6 research outputs found
Accelerated Randomized Methods for Receiver Design in Extra-Large Scale MIMO Arrays
Recent interest has been cast on accelerated versions of the randomized
Kaczmarz (RK) algorithm due to the increase in applications that consider
sparse linear systems. In particular, considering the context of massive
multiple-input-multiple-output (M-MIMO) communication systems, a low complexity
naive RK-based receiver has recently been proposed. This method can take
advantage of non-stationarities emerging from extra-large M-MIMO systems, but
it performs poorly on highly spatially correlated channels. To address this
problem, in this paper, we propose a new class of accelerated RK-based receiver
designs, where convergence acceleration is based on the residual information.
However, we show that the cost of obtaining this knowledge on an iteration
basis is not worth it due to the lousy convergence effects caused by system and
channel parameters. Inspired by this observation, we further propose a RK-based
receiver with sampling without replacement, referred to as RK-RZF. This simple
technique is more effective in performing signal detection under reduced
complexity. Future works suggest advantage of RK-based receivers to improve
current 5G commercial systems and solve the problem of signal detection in
other paradigms beyond 5G.Comment: 11 pages, 4 figures, submitted to IEEE TV