Performance Analysis of Cell-Free Massive MIMO Systems: A Stochastic Geometry Approach

Abstract

Cell-free (CF) massive multiple-input-multipleoutput (MIMO) has emerged as an alternative deployment for conventional cellular massive MIMO networks. As revealed by its name, this topology considers no cells, while a large number of multi-antenna access points (APs) serves simultaneously a smaller number of users over the same time/frequency resources through time-division duplex (TDD) operation. Prior works relied on the strong assumption (quite idealized) that the APs are uniformly distributed, and actually, this randomness was considered during the simulation and not in the analysis. However, in practice, ongoing and future networks become denser and increasingly irregular. Having this in mind, we consider that the AP locations are modeled by means of a Poisson point process (PPP) which is a more realistic model for the spatial randomness than a grid or uniform deployment. In particular, by virtue of stochastic geometry tools, we derive both the downlink coverage probability and achievable rate. Notably, this is the only work providing the coverage probability and shedding light on this aspect of CF massive MIMO systems. Focusing on the extraction of interesting insights, we consider small-cells (SCs) as a benchmark for comparison. Among the findings, CF massive MIMO systems achieve both higher coverage and rate with comparison to SCs due to the properties of favorable propagation, channel hardening, and interference suppression. Especially, we showed for both architectures that increasing the AP density results in a higher coverage which saturates after a certain value and increasing the number of users decreases the achievable rate but CF massive MIMO systems take advantage of the aforementioned properties, and thus, outperform SCs. In general, the performance gap between CF massive MIMO systems and SCs is enhanced by increasing the AP density. Another interesting observation concerns that a higher path-loss exponent decreases the rate while the users closer to the APs affect more the performance in terms of the rate.

Towards Optimal Energy Efficiency in Cell-Free Massive MIMO Systems

“…Unfortunately, these gains from CF mMIMO are achieved by deploying more hardware, which in turn, may increase the power consumption. Notably, even though CF mMIMO systems come with plausible potentials, the study of this technology is limited as literature reveals [12], [13], [13], [14], [16]- [26]. For example, the authors in [17] achieved better data rates by suggesting a user-centric approach of CF mMIMO systems, where the APs serve a group of users instead of all of them.…”

Cell-Free Massive MIMO : Scalability, Signal Processing and Power Control

“…Deep Learning [176][177][178][179][180] Channel Model Rician fading [181][182][183] Spatially correlated Rayleigh fading [184][185][186] Miscellaneous Full-duplex [187][188][189] Channel non-reciprocity [190] Asynchronous reception [191] System information broadcast [192] Over-the-air signaling [193,194] Multi-antenna users [195,196] Frequency division duplex [197] Stochastic geometry [198] Dynamic Resource allocation [199] Wireless power transfer [200]…”

Research on Resource Efficiency Optimization Model of TDMA-Based Distributed Wireless Ad Hoc Networks

“…The stochastic geometry was traditionally used for wireless local area networks without interference. The spatial distribution of randomly located transmitters with ALOHA MAC can be captured by the Poisson point process (PPP) [14]. Different from the traditional PPP in which nodes are independently distributed, HCPP use the concept of repulsion and can precisely describe the effect of interference suppression by scheduling in actual networks.…”