Energy Efficiency of the Cell-Free Massive MIMO Uplink With Optimal Uniform Quantization

Cell-free Massive multiple-input multiple-output (MIMO) is considered, where distributed access points (APs) multiply the received signal by the conjugate of the estimated channel, and send back a quantized version of this weighted signal to a central processing unit (CPU). For the first time, we present a performance comparison between the case of perfect fronthaul links, the case when the quantized version of the estimated channel and the quantized signal are available at the CPU, and the case when only the quantized weighted signal is available at the CPU. The Bussgang decomposition is used to model the effect of quantization. The max-min problem is studied, where the minimum rate is maximized with the power and fronthaul capacity constraints. To deal with the non-convex problem, the original problem is decomposed into two sub-problems (referred to as receiver filter design and power allocation). Geometric programming (GP) is exploited to solve the power allocation problem whereas a generalized eigenvalue problem is solved to design the receiver filter. An iterative scheme is developed and the optimality of the proposed algorithm is proved through uplink-downlink duality. A user assignment algorithm is proposed which significantly improves the performance. Numerical results demonstrate the superiority of the proposed schemes.

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“…Note that the simulation results confirm that this approximation is very tight [49]. The first term in (61) can be obtained as…”

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confidence: 71%

Max–Min Rate of Cell-Free Massive MIMO Uplink With Optimal Uniform Quantization

Bashar

Cumanan

Burr

et al. 2019

IEEE Trans. Commun.

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116

146

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“…Based on the Bussgang decomposition [10], the output of a quantizer can be represented by a scalar multiple of the input plus uncorrelated distortion as follows [1], [2], [9]:…”

Section: A Optimal Uniform Quantization With Bussgang Theoremmentioning

confidence: 99%

On the Performance of Backhaul Constrained Cell-Free Massive MIMO with Linear Receivers

Bashar

Ngo

Burr

et al. 2018

2018 52nd Asilomar Conference on Signals, Systems, and Computers

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Limited-backhaul cell-free Massive multiple-input multiple-output (MIMO), in which the fog radio access network (F-RAN) is implemented to exchange the information between access points (APs) and the central processing unit (CPU), is investigated. We introduce a novel approach where the APs estimate the channel and send back the quantized version of the estimated channel and the quantized version of the received signal to the central processing unit. The Max algorithm and the Bussgang theorem are exploited to model the optimum uniform quantization. The ergodic achievable rates are derived. We show that exploiting microwave wireless backhaul links and using a small number of bits to quantize the estimated channel and the received signal, the performance of limited-backhaul cell-free Massive MIMO closely approaches the performance of cell-free Massive MIMO with perfect backhaul links.

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“…This, in turn, brings different challenges on the aspects surrounding the energy consumption in wireless networks. The energy consumption, regardless of the underpinning technologies, has a knock-on effect on the environment we live in, carbon footprint, global warming, and thus unanticipated financial consequences [4]- [6].…”

Section: Introductionmentioning

confidence: 99%

Energy Efficiency Optimization for Secure Transmission in MISO Cognitive Radio Network With Energy Harvesting

Zhang¹,

Cumanan²,

Thiyagalingam³

et al. 2019

IEEE Access

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In this paper, we investigate different secrecy energy efficiency (SEE) optimization problems in a multiple-input single-output underlay cognitive radio (CR) network in the presence of an energy harvesting receiver. In particular, these energy efficient designs are developed with different assumptions of channels state information (CSI) at the transmitter, namely perfect CSI, statistical CSI and imperfect CSI with bounded channel uncertainties. In particular, the overarching objective here is to design a beamforming technique maximizing the SEE while satisfying all relevant constraints linked to interference and harvested energy between transmitters and receivers. We show that the original problems are non-convex and their solutions are intractable. By using a number of techniques, such as non-linear fractional programming and difference of concave (DC) functions, we reformulate the original problems so as to render them tractable. We then combine these techniques with the Dinkelbach's algorithm to derive iterative algorithms to determine relevant beamforming vectors which lead to the SEE maximization. In doing this, we investigate the robust design with ellipsoidal bounded channel uncertainties, by mapping the original problem into a sequence of semidefinite programs This paper has been presented in part at the IEEE Wireless Communications and Signal Processing (WCSP), October 11-13, 2017 [1]. M. Zhang is with the ). Z. Ding is with the School of Electrical and Electronic Engineering, The University of Manchester, Manchester, UK (email: zhiguo.ding@manchester.ac.uk). O. A. Dobre is with the employing the semidefinite relaxation, non-linear fractional programming and S-procedure. Furthermore, we show that the maximum SEE can be achieved through a search algorithm in the single dimensional space. Numerical results, when compared with those obtained with existing techniques in the literature, show the effectiveness of the proposed designs for SEE maximization.

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Energy Efficiency of the Cell-Free Massive MIMO Uplink With Optimal Uniform Quantization

Cited by 117 publications

References 46 publications

Max–Min Rate of Cell-Free Massive MIMO Uplink With Optimal Uniform Quantization

Max–Min Rate of Cell-Free Massive MIMO Uplink With Optimal Uniform Quantization

On the Performance of Backhaul Constrained Cell-Free Massive MIMO with Linear Receivers

Energy Efficiency Optimization for Secure Transmission in MISO Cognitive Radio Network With Energy Harvesting

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