2017/11/30 by Mahdi Nouri Boroujerdi, Boroujerdi, Mahdi Nouri, Saeid Haghighatshoar +3
Computer Science · Engineering · #Advanced MIMO Systems Optimization #Advanced Wireless Communication Techniques #Cooperative Communication and Network Coding #FOS: Computer and information sciences #FOS: Electrical engineering #Information Theory (cs.IT) #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1711.11405
openalex publication_date 2017/11/30 · openalex created_date 2022/10/03 · openalex updated_date 2026/07/28
Massive MIMO is a variant of multiuser MIMO in which the number of antennas\nat the base station (BS) M is very large and typically much larger than the\nnumber of served users (data streams) K. Recent research has illustrated the\nsystem-level advantages of such a system and in particular the beneficial\neffect of increasing the number of antennas M. These benefits, however, come\nat the cost of dramatic increase in hardware and computational complexity. This\nis partly due to the fact that the BS needs to compute suitable beamforming\nvectors in order to coherently transmit/receive data to/from each user, where\nthe resulting complexity grows proportionally to the number of antennas M and\nthe number of served users K. Recently, different algorithms based on tools\nfrom random matrix theory in the asymptotic regime of M,K \→ \∞ with\n\(K)/(M) \→ \ρ \∈ (0,1) have been proposed to reduce such complexity.\nThe underlying assumption in all these techniques, however, is that the exact\nstatistics (covariance matrix) of the channel vectors of the users is a priori\nknown. This is far from being realistic, especially that in the high-dim regime\nof M\→ \∞, estimation of the underlying covariance matrices is well\nknown to be a very challenging problem.\n In this paper, we propose a novel technique for designing beamforming vectors\nin a massive MIMO system. Our method is based on the randomized Kaczmarz\nalgorithm and does not require knowledge of the statistics of the users channel\nvectors. We analyze the performance of our proposed algorithm theoretically and\ncompare its performance with that of other competitive techniques via numerical\nsimulations. Our results indicate that our proposed technique has a comparable\nperformance while it does not require the knowledge of the statistics of the\nusers channel vectors.\n