On the Total Energy Efficiency of Cell-Free Massive MIMO

By Hien Quoc Ngo, Le-Nam Tran, Trung Quang Duong, Michail Matthaiou, Erik G. Larsson. IEEE Transactions on Green Communications and Networking, 2017

637 citations recorded by the source corpus.

We consider the cell-free massive multiple-input multiple-output (MIMO) downlink, where a very large number of distributed multiple-antenna access points (APs) serve many single-antenna users in the same time-frequency resource. A simple (distributed) conjugate beamforming scheme is applied at each AP via the use of local channel state information (CSI). This CSI is acquired through time-division duplex operation and the reception of uplink training signals transmitted by the users. We derive a closed-form expression for the spectral efficiency taking into account the effects of channel estimation errors and power control. This closed-form result enables us to analyze the effects of backhaul power consumption, the number of APs, and the number of antennas per AP on the total energy efficiency, as well as, to design an optimal power allocation algorithm. The optimal power allocation algorithm aims at maximizing the total energy efficiency, subject to a per-user spectral efficiency constraint and a per-AP power constraint. Compared with the equal power control, our proposed power allocation scheme can double the total energy efficiency. Furthermore, we propose AP selections schemes, in which each user chooses a subset of APs, to reduce the power consumption caused by the backhaul links. With our proposed AP selection schemes, the total energy efficiency increases significantly, especially for large numbers of APs. Moreover, under a requirement of good quality-of-service for all users, cell-free massive MIMO outperforms the colocated counterpart in terms of energy efficiency.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/ad7722c4a30fc14f392e7a96d694f74fd2b11f88

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Citations in the corpus

Prospective Multiple Antenna Technologies for Beyond 5G (2019, methodology): “Moreover, the energy efficiency of cell-free massive MIMO was considered in [51], [52], which showed that it can improve the energy efficiency (measured in bit/Joule) by nearly ten times compared to cellular massive MIMO.”
Making Cell-Free Massive MIMO Competitive With MMSE Processing and Centralized Implementation (2019, background): “1Previous comparisons are found in [25], [26] but only for a single cell, so it is not cellular, and only MR is used, which is known to perform badly [3].”
Scalable Cell-Free Massive MIMO Systems (2019, methodology): “We first note that the algorithms for user-centric clustering in previous works have two deficiencies: [28], [33] do not limit how many UEs that an AP can serve, making them unscalable as K → ∞, and [27], [29] do not guarantee that all UEs are served.”
The Road to 6G: Ten Physical Layer Challenges for Communications Engineers (2020, methodology): “There are several simple methods to implement user-centric approach, such as each user chooses some of its closest APs or chooses a subset of APs which contribute most of the total received power of the desired signal [10].”
User-Centric Cell-Free Massive MIMO Networks: A Survey of Opportunities, Challenges and Solutions (2021, methodology): “[58] Mathematical optimization EE Centralized DL TDD Single No Random Conjugate beamforming, serving clusters.”
Cell-Free Massive MIMO: A New Next-Generation Paradigm (2019, background): “[23], who considered the practical effects of power allocation, channel estimation, the number of antennas, the power consumption of the hardware and of the entire fronthaul.”
User-Centric 5G Cellular Networks: Resource Allocation and Comparison With the Cell-Free Massive MIMO Approach (2018, background): “The energy-efficiency of CF massive MIMO systems is also considered in [12], which proposes a power allocation algorithm aiming at maximizing the total energy efficiency, subject to a per-user spectral efficiency constraint and a per-AP power constraint.”