Ergodic Spectral Efficiency in MIMO Cellular Networks

Abstract

This paper shows how the application of stochastic geometry to the analysis of wireless networks is greatly facilitated by (i) a clear separation of time scales, (ii) the abstraction of small-scale effects via ergodicity, and (iii) an interference model that reflects the receiver's lack of knowledge of how each individual interference term is faded. These procedures render the analysis both more manageable and more precise, as well as more amenable to the incorporation of subsequent features. In particular, the paper presents analytical characterizations of the ergodic spectral efficiency of cellular networks with single-user multiple-input multiple-output (MIMO) and sectorization. These characterizations, in the form of easy-to-evaluate expressions, encompass the coverage, the distribution of spectral efficiency over the network locations, and the average thereof.Comment: 35 pages, 12 figure

Massive MIMO Forward Link Analysis for Cellular Networks

“…The closeness of our analytical abstractions (which correspond to σ dB → ∞) to the behaviors with typical outdoor values of σ dB is conspicuous, similar to the corresponding observation made in the context of nonmassive single-user communication [21]. For σ dB = 10 dB, with either power allocation and regardless of whether K is fixed or a truncated Poisson random quantity, the performance in a hexagonal network is within 1 dB of our analytical characterizations.…”

“…of massive MIMO settings, we invoke tools from stochastic geometry that have been successfully applied already in non-MIMO [14]- [20] and in MIMO contexts [21]- [24].…”

Asymptotic Analysis of Area Spectral Efficiency and Energy Efficiency in PPP Networks With SLNR Precoder

“…In this work, SLNR precoder is used and then the channel state information of active users in the network are required at transmitters in order to realize the benefits of SLNR precoding in MU-MISO system. Therefore, the approach followed in [36] is not applicable to our case. Now, (19) can be calculated using coverage probability [21] or moment generating function approaches [37].…”

“…Note that (19) holds only when x ij are Gaussian and when transmitters have the knowledge of CSI of active users in the network, which is assumed in order to implement the SLNR precoder. The authors in [36] assumed the aggregated interference-plus-noise to be Gaussian distributed, but without the strain of receiver's knowledge about the fading of each individual interference term. This enabled the authors to express the ergodic SE as a function of the local-average SINR and involving the expectation over the intended fading channel only.…”

Full-Duplex MIMO in Cellular Networks: System-Level Performance

“…We follow the approach in [29]- [31] to model the cochannel interference terms z 0 and z 0 . The local-average distributions of z 0 and z 0 are modeled as zero-mean complex Gaussian with respective matched conditional covariances…”