Efficient beamforming scheme in distributed massive MIMO system

Efficient receivers for cellular massive mimo systems and random access in cell-free networks.

“…The rKA is an iterative algorithm that solves systems of linear equations (SLEs) and has been recently applied to efficiently tackle the problem of relaxing linear signal processing schemes in the context of M-MIMO. This procedure was first presented in [55] and deepened in [57,58]. The randomization in rKA is related to the order in which the SLE equations are being selected when solved.…”

“…In the context of rKA, the manner and order in which selection of the random rows occurs is often called as the update schedule. The convergence speed of the rKA is closely tied to the updating schedule strategy, and this has motivated the study of randomized variants in new application scenarios, such as [57], [26]. This basically translates into the choice of the probability vector p ( ) = [ ( ) 1 , .…”

“…Contributions: Inspired by the promising results obtained for M-MIMO [55,57,58], this work proposes the application of the randomized Kaczmarz algorithm (rKA) as a way to circumvent the high-dimensional matrix inversion that comes with zero-forcing (ZF) and regularized zero-forcing (RZF) schemes when these are applied to recover the signal estimates of a crowded XL-MIMO scenario [8]. The contributions are listed as follows: (i) extension of rKA to resemble the performance of ZF and RZF schemes for a XL-MIMO system with a fixed number of subarrays; (ii) consideration of non-stationary properties through the concept of visibility regions (VRs) when taking into account two different power normalization methods of non-stationary channels [19]; (iii) exploitation of non-stationary features in the randomness design of rKA; (iv) complexity analysis considering the different random variants of the proposed algorithm.…”

“…Simplicity: the only tuning parameter needed to be set is the number of iterations at each subarray. The others stem from network design choices and environment characteristics, which obviously affect the convergence of the algorithm, as discussed in [55], [57], and [58]. In opposite to that, this also means that a convergence analysis is sufficient to characterize the efficiency of the algorithm in achieving its goal.…”

“…A second strategy for the sample probability was suggested by the authors in [57]. The authors of [57] proved that, if the selection of the rows is defined to be uniform with respect to the users i.e., ( ) ( ) = 1/ ( ) , the rKA also achieves an expected rate of convergence, where ( ) denotes the number of active users at subarray . This method can be considered to bring fairness to the update schedule, in the sense that no user-specific equations are preferable.…”