Learning-based Precoding-aware Radio Resource Scheduling for Cell-free
mMIMO Networks
Abstract
Communication by jointly precoded transmission from many distributed
access points (APs), called cell-free massive multiple-input
multiple-output (CF mMIMO), is a promising concept for beyond 5G
systems. One of the challenging aspects of CF mMIMO is an efficient
management of the radio resources. We propose both reinforcement
learning (RL)-based and heuristic precoding aware radio resource
scheduling (RRS) algorithms aiming at maximizing sum spectral efficiency
(SE). The proposed algorithms allocate resources for Maximum Ratio
Transmission (MRT), Zero-Forcing (ZF) and Regularised Zero-Forcing (RZF)
precoders. For the resource allocation, both the set of serving APs and
the Physical Resource Blocks are considered. In high noise scenarios,
the proposed RL-based RRS algorithm combined with the MRT precoder shows
2.4 times higher sum SE than the standard Round Robin scheduler.
Moreover, we demonstrate that the proposed heuristic algorithms offer
similar sum SE while significantly reducing the complexity compared to
the RL-based solution. We also show that the RZF precoding, which is
superior to the ZF precoding in noisy environments, results overall in
more transmitted power. Therefore, assuming the same radio resource
schedule and precoding strategy in the neighbouring cells, it will
result in more inter-cell interference and an overall reduced
performance.