Cell-Free Massive MIMO Systems Research Articles

Multi-agent Reinforcement Learning-based Joint Precoding and Phase Shift Optimization for RIS-aided Cell-Free Massive MIMO Systems

Multi-agent Reinforcement Learning-based Joint Precoding and Phase Shift Optimization for RIS-aided Cell-Free Massive MIMO Systems

Centralized and Decentralized IDD Schemes for Cell-Free Massive MIMO Systems: AP Selection and LLR Refinement

Centralized and Decentralized IDD Schemes for Cell-Free Massive MIMO Systems: AP Selection and LLR Refinement

Distributed Combined Channel Estimation and Optimal Uplink Receive Combining for User- Centric Cell-Free Massive MIMO Systems

Distributed Combined Channel Estimation and Optimal Uplink Receive Combining for User- Centric Cell-Free Massive MIMO Systems

RIS-Aided Cell-Free Massive MIMO System: A Two-Timescale Optimization Scheme

RIS-Aided Cell-Free Massive MIMO System: A Two-Timescale Optimization Scheme

On the Performance of RIS-Aided Cell-Free Massive MIMO Systems Under Channel Aging

On the Performance of RIS-Aided Cell-Free Massive MIMO Systems Under Channel Aging

Low-complexity detector performance evaluation for cell-free massive MIMO systems

Low-complexity detector performance evaluation for cell-free massive MIMO systems

Performance Analysis and Power Allocation for Uplink Cell-Free Massive MIMO System with Nonlinear Power Amplifier

Performance Analysis and Power Allocation for Uplink Cell-Free Massive MIMO System with Nonlinear Power Amplifier

Rate Analysis and Optimization of IRS-Aided Hardware-Impaired Cell-Free Massive MIMO Systems Under Correlated Ricean fading Channels

Rate Analysis and Optimization of IRS-Aided Hardware-Impaired Cell-Free Massive MIMO Systems Under Correlated Ricean fading Channels

Superimposed Versus Regular Pilots for Hardware Impaired Rician-Faded Cell-Free Massive MIMO Systems

Superimposed Versus Regular Pilots for Hardware Impaired Rician-Faded Cell-Free Massive MIMO Systems

RIS-Aided Cell-Free Massive MIMO Systems With Channel Aging

RIS-Aided Cell-Free Massive MIMO Systems With Channel Aging

Performance Analysis of RIS-Assisted Cell-Free Massive MIMO Systems With Transceiver Hardware Impairments

Performance Analysis of RIS-Assisted Cell-Free Massive MIMO Systems With Transceiver Hardware Impairments

A Scalable Deep-Learning-Based Active User Detection Approach for SEU-Assisted Cell-Free Massive MIMO Systems

Massive ultra-reliable and low-latency communications (mURLLC) is an emerging and dominate traffic service in 6G. To reduce the signaling overhead and access delay, grant-free random access (GFRA) is widely used in mURLLC. As the first step in GFRA, active user detection (AUD) is aimed to identify the set of active users accurately and timely. Conventional AUD schemes relying on iterative computations over massive users bring redundant computing overload and processing delay, which seriously affect the system scalability in the mURLLC scenario. Considering the near-real-time requirement of mURLLC, we propose a scalable deep learning-based AUD approach utilizing similar channel sparsity in cell-free (CF) massive multiple input multiple-output (mMIMO) systems. By exploiting the distributed computing unit, i.e., space expansion unit (SEU), we design a SEU-assisted CF mMIMO to improve the scalability of the traditional centralized CF computing architecture. In the proposed system, all access points (APs) are divided into several clusters, and the SEU in each cluster provides a reliable distributed AUD scheme through a one-dimensional convolutional network (1D CNN). In addition, a transfer learning-based ensemble model is established at the CPU to achieve a better global detection decision. Simulation results demonstrate the superiority of our scalable deep learning-based approach, and reveal that through the transfer learning-based model fusion at the CPU, our proposed scalable SEU-assisted approach can obtain success probability close to that of the centralized CF computing scheme with less access delay. In addition, our scheme requires fewer pilots than other CS-based schemes.

Performance Analysis of DoA Estimation for FDD Cell Free Systems Based on Compressive Sensing Technique

The concept of cell free (CF) massive MIMO systems is a prospective fifth generation communication technology that effort with base stations for the privilege of user-centric coverage. Most studies on the CF massive MIMO system in the past imply that systems that use time division duplexing (TDD), even despite the systems using frequency division duplex (FDD) predominate in today’s wireless communications. When the number of antennas increases in FDD systems, channel state information (CSI) collection and feedback overhead become major issues. In order to mitigate these issues, we make use of the condition that the so-called uplink and downlink multipath components are comparable. Base station takes use of the angle reciprocity may immediately obtain information on channel parameters from the uplink training signal. In this paper, for CF massive MIMO system based on FDD, we provide compressive sensing (CS) of directions of arrival (DoAs) estimation approach of access point cooperation based on the channel parameters. The suggested estimation approach outperforms the established subspace-based technique, according to simulation findings. Additionally, we showed that the results of our compressive sensing estimator against the conventional estimation method. The former demonstrates way far better outcome and performance accordingly than the latter.

Symbiotic Backscatter Communication Underlying a Cell-Free Massive MIMO System

In ambient backscatter communications, backscatter devices (BDs) utilize ambient “legacy” radio signals as both a harvested energy source and a carrier on which to modulate data. Symbiotic radio, a subtype in which the legacy system assists both its own users and underlaid BDs, has attracted much research interest recently. Furthermore, cell-free massive multiple-input multiple-output (CF mMIMO) systems are promising for beyond-5G networks from both spectral and energy efficiency perspectives. To reap the benefits of both, we consider a primary CF mMIMO system where the access points (APs) aid an underlaid BD layer to both harvest energy and reflect information toward the primary receivers (which receive data from both layers). To acquire separate channel state information (CSI) of the direct and backscattered channels at the APs, a two-phase uplink pilot training method is proposed, with the effects of pilot contamination and spatial correlation between antennas accounted for. However, the receivers are assumed to only have partial CSI (statistical knowledge plus instantaneous phase information for partially-coherent reception). Assuming uplink/downlink radio channel reciprocity, the CSI is used to design downlink precoding vectors for the APs such that channel hardening is enhanced and both primary receivers and BDs benefit. We derive expressions for the average signal-to-interference-plus-noise ratios of both primary and backscatter signals, accounting for the effects of imperfect CSI, spatial correlation, pilot contamination, and channel hardening. Furthermore, the average power harvested in the BDs is derived. Simulation results demonstrate that the performance of the proposed scheme is much more uniform across all devices with CF mMIMO than with conventional co-located mMIMO. The use of CF mMIMO also largely removes the need for special consideration of the BD layer in the symbiotic system, which is not the case with co-located mMIMO.

Design and Analysis of MMSE Precoder for Dual-Polarized Cell-Free Massive MIMO Systems

Design and Analysis of MMSE Precoder for Dual-Polarized Cell-Free Massive MIMO Systems

Energy Efficiency Maximization in IRS-Aided Cell-Free Massive MIMO System

Energy Efficiency Maximization in IRS-Aided Cell-Free Massive MIMO System

Manifold optimization assisted centralized hybrid precoding for cell-free massive MIMO systems

Manifold optimization assisted centralized hybrid precoding for cell-free massive MIMO systems

Impact of mobility on the downlink performance of cell-free massive MIMO systems

In this paper, we analyze the achievable downlink spectral efficiency of cell-free massive multiple input multiple output (CF-mMIMO) systems, accounting for the effects of channel aging (caused by user mobility) and pilot contamination. We consider two cases, one where user equipments (UEs) rely on downlink pilots beamformed by the access points (APs) to estimate downlink channel, and another where UEs utilize statistical channel state information (CSI) for data decoding. For comparison, we also consider cellular mMIMO and derive its achievable spectral efficiency with channel aging and pilot contamination in the above two cases. Our results show that, in CF-mMIMO, downlink training is preferable over statistical CSI when the length of the data sequence is chosen optimally to maximize the spectral efficiency. In cellular mMIMO, however, either one of the two schemes may be better depending on whether user fairness or sum spectral efficiency is prioritized. Furthermore, the CF-mMIMO system generally outperforms cellular mMIMO even after accounting for the effects of channel aging and pilot contamination. Through numerical results, we illustrate the effect of various system parameters such as the maximum user velocity, uplink/downlink pilot lengths, data duration, network densification, and provide interesting insights into the key differences between cell-free and cellular mMIMO systems.

Access Point-User Association and Auction Algorithm-Based Pilot Assignment Schemes for Cell-Free Massive MIMO Systems

In this article, two effective schemes are proposed for association and pilot assignment to tackle the problem of spectral efficiency (SE) maximization and interference management in a user-centric cell-free (CF) massive multiple-input–multiple-output (mMIMO) system. First, a novel access point (AP)–user equipment (UE) association scheme is proposed, which ensures both scalability and association for all the UEs in a CF mMIMO system. After that, a pilot assignment scheme is also proposed which has an iterative structure that utilizes the auction algorithm to solve symmetrical assignment problems for pilot allocation. Numerical results demonstrate the superior performance of the proposed AP–UE association scheme compared to the existing AP preference-based association scheme. Moreover, the proposed pilot assignment scheme provides up to 30% average SE performance and up to 50% reduction in the average interference to signal power ratio (ISR) over that of several competing alternatives. Numerical simulated results further reveal that the proposed pilot assignment scheme improves the average SE and ISR reduction performance regardless of the number of UEs, APs, and pilot sequences in the system. Finally, the proposed pilot assignment scheme manifests the best balance in the tradeoff among the considered performance criteria relative to its comparatives.

Hardware-Impaired Rician-Faded Cell-Free Massive MIMO Systems With LSFD and Channel Aging: SE Analysis and Optimization

We study the impact of channel aging on the uplink of a cell-free (CF) massive multiple-input multiple-output (mMIMO) system by considering i) spatially-correlated Rician-faded channels; ii) hardware impairments at the access points and user equipments (UEs); and iii) two-layer large-scale fading decoding (LSFD). We first derive a closed-form spectral efficiency (SE) expression for this system, and later propose two novel optimization techniques to optimize the non-convex SE metric by exploiting the minorization-maximization (MM) method. The first one requires a numerical optimization solver, and has a high computation complexity. The second one with closed-form transmit power updates, has a trivial computation complexity. We numerically show that i) the two-layer LSFD scheme effectively mitigates the interference due to channel aging for both low- and high-velocity UEs; and ii) increasing the number of AP antennas does not mitigate the SE deterioration due to channel aging. We numerically characterize the optimal pilot length required to maximize the SE for various UE speeds. We also numerically show that the proposed closed-form MM optimization yields the same SE as that of the first technique, which requires numerical solver, and that too with a much reduced time-complexity.