GNN-based Precoder Design and Fine-tuning for Cell-free Massive MIMO with Real-world CSI

Cell-free massive MIMO (CF-mMIMO) has emerged as a promising paradigm for delivering uniformly high-quality coverage in future wireless networks. To address the inherent challenges of precoding in such distributed systems, recent studies have explored the use of graph neural network (GNN)-based methods, using their powerful representation capabilities. However, these approaches have predominantly been trained and validated on synthetic datasets, leaving their generalizability to real-world propagation environments largely unverified. In this work, we initially pre-train the GNN using simulated channel state information (CSI) data, which incorporates standard propagation models and small-scale Rayleigh fading. Subsequently, we finetune the model on real-world CSI measurements collected from a physical testbed equipped with distributed access points (APs). To balance the retention of pre-trained features with adaptation to real-world conditions, we adopt a layer-freezing strategy during fine-tuning, wherein several GNN layers are frozen and only the later layers remain trainable. Numerical results demonstrate that the fine-tuned GNN significantly outperforms the pre-trained model, achieving an approximate 8.2 bits per channel use gain at 20 dB signal-to-noise ratio (SNR), corresponding to a 15.7 % improvement. These findings highlight the critical role of transfer learning and underscore the potential of GNN-based precoding techniques to effectively generalize from synthetic to real-world wireless environments.

Multi-Antenna Users in Cell-Free Massive MIMO: Stream Allocation and Necessity of Downlink Pilots

We consider a cell-free massive multiple-input multiple-output (MIMO) system with multiple antennas on the users and access points (APs). In previous works, the downlink spectral efficiency (SE) has been evaluated using the hardening bound that requires no downlink pilots. This approach works well for single-antenna users. In this paper, we show that much higher SEs can be achieved if downlink pilots are sent when having multi-antenna users. The reason is that the effective channel matrix does not harden. We propose a pilot-based downlink estimation scheme, derive a new SE expression, and show numerically that it yields substantially higher performance when having correlated Rayleigh fading channels. In cases with multi-antenna users, the APs can either transmit the same or different data streams. The latter reduces the fronthaul signaling but comes with a SE loss. We propose precoding and combining schemes for these cases and consider whether channel knowledge is shared between the APs. Finally, we show numerically how the number of users, APs, and the number of antennas on users and APs affect the SE.

Cell-Free Massive MIMO-Assisted SWIPT for IoT Networks

This paper studies cell-free massive multiple-input multiple-output (CF-mMIMO) systems that underpin simultaneous wireless information and power transfer (SWIPT) for separate information users (IUs) and energy users (EUs) in Internet of Things (IoT) networks. We propose a joint access point (AP) operation mode selection and power control design, wherein certain APs are designated for energy transmission to EUs, while others are dedicated to information transmission to IUs. The performance of the system, from both a spectral efficiency (SE) and energy efficiency (EE) perspective, is comprehensively analyzed. Specifically, we formulate two mixed-integer nonconvex optimization problems for maximizing the average sum-SE and EE, under realistic power consumption models and constraints on the minimum individual SE requirements for individual IUs, minimum HE for individual EUs, and maximum transmit power at each AP. The challenging optimization problems are solved using successive convex approximation (SCA) techniques. The proposed framework design is further applied to the average sum-HE maximization and energy harvesting fairness problems. Our numerical results demonstrate that the proposed joint AP operation mode selection and power control algorithm can achieve EE performance gains of up to $4$-fold and $5$-fold over random AP operation mode selection, with and without power control respectively.

Performance of Cell-Free Massive MIMO in Realistic Urban Propagation Environments

While UE-centric cell-free massive MIMO (CF-mMIMO) provides high and uniform throughput performance under the assumption of a uniform propagation environment modeled by the log-distance path loss channel model, the performance under a realistic urban propagation environment is not yet fully addressed. In this paper we conduct the first comparative performance study of CF-mMIMO under both the widely assumed log-distance channel model and the realistic urban propagation environment obtained via raytracing using real 3D city layouts and practical AP locations. Our results show that with the raytracing channel model, CF-mMIMO cannot achieve as high and uniform throughput performance as observed with the log-distance channel model, putting into question the attractiveness in practice of CF-mMIMO for real urban deployments.

Hierarchical Cell-Free Massive MIMO: A Simplified Design for Uniform Service Quality

In traditional cellular networks, users at the cell edge often suffer from poor quality of service (QoS) due to large distance-dependent path loss and severe inter-cell interference. While cell-free (CF) massive multi-input multi-out (MIMO) mitigates this issue by distributing access points (APs) to ensure uniform QoS, the deployment of numerous distributed APs and a fronthaul network incurs high infrastructure costs. To balance performance and cost efficiency, this article proposes a simplified design called hierarchical cell-free (HCF) massive MIMO. The key idea is to reduce the number of APs, thus minimizing the scale of the fronthaul network. The antennas from the decommissioned APs are aggregated at a central base station (cBS), which also serves as the coordinator for distributed APs. We derive closed-form expressions for uplink and downlink spectral efficiency (SE) for HCF, CF, and cellular massive MIMO under pilot contamination and correlated fading channels, considering the use of multi-antenna APs. Numerical results confirm that the hierarchical architecture achieves $95\%$-likely per-user SE comparable to CF, enhancing cell-edge user rates in cellular systems by over 100 times, while significantly reducing the complexity and cost of the fronthaul network in CF. We develop max-min fairness algorithms for joint power control of the cBS and APs in the downlink, and the users in the uplink. These algorithms not only boost fairness and system capacity but also dramatically lower transmission power, e.g., achieving over $70\%$ savings in uplink, particularly beneficial for battery-powered mobile devices.

Experimental Analysis of Multipath Characteristics in Indoor Distributed Massive MIMO Channels

Distributed massive multiple-input multiple-output (MIMO), also known as cell-free massive MIMO, has emerged as a promising technology for sixth-generation (6G) wireless networks. This letter introduces an indoor channel measurement campaign designed to explore the behavior of multipath components (MPCs) in distributed massive MIMO channels. Fully coherent channels were measured between eight distributed uniform planar arrays (128 elements in total) and a 12-meter user equipment route. Furthermore, a method is introduced to determine the order (single- or multi-bounce) of MPC interaction by leveraging map information and MPC parameters. In addition, a Kalman filter-based framework is used for identifying the MPC interaction mechanisms (reflection or scattering/diffraction/mixed). Finally, a comprehensive MPC-level characterization is performed based on the measured channels, including the significance of the single-bounce MPCs, the spherical wavefront features, the birth-and-death processes of the MPCs, and the spatial distribution of reflections. The findings serve as a valuable reference for understanding MPC propagation behavior, which is necessary for accurate modeling of indoor distributed massive MIMO channels.

Interacting Object-Enabled Clustering and Characterization of Distributed MIMO Channels

Distributed multiple-input multiple-output (MIMO), also known as cell-free massive MIMO, emerges as a promising technology for sixth-generation (6G) systems to support uniform coverage and reliable communication. For the design and optimization of such systems, measurement-based investigations of real-world distributed MIMO channels are essential. In this paper, we present an indoor channel measurement campaign, featuring eight distributed antenna arrays with 128 elements in total. Multi-link channels are measured at 50 positions along a 12-meter user route. A clustering algorithm enabled by interacting objects is proposed to identify clusters in the measured channels. The algorithm jointly clusters the multipath components for all links, effectively capturing the dynamic contributions of common clusters to different links. In addition, a Kalman filter-based tracking framework is introduced for cluster prediction, tracking, and updating along the user movement. Using the clustering and tracking results, cluster-level characterization of the measured channels is performed. First, the number of clusters and their visibility at both link ends are analyzed. Next, a maximum-likelihood estimator is utilized to determine the entire cluster visibility region length. Finally, key cluster-level properties, including the common cluster ratio, cluster power, shadowing, spread, among others, are statistically investigated. The results provide valuable insights into cluster behavior in typical multi-link channels, necessary for accurate modeling of distributed MIMO channels.

Quantum Manifold Optimization: A Design Framework for Future Communications Systems

Inspired by recent developments in various areas of science relevant to quantum computing, we introduce quantum manifold optimization (QMO) as a promising framework for solving constrained optimization problems in next-generation wireless communication systems. We begin by showing how classical wireless design problems - such as pilot design in cell-free (CF)-massive MIMO (mMIMO), beamformer optimization in gigantic multiple input multiple output (MIMO), and reconfigurable intelligent surface (RIS) phase tuning - naturally reside on structured manifolds like the Stiefel, Grassmannian, and oblique manifolds, with the latter novelly formulated in this work. Then, we demonstrate how these problems can be reformulated as trace-based quantum expectation values over variationally-encoded quantum states. While theoretical in scope, the work lays a foundation for a new class of quantum optimization algorithms with broad application to the design of future beyond-sixth-generation (B6G) systems.

Fair and Energy-Efficient Activation Control Mechanisms for Repeater-Assisted Massive MIMO

Massive multiple-input multiple-output (mMIMO) has been the core of 5G due to its ability to improve spectral efficiency and spatial multiplexing significantly; however, cell-edge users still experience performance degradation due to inter-cell interference and uneven signal distribution. While cell-free mMIMO (cfmMIMO) addresses this issue by providing uniform coverage through distributed antennas, it requires significantly more deployment cost due to the fronthaul and tight synchronization requirements. Alternatively, repeater-assisted massive MIMO (RA-MIMO) has recently been proposed to extend the coverage of cellular mMIMO by densely deploying low-cost single-antenna repeaters capable of amplifying and forwarding signals. In this work, we investigate amplification control for the repeaters for two different goals: (i) providing a fair performance among users, and (ii) reducing the extra energy consumption by the deployed repeaters. We propose a max-min amplification control algorithm using the convex-concave procedure for fairness and a joint sleep mode and amplification control algorithm for energy efficiency, comparing long- and short-term strategies. Numerical results show that RA-MIMO, with maximum amplification, improves signal-to-interference-plus-noise ratio (SINR) by over 20 dB compared to mMIMO and performs within 1 dB of cfmMIMO when deploying the same number of repeaters as access points in cfmMIMO. Additionally, our majority-rule-based long-term sleep mechanism reduces repeater power consumption by 70% while maintaining less than 1% spectral efficiency outage.

STAR-RIS-Assisted Cell-Free Massive MIMO with Multi-antenna Users and Hardware Impairments Over Correlated Rayleigh Fading Channels

Integrating cell-free massive multiple-input multiple-output (MIMO) with simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) can provide ubiquitous connectivity and enhance coverage. This paper explores a STAR-RIS-assisted cell-free massive MIMO system featuring multi-antenna users, multi-antenna access points (APs), and multi-element STAR-RISs, accounting for transceiver hardware impairments. We first establish the system model of STAR-RIS-assisted cell-free massive MIMO systems with multi-antenna users. Subsequently, we analyze two uplink implementations: local processing and centralized decoding (Level 1), and fully centralized processing (Level 2), both implementations incorporating hardware impairments. We study the local and global minimum mean square error (MMSE) combining schemes to maximize the uplink spectral efficiency (SE) for Level 1 and Level 2, respectively. The MMSE-based successive interference cancellation detector is utilized to compute the uplink SE. We introduce the optimal large-scale fading decoding at the central processing unit and derive closed-form SE expressions utilizing maximum ratio combining at APs for Level 1. Our numerical results reveal that hardware impairments negatively affect SE performance, particularly at the user end. However, this degradation can be mitigated by increasing the number of user antennas. Enhancing the number of APs and STAR-RIS elements also improves performance and mitigates performance degradation. Notably, unlike conventional results based on direct links, our findings show that Level 2 consistently outperforms Level 1 with arbitrary combining schemes for the proposed STAR-RIS-assisted system.