Cell-free massive multi-input-multi-output (CF-mMIMO) provides significant improvement owing to the distributed architecture. However, it suffers from the constraints including information constraints, and computation resources constraints. In this paper, we propose 4 ranks of available information in CF-mMIMO and aim to find a distributed precoding exploiting randomly accessible side information, which is one-step without iterations and robust against the unreliable fronthaul between distributed central processing units. Quadratic team learning (QTL) is devised which is derived from team theory to handle the distributed underdetermined quadratic programming. The extensive 1440 experiments validate the superiority of QTL and we believe QTL is a definitely excellent choice for CF-mMIMO distributed precoding. To the best of our knowledge, this is the first work utilizing team theory to help the design of artificial intelligence architecture for wireless communications. To prompt the development of QTL, we have open-sourced the implementation code on https://github.com/hzy238221seu/QTL4CF-Precoding.git.

Cell-free massive MIMO (CF-mMIMO) represents the pinnacle of distributed antenna systems, offering superior service to all users. However, the distributed nature of access points leads to fragmented information processing, limiting performance in practical deployments. Additionally, existing studies often overlook the impact of limited fronthaul capacity, which introduces noise and degrades the reliability of shared information. In this work, we implement a practical CF-mMIMO prototype under fifth generation new radio standards and propose a diffusion-based uplink scheme that outperforms conventional distributed cell-free systems without cooperation. Our approach adapts to arbitrary fronthaul topologies by leveraging the law of large numbers. We further analyze the linear effects of fronthaul noise and the correlation of uploaded data, demonstrating that the diffusion uplink excels in Rician fading environments while maintaining robust performance in Rayleigh fading. To the best of our knowledge, this is the first work to employ a diffusion model for mitigating fronthaul non-idealities, enabling distributed cooperative uplink in a real-world CF-mMIMO system.

Cell-free radio access networks (CF-RANs) with network-assisted free-duplex (NA-FD) architecture unify flexible-duplex, hybrid-duplex, and full-duplex operations, but the coupled uplink/downlink interference and centralized processing impose substantial computational and signaling burdens. To address these challenges, this paper studies distributed transceiver design and access point (AP) duplex mode selection under edge distributed unit (EDU)-level information constraints and per- AP power limitations. We develop a partial distributed block coordinate descent (PDBCD) algorithm that decomposes the original sum-rate maximization into three tractable subproblems and iteratively approximates MMSE performance. The proposed design fully leverages EDU computing resources, reducing the computation load at the cloud computing unit (CCU), and significantly lowering fronthaul signaling overhead through an adaptive inter-EDU information sharing mechanism. Furthermore, by integrating a low-complexity greedy search for duplex mode assignment, the algorithm effectively mitigates cross-link interference (CLI) in NA-FD systems. Simulation results show that the proposed scheme improves spectral efficiency compared with conventional duplex mode, and centralized/distributed MMSE baselines, while maintaining strong scalability under practical deployment constraints.

The scalable cell-free radio access network (CF-RAN), built on cell-free massive multiple-input multiple-output (CF-mMIMO) networks, achieves a critical trade-off between computational complexity and system performance in cooperative transmission through the rational division of physical-layer functionalities. However, due to its distributed transmission architecture, unavoidable propagation delay differences arise in signal arrival times across different receivers during cooperative transmission, leading to asynchronous reception effects that severely degrade system performance. In this paper, we analyze the specific impacts of asynchronous reception effects in scalable CF-RAN systems, including accumulated phase offset on received signals, as well as inter-carrier interference (ICI) and intersymbol interference (ISI). We investigate channel estimation under non-ideal channel state information (CSI) acquisition caused by non-orthogonal pilot sequences and asynchronous reception effects, deriving closed-form expressions for the achievable uplink and downlink spectral efficiency (SE) in scalable CF-RAN systems under asynchronous conditions. To mitigate asynchronous reception effects, we introduce a beam division multiple access (BDMA) transmission scheme into scalable CF-RAN systems, leveraging large-scale antenna arrays at remote radio units (RRUs) to achieve beam-domain multi-user spatial multiplexing. Building on this framework, we implement per-beam time delay compensation (PBTDC) on RRU antenna arrays to approximate asynchronous received signals as synchronized and derive closed-form expressions for the achievable SE of uplink/downlink in asynchronous scalable CF-RAN systems with PBTDC architecture. Numerical simulations demonstrate that asynchronous reception effects significantly degrade channel estimation and data transmission performance in scalable CF-RAN systems. In contrast, the proposed PBTDC architecture based on BDMA transmission effectively mitigates these adverse effects and substantially enhances system performance.

