User-Centric Cell-Free Massive MIMO Enhanced by Fluid-Antenna Access Points: Uplink Analysis

In this paper, we investigate cell-free massive MIMO (CF-mMIMO) systems in which access points (APs) are equipped with fluid antennas (FAs) and develop a comprehensive framework for channel estimation, antenna port selection, and uplink spectral efficiency (SE) optimization. We propose a generalized LMMSE-based uplink channel estimation scheme that dynamically activates FA ports during pilot transmission, efficiently exploiting antenna reconfigurability under practical training constraints. Building on this, we design a distributed port selection strategy that minimizes per-AP channel estimation error by exploiting spatial correlation among FA ports. We systematically analyze the impact of antenna geometry and spatial correlation using the Jakes' channel model for different AP array configurations, including uniform linear and planar arrays. We then derive SINR expressions for centralized and distributed uplink processing and obtain a closed-form uplink SE expression for centralized maximum-ratio combining using the use-and-then-forget bound. Finally, we propose an alternating-optimization framework to select FA port configurations that maximize the uplink sum SE. Numerical results show that the proposed FA-aware channel estimation and port optimization strategies greatly reduce channel estimation error and significantly improve sum-SE over fixed-antenna and non-optimized FA baselines, confirming FAs as a key enabler for scalable, adaptive CF-mMIMO networks.

Power Control Design for ISAC Optimization in User-Target-Centric Cell-Free mMIMO Networks

This paper addresses the power control design for a cell-free massive MIMO (CF-mMIMO) system that performs integrated sensing and communications (ISAC). Specifically, the case where many access points are deployed to simultaneously communicate with mobile users and monitor the surrounding environment at the same time-frequency slot is considered. On top of the user-centric architecture used for the data services, a target-centric approach is introduced for the detection tasks. As a valuable performance metric, we derive the receive sensing signal-to-noise (SNR) ratio under generalized likelihood ratio test processing. Based on that, we formulate a quality-of-service (QoS) scheme that maximizes the two figures of merit: achievable data rate and effective sensing SNR. Simulations demonstrate that our proposal surpasses orthogonal resource algorithms, underscoring the potential of ISAC-enabled CF-mMIMO networks.

Differential Space-Time Block Coding for Phase-Unsynchronized Cell-Free MIMO Downlink

In the downlink of a cell-free massive multiple-input multiple-output (CF-mMIMO) system, spectral efficiency gains critically rely on joint coherent transmission, as all access points (APs) must align their transmitted signals in phase at the user equipment (UE). Achieving such phase alignment is technically challenging, as it requires tight synchronization among geographically distributed APs. In this paper, we address this issue by introducing a differential space-time block coding (DSTBC) approach that bypasses the need for AP phase synchronization. We first provide analytic bounds to the achievable spectral efficiency of CF-mMIMO with phase-unsynchronized APs. Then, we propose a DSTBC-based transmission scheme specifically tailored to CF-mMIMO, which operates without channel state information and does not require any form of phase synchronization among the APs. We derive a closed-form expression for the resulting signal-to-interference-plus-noise ratio (SINR), enabling quantitative comparisons among different DSTBC schemes. Numerical simulations confirm that phase misalignments can significantly impair system performance. In contrast, the proposed DSTBC scheme successfully mitigates these effects, achieving performance comparable to that of fully synchronized systems.

Track-before-detect in RIS-aided Integrated Sensing and Communication

This study considers a base station equipped with sensing and communication capabilities, which serves a ground user and scans a portion of the sky via a passive reconfigurable intelligent surface. To achieve more favorable system tradeoffs, we utilize a multi-frame radar detector, comprising a detector, a plot-extractor, and a track-before-detect processor. The main idea proposed here is that user spectral efficiency can be enhanced by increasing the number of scans jointly processed by the multi-frame radar detector while maintaining the same sensing performance. A numerical analysis is conducted to verify the effectiveness of the proposed solution and to evaluate the achievable system tradeoffs.

Joint Optimization of Uplink and Downlink Power in Full-Duplex Integrated Access and Backhaul

We examine the performance of an Integrated Access and Backhaul (IAB) node as a range extender for beyond-5G networks, focusing on the significant challenges of effective power allocation and beamforming strategies, which are vital for maximizing users' spectral efficiency (SE). We present both max-sum SE and max-min fairness power allocation strategies, to assess their effects on system performance. The results underscore the necessity of power optimization, particularly as the number of users served by the IAB node increases, demonstrating how efficient power allocation enhances service quality in high-load scenarios. The results also show that the typical line-of-sight link between the IAB donor and the IAB node has rank one, posing a limitation on the effective SEs that the IAB node can support.

Leveraging Line-of-Sight Propagation for Near-Field Beamfocusing in Cell-Free Networks

Cell-free (CF) massive multiple-input multiple-output (MIMO) is a promising approach for next-generation wireless networks, enabling scalable deployments of multiple small access points (APs) to enhance coverage and service for multiple user equipments (UEs). While most existing research focuses on low-frequency bands with Rayleigh fading models, emerging 5G trends are shifting toward higher frequencies, where geometric channel models and line-of-sight (LoS) propagation become more relevant. In this work, we explore how distributed massive MIMO in the LoS regime can achieve near-field-like conditions by forming artificially large arrays through coordinated AP deployments. We investigate centralized and decentralized CF architectures, leveraging structured channel estimation (SCE) techniques that exploit the line-of-sight properties of geometric channels. Our results demonstrate that dense distributed AP deployments significantly improve system performance w.r.t. the case of a co-located array, even in highly populated UE scenarios, while SCE approaches the performance of perfect CSI.

Scalable Integrated Sensing and Communications for Multi-Target Detection and Tracking in Cell-Free Massive MIMO: A Unified Framework

This paper investigates a cell-free massive MIMO (multiple-input multiple-output) system where distributed access points (APs) perform integrated sensing and communications (ISAC) tasks, enabling simultaneous user communication and target detection/tracking. A unified framework and signal model are developed for the detection of potential targets and tracking of previously detected ones, even in arbitrary positions. Leveraging the Generalized Likelihood Ratio Test technique, novel detection/tracking algorithms are proposed to handle unknown target responses and interference. Scalable AP-user and AP-target association rules are evaluated, explicitly considering multi-zone sensing scenarios. Additionally, a scalable power control mechanism extends fractional power control principles to ISAC, balancing power allocation between communication and sensing tasks. For benchmarking, a non-scalable power control optimization problem is also formulated to maximize the minimum user data rate while ensuring a Quality of Service constraint for sensing, solved via successive convex approximation. Extensive numerical results validate the proposed framework, demonstrating its effectiveness in both communication and sensing, revealing the impact of interference from other targets, and highlighting fundamental trade-offs between sensing and communication performance.

A General Framework for Scalable UE-AP Association in User-Centric Cell-Free Massive MIMO based on Recurrent Neural Networks

This study addresses the challenge of access point (AP) and user equipment (UE) association in cell-free massive MIMO networks. It introduces a deep learning algorithm leveraging Bidirectional Long Short-Term Memory cells and a hybrid probabilistic methodology for weight updating. This approach enhances scalability by adapting to variations in the number of UEs without requiring retraining. Additionally, the study presents a training methodology that improves scalability not only with respect to the number of UEs but also to the number of APs. Furthermore, a variant of the proposed AP-UE algorithm ensures robustness against pilot contamination effects, a critical issue arising from pilot reuse in channel estimation. Extensive numerical results validate the effectiveness and adaptability of the proposed methods, demonstrating their superiority over widely used heuristic alternatives.

Eliminating Phase Misalignments in Cell-Free Massive MIMO via Differential Transmission

This paper proposes two approaches for overcoming access points' phase misalignment effects in the downlink of cell-free massive MIMO (CF-mMIMO) systems. The first approach is based on the differential space-time block coding technique, while the second one is based on the use of differential modulation schemes. Both approaches are shown to perform exceptionally well and to restore system performance in CF-mMIMO systems where phase alignment at the access points for downlink joint coherent transmission cannot be achieved.

Integrated Communication and RIS-aided Track-Before-Detect Radar Sensing

This paper investigates an integrated sensing and communication system where the base station serves multiple downlink users, while employing a passive reconfigurable intelligent surface to detect small, noncooperative airborne targets. We propose a method to design the two-way beampattern of the RIS-assisted monostatic radar, which allows controlling the sidelobe levels in the presence of eavesdroppers, jammers, and other scattering objects and avoiding any radar interference to the users. To obtain more favorable system tradeoffs, we exploit the correlation of the target echoes over consecutive scans by resorting to a multi-frame radar detector, which includes a detector, a plot-extractor, and a track-before-detect processor. A numerical analysis is provided to verify the effectiveness of the proposed solutions and to assess the achievable tradeoffs. Our results show that, by increasing the number of scans processed by the radar detector (and therefore its implementation complexity), we can reduce the amount of power dedicated to the radar function while maintaining the same sensing performance (measured in terms of probability of target detection and root mean square error in the estimation of target position); this excess power can be reused to increase the user sum-rate.