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This paper studies the uplink spectral efficiency (SE) achieved by two single-antenna user equipments (UEs) communicating with a Large Intelligent Surface (LIS), defined as a planar array consisting of N antennas that each has area A. The analysis is carried out with a deterministic line-of-sight propagation channel model that captures key fundamental aspects of the so-called geometric near-field of the array. Maximum ratio (MR) and minimum mean squared error (MMSE) combining schemes are considered. With both schemes, the signal and interference terms are numerically analyzed as a function of the position of the transmitting devices when the width/height L = root NA of the square-shaped array grows large. The results show that an exact near-field channel model is needed to evaluate the SE whenever the distance of transmitting UEs is comparable with the LIS dimensions. It is shown that, if L grows, the UEs are eventually in the geometric near-field and the interference does not vanish. MMSE outperforms MR for an LIS of practically large size.

Intelligent reflecting surfaces (IRSs) have recently attracted the attention of communication theorists as a means to control the wireless propagation channel. It has been shown that the signal-to-noise ratio (SNR) of a single-user IRS-aided transmission increases as N 2, with N being the number of passive reflecting elements in the IRS. This has been interpreted as a major potential advantage of using IRSs, instead of conventional Massive MIMO (mMIMO) whose SNR scales only linearly in N. This paper shows that this interpretation is incorrect. We first prove analytically that mMIMO always provides higher SNRs, and then show numerically that the gap is substantial; a very large number of reflecting elements is needed for an IRS to obtain SNRs comparable to mMIMO., Funding Agencies|ELLIIT; Swedish Research CouncilSwedish Research Council; University of Pisa under the PRA 2018-2019 Research Project CONCEPT

Communication by joint signal processing from many distributed access points, called Cell-free Massive MIMO, is a potential beyond-5G network infrastructure. The aim of this paper is to provide the first comprehensive comparison with Cellular Massive MIMO. The uplink spectral efficiencies of four different cell-free implementations are analyzed, with spatially correlated fading and arbitrary processing. It turns out that it is possible to outperform cellular networks by a wide margin, but only using the right signal processing. A centralized implementation with optimal processing maximizes performance and, surprisingly, also reduces the fronthaul signaling., Funding Agencies|ELLIIT; Wallenberg AI, Autonomous Systems and Software Program (WASP); University of Pisa under the PRA 2018-2019 Research Project CONCEPT

Cell-free Massive MIMO systems consist of a large number of geographically distributed access points (APs) that serve users by coherent joint transmission. Downlink power allocation is important in these systems, to determine which APs should transmit to which users and with what power. If the system is implemented correctly, it can deliver a more uniform user performance than conventional cellular networks. To this end, previous works have shown how to perform system-wide max-min fairness power allocation when using maximum ratio precoding. In this paper, we first generalize this method to arbitrary precoding, and then train a neural network to perform approximately the same power allocation but with reduced computational complexity. Finally, we train one neural network per AP to mimic system-wide max-min fairness power allocation, but using only local information. By learning the structure of the local propagation environment, this method outperforms the state-of-the-art distributed power allocation method from the Cell-free Massive MIMO literature., Funding Agencies|Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation

The spectral efficiency of wireless networks can be made nearly infinitely large by deploying many antennas, but the deployment of very many antennas requires new topologies beyond the compact and discrete antenna arrays used by conventional base stations. In this paper, we consider the large intelligent surface scenario where small antennas are deployed on a large and dense two-dimensional grid. Building on the heritage of MIMO, we first analyze the beamwidth and sidelobes when transmitting from large intelligent surfaces. We compare different precoding schemes and determine how to optimize the transmit power with respect to different utility functions., Funding Agencies|ELLIIT; CENIIT; Pisa University under the PRA 2018-2019 Project CONCEPT

We consider the uplink of a Massive MIMO network with L cells, each comprising a BS with M antennas and K single-antenna user equipments. Recently, [1] studied the asymptotic spectral efficiency of such networks with optimal multicell minimum mean-squared error (M-MMSE) processing when M -> infinity and K is kept fixed. Remarkably, [1] proved that, for practical channels with spatial correlation, the spectral efficiency grows unboundedly, even with pilot contamination. In this paper, we extend the analysis from [1] to the alternative regime in which M,K -> infinity with a given ratio. Tools from random matrix theory are used to compute low-complexity approximations which are proved to be asymptotically tight, but accurate for realistic system dimensions, as shown by simulations.

This paper considers the uplink of a distributed Massive MIMO network where N base stations (BSs), each equipped with M antennas, receive data from K = 2 users. We study the asymptotic spectral efficiency (as M -amp;gt; infinity) with spatial correlated channels, pilot contamination, and different degrees of channel state information (CSI) and statistical knowledge at the BSs. By considering a two-user setup, we can simply derive fundamental asymptotic behaviors and provide novel insights into the structure of the optimal combining schemes. In line with In when global CSI is available at all BSs, the optimal minimum-mean squared error combining has an unbounded capacity as M -amp;gt; infinity, if the global channel covariance matrices of the users are asymptotically linearly independent. This result is instrumental to derive a suboptimal combining scheme that provides unbounded capacity as M -amp;gt; infinity using only local CSI and global channel statistics. The latter scheme is shown to outperform a generalized matched filter scheme, which also achieves asymptotic unbounded capacity by using only local CSI and global channel statistics, but is derived following [2] on the basis of a more conservative capacity bound.

This paper analyzes how the distortion created by hardware impairments in a multiple-antenna base station affects the uplink spectral efficiency (SE), with focus on Massive MIMO. The distortion is correlated across the antennas, but has been often approximated as uncorrelated to facilitate (tractable) SE analysis. To determine when this approximation is accurate, basic properties of the distortion correlation are first uncovered. Then, we focus on third-order non-linearities and prove analytically and numerically that the correlation can be neglected in the SE analysis when there are many users. In i.i.d. Rayleigh fading with equal signal-to-noise ratios, this occurs when having five users.

This work aims to design a cellular network for maximal energy efficiency (EE). In particular, we consider the uplink with multi-antenna base stations and assume that zero-forcing (ZF) combining is used for data detection with imperfect channel state information. Using stochastic geometry and a new lower bound on the average per-user spectral efficiency of the network, we optimize the pilot reuse factor, number of antennas and users per base station. Closed-form expressions are computed from which valuable insights into the interplay between the optimization variables, hardware characteristics, and propagation environment are obtained. Numerical results are used to validate the analysis and make comparisons with a network using maximum ratio (MR) combining. The results show that a Massive MIMO setup arises as the EE-optimal network configuration. In addition, ZF provides higher EE than MR while allowing a smaller pilot reuse factor and a more dense network deployment., Funding Agencies|ERC [305123 MORE]; Swedish Foundation for Strategic Research (SFF)