Rate-Splitting--Inspired Uplink Near-Field ISAC

Integrated sensing and communication (ISAC) enables sensing and communication (S&C) functionalities to share spectrum, hardware, and signal-processing resources, but the resulting inter-functionality interference creates a fundamental receiver-design challenge, particularly in uplink operation. This paper develops a rate-splitting (RS)-inspired framework for uplink near-field ISAC. The framework generalizes the sensing-centric (S-C) and communication-centric (C-C) endpoint orders of non-orthogonal multiple access (NOMA)-inspired ISAC by splitting the communication message across the sensing operation. Closed-form expressions are derived for the communication-rate (CR) and sensing-rate (SR), accounting for residual sensing interference from target-response estimation uncertainty. The achievable CR-SR rate region is characterized under sensing-matched illumination, where the proposed single-frame RS-inspired boundary contains the NOMA-inspired time-sharing region. Unlike the classical Gaussian uplink multiple access channel, where RS recovers the time-sharing dominant face, the split factor in uplink ISAC also reshapes the sensing-stage interference, allowing the RS-inspired boundary to match or strictly enlarge the S&C tradeoff. High-SNR analysis shows that, for non-aligned S&C channels, residual sensing interference changes the rate offsets but not the leading S&C slopes, whereas in the fully-aligned case it becomes slope-limiting. Using an aperture-aware near-field channel model, large-array limits are derived, showing that achievable rates remain finite as the array grows. Numerical results validate the analysis and demonstrate the benefits of the RS-inspired scheme, the impact of residual sensing interference, and the bounded large-array behaviour induced by physically consistent near-field modelling.

Dynamic FDD for Spectrum Sharing in Non-Terrestrial Networks

Future 6G networks are envisioned to integrate low Earth orbit satellite mega-constellations to enable seamless global connectivity, particularly in underserved and remote areas. However, the deployment of dense mega-constellations introduces interference among satellites operating over shared frequency bands. This represents a rather new setup for studying spectrum sharing, which exacerbates the limited flexibility of conventional FDD systems based on fixed bands for downlink and uplink transmissions. We address this spectrum-sharing problem and propose dynamic re-assignment of FDD bands for improved interference management in dense deployments, as well as evaluate the performance gain of this approach. To this end, we formulate a joint optimization problem that incorporates dynamic band assignment, user scheduling, and power allocation in both directions. This non-convex mixed integer problem is solved using a combination of equivalence transforms, alternating optimization, and state-of-the-art industrial-grade mixed integer solvers. Numerical results demonstrate that the proposed approach of dynamic FDD band assignment significantly enhances system performance over conventional FDD, achieving up to 30\% improvement in throughput in dense deployments.

Access Protocols for Segmented Waveguide-Enabled Pinching-Antenna Systems (SWANs)

This paper proposes an access protocol framework for segmented waveguide-enabled pinching-antenna systems (SWANs), which exploits SWAN-induced reconfigurable channel diversity as a protocol-level resource for uplink random access. The framework consists of two stages, a channel-oracle stage and an access stage, designed under three SWAN operating modes: (i) one-segment selection (OS), (ii) segment aggregation (SA), and (iii) segment multiplexing (SM). Specifically, in the channel oracle stage, the OS mode is adopted to acquire sparse pilot observations and infer the channel responses across the SWAN configuration space. In this way, high-dimensional uplink channel acquisition is recast as a low-dimensional geometric localization problem, thereby reducing pilot overhead while preserving channel reconstruction accuracy. For the access stage, we construct two oracle-guided access codebooks under the SA and SM modes, respectively, which address the tradeoff between hardware complexity and multiuser access resolution. In particular, the SA-based scheme supports single radio frequency (RF) chain access through randomized segment-group activation, whereas the SM-based R-access scheme exploits multiple RF chains to construct deterministic access slots and enhance collision resolution. Finally, our numerical results demonstrate that (i) the proposed two-stage framework improves access performance under the same training overhead, (ii) anchor densification is more effective than aggressive segment aggregation for SA, and (iii) SM-based R-access achieves deterministic coverage and higher throughput in moderate- and high-load regimes, whereas SA-based access remains attractive for low-complexity implementations.

Multi-Agent Conformal Prediction with Personalized Statistical Validity

Uncertainty quantification is essential in high-stakes machine learning tasks. However, one of the principled solutions, conformal prediction, faces challenges under limited local calibration data, privacy constraints, and data heterogeneity. In multi-agent settings, existing works do not simultaneously and satisfactorily address these challenges with guarantees either limited to averages across agents or losing validity in heterogeneous settings. Hence, we propose personalized federated weighted conformal prediction (PFWCP), a framework that combines local density ratio weighting with weighted quantile aggregation to correct for heterogeneity while preserving privacