Cell-Free Massive MIMO Systems Research Articles

Learning-Based Intelligent Reflecting Surface-Aided Cell-Free Massive MIMO Systems

Intelligent reflecting surface (IRS) is amenable to assisting cell-free wireless networks because of its low cost and high spectrum efficiency. However, solving the IRS optimization problem usually requires high computational complexity. Therefore, we propose a novel multi-agent reinforcement learning (MARL) based joint optimization scheme to improve the system performance. First, we model the MARL framework to portray the dynamic wireless communication environment and interactive agent entities, which interprets the relationship between reinforcement learning and communication strategies. Moreover, in an IRS-aided cell-free massive MIMO system, entries are collaborated by the CPU for joint optimization, and the actions are continuous. Thus we propose a multi-agent deep deterministic policy gradient scheme for jointly optimizing the user's power control and IRS's passive beamforming (MADDPG-JPA-PB). Via centralized training and decentralized execution, the system gradually converges into an optimized policy associated with the maximization of the success transmission rate. Simulation results verify that the performance of the proposed scheme outperforms various independent deep deterministic policy gradient (DDPG) schemes and traditional optimization methods.

Access Point Selection Algorithm Based on Coevolution Particle Swarm in Cell-Free Massive MIMO Systems

Access Point Selection Algorithm Based on Coevolution Particle Swarm in Cell-Free Massive MIMO Systems

Bayesian Receiver Design via Bilinear Inference for Cell-Free Massive MIMO With Low-Resolution ADCs

We propose a novel joint channel and data estimation (JCDE) scheme to combat the rate limitation in fronthaul links of cell-free massive MIMO (CF-mMIMO) systems introduced by the use of analog-to-digital converters (ADCs) at access points (APs), which makes channel estimation and multi-user detection at the central AP (CAP) challenging. The latter problem is solved here via the new JCDE scheme which differs from state-of-the-art (SotA) alternatives due to two contributions. The first is the design and incorporation of de-quantization (DQ) step which relies only on scalar Gaussian approximation (SGA) assumptions in conformity with mild central limit theorem (CLT), in contrast to the much harder asymptotic conditions required by the classic bilinear generalized approximate message passing (BiGAMP) algorithm. The second is a modification of bilinear Gaussian belief propagation (BiGaBP), whereby quantized outputs are linearized via the Bussgang decomposition enabling tractable signal processing. The resulting DQ-aided JCDE method achieves both low-complexity and high-accuracy by exploiting both the spatial degrees of freedom (DoF) obtained from, and the observations at the CAP to compensate for the low-resolution distortion introduced by, the distributed APs. The efficacy of the proposed method over the SotA is confirmed via computer simulations.

Unsupervised Deep Learning for Power Control of Cell-Free Massive MIMO Systems

The problem of power control for the uplink and the downlink in cell-free massive multiple-input multiple-output (CF mMIMO) systems is considered in this paper. In order to achieve a balance between the conflicting requirements of good performance levels and of low computational complexity, we employ unsupervised learning based on deep learning (DL). The proposed power control strategies can work using as input the large scale fading coefficients only and are capable to obtain very satisfactory performance levels, as compared to conventional, highly complex, optimization methods and to heuristic methodologies. Moreover, they can be used also in a user centric system wherein each mobile station is served by a subset of the active access points.

Federated Learning-Based Cell-Free Massive MIMO System for Privacy-Preserving

Cell-free massive MIMO (CF mMIMO) is a promising next generation wireless architecture to realize federated learning (FL). However, sensitive information of user equipments (UEs) may be exposed to the involved access points or the central processing unit in practice. To guarantee data privacy, effective privacy-preserving mechanisms are defined in this paper. In particular, we demonstrate and characterize the possibility in exploiting the inherent quantization error, caused by low-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs), for privacy-preserving in a FL CF mMIMO system. Furthermore, to reduce the required uplink training time in such a system, a stochastic non-convex design problem that jointly optimizing the transmit power and the data rate is formulated. To address the problem at hand, we propose a novel power control method by utilizing the successive convex approximation approach to obtain a suboptimal solution. Besides, an asynchronous protocol is established for mitigating the straggler effect to facilitate FL. Numerical results show that compared with the conventional full power transmission, adopting the proposed power control method can effectively reduce the uplink training time under various practical system settings. Also, our results unveil that our proposed asynchronous approach can reduce the waiting time at the central processing unit for receiving all user information, as there are no stragglers that requires a long time to report their local updates.

