Cell-Free Massive MIMO Systems Research Articles

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

Physical Layer Security in Downlink of Cell-Free Massive MIMO With Imperfect CSI

This paper investigates the threat of passive eavesdropping on downlink cell-free massive MIMO (CF-MaMIMO) systems, examining in a particular system under the effects of imperfect channel estimation. Physical layer security (PLS) techniques and power allocation algorithms are typically adopted to deteriorate the quality of eavesdropped signals. In the downlink stream, artificial noise (AN) is broadcasted simultaneously with the users’ data streams, with the aim of jamming the eavesdropper’s signal without sacrificing the quality of service (QoS). However, imperfect channel estimation results in AN leakage to legitimate users. Our work considers this condition, and the power allocation algorithms we present determine the minimum power that is needed to maintain the user’s Qos even in the presence of leaked AN. We first propose the cooperative PLS algorithm (COP), which facilitates AN broadcasting via access point (AP) cooperation. Unfortunately, this cooperation wastes some of the power used to send the AN and, in most cases, increases the computational complexity. We then present the independent PLS algorithm (IND), where the goal is to regain this power by allowing each AP to broadcast AN independently. However, to meet the feasibility criterion, this occurs at the cost of increasing the number of used antennas. This technique reduces the power allocation complexity but increases the computational complexities of the precoder design and the channel estimation process. Our results reveal that the proposed algorithms enhance the security performance of CF-MaMIMO. We evaluate such performance from numerous angles including the number of users, number of APs, QoS, beamforming, AN leakage, and channel estimation techniques.

Secrecy Performance Evaluation of Scalable Cell-Free Massive MIMO Systems: A Stochastic Geometry Approach

This paper presents the first performance analysis of physical layer downlink secure transmissions in a scalable cell-free massive MIMO (SCF-mMIMO) system. A stochastic geometry approach is used to model the locations of the access points (APs), user equipments (UEs) and eavesdroppers (Eves) as independent homogeneous Poisson point processes (HPPPs). In addition to applying maximum ratio transmission (MRT) to send the confidential messages, null-space artificial noise is also injected for secrecy enhancement. We analytically characterize the secrecy performance in terms of both the outage-based secrecy transmission rate (STR) and the ergodic secrecy rate (ESR), appropriate for slow quasi-static fading channels and fast block-fading channels, respectively. By utilizing moment matching and Gil-Pelaez inversion theorem, we are able to obtain mathematically tractable approximations for the performance metrics. These approximations are shown to have high accuracy as compared to simulation results. Our numerical results reveal useful design insights that cannot be inferred from existing studies. These insights answer important questions such as whether it is best to deploy as many APs each with fewer antennas and to what extent the artificial noise insertion is beneficial.

Secure Optimal Precoding for User-Centric Cell-Free Massive MIMO System

The security of user-centric cell-free massive multiple-input multiple-output (UC-CF mMIMO) system in the presence of multiple collusive eavesdroppers (Eves) is investigated in this letter. The access points (APs) share the estimated channel state informations (CSIs) of serving users to central processing unit (CPU). Utilizing the received partial CSIs, the CPU optimizes precoding via formulating a secrecy rate maximization problem under minimum rate requirements of users and power constraints of APs. The formulated problem is non-convex, and the optimum solution is acquired by iteratively solving a sequence of second-order cone programming problems. The numerical simulations present the comparison of secrecy performance achieved in different AP selection methods of UC-CF system, and CF system.

Sum-Rate Maximization and Leakage Minimization for Multi-User Cell-Free Massive MIMO Systems

Sum-Rate Maximization and Leakage Minimization for Multi-User Cell-Free Massive MIMO Systems

Reconfigurable Intelligent Surface-Aided Cell-Free Massive MIMO with Low-Resolution ADCs: Secrecy Performance Analysis and Optimization

