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

Massive MIMO Systems with Non-Ideal Hardware: Energy Efficiency, Estimation, and Capacity Limits

The use of large-scale antenna arrays can bring substantial improvements in energy and/or spectral efficiency to wireless systems due to the greatly improved spatial resolution and array gain. Recent works in the field of massive multiple-input multiple-output (MIMO) show that the user channels decorrelate when the number of antennas at the base stations (BSs) increases, thus strong signal gains are achievable with little inter-user interference. Since these results rely on asymptotics, it is important to investigate whether the conventional system models are reasonable in this asymptotic regime. This paper considers a new system model that incorporates general transceiver hardware impairments at both the BSs (equipped with large antenna arrays) and the single-antenna user equipments (UEs). As opposed to the conventional case of ideal hardware, we show that hardware impairments create finite ceilings on the channel estimation accuracy and on the downlink/uplink capacity of each UE. Surprisingly, the capacity is mainly limited by the hardware at the UE, while the impact of impairments in the large-scale arrays vanishes asymptotically and inter-user interference (in particular, pilot contamination) becomes negligible. Furthermore, we prove that the huge degrees of freedom offered by massive MIMO can be used to reduce the transmit power and/or to tolerate larger hardware impairments, which allows for the use of inexpensive and energy-efficient antenna elements.Comment: To appear in IEEE Transactions on Information Theory, 28 pages, 15 figures. The results can be reproduced using the following Matlab code: https://github.com/emilbjornson/massive-MIMO-hardware-impairment

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, we model future cellular networks using stochastic geometry and obtain a new lower bound on the average uplink spectral efficiency. This enables us to formulate a tractable 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 obtained 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

RIS-Aided Cell-Free Massive MIMO Systems for 6G: Fundamentals, System Design, and Applications

An introduction of intelligent interconnectivity for people and things has posed higher demands and more challenges for sixth-generation (6G) networks, such as high spectral efficiency and energy efficiency, ultra-low latency, and ultra-high reliability. Cell-free (CF) massive multiple-input multiple-output (mMIMO) and reconfigurable intelligent surface (RIS), also called intelligent reflecting surface (IRS), are two promising technologies for coping with these unprecedented demands. Given their distinct capabilities, integrating the two technologies to further enhance wireless network performances has received great research and development attention. In this paper, we provide a comprehensive survey of research on RIS-aided CF mMIMO wireless communication systems. We first introduce system models focusing on system architecture and application scenarios, channel models, and communication protocols. Subsequently, we summarize the relevant studies on system operation and resource allocation, providing in-depth analyses and discussions. Following this, we present practical challenges faced by RIS-aided CF mMIMO systems, particularly those introduced by RIS, such as hardware impairments and electromagnetic interference. We summarize corresponding analyses and solutions to further facilitate the implementation of RIS-aided CF mMIMO systems. Furthermore, we explore an interplay between RIS-aided CF mMIMO and other emerging 6G technologies, such as next-generation multiple-access (NGMA), simultaneous wireless information and power transfer (SWIPT), and millimeter wave (mmWave). Finally, we outline several research directions for future RIS-aided CF mMIMO systems.Comment: 30 pages, 15 figure

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

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Cell-free (CF) massive multiple-input-multiple-output (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.Peer reviewe

Comparative Analysis of Procedures and Solutions to Improve Energy Efficiency of Massive MIMO

The blustery growth of high data rate applications leadsto more energy consumption in wireless networks to satisfy servicequality.Therefore, energy-efficient communications have been paidmore attention to limited energy resources and environmentallyfriendly transmission functioning. Countless publications arepresent in this domain which focuses on intensifying networkenergy efficiency for uplink-downlink transmission.It is done eitherby using linear precoding schemes, by amending the number ofantennas per BS, by power control problem formulation, antennaselection schemes, level of hardware impairments, and byconsidering cell-free (CF) Massive-MIMO.After reviewing thesetechniques, still there are many barriers to implement thempractically. The strategies mentioned in this review show theperformance of EE under the schemes as raised above. The chiefcontribution of this work is the comparative study of how MassiveMIMO EE performs under the background of different methodsand architectures and the solutions to few problem formulationsthat affect the EE of network systems. This study will help choosethe best criteria to improve EE of Massive MIMO whileformulating a newer edition of testing stand-ards.This surveyprovides the base for interested readers in energy efficient MassiveMIMO

Comparative Analysis of Procedures and Solutions to Improve Energy Efficiency of Massive MIMO

The blustery growth of high data rate applications leadsto more energy consumption in wireless networks to satisfy servicequality.Therefore, energy-efficient communications have been paidmore attention to limited energy resources and environmentallyfriendly transmission functioning. Countless publications arepresent in this domain which focuses on intensifying networkenergy efficiency for uplink-downlink transmission.It is done eitherby using linear precoding schemes, by amending the number ofantennas per BS, by power control problem formulation, antennaselection schemes, level of hardware impairments, and byconsidering cell-free (CF) Massive-MIMO.After reviewing thesetechniques, still there are many barriers to implement thempractically. The strategies mentioned in this review show theperformance of EE under the schemes as raised above. The chiefcontribution of this work is the comparative study of how MassiveMIMO EE performs under the background of different methodsand architectures and the solutions to few problem formulationsthat affect the EE of network systems. This study will help choosethe best criteria to improve EE of Massive MIMO whileformulating a newer edition of testing stand-ards.This surveyprovides the base for interested readers in energy efficient MassiveMIMO