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 shortterm 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.

Wireless Powered IoE for 6G: Massive Access Meets Scalable Cell-Free Massive MIMO

A key challenge to the scalable deployment of the energy self-sustainability(ESS)Internet of Everything(IoE)for sixth-generation(6G)networks is juggling massive connectivity and high spectral efficiency(SE).Cell-free massive multiple-input multiple-output(CF mMIMO)is considered as a promising solution,where many wireless access points perform coherent signal processing to jointly serve the users.However,massive connectivity and high SE are difficult to obtain at the same time because of the limited pilot resource.To solve this problem,we propose a new framework for ESS IoE networks where the user activity detection(UAD)and channel estimation are decoupled.A UAD detector based on deep convolutional neural networks,an initial access scheme,and a scalable power control policy are proposed to enable the practical scalable CF mMIMO implementation.We derive novel and exact closed-form expressions of harvested energy and SE with maximum ratio(MR)processing.Using local partial minimum mean-square error and MR combining,simulation results prove that the proposed framework can serve more users,improve the SE performance,and achieve better user fairness for the considered ESS IoE networks.

Spectral Efficiency of Superimposed Pilots in Cell-Free Massive MIMO Systems with Hardware Impairments

In this paper,the spectral efficiency(SE)of an uplink hardware-constrained cell-free massive multi-input multi-output(MIMO)system with maximal ratio combining(MRC)receiver filters in the context of superimposed pilots(SPs)is investigated.Tractable closed-form SE expressions for the considered system are derived,which share us with opportunities to explore the impacts of the hardware quality coefficient,the length of coherence interval,and the power balance factor between pilot and data signals.Numerical results indicate that the achievable SE deteriorates as the hardware quality decreases and is more susceptible to the hardware impairments at the user equipments(UEs).Besides,we observe that SPs outperform regular pilots(RPs)in terms of SE and this performance gain is heavily dependent on the values of power balance factor and coherence interval.However,the superiorities of SPs over RPs have vanished when severe hardware imperfections are considered.

Cell-Free Massive MIMO for 6G Wireless Communication Networks

The recently commercialized fifth-generation(5G)wireless networks have achieved many improvements,including air interface enhancement,spectrum expansion,and network intensification by several key technologies,such as massive multiple-input multipleoutput(MIMO),millimeter-wave communications,and ultra-dense networking.Despite the deployment of 5G commercial systems,wireless communications is still facing many challenges to enable connected intelligence and a myriad of applications such as industrial Internet-ofthings,autonomous systems,brain-computer interfaces,digital twin,tactile Internet,etc.Therefore,it is urgent to start research on the sixth-generation(6G)wireless communication systems.Among the candidate technologies for 6G,cell-free massive MIMO,which combines the advantages of distributed systems and massive MIMO,is a promising solution to enhance the wireless transmission efficiency and provide better coverage.In this paper,we present a comprehensive study on cell-free massive MIMO for 6G wireless communication networks with a special focus on the signal processing perspective.Specifically,we introduce enabling physical layer technologies for cell-free massive MIMO,such as user association,pilot assignment,transmitter,and receiver design,as well as power control and allocation.Furthermore,some current and future research problems are described.