The secure communication in reconfigurable intelligent surface-aided cell-free massive MIMO system is investigated with low-resolution ADCs and with the existence of an active eavesdropper. Specifically, an aggregated channel estimation approach is applied to decrease the overhead required to estimate the channels. Using the available imperfect channel state information (CSI), the conjugate beamforming and random beamforming are applied at the APs and the RIS for downlink data transmission, respectively. The closed-form expression of the achievable secrecy rate is acquired to appraise the achievable secrecy performance using only the channel statistics. With the achievable analytical results, the impacts of the quantization bit of ADCs, channel estimation error, the number of RIS elements, and the number of the APs can be unveiled. Aiming to maximize the minimum achievable rate of all legitimate users subject to security constraints, the power control optimization scheme is first formulated. To tackle this nonconvex property of the proposed optimization problem, a path-following algorithm is then utilized to solve the initial problem with continuous approximations and iterative optimization. Numerical results are presented to verify the achieved results along with availability of the presented power allocation approach.

Enabling Cell-Free Massive MIMO Systems With Wireless Millimeter Wave Fronthaul

Cell-free massive MIMO systems have promising data rate and coverage gains. These systems, however, typically rely on fiber based fronthaul for the communication between the central processing unit and the distributed access points (APs), which increases the infrastructure cost and installation complexity. To address these challenges, this paper proposes two architectures for cell-free massive MIMO systems based on wireless fronthaul that is operating at a higher-band compared to the access links. These dual-band architectures ensure high data rate fronthaul while reducing the infrastructure cost and enhancing the deployment flexibility and adaptability. To investigate the achievable data rates with the proposed architectures, we formulate the end-to-end data rate optimization problem accounting for the various practical aspects of the fronthaul and access links. Then, we develop a low-complexity yet efficient joint beamforming and resource allocation solution for the proposed architectures based on user-centric AP grouping. With this solution, we show that the proposed architectures can achieve comparable data rates to those obtained with optical fiber-based fronthaul under realistic assumptions on the fronthaul bandwidth, hardware constraints, and deployment scenarios. This highlights a promising path for realizing the cell-free massive MIMO gains in practice while reducing the infrastructure and deployment overhead.

Iterative Matrix Inversion Methods for Precoding in Cell-Free Massive MIMO Systems

Cell-free massive multiple-input multiple-output (mMIMO) systems are an alternative topology for mMIMO deployment, wherein a large number of access points are distributed over the coverage area to jointly serve users. Linear precoding methods such as zero-forcing are sufficient to achieve near-optimal performance in mMIMO systems. However, a key challenge in implementing these precoders can be a channel matrix inversion operation, which results in significant computational complexity in systems with large-scale antenna arrays. Hence, instead of direct matrix inversion, we examine several iterative methods to calculate the precoding matrix in a cell-free mMIMO system. We investigate their computational complexity and convergence rate in the presence of small- and large-scale fading and spatial correlation between antennas. Notably, we demonstrate that some iterative methods previously proposed for conventional (co-located) mMIMO do not always converge for cell-free mMIMO. Our main focus is the hyper-power iterative inversion method, which can be applied to both matrix inverses and pseudoinverses with guaranteed convergence; its factorized version also reduces its computational complexity. Although the hyper-power method does not reduce the complexity compared to direct matrix inversion, it converges quickly with high accuracy and strong numerical stability, and is conducive to parallel computation. These qualities make it a good candidate for matrix inversion in cell-free mMIMO system precoders.

FD Cell-Free Massive MIMO Systems With Downlink Pilots: Analysis and Optimization

We consider a full duplex (FD) massive multiple-input multiple-output (mMIMO) cell-free (CF) system, where a large number of multi-antenna FD access points (APs) jointly serve multiple FD user equipments (UEs). The APs transmit precoded downlink pilots for the UEs to estimate the effective downlink channel. For this system, we derive closed-form uplink and downlink spectral efficiency (SE) expressions by considering i) radio-frequency (RF) impairments at the APs and UEs; ii) dynamic resolution analog-to-digital converter/digital-to-analog converter (ADC/DAC) architecture at the APs and low-resolution ADC/DACs at the UEs; and iii) spatially-correlated Rician channels. We then maximize the non-convex global energy efficiency metric by using the block minorization-maximization technique, which decomposes the main optimization into multiple convex surrogate sub-problems. We analytically show that the SE gain obtained with downlink training is limited for practical CF systems with RF and ADC/DAC impairments, Rician channel and pilot contamination. We also extensively investigate the impact of FD interferences on the downlink training gain.

