Repeater-Aided Over-the-Air Phase Synchronization in Distributed MIMO

Phase synchronization of access points (APs) in a distributed multiple-input multiple-output (D-MIMO) system is critical to leverage the performance benefits of D-MIMO. Existing over-the-air phase synchronization methods assume that APs can communicate directly to perform necessary measurements. However, this assumption might not hold in scenarios where inter-AP signaling is too weak for effective communication. To address this, in this paper, we propose a novel over-the-air calibration scheme that uses repeater nodes to facilitate phase synchronization when direct AP signaling is infeasible. We give the steps of the algorithm for phase calibration in closed form, and show how it enables coherent joint transmission (CJT) by the APs. The framework expands the applicability of D-MIMO systems to challenging environments, where existing over-the-air synchronization techniques fall short.

Hybrid Channel Estimation with Quantized Phase Feedback for Over-the-Air Computation

To reduce the signaling overhead of over-the-air computation, a hybrid channel estimation scheme is proposed, where reciprocity-based and feedback-based channel estimation are combined. In particular, the impact of quantized phase-feedback is studied while the amplitude is assumed estimated exactly. The scheme enables selecting the estimation precision of amplitude and phase separately, depending on the importance of each. Two variants of the scheme are proposed: As shown through simulations and theory, the second variant with reciprocity-based estimation of the channel phase, and optimal quantization of phase feedback, can outperform the first variant estimating the phase by feedback only.

Drone Surveillance via Coordinated Beam Sweeping in MIMO-ISAC Networks

This paper introduces a scheme for drone surveillance coordinated with the fifth generation (5G) synchronization signal block (SSB) cell-search procedure to simultaneously detect low-altitude drones within a volumetric surveillance grid. Herein, we consider a multistatic configuration where multiple access points (APs) collaboratively illuminate the volume while independently transmitting SSB broadcast signals. Both tasks are performed through a beam sweeping. In the proposed scheme, coordinated APs send sensing beams toward a grid of voxels within the volumetric surveillance region simultaneously with the 5G SSB burst. To prevent interference between communication and sensing signals, we propose a precoder design that guarantees orthogonality of the sensing beam and the SSB in order to maximize the sensing signal-to-interference-plus-noise ratio (SINR) while ensuring a specified SINR for users, as well as minimizing the impact of the direct link. The results demonstrate that the proposed precoder outperforms the non-coordinated precoder and is minimally affected by variations in drone altitude.

A Unified Convergence Analysis for Semi-Decentralized Learning: Sampled-to-Sampled vs. Sampled-to-All Communication

In semi-decentralized federated learning, devices primarily rely on device-to-device communication but occasionally interact with a central server. Periodically, a sampled subset of devices uploads their local models to the server, which computes an aggregate model. The server can then either (i) share this aggregate model only with the sampled clients (sampled-to-sampled, S2S) or (ii) broadcast it to all clients (sampled-to-all, S2A). Despite their practical significance, a rigorous theoretical and empirical comparison of these two strategies remains absent. We address this gap by analyzing S2S and S2A within a unified convergence framework that accounts for key system parameters: sampling rate, server aggregation frequency, and network connectivity. Our results, both analytical and experimental, reveal distinct regimes where one strategy outperforms the other, depending primarily on the degree of data heterogeneity across devices. These insights lead to concrete design guidelines for practical semi-decentralized FL deployments.

Joint Access Point Selection and Beamforming Design for Bistatic Backscatter Communication

Future Internet-of-Things networks are envisioned to use small and cheap sensor nodes with extremely low power consumption to avoid the extensive use of batteries. To provide connectivity to a massive number of these nodes, backscatter communication (BC) is emerging as an energy- and cost-efficient technology exploiting the reflection of radio frequency signals. However, challenges such as round-trip path loss and direct link interference (DLI) between the carrier emitter and the reader limit its performance. To tackle these limitations, this paper proposes a joint access point role selection and a novel beamforming technique for bistatic BC in a distributed multiple-input multiple-output setup. The proposed approach boosts the received backscattered energy while effectively mitigating DLI, thereby reducing the error probability. We also propose a channel estimation method tailored to operate under DLI conditions and propose a mismatch detector using estimated channel coefficients. Furthermore, we derive a closed-form expression for the probability of error for the detectors and model the quantization noise caused by DLI. Finally, comprehensive simulation results show that the proposed method with 1-bit analog-to-digital converters (ADCs) effectively mitigates DLI, reduces the quantization noise, and enhances backscattered signal energy, achieving performance comparable to the benchmark scenario with 8-bit ADCs.

DeMuon: A Decentralized Muon for Matrix Optimization over Graphs

In this paper, we propose DeMuon, a method for decentralized matrix optimization over a given communication topology. DeMuon incorporates matrix orthogonalization via Newton-Schulz iterations-a technique inherited from its centralized predecessor, Muon-and employs gradient tracking to mitigate heterogeneity among local functions. Under heavy-tailed noise conditions and additional mild assumptions, we establish the iteration complexity of DeMuon for reaching an approximate stochastic stationary point. This complexity result matches the best-known complexity bounds of centralized algorithms in terms of dependence on the target tolerance. To the best of our knowledge, DeMuon is the first direct extension of Muon to decentralized optimization over graphs with provable complexity guarantees. We conduct preliminary numerical experiments on decentralized transformer pretraining over graphs with varying degrees of connectivity. Our numerical results demonstrate a clear margin of improvement of DeMuon over other popular decentralized algorithms across different network topologies.

Industrial Viewpoints on RAN Technologies for 6G

6G standardization is to start imminently, with commercial deployments expected before 2030. Its technical components and performance requirements are the focus of this article. Our emphasis is on the 6G radio access, especially MIMO, AI, waveforms, coding, signal constellations and integration with non-terrestrial networks. Whilst standardization has not yet formally started, the scope of the 6G study items has been defined. Our predictions in this paper are speculative as there are no results of the study yet, but our views are guided by implementation and deployment aspects. We expect that the views here will guide researchers and industry practitioners.

Breaking the TDD Flow for Over-the-Air Phase Synchronization in Distributed Antenna Systems

Phase synchronization between distributed antenna arrays requires measurements that break the standard time-division duplex (TDD) operation. We present a feasibility study on implementing such synchronization and analyze its impact on the quality of service. Considering two antenna arrays with independent local oscillators (LOs), we propose a modified TDD flow to accommodate the transmission of phase synchronization signals, formulate the phase estimation and compensation problem, and derive the achievable downlink spectral efficiency (SE). Numerical results show that frequent re-estimation of the interarray phase disparity is essential for maximizing SE in systems with low-quality LOs. Furthermore, applying a Kalman filter for phase tracking substantially improves the SE, especially if phase estimation errors are large compared to LOs phase drifts.

Robust and Efficient Average Consensus with Non-Coherent Over-the-Air Aggregation

Non-coherent over-the-air (OTA) computation has garnered increasing attention for its advantages in facilitating information aggregation among distributed agents in resource-constrained networks without requiring precise channel estimation. A promising application scenario of this method is distributed average consensus in wireless multi-agent systems. However, in such scenario, non-coherent interference from concurrent OTA transmissions can introduce bias in the consensus value. To address this issue, we develop a robust distributed average consensus algorithm by formulating the consensus problem as a distributed optimization problem. Using decentralized projected gradient descent (D-PGD), our proposed algorithm can achieve unbiased mean square average consensus even in the presence of non-coherent interference and noise. Additionally, we implement transmit power control and receive scaling mechanisms to further accelerate convergence. Simulation results demonstrate that our method can significantly enhance the convergence speed of the D-PGD algorithm for OTA average consensus without compromising accuracy.

Impact of Network-Controlled Repeaters in Integrated Sensing and Communication Systems

Integrating sensing capabilities into existing massive MIMO communication networks has become crucial, stemming from a need for a more interconnected society. Improved coverage and performance can be obtained by incorporating new network components, such as reconfigurable intelligent surfaces or network-controlled repeaters (NCR). Integrating such components into modern networks brings a number of challenges. Thus, this paper contributes with the analysis of NCR impact in integrated sensing and communication networks. Particularly, the Cram\'er-Rao bound for a range estimator is derived where the interference from the repeater is taken into consideration. Additionally, a joint procedure for determining the repeater amplification factor, along with the precoders of the transmitting access point, is proposed.