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.

Unified Analysis of Decentralized Gradient Descent: a Contraction Mapping Framework

The decentralized gradient descent (DGD) algorithm, and its sibling, diffusion, are workhorses in decentralized machine learning, distributed inference and estimation, and multi-agent coordination. We propose a novel, principled framework for the analysis of DGD and diffusion for strongly convex, smooth objectives, and arbitrary undirected topologies, using contraction mappings coupled with a result called the mean Hessian theorem (MHT). The use of these tools yields tight convergence bounds, both in the noise-free and noisy regimes. While these bounds are qualitatively similar to results found in the literature, our approach using contractions together with the MHT decouples the algorithm dynamics (how quickly the algorithm converges to its fixed point) from its asymptotic convergence properties (how far the fixed point is from the global optimum). This yields a simple, intuitive analysis that is accessible to a broader audience. Extensions are provided to multiple local gradient updates, time-varying step sizes, noisy gradients (stochastic DGD and diffusion), communication noise, and random topologies.

Physically Large Apertures for Wireless Power Transfer: Performance and Regulatory Aspects

Wireless power transfer (WPT) is a promising service for the Internet of Things, providing a cost-effective and sustainable solution to deploy so-called energy-neutral devices on a massive scale. The power received at the device side decays rapidly with the distance from a conventional transmit antenna with a physically small aperture. New opportunities arise from the transition from conventional far-field beamforming to near-field beam focusing. We argue that a "physically large" aperture, i.e., large w.r.t. the distance to the receiver, enables a power budget that remains practically independent of distance. Distance-dependent array gain patterns allow focusing the power density maximum precisely at the device location, while reducing the power density near the infrastructure. The physical aperture size is a key resource in enabling efficient yet regulatory-compliant WPT. We use real-world measurements to demonstrate that a regulatory-compliant system operating at sub-10GHz frequencies can increase the power received at the device into the milliwatt range. Our empirical demonstration shows that power-optimal near-field beam focusing inherently exploits multipath propagation, yielding both increased WPT efficiency and improved human exposure safety in real-world scenarios.

Whisper D-SGD: Correlated Noise Across Agents for Differentially Private Decentralized Learning

Decentralized learning enables distributed agents to train a shared machine learning model through local computation and peer-to-peer communication. Although each agent retains its dataset locally, the communication of local models can still expose private information to adversaries. To mitigate these threats, local differential privacy (LDP) injects independent noise per agent, but it suffers a larger utility gap than central differential privacy (CDP). We introduce Whisper D-SGD, a novel covariance-based approach that generates correlated privacy noise across agents, unifying several state-of-the-art methods as special cases. By leveraging network topology and mixing weights, Whisper D-SGD optimizes the noise covariance to achieve network-wide noise cancellation. Experimental results show that Whisper D-SGD cancels more noise than existing pairwise-correlation schemes, substantially narrowing the CDP-LDP gap and improving model performance under the same privacy guarantees.

A Flexible Framework for Grant-Free Random Access in Cell-Free Massive MIMO Systems

We propose a novel generalized framework for grant-free random-access (GFRA) in cell-free massive multiple input multiple-output systems where multiple geographically separated access points (APs) or base stations (BSs) aim to detect sporadically active user-equipment (UEs). Unlike a conventional architecture in which all the active UEs transmit their signature or pilot sequences of equal length, we admit a flexible pilot length for each UE, which also enables a seamless integration into conventional grant-based wireless systems. We formulate the joint UE activity detection and the distributed channel estimation as a sparse support and signal recovery problem, and describe a Bayesian learning procedure to solve it. We develop a scheme to fuse the posterior statistics of the latent variables inferred by each AP to jointly detect the UEs' activities, and utilize them to further refine the channel estimates. In addition, we allude to an interesting point which enables this flexible GFRA framework to encode the information bits from the active UEs. We numerically evaluate the normalized mean square error and the probability of miss-detection performances obtained by the Bayesian algorithm and show that the latent-variable fusion enhances the detection and the channel estimation performances by a large margin. We also benchmark against a genie-aided algorithm which has a prior knowledge of the UEs' activities.

Reducing Dynamic Range in Bistatic Backscatter Communication via Beamforming Design

Considering the exponential growth of Internet-of-Things devices and the goals toward sustainable networks, the complexity should be focused on the infrastructure side. For a massive number of passive devices, backscatter communication (BC) is a promising technology that reduces cost and increases energy efficiency by enabling transmitting information by backscattering radio frequency signals. Two main limitations that restrict the performance of BC are the round-trip path loss effect and the direct link interference (DLI) from the carrier emitter (CE). To circumvent this, we propose a novel transmit beamforming design for a multiple antenna bistatic BC (BiBC) system that realizes both purposes: mitigation of the DLI and increasing the power towards the backscatter device (BD). Additionally, we provide a detector design and the performance is evaluated in terms of the probability of error, for which we also provide a closed-form expression. Finally, simulation results show the superiority of the proposed beamforming design in decreasing DLI over a benchmark scenario that considers maximum-ratio transmission.

Location and Map-Assisted Wideband Phase and Time Calibration Between Distributed Antennas

Distributed massive multiple-input multiple-output networks utilize a large number of distributed access points (APs) to serve multiple user equipments (UEs), offering significant potential for both communication and localization. However, these networks require frequent phase and time calibration between distributed antennas due to oscillator phase drifts, crucial for reciprocity-based coherent beamforming and accurate localization. While this calibration is typically performed through bi-directional measurements between antennas, it can be simplified to unidirectional measurement under perfect knowledge of antenna locations. This paper extends a recent phase calibration narrowband line-of-sight (LoS) model to a phase and time calibration wideband orthogonal frequency division multiplexing model, including both LoS and reflection paths and allowing for joint phase and time calibrations. We explore different scenarios, considering whether or not prior knowledge of antenna locations and the map is available. For each case, we introduce a practical maximum likelihood estimator and conduct Cramer-Rao lower bound (CRLB) analyses to benchmark performance. Simulations validate our estimators against the CRLB in these scenarios.

A Unified Activity Detection Framework for Massive Access: Beyond the Block-Fading Paradigm

The wireless channel changes continuously with time and frequency and the block-fading assumption, which is popular in many theoretical analyses, never holds true in practical scenarios. This discrepancy is critical for user activity detection in grant-free random access, where joint processing across multiple coherence blocks is undesirable, especially when the environment becomes more dynamic. In this paper, we develop a framework for low-dimensional approximation of the channel to capture its variations over time and frequency, and use this framework to implement robust activity detection algorithms. Furthermore, we investigate how to efficiently estimate the principal subspace that defines the low-dimensional approximation. We also examine pilot hopping as a way of exploiting time and frequency diversity in scenarios with limited channel coherence, and extend our algorithms to this case. Through numerical examples, we demonstrate a substantial performance improvement achieved by our proposed framework.

Propagation Distance Estimation for Radio over Fiber with Cascaded Structure

Recent developments in polymer microwave fiber (PMF) have opened great opportunities for robust, low-cost, and high-speed sub-terahertz (THz) communications. Noticing this great potential, this paper addresses the problem of estimation of the propagation distance of a sub-Thz signal along a radio over fiber structure. Particularly, this paper considers a novel cascaded structure that interconnects multiple radio units (RUs) via fiber for applications in indoor scenarios. Herein, we consider the cascaded effects of distortions introduced by non-linear power amplifiers at the RUs, and the propagation channel over the fiber is based on measurements obtained from transmissions of sub-THz signals on high-density polyethylene fibers. For the estimation of the propagation distance, non-linear least-squares algorithms are proposed, and our simulation results demonstrate that the proposed estimators present a good performance on the propagation distance estimation even in the presence of the cascaded effect of non-linear PAs.

User-to-User Interference Mitigation in Dynamic TDD MIMO Systems with Multi-Antenna Users

We propose a novel method for user-to-user interference (UUI) mitigation in dynamic time-division duplex multiple-input multiple-output communication systems with multi-antenna users. Specifically, we consider the downlink data transmission in the presence of UUI caused by a user that simultaneously transmits in uplink. Our method introduces an overhead for estimation of the user-to-user channels by transmitting pilots from the uplink user to the downlink users. Each downlink user obtains a channel estimate that is used to design a combining matrix for UUI mitigation. We analytically derive an achievable spectral efficiency for the downlink transmission in the presence of UUI with our mitigation technique. Through numerical simulations, we show that our method can significantly improve the spectral efficiency performance in cases of heavy UUI.