  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.