Capacity Maximization in Cell Free Massive MIMO Network with Access Point Selection Method

Background: Cell Free massive MIMO, containing a very large number of distributed access points (APs), which is a promising technology to provide high data rate, spectral efficiency (SE), and energy efficiency (EE). The system performance of cell-free M-MIMO is maximum when selecting optimal access points (AP) from the large number of APs. The linear precoding methods of zeroforcing (ZF) and minimum mean square error (MMSE) are utilized in this study because they are devoid of self-interference and so improve the system capacity. Objective: The objective of this study is to maximize the system data rate in a cell-free M-MIMO network. Method: To maximize the system data rate, the maximum channel gain-based Access Point Selection (MCGAPS), Distance based Access Point Selection (DAPS), and Random-Access Point Selection (RAPS) algorithms are used to pick access points (APs) in a cell-free M-MIMO network. Because the MCGAPS algorithm selects those APs with the highest channel gain, the system’s rate is improved. Result & Discussion: The DAPS algorithm is used to choose the closest APs to the user. The APs were randomly chosen using RAPS. Random user selection (RUS) algorithm schedules the same number of users. Conclusion: It is observed that the DAPS and RUS algorithms jointly improve the system rate significantly in cell-free massive MIMO system compared to the other proposed algorithms.

Multi-UE Multi-AP Beam Alignment in User-Centric Cell-Free Massive MIMO Systems Operating at mmWave

This paper considers the problem of beam alignment in a cell-free massive MIMO deployment with multiple access points (APs) and multiple user equipments (UEs) simultaneously operating in the same millimeter wave frequency band. Assuming the availability of a control channel at sub-6 GHz frequencies, a protocol is developed that permits estimating, for each UE, the strongest propagation path from each of the surrounding APs, and to perform user-centric association between the UEs and the APs. Estimation of the strongest paths from nearby APs is realized at the UE in a one-phase procedure, during which all the APs simultaneously transmit on pseudo-randomly selected channels with pseudo-random transmit beamformers. An algorithm for orthogonal channels assignment to the APs is also proposed, with the aim of minimizing the mutual interference between APs that transmit on the same channels. The performance of the proposed strategy is evaluated both in terms of probability of correct detection of the directions of arrival and of departure associated to the strongest beam from nearby APs, and in terms of downlink and uplink signal-to-interference-plus-noise ratio. Numerical results show that the proposed approach is effective and capable of efficiently realizing beam alignment in a multi-UE multi-AP wireless scenario.

User-Centric Cell-Free Massive MIMO System for Indoor Industrial Networks

The cell-free massive multiple-input multiple-output (CFmMIMO) aims to provide uniform quality of service (QoS) for all users, and can be used in small-area scenarios such as indoor industrial networks. This paper studies the CFmMIMO system for indoor industrial scenarios. Firstly, an access point (AP) grouping based hierarchical network topology is proposed. Based on this, we propose an effective AP selection method. To reduce pilot contamination, a pilot assignment scheme based on inspection robot (IR) location is proposed. Considering the high reliability requirement of industrial data transmission, the power control and backhaul combining are jointly optimized to maximize the minimum signal to interference plus noise ratio (SINR). The scalability of the proposed CFmMIMO system is analyzed, and a scalable power control method is proposed. The simulations demonstrate the effectiveness of the AP selection method, pilot assignment scheme, and the joint optimization algorithm for power control and backhaul combining. Moreover, the impact of network scale and network load on system performance is evaluated and analyzed in the simulations.

Power Allocation in Smart Grid Aided Cell Free Massive MIMO Systems for URLLC Applications

This paper investigates power allocation in cell-free massive MIMO for URLLC applications, where each access point is powered by smart grid or solar panels/wind-turbines. In particular, the closed-form achievable rate for URRLC is derived. By exploiting the derived results, a non-convex and non-smooth optimization problem is formulated for maximizing the minimum URRLC rate of all multicast group, and is NP-hard. To deal with this problem, the formulated optimization problem is transformed into a tractable version, then the classical CVX tool is adopted to solve the resultant problem. Numerical simulation verifies the practicability of the derived theoretical results and the proposed algorithm.

