Optimal Design of Energy-Efficient Cell-Free Massive Mimo: Joint Power Allocation and Load Balancing

Chien, Trinh Van; Björnson, Emil; Larsson, Erik G.

doi:10.1109/icassp40776.2020.9054083

Cited by 18 publications

(11 citation statements)

References 21 publications

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“…On the other hand, under ideal conditions, the set of selected APs should be adapted to scenario variations due, among others, to changes in the location of the MSs and/or the geographical distribution of shadow fading. In particular, Chien et al [41] have very recently proposed an energy-efficient cell-free massive MIMO scheme aiming at minimizing the total DL power consumption at the APs assuming that some of them can be turned off. Critically, the proposed strategy implies solving a computationally-intensive non-convex optimization problem at the rate of change of the large-scale parameters of the system.…”

Section: Ap Switch-on/off Strategiesmentioning

confidence: 99%

Access Point Switch ON/OFF Strategies for Green Cell-Free Massive MIMO Networking

2020

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The combination of user-centric network densification and distributed massive multiple-input multiple-output (MIMO) operation has recently brought along a new paradigm in the wireless communications arena, referred to as cell-free massive MIMO networking. In these networks, a large number of distributed access points (APs), coordinated by a central processing unit (CPU), cooperate to coherently serve a large number of mobile stations (MSs) in the same time/frequency resource. Similar to what has been traditionally done with conventional cellular networks, cell-free massive MIMO networks will be dimensioned to provide the required quality of service (QoS) to MSs under heavy traffic load conditions, and thus they might be underutilized during low traffic load periods, leading to an inefficient use of both spectral and energy resources. Aiming at the implementation of green cell-free massive MIMO networks, this paper proposes and analyzes the performance of different AP switch ON/OFF (ASO) strategies designed to dynamically turn ON/OFF some of the APs based on the number and/or location of the active MSs in the network. The proposed framework considers line-of-sight (LOS) and non-line-of-sight (NLOS) links between APs and MSs, the use of different antenna array architectures at the access points (APs), suitably characterized by array-dependent spatial correlation matrices, and specific power consumption models for APs, MSs and fronthaul links between the APs and the CPU. Numerical results show that the use of properly designed ASO strategies in cell-free massive MIMO networks clearly improve the achievable energy efficiency. Moreover, they also reveal the existing trade-offs among the achievable energy efficiency, the available network-state information, and the hardware configuration (i.e., number of APs, number of transmit antennas per AP, and number of MSs). INDEX TERMS AP ON/OFF switching, green networking, cell-free massive MIMO, zero-forcing precoding.

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Section: Ap Switch-on/off Strategiesmentioning

confidence: 99%

Access Point Switch ON/OFF Strategies for Green Cell-Free Massive MIMO Networking

2020

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“…Combining ( 60) and ( 61), the denominator of ( 15) is formulated as CI lk , and therefore the asymptotic SINR expression as M → ∞ for a given finite set of the scatterers and covariance matrices as shown in (26).…”

Section: Proof Of Theoremmentioning

confidence: 99%

“…However, for large-scale networks with many base stations and users, the fairness level will approach a zero rate [25]. In contrast, one can include separate SE constraints in the optimization problems to simultaneously maintain the quality of service for all the users [26], [27]. However, since the users were randomly distributed, many user locations with poor channel conditions leads the optimization problems to be infeasible.…”

Section: Introductionmentioning

confidence: 99%

Uplink Power Control in Massive MIMO With Double Scattering Channels

Chien

Ngo

Chatzinotas

et al. 2022

IEEE Trans. Wireless Commun.

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Massive multiple-input multiple-output (MIMO) is a key technology for improving the spectral and energy efficiency in 5G-and-beyond wireless networks. For a tractable analysis, most of the previous works on Massive MIMO have been focused on the system performance with complex Gaussian channel impulse responses under rich-scattering environments. In contrast, this paper investigates the uplink ergodic spectral efficiency (SE) of each user under the double scattering channel model. We derive a closed-form expression of the uplink ergodic SE by exploiting the maximum ratio (MR) combining technique based on imperfect channel state information. We further study the asymptotic SE behaviors as a function of the number of antennas at each base station (BS) and the number of scatterers available at each radio channel. We then formulate and solve a total energy optimization problem for the uplink data transmission that aims at simultaneously satisfying the required SEs from all the users with limited data power resource. Notably, our proposed algorithms can cope with the congestion issue appearing when at least one user is served by lower SE than requested. Numerical results illustrate the effectiveness of the closed-form ergodic SE over Monte-Carlo simulations. Besides, the system can still provide the required SEs to many users even under congestion.

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“…Recently, a system with a massive number of access points (APs) distributed over a large area coherently serves a massive number of users, called Cell-free Massive MIMO, has attracted lots of interest [41][42][43][44][45]. In Paper E [46], we investigate how APs can be turned on and off in Cell-free Massive MIMO systems to save energy.…”

Section: Introductionmentioning

confidence: 99%

Spatial Resource Allocation in Massive MIMO Communications : From Cellular to Cell-Free

Chien

2020

Linköping Studies in Science and Technology. Dissertations

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Printed in Sweden by LiU-Tryck, Linköping 2020 investigates the use of deep learning for power control optimization in Massive MIMO. We formulate the joint data and pilot power optimization for maximum sum SE in multi-cell Massive MIMO systems, which is a non-convex problem. We propose a new optimization algorithm, inspired by the weighted MMSE approach, to obtain a stationary point in polynomial time. We then use this algorithm together with deep learning to train a convolutional neural network to perform the joint data and pilot power control in sub-millisecond runtime. The solution is suitable for online optimization.iv Finally, the fifth part of this thesis considers a large-scale distributed antenna system that serves the users by coherent joint transmission called Cell-free Massive MIMO. For a given user set, only a subset of the access points (APs) is likely needed to satisfy the users' performance demands. To find a flexible and energy-efficient implementation, we minimize the total power consumption at the APs in the DL, considering both the hardwareconsumed and transmit powers, where APs can be turned off to reduce the former part. Even though this is a non-convex optimization problem, a globally optimal solution is obtained by solving a mixed-integer second-order cone program (SOCP). We also propose low-complexity algorithms that exploit group-sparsity or received power strength in the problem formulation.

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Optimal Design of Energy-Efficient Cell-Free Massive Mimo: Joint Power Allocation and Load Balancing

Cited by 18 publications

References 21 publications

Access Point Switch ON/OFF Strategies for Green Cell-Free Massive MIMO Networking

Access Point Switch ON/OFF Strategies for Green Cell-Free Massive MIMO Networking

Uplink Power Control in Massive MIMO With Double Scattering Channels

Spatial Resource Allocation in Massive MIMO Communications : From Cellular to Cell-Free

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