This paper investigates the downlink performance of cell-free radio access networks (CF-RAN) employing distributed regularized zero-forcing (RZF) precoding under imperfect channel state information (CSI). We derive a deterministic equivalent expression for the spectral efficiency (SE) lower bound under large-system regime. The proposed bound accurately approximates the system performance while requiring only statistical channel information, formulated based on the principles of large dimensional random matrix theory (RMT). Simulation results demonstrate that the deterministic approximation achieves high accuracy across various configurations, especially in scenarios with a limited number of edge distribution units (EDUs) per user. The derived lower bound provides a tractable and efficient analytical tool for evaluating CF-RAN performance in extreme MIMO (E-MIMO) scenarios, and offers practical guidance for system design, resource allocation, and 6G network planning.

Orthogonal Time Frequency Space (OTFS) modulation can effectively enhance transmission efficiency in doubly selective channels and is considered as one of the modulation techniques for next generation wireless communication. To meet the demands of machine-type communication in high-speed scenarios, this paper conducts research on a massive random access scheme based on OTFS modulation. Specifically, the transmission and reception model of multiple-input multiple-output (MIMO) OTFS signals is analyzed, where channel estimation is formulated as a block-sparse signal recovery problem. Therefore, based on existing superimposed and embedded pilot schemes, we propose a novel hybrid preamble scheme. This scheme utilizes the superimposed preamble and embedded preamble to achieve rough active user detection (AUD) and precise AUD, respectively, enabling accurate detection and channel estimation while supporting a large number of device access. On this basis, this paper employs a generalized approximate message passing with pattern-coupled sparse Bayesian learning (GAMP-PCSBL) algorithm that can capture the block sparsity characteristics of the channel matrix and achieve accurate estimation. The results of numerical simulations verify the effectiveness and superiority of the proposed scheme.

The integration of autonomous aerial vehicles (UAVs) and low-Earth orbit (LEO) satellites has become attractive for Internet of Things (IoT) task processing, as it can overcome obstacles in terrestrial network coverage, such as those in oceans or desert areas. However, it lacks a collaborative approach for allocating the communication and computing resources among UAVs and LEO satellites and optimizing the hovering point of UAVs to prolong their endurance. In this article, we investigate energy-efficient resource allocation in LEO-assisted UAV networks for the IoT. A novel optimization algorithm, that jointly IoT tasks’ offloading decision, UAVs’ region selection, hovering point chosen, and communication and computing resource allocation (ORHCC), is proposed to optimize UAV trajectories and hovering points, enhancing endurance and minimizing energy consumption. In particular, the UAVs’ region selection and IoT tasks offloading are under the dueling deep Q-network (DuDQN) framework, the hovering point chosen and communication and computing resource allocation via the convex solution. The results show that the proposed ORHCC reduces 12.5% and 20.76% energy consumption compared with the proximal policy optimization and greedy baseline, respectively.

As a key technology for the evolution to the sixth generation (6G) systems, cell-free massive multiple-input multiple-output (CF-mMIMO) can effectively improve the spectrum efficiency, peak rate, and reliability of wireless communication systems. Starting from the scalable implementation of CF-mMIMO, we study a cell-free RAN (CF-RAN) with distributed cooperative transceivers under the open RAN (ORAN) architecture. Through theoretical analysis and numerical simulation, we investigate the uplink and downlink spectral efficiencies of CF-mMIMO with the distributed transceivers. We then discuss the implementation issues of CF-RAN under ORAN architecture, including time-frequency synchronization and over-the-air reciprocity calibration, low layer splitting, deployment of ORAN radio units (O-RU), and artificial intelligent-based user associations. Finally, we present some representative experimental results for the uplink distributed reception and downlink coherent joint transmission of CF-RAN with commercial off-the-shelf O-RUs.

This paper considers a decentralized massive random access scheme applicable to a user-centric cell-free massive MIMO system. In this system, a large number of user equipment (UEs) select access points (APs) based on the quality of channel, and adjacent APs can achieve lossless data exchange with a finite data volume. Each AP is equipped with an Edge Distributed Unit (EDU) capable of independently performing Maximum Likelihood (ML) estimation of the large-scale fading coefficients (LSFC) for the UEs associated with. Subsequently, the APs collectively assess the activity of associated UEs based on the estimated LSFC vectors obtained from adjacent APs, and employ a heuristic method to identify primary interference sources. On this basis, each AP's EDU uses the approximate message passing - sparse Bayesian learning (AMP-SBL) algorithm for channel estimation (CE). The proposed scheme concentrates on the associated user set, reducing computational complexity and signaling overhead while maintaining accurate performance. Numerical simulations validate the effectiveness and superiority of the approach presented in this paper.

A cell-free radio access network (CF-RAN) is a novel architecture that can effectively solve the scalability problems and lack of collaboration capabilities of other cell-free networks and achieve a better trade-off between performance and complexity. There are multiple edge distributed units (EDUs) in a CF-RAN system and each EDU mounts multiple APs. This paper inves-tigates the spectral efficiency (SE) performance of a downlink CF-RAN with zero-forcing (ZF) beamforming in the presence of pilot reuse, considering power coefficients and compression noise. An asymptotic expression for the downlink signal-to-interference-plus-noise ratio (SINR) of the system can be derived by using large-scale random matrix theory. To improve the performance of the system, we consider the EDU-AP association strategy and propose a heuristic greedy algorithm. Simulation results verify the accuracy of the asymptotic expression and the effectiveness of the proposed association algorithm.

The demand for higher transmission efficiency and denser user access has been put forth by the next generation of wireless communication systems. To cater to the future communication development, this article focuses on massive random access schemes under the user-centric cell-free massive multiple-input-multiple-output (MIMO) architecture. For uplink transmission, a data frame structure is designed to enable active user detection (AUD), channel estimation (CE), and data transmission. The association between access points (APs) and user equipment (UEs) is presented to facilitate an user-centric cell-free scalable architecture. In this article, a maximum likelihood (ML)-based method is proposed for AUD to obtain the set of active UEs. By setting appropriate thresholds and combining the UE-AP association, accurate active detection results can be obtained. CE can be accomplished with lower computational complexity by utilizing the detected active UE set in AUD module. Specifically, the generalized approximate message passing-based sparse Bayesian learning with Dirichlet process (GAMP-DP-SBL) is adopted as the CE algorithm, leveraging the spatial aggregation and dispersion characteristics of APs to enhance the estimation accuracy. Building upon GAMP-DP-SBL algorithm, a clustered algorithm (GAMP-CDP-SBL) is proposed to reduce the scale of the sensing matrix and improve the accuracy of CE for associated active UEs. Moreover, to enhance system scalability, decentralized AUD and CE algorithms are proposed in this article. Simulation results under various parameter settings and different scenarios exhibit the superior performance of the proposed scheme.

The Internet of Things (IoT) system provides sensing and computing services via terrestrial networks. However, the restricted coverage of terrestrial networks, such as base stations, limits the ubiquitous IoT services. Low-Earth orbit (LEO) satellites are able to provide network coverage for terrestrial IoT devices in unconnected scenarios, e.g., maritime. IoT devices in such scenarios usually have restricted onboard computation and power resources. In this article, we present an LEO-assisted IoT network (L-IoT) architecture where a device splits its task and offloads a portion of its task to the LEO to process within the coverage time. We formulate a task split problem with communication and computation resource allocation (SCC) to minimize the L-IoT energy consumption. We proposed an alternating optimization for split ratio and resource allocation (AOSR) algorithm. In particular, we use the outputs of Karush-Kuhn–Tucker (KKT) for resource allocation as part of the reward that feeds twin-delayed deep deterministic policy gradient. Lastly, the results of numerical simulations show that the proposed AOSR approach reduces 12.7% energy consumption compared to soft actor-critic (SAC) and 15% to deep deterministic policy gradient (DDPG).

This paper considers a cell-free (CF) massive multiple-input multiple-output (MIMO) with network-assisted full-duplex (NAFD) system for the ultra-reliable and low-latency (URLLC) communications, jointly optimizing duplex mode selection and transceiver design. To reduce the cross-link interference (CLI) and improve the weighted URLLC sum-rate of downlink and uplink users, we propose an optimization problem to maximize the achievable sum rates for the cell-free massive MIMO with NAFD system under a finite blocklength, limited by data rates and transmission power constraints. Then, a concave-convex procedure (CCCP) algorithm is used to solve this optimization problem. Simulation results show that the proposed algorithm has better performance than the traditional co-frequency co-time full-duplex (CCFD) and half-duplex (HD) schemes.

As an important part of beyond the fifth-generation (B5G) and the sixth-generation (6G) mobile communication systems, ultra-reliable and low latency communications (uRLLC) puts forward strict requirements for delay and reliability (e.g., 99.9999% reliability and$500 \mu \text{s}$latency). At present, the evaluation measures of delay and reliability are usually based on infinite block length and rely on long-term statistics, which cannot meet the requirement of low latency. The cell-free system, with a very large number of distributed antennas, has the characteristics of macro-diversity and spatial sparsity, which can further enhance the performance of uRLLC. In this paper, the downlink multidevice cell-free system with hard deadlines is considered and analyzed in the finite block length (FBL) regime. The communication’s delay and reliability are described based on two instantaneous evaluation measures: transmission error (TE) and time overflow (TO) probability. From the perspective of information theory, this paper analyzes the analytic expression of TE probability for a single device and the performance impact of FBL on the traditional channel capacity analysis. Considering the multidevice TO probability in a cell-free system, the closed-form expressions of upper and lower bounds are derived and compared with the gamma approximation results. This paper further provides three methods, namely, transmission rate selection, device grouping and space division multiplexing, to balance the delay and reliability of the system and analyzes the performance.

In this paper, we investigate the problem of optimizing the overall system secrecy spectral efficiency of cell-Free massive multiple-input multiple-output (CF-mMIMO) with network-assisted full-duplexing (NAFD) system, considering resisting interception by superimposing artificial noise (AN) on the transmitted signal. The access points (APs) are selected by binary mode selection vectors as transmitting or receiving APs flexibly to serve both uplink and downlink users simultaneously. Since optimization variables are tightly coupled, a double-loop strategy is developed to solve the non-covex combinatorial optimization problem. The outer loop is constructed according to greedy search for duplex mode selection, while the inner loop based on the proposed successive convex approximation (SCA) algorithm aims to optimize the transceivers and AN. Simulation results show that the proposed solution is superior to the fixed-mode duplex scheme in terms of secure spectral efficiency and able to achieve similar performance to that of the optimal exhaustive search scheme.

In this paper, we investigate the problem of sum rate maximization by guaranteeing the quality of service (QoS) of both uplink and downlink in cell-free with network-assisted full Duplexing (NAFD), where the users operate in half-duplex mode and access points (APs) operate in either full-duplex mode or half-duplex mode. In the considered network, the central processor unit (CPU) sends the compressed beamformed signals to the transmit-APs (T-APs) over the downlink fronthaul, and the T-APs forward the signals to downlink users. At the same time, the receive-APs (R-APs) compress the signals transmitted by uplink users and forward them to the CPU via uplink fronthaul. We aim to maximize the spectral efficiency and the number of users that should be admitted by the network, where users’ requirements of both downlink and uplink signal-to-interference-plus-noise (SINR) constraints, fronthaul capacity constraints, energy harvesting constraints and simultaneous wireless information and power transfer (SWIPT) ratio design are considered. A successive convex approximation-based algorithm is proposed to solve the highly coupled problem, which is guaranteed to converge to the Karush-Kuhn-Tucker (KKT) conditions. We conduct a comprehensive comparison between the NAFD scheme and the traditional co-frequency co-time full duplex (CCFD) scheme and time division duplex (TDD) scheme and offer some valuable opinions about the system design.

This paper studies transceiver design for a large-scale distributed antenna system (L-DAS) with network assisted full duplexing (NAFD), where all the users and remote antenna units (RAUs) operate in either half-duplex (HD) or full-duplex (FD) mode. In the considered network, transmitting-RAUs (TRAUs) transmit information to downlink users (DUs) while receiving-RAUs (R-RAUs) receive signal from uplink users (UUs). All the T-RAUs and R-RAUs are connected to the central processor (CP) via high-speed backhaul links. T-RAUs obtain DUs' data from the CP via downlink backhaul (D-backhaul), and forward the data to DUs by sparse beamforming. Meanwhile, R-RAUs detect the signal transmited by UUs, and forward the signal to the CP via uplink backhaul (U-backhaul). We aim to maximize the the spectral efficiency subject to quality of service (QoS) constraints and backhaul constraints. Since various design parameters, such as the downlink sparse beamformers, the uplink transmit power, and the receiver, are tightly coupled together in both the subject function and the constraints, the solution of the problem is challenging. By converting the object function to the difference between two convex functions (D.C.) structure with semi definite relax (SDR), an iterative SDR-block coordinate descent (SDR-BCD) method is proposed. Simulation results show that the proposed algorithm yield a higher spectral efficiency (SE) gain compared with the traditional time-division duple (TDD) scheme.

To achieve the expected attractive potential of massive multiple‐input multiple‐output (MIMO), accurate match between the receiver and the actual channel is necessary. Due to the channel aging, this kind of mismatch appears in the vast majority of practical propagations. Consequently, the impact of channel aging for massive MIMO is investigated in this study. The authors first propose an equivalent estimated channel model by considering the inevitable time‐varying nature of channels in realistic mobile communications. Then, they obtain the achievable uplink rate based on the proposed equivalent channel models. In particular, the closed‐form expression of the asymptotic uplink rate is derived. Different from the existing works, this study considers the joint multi‐cell minimum‐mean‐squared‐error (MMSE) channel estimator and MMSE receiver for massive MIMO systems over aging channels. Through the simulation, it demonstrates that adopting massive MIMO can compensate for the performance loss caused by channel aging. In addition, this pstudy gives some insights into the system design: to achieve the best performance, system with higher‐mobility terminals should be operated with more pilot resources, and it can support more users, moreover, the optimal number of users supported in a cell is less than half of the coherence time interval.