Structured Tensor CP Decomposition-Aided Pilot Decontamination for UAV Communication in Cell-Free Massive MIMO Systems

This letter proposes a structured CANDECOMP/PARAFAC (CP) decomposition-aided (SCPD) pilot decontamination (PDC) scheme for UAV communication in cell-free massive MIMO systems. We design a two-stage pilot transmission scheme and intentionally generate pilot contamination for pilot decontamination. At first, we formulate the received signals as a second-order tensor. Then we turn the PDC problem into a joint channel parameter estimation problem. By exploiting the Vandermonde structure of the factor matrix, we propose a SCPD-aided scheme for channel estimation, which only leverages standard linear algebra and avoids numerous iterations. The simulation results demonstrate the effectiveness of the proposed SCPD scheme in terms of accuracy and complexity.

Uplink Performance of Cell-Free Massive MIMO With Multi-Antenna Users Over Jointly-Correlated Rayleigh Fading Channels

In this paper, we investigate a cell-free massive MIMO system with both access points (APs) and user equipments (UEs) equipped with multiple antennas over jointly-correlated Rayleigh fading channels. We study four uplink implementations, from fully centralized processing to fully distributed processing, and derive their achievable spectral efficiency (SE) expressions with minimum mean-squared error successive interference cancellation (MMSE-SIC) detectors and arbitrary combining schemes. Furthermore, the global and local MMSE combining schemes are derived based on full and local channel state information (CSI) obtained under pilot contamination, which can maximize the achievable SE for the fully centralized and distributed implementation, respectively. We study a two-layer decoding implementation with an arbitrary combining scheme in the first layer and optimal large-scale fading decoding (LSFD) in the second layer. Besides, we compute novel closed-form SE expressions for the two-layer decoding implementation with maximum ratio (MR) combining. In the numerical results, we compare the SE performance for different implementation levels, combining schemes, and channel models. It is important to note that increasing the number of antennas per UE may degrade the SE performance.

Uplink Channel Estimation With Reduced Fronthaul Overhead in Cell-Free Massive MIMO Systems

This letter focuses on the problem of uplink channel estimation with the reduced fronthaul overhead in cell-free massive multiple-input multiple-output (mMIMO) systems. First, we propose a sub-sampling scheme to reduce the dimension of fronthaul. Then, we exploit the inherent channel sparsity and model the underdetermined channel estimation problem as an off-grid sparse signal recovery problem. Finally, an enhanced sparse Bayesian learning (ESBL) channel estimation algorithm is proposed to refine the sampled grid points and recover the sparse channel iteratively. Simulation results demonstrate that the proposed algorithm achieves a significant reduction on the fronthaul overhead and offers a better channel estimation performance.

Secure Downlink Transmission in Cell-Free Massive MIMO System Enhanced by Intelligent Reflecting Surfaces

This paper presents the application of intelligent reflecting surfaces (IRSs) in a cell-free massive MIMO network to enhance secure transmission in the system. Multiantenna access points (APs) need to transmit information to users safely and reliably via IRSs without fully knowing the channel state information (CSI) of a multiantenna eavesdropper (Eve) or the accurate beamforming information of a jammer. Specifically, through joint optimization of the AP active beamforming (ABF) and IRS passive beamforming (PBF), the information leaked to the Eve is limited by a set threshold, and restrictions on the IRS reflection phase are considered to maximize the weighted sum rate of the system’s downlink transmission. To solve this multivariate-coupled nonconvex problem, we propose a joint precoding framework under imperfect CSI, using the generalized S-procedure, fractional programming (FP), and multidimensional complex quadratic transformation (MCQT) to transform the original complex nonconvex problem into an easily solvable convex optimization problem, and an alternating algorithm to obtain the optimal solution for precoding and IRS phase shifting. Simulation results show that the proposed scheme can significantly increase the weighted sum rate of the system compared to conventional antijamming methods while revealing that the scheme is effective in enhancing secure system transmission.