Multi-dimensional Parameter Estimation in RIS-aided MU-MIMO Channels

We address the channel estimation problem in reconfigurable intelligent surface (RIS) aided broadband systems by proposing a dual-structure and multi-dimensional transformations (DS-MDT) algorithm. The proposed approach leverages the dual-structure features of the channel parameters to assist users experiencing weaker channel conditions, thereby enhancing estimation performance. Moreover, given that the channel parameters are distributed across multiple dimensions of the received tensor, the proposed algorithm employs multi-dimensional transformations to effectively isolate and extract distinct parameters. The numerical results demonstrate the proposed algorithm reduces the normalized mean square error (NMSE) by up to 10 dB while maintaining lower complexity compared to state-of-the-art methods.

Sense-then-Charge: Wireless Power Transfer to Unresponsive Devices with Unknown Location

This paper explores a multi-antenna dual-functional radio frequency (RF) wireless power transfer (WPT) and radar system to charge multiple unresponsive devices. We formulate a beamforming problem to maximize the minimum received power at the devices without prior location and channel state information (CSI) knowledge. We propose dividing transmission blocks into sensing and charging phases. First, the location of the devices is estimated by sending sensing signals and performing multiple signal classification and least square estimation on the received echo. Then, the estimations are used for CSI prediction and RF-WPT beamforming. Simulation results reveal that there is an optimal number of blocks allocated for sensing and charging depending on the system setup. Our sense-then-charge (STC) protocol can outperform CSI-free benchmarks and achieve near-optimal performance with a sufficient number of receive antennas and transmit power. However, STC struggles if using insufficient antennas or power as device numbers grow.

Online Conformal Compression for Zero-Delay Communication with Distortion Guarantees

We investigate a lossy source compression problem in which both the encoder and decoder are equipped with a pre-trained sequence predictor. We propose an online lossy compression scheme that, under a 0-1 loss distortion function, ensures a deterministic, per-sequence upper bound on the distortion (outage) level for any time instant. The outage guarantees apply irrespective of any assumption on the distribution of the sequences to be encoded or on the quality of the predictor at the encoder and decoder. The proposed method, referred to as online conformal compression (OCC), is built upon online conformal prediction--a novel method for constructing confidence intervals for arbitrary predictors. Numerical results show that OCC achieves a compression rate comparable to that of an idealized scheme in which the encoder, with hindsight, selects the optimal subset of symbols to describe to the decoder, while satisfying the overall outage constraint.

Calibrating Wireless AI via Meta-Learned Context-Dependent Conformal Prediction

Modern software-defined networks, such as Open Radio Access Network (O-RAN) systems, rely on artificial intelligence (AI)-powered applications running on controllers interfaced with the radio access network. To ensure that these AI applications operate reliably at runtime, they must be properly calibrated before deployment. A promising and theoretically grounded approach to calibration is conformal prediction (CP), which enhances any AI model by transforming it into a provably reliable set predictor that provides error bars for estimates and decisions. CP requires calibration data that matches the distribution of the environment encountered during runtime. However, in practical scenarios, network controllers often have access only to data collected under different contexts -- such as varying traffic patterns and network conditions -- leading to a mismatch between the calibration and runtime distributions. This paper introduces a novel methodology to address this calibration-test distribution shift. The approach leverages meta-learning to develop a zero-shot estimator of distribution shifts, relying solely on contextual information. The proposed method, called meta-learned context-dependent weighted conformal prediction (ML-WCP), enables effective calibration of AI applications without requiring data from the current context. Additionally, it can incorporate data from multiple contexts to further enhance calibration reliability.

Sparse Incremental Aggregation in Satellite Federated Learning

This paper studies Federated Learning (FL) in low Earth orbit (LEO) satellite constellations, where satellites are connected via intra-orbit inter-satellite links (ISLs) to their neighboring satellites. During the FL training process, satellites in each orbit forward gradients from nearby satellites, which are eventually transferred to the parameter server (PS). To enhance the efficiency of the FL training process, satellites apply in-network aggregation, referred to as incremental aggregation. In this work, the gradient sparsification methods from [1] are applied to satellite scenarios to improve bandwidth efficiency during incremental aggregation. The numerical results highlight an increase of over 4 x in bandwidth efficiency as the number of satellites in the orbital plane increases.

Experimental Study of Low-Latency Video Streaming in an ORAN Setup with Generative AI

Video streaming services depend on the underlying communication infrastructure and available network resources to offer ultra-low latency, high-quality content delivery. Open Radio Access Network (ORAN) provides a dynamic, programmable, and flexible RAN architecture that can be configured to support the requirements of time-critical applications. This work considers a setup in which the constrained network resources are supplemented by \gls{GAI} and \gls{MEC} {techniques} in order to reach a satisfactory video quality. Specifically, we implement a novel semantic control channel that enables \gls{MEC} to support low-latency applications by tight coupling among the ORAN xApp, \gls{MEC}, and the control channel. The proposed concepts are experimentally verified with an actual ORAN setup that supports video streaming. The performance evaluation includes the \gls{PSNR} metric and end-to-end latency. Our findings reveal that latency adjustments can yield gains in image \gls{PSNR}, underscoring the trade-off potential for optimized video quality in resource-limited environments.

Scalable Data Transmission Framework for Earth Observation Satellites with Channel Adaptation

The immense volume of data generated by Earth observation (EO) satellites presents significant challenges in transmitting it to Earth over rate-limited satellite-to-ground communication links. This paper presents an efficient downlink framework for multi-spectral satellite images, leveraging adaptive transmission techniques based on pixel importance and link capacity. By integrating semantic communication principles, the framework prioritizes critical information, such as changed multi-spectral pixels, to optimize data transmission. The process involves preprocessing, assessing pixel importance to encode only significant changes, and dynamically adjusting transmissions to match channel conditions. Experimental results on the real dataset and simulated link demonstrate that the proposed approach ensures high-quality data delivery while significantly reducing number of transmitted data, making it highly suitable for satellite-based EO applications.

On Models with Power Conservation in Reflective Intelligent Surfaces and their Design Implications

Reconfigurable intelligent surfaces (RISs) are potential enablers of future wireless communications and sensing applications and use-cases. The RIS is envisioned as a dynamically controllable surface that is capable of transforming impinging electromagnetic waves in terms of angles and polarization. Many models has been proposed to predict the wave-transformation capabilities of potential RISs, where power conservation is ensured by enforcing that the scattered power equals the power impinging upon the aperture of the RIS, without considering whether the scattered field adds coherently of destructively with the source field. In effect, this means that power is not conserved, as elaborated in this paper. With the goal of investigating the implications of global and local power conservation in RISs, work considers a single-layer metasurface based RIS. A complete end-to-end communications channel is given through polarizability modeling and conditions for power conservation and channel reciprocity are derived. The implications of the power conservation conditions upon the end-to-end communications channel is analyzed.

Asynchronous Random Access in Massive MIMO Systems Facilitated by the Delay-Angle Domain

The problem of uplink transmissions in massive connectivity is commonly dealt with using schemes for grant-free random access. When a large number of devices transmit almost synchronously, the receiver may not be able to resolve the collision. This could be addressed by assigning dedicated pilots to each user, leading to a contention-free random access (CFRA), which suffers from low scalability and efficiency. This paper explores contention-based random access (CBRA) schemes for asynchronous access in massive multiple-input multiple-output (MIMO) systems. The symmetry across the accessing users with the same pilots is broken by leveraging the delay information inherent to asynchronous systems and the angle information from massive MIMO to enhance activity detection (AD) and channel estimation (CE). The problem is formulated as a sparse recovery in the delay-angle domain. The challenge is that the recovery signal exhibits both row-sparse and cluster-sparse structure, with unknown cluster sizes and locations. We address this by a cluster-extended sparse Bayesian learning (CE-SBL) algorithm that introduces a new weighted prior to capture the signal structure and extends the expectation maximization (EM) algorithm for hyperparameter estimation. Simulation results demonstrate the superiority of the proposed method in joint AD and CE.

Coexistence of Real-Time Source Reconstruction and Broadband Services Over Wireless Networks

Achieving a flexible and efficient sharing of wireless resources among a wide range of novel applications and services is one of the major goals of the sixth-generation of mobile systems (6G). Accordingly, this work investigates the performance of a real-time system that coexists with a broadband service in a frame-based wireless channel. Specifically, we consider real-time remote tracking of an information source, where a device monitors its evolution and sends updates to a base station (BS), which is responsible for real-time source reconstruction and, potentially, remote actuation. To achieve this, the BS employs a grant-free access mechanism to serve the monitoring device together with a broadband user, which share the available wireless resources through orthogonal or non-orthogonal multiple access schemes. We analyse the performance of the system with time-averaged reconstruction error, time-averaged cost of actuation error, and update-delivery cost as performance metrics. Furthermore, we analyse the performance of the broadband user in terms of throughput and energy efficiency. Our results show that an orthogonal resource sharing between the users is beneficial in most cases where the broadband user requires maximum throughput. However, sharing the resources in a non-orthogonal manner leads to a far greater energy efficiency.