器类型通信，以及用于远程医疗保健和车辆通信的超可靠低延迟服务。在本文中，我们介绍了智能可重构物理层 (PHY) 的设计，以将这些服务变为现实。首先，我们通过硬件-软件协同设计方法在由 ARM 处理器和现场可编程门阵列 (FPGA) 组成的片上系统上设计和实现可重构 PHY。然后，通过使用基于在线机器学习 (OML) 的决策算法来增强可重构 PHY，使其变得智能化。此类 PHY 可以了解环境（例如，无线信道）并动态调整收发器的配置（即调制方案、字长）并即时选择无线信道。由于环境是未知的并且随着时间的推移而变化，我们使 OML 架构可重新配置，以实现各种 OML 算法之间的动态切换。我们已经证明了所提出的架构对于不同环境和字长的功能正确性。详细的吞吐量、延迟和复杂性分析验证了所提出的智能和可重构 PHY 在下一代网络中的可行性和重要性。