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Despite the extensive use of a centralized approach to design receivers atthe base station for massive multiple-input multiple-output (M-MIMO) systems,their actual implementation is a major challenge due to several bottlenecksimposed by the large number of antennas. One way to deal with this problem isby fully decentralizing the classic zero-forcing receiver across multipleprocessing nodes based on the gradient descent method. In this paper, we firstexplicitly relate this decentralized receiver to a distributed version of theKaczmarz algorithm and to the use of the successive interference cancellation(SIC) philosophy to mitigate the residual across nodes. In addition, we proposetwo methods to further accelerate the initial convergence of these iterativedecentralized receivers by exploring the connection with the Kaczmarzalgorithm: 1) a new Bayesian distributed receiver, which can eliminate noise onan iteration basis; 2) a more practical method for choosing the relaxationparameter. The discussion also consider spatial non-stationarities that arisewhen the antenna arrays are extremely large (XL-MIMO). We were able to improvethe numerical results for both spatially stationary and non-stationarychannels, but mainly the non-stationary performance can still be improvedcompared to the centralized ZF receiver. Future research directions areprovided with the aim of further improving the applicability of the receiverbased on the principle of successive residual cancellation (SRC).

Decentralized Design of Fast Iterative Receivers for Massive and Extreme-Large MIMO Systems