Distributed MMSE-based uplink receive combining, downlink transmit precoding and optimal power allocation in cell-free massive MIMO systems

In cell-free massive multiple-input multiple-output systems, a large number of distributed wireless access points (AP) simultaneously serve a number of user equipments (UEs). This setup has recently been introduced as a promising alternative for the current 5G cellular networks. The setup has the ability to offer a good quality of service, especially for the UEs on the cell edges, be it that there is still a need for low-complexity signal processing algorithms. In this paper, the problem of optimal power allocation combined with uplink receive combining and downlink transmit precoding is tackled by providing efficient distributed MMSE-based algorithms. The necessary fronthaul communications to estimate the combining/precoding vectors and the necessary large-scale channel statistics are reduced to a minimum and rely on in-network summation that can be accomplished whenever the APs can be arranged into a tree-topology. Non-weighted max-sum and max–min are used as utilities for the power allocation, but the algorithms are not limited to these cases. Simulations show that the proposed algorithms outperform heuristic power allocation methods, both in uplink and downlink.

Scalable Cell-Free Massive MIMO Systems With Finite Resolution ADCs/DACs Over Spatially Correlated Rician Fading Channels

Scalable Cell-Free Massive MIMO Systems With Finite Resolution ADCs/DACs Over Spatially Correlated Rician Fading Channels

Experimental Performance Evaluation of Cell-Free Massive MIMO Systems Using COTS RRU With OTA Reciprocity Calibration and Phase Synchronization

Experimental Performance Evaluation of Cell-Free Massive MIMO Systems Using COTS RRU With OTA Reciprocity Calibration and Phase Synchronization

RIS-Aided Cell-Free Massive MIMO System: Joint Design of Transmit Beamforming and Phase Shifts

This article studies a reconfigurable intelligent surface (RIS)-aided cell-free massive multiple-input multiple-output system and formulate the max–min fairness problem that maximizes the minimum achievable rate among all the users by jointly optimizing the transmit beamforming at access points and the phase shifts at RISs. To address such a challenging problem, we first study the special single-user scenario and propose an algorithm that can transform the optimization problem into a semidefinite program (SDP) or an integer linear program for the cases of continuous or discrete phase shifts, respectively. Then, in order to solve the optimization problem for the multiuser scenario with continuous phase shifts, we propose an alternating optimization algorithm, which can alternately transform the problem into a second-order-cone program and an SDP. Finally, for the multiuser scenario with discrete phase shifts, we design a zero-forcing-based successive refinement algorithm, which can find the suboptimal transmit beamforming and phase shifts by means of alternating optimization. Numerical results show that compared with the benchmark schemes of random phase shifts and without using the RIS, the proposed algorithms can significantly increase the minimum achievable rate. It is also demonstrated that, compared with the case of programming continuous phase shifts, using 2-bit discrete phase shifts can practically achieve the same performance.

Joint flexible duplexing and power allocation with deep reinforcement learning in cell-free massive MIMO system

Network-assisted full duplex (NAFD) cellfree (CF) massive MIMO has drawn increasing attention in 6G evolvement. In this paper, we build an NAFD CF system in which the users and access points (APs) can flexibly select their duplex modes to increase the link spectral efficiency. Then we formulate a joint flexible duplexing and power allocation problem to balance the user fairness and system spectral efficiency. We further transform the problem into a probability optimization to accommodate the short-term communications. In contrast with the instant performance optimization, the probability optimization belongs to a sequential decision making problem, and thus we reformulate it as a Markov Decision Process (MDP). We utilizes deep reinforcement learning (DRL) algorithm to search the solution from a large state-action space, and propose an asynchronous advantage actor-critic (A3C)-based scheme to reduce the chance of converging to the suboptimal policy. Simulation results demonstrate that the A3C-based scheme is superior to the baseline schemes in term of the complexity, accumulated log spectral efficiency, and stability.

Whale Swarm Reinforcement Learning Based Dynamic Cooperation Clustering Method for Cell-Free Massive MIMO Systems

Dynamic cooperation clustering (DCC) becomes a main enabler for cell-free massive MIMO systems since it can improve the energy efficiency and reduce the complexity of signal processing significantly. However, DCC formation for all served users simultaneously is a very complicated mixed binary nonlinear programming problem, a single agent has limited capability to search the optimal schemes. In this paper, we propose a whale swarm reinforcement learning (WSRL) based DCC method. Exploiting multiple searching agent imitated by a group of whales, whale swarm optimization (WOA) algorithm searches the optimal DCC scheme simultaneously and learn the searching experience from each other. Moreover, the reinforcement learning is integrated to select the most efficient hunting action for each whale, which can accelerate the convergence and avoid the local trap. Simulation results demonstrate that the proposed method has better searching ability and higher convergence speed than the existing works.

On the Sum Spectral Efficiency of Dynamic TDD-Enabled Cell-Free Massive MIMO Systems

We examine the sum spectral efficiency (SE) performance of a cell-free massive multiple-input multiple-output (CF-mMIMO) system, where each access point (AP) can operate either in the uplink or downlink mode in each slot, corresponding to dynamic time-division duplexing (DTDD) across the APs. We derive the sum SE of the system under a weighted combining of the signals received at the central processing unit. We show that the sum SE is a sub-modular function of the subset of active APs. We exploit this to develop a novel, low-complexity, greedy algorithm for choosing the mode of operation of the APs which is guaranteed to achieve within <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(1-1/e)$ </tex-math></inline-formula> of the sum SE attained via a full-complexity brute-force search. Our results show that DTDD with greedy AP mode selection can nearly double the sum SE compared to a TDD based CF-system where all APs operate in the uplink or downlink modes simultaneously. Thus, it is a promising duplexing scheme for beyond 5G communications.

A Review on Cell-Free Massive MIMO Systems

Cell-free massive multiple-input multiple-output (CF mMIMO) can be considered as a potential physical layer technology for future wireless networks since it can benefit from all the advantages of distributed antenna systems (DASs) and network MIMOs, such as macro-diversity gain, high channel capacity, and link reliability. CF mMIMO systems offer remarkable spatial degrees of freedom and array gains to mitigate the inherent inter-cell interference (ICI) of cellular networks. In such networks, several distributed access points (APs) together with precoding/detection processing can serve many users while sharing the same time-frequency resources. Each AP can be equipped with single or multiple antennas, and hence, can provide a consistently adequate service to all users regardless of their locations in the network. This paper presents a detailed overview of the current state-of-the-art on CF systems. First, it performs a literature review of the conventional CF and scalable user-centric (UC) CF mMIMO systems in terms of the limited capacity of the fronthaul links and the connection between APs and user equipments (UEs). As beyond networks will rely on higher frequency bands, it is of paramount importance to discuss the impact of beamforming techniques that are being investigated. Finally, some of the CF promising enabling technologies are presented to emphasize the main applications in these networks.

Enhanced Subset Greedy Multiuser Scheduling in Clustered Cell-Free Massive MIMO Systems

In this work, we consider the problem of multiuser scheduling for the downlink of cell-free massive multi-input multi-output networks with clustering. In particular, we develop a multiuser scheduling algorithm based on an enhanced greedy method that is deployed with linear precoding and clustering. Closed-form expressions for the sum-rate performance are derived when imperfect channel state information is considered. The proposed scheduling algorithm is then analyzed along with its computational cost and network signaling load. Numerical results show that the proposed scheduling method outperforms the existing methods and in low signal-to-noise ratios, its performance becomes much closer to the optimal approach.

Lower energy consumption in cache-aided cell-free massive MIMO systems

Cell-free (CF) massive MIMO systems provide uniform and fast service to users by the cooperation of multiple distributed access points (APs) at the expense of high energy consumption (EC) due to the heavy use of fronthaul and backhaul links. To tackle this issue, we propose a cache-aided cell-free (CCF) system where all APs are deployed with edge cache to reduce the load on links. In this paper, a scalable total EC model of CCF systems is established with two parts: EC of APs (ECAP) and EC for cache transmission (ECCT). An improved partial zero-forcing precoding scheme is proposed, which can provide high spectral efficiency and transmit few control data to reduce ECAP greatly. Then a minimum ECCT strategy is formulated. We transform ECCT minimization problem into a series of geometric programming problems by the successive convex approximation and an ingenious equivalent transformation. The obtained optimal cache placement makes the ECCT of CCF systems lower than that obtained by maximum hit ratio, equal probability and most popular caching strategies. Simulation results show that the proposed CCF systems are much more energy efficient than CF systems.

Datasets associated with the article "Learning-Based Downlink Power Allocation in Cell-Free Massive MIMO Systems" published in IEEE Transactions on Wireless Communications

Datasets associated with the article "Learning-Based Downlink Power Allocation in Cell-Free Massive MIMO Systems" published in IEEE Transactions on Wireless Communications

Optimization of IRS-NOMA-Assisted Cell-Free Massive MIMO Systems Using Deep Reinforcement Learning

Optimization of IRS-NOMA-Assisted Cell-Free Massive MIMO Systems Using Deep Reinforcement Learning

Integrated Access and Backhaul (IAB) in Cell-Free Massive MIMO Systems

Integrated Access and Backhaul (IAB) in Cell-Free Massive MIMO Systems

An Architecture for AoI and Cache Hybrid Multicast/Unicast/D2D With Cell-Free Massive MIMO Systems

An Architecture for AoI and Cache Hybrid Multicast/Unicast/D2D With Cell-Free Massive MIMO Systems

Federated Learning for Precoding Design in Cell-Free Massive MIMO Systems

Federated Learning for Precoding Design in Cell-Free Massive MIMO Systems