Towards Intelligent Reconfigurable Wireless Physical Layer (PHY)

TL;DR

This paper proposes an intelligent, reconfigurable wireless physical layer that uses online machine learning to adapt to changing environments, enhancing next-generation network capabilities with hardware-software co-design on FPGA and ARM.

Contribution

It introduces a hardware-software co-designed reconfigurable PHY augmented with reconfigurable online machine learning algorithms for dynamic adaptation in wireless networks.

Findings

Demonstrated functional correctness across various environments.

Validated throughput, latency, and complexity metrics.

Showcased dynamic environment adaptation capabilities.

Abstract

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 paper, we present the design of an 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.