Stochastic array antenna figures-of-merit for quality-of-service-enhanced massive MIMO

This paper shows that the signal-to-interference-plus-noise ratio (SINR) at a base station (BS) equipped with an arbitrary physical array antenna can be expressed as a function of two fundamental, stochastic figure of merits (FoMs): (I) the instantaneous effective gain (IEG) and (II) the beamforming-channel correlation (BCC). This result is achieved by applying a novel channel normalization approach using a reference array to preserve effects induced by the embedded element patterns of physical antenna elements. It is shown that both FoMs provide essential insights for quality-of-service (QoS)-based array antenna design by investigating their statistics for BSs applying full-digital (FD) zero forcing (ZF) beamforming. Various array designs are evaluated, and it is shown that arrays with higher IEGs and a reduced probability of low BCCs can increase the ergodic sum rate and reduce the need for scheduling.</p

Stochastic Phased Array Performance Indicators for Quality-of-Service-Enhanced Massive MIMO

In this paper, we show that the signal-to-interference-plus-noise ratio (SINR) at a base station (BS) equipped with an arbitrary physical array antenna can be expressed as a function of two fundamental figures-of-merit (FoMs): (I) the instantaneous effective gain (IEG), and (II) the beamforming-channel correlation (BCC). These two FoMs are functions of the array antenna layout, the antenna elements, the propagation channel and the applied signal processing algorithms, and hence they are random variables (RVs) in general. We illustrate that both FoMs provide essential insights for quality-of-service (QoS)-based phased array design by investigating their statistics for BSs applying full-digital (FD) zero forcing (ZF) beamforming. We evaluate various array designs and show that arrays with higher IEGs and a reduced probability of low BCCs can increase the ergodic sum rate and reduce the need for scheduling

Capacity bounds for dense massive MIMO in a line-of-sight propagation environment

The use of large-scale antenna arrays grants considerable benefits in energy and spectral efficiency to wireless systems due to spatial resolution and array gain techniques. By assuming a dominant line-of-sight environment in a massive multiple-input multiple-output scenario, we derive analytical expressions for the sum-capacity. Then, we show that convenient simplifications on the sum-capacity expressions are possible when working at low and high signal-to-noise ratio regimes. Furthermore, in the case of low and high signal-to-noise ratio regimes, it is demonstrated that the Gamma probability density function can approximate the probability density function of the instantaneous channel sum-capacity as the number of served devices and base station antennas grows, respectively. A second important demonstration presented in this work is that a Gamma probability density function can also be used to approximate the probability density function of the summation of the channel's singular values as the number of devices increases. Finally, it is important to highlight that the presented framework is useful for a massive number of Internet of Things devices as we show that the transmit power of each device can be made inversely proportional to the number of base station antennas.20

Deploying Dense Networks for Maximal Energy Efficiency: Small Cells Meet Massive MIMO

How would a cellular network designed for maximal energy efficiency look like? To answer this fundamental question, tools from stochastic geometry are used in this paper to model future cellular networks and obtain a new lower bound on the average uplink spectral efficiency. This enables us to formulate a tractable uplink energy efficiency (EE) maximization problem and solve it analytically with respect to the density of base stations (BSs), the transmit power levels, the number of BS antennas and users per cell, and the pilot reuse factor. The closed-form expressions obtained from this general EE maximization framework provide valuable insights on the interplay between the optimization variables, hardware characteristics, and propagation environment. Small cells are proved to give high EE, but the EE improvement saturates quickly with the BS density. Interestingly, the maximal EE is achieved by also equipping the BSs with multiple antennas and operate in a "massive MIMO" fashion, where the array gain from coherent detection mitigates interference and the multiplexing of many users reduces the energy cost per user.Comment: To appear in IEEE Journal on Selected Areas in Communications, 15 pages, 7 figures, 1 tabl