Decentralized Design of Fast Iterative Receivers for Massive and Extreme-Large MIMO Systems
Victor Croisfelt, Taufik Abr\~ao, Abolfazl Amiri, Elisabeth, de Carvalho, Petar Popovski

TL;DR
This paper introduces decentralized iterative receivers for massive MIMO systems, leveraging gradient descent and Kaczmarz algorithms to improve scalability and convergence, especially in extremely large antenna arrays.
Contribution
It proposes novel decentralized receiver designs based on gradient descent and Kaczmarz algorithms, with acceleration techniques for faster convergence in massive MIMO systems.
Findings
Improved convergence speed of decentralized receivers.
Enhanced performance in non-stationary XL-MIMO channels.
Potential for better scalability in massive MIMO deployments.
Abstract
Despite the extensive use of a centralized approach to design receivers at the base station for massive multiple-input multiple-output (M-MIMO) systems, their actual implementation is a major challenge due to several bottlenecks imposed by the large number of antennas. One way to deal with this problem is by fully decentralizing the classic zero-forcing receiver across multiple processing nodes based on the gradient descent method. In this paper, we first explicitly relate this decentralized receiver to a distributed version of the Kaczmarz algorithm and to the use of the successive interference cancellation (SIC) philosophy to mitigate the residual across nodes. In addition, we propose two methods to further accelerate the initial convergence of these iterative decentralized receivers by exploring the connection with the Kaczmarz algorithm: 1) a new Bayesian distributed receiver, which…
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Taxonomy
TopicsAdvanced MIMO Systems Optimization · Radio Frequency Integrated Circuit Design · Energy Harvesting in Wireless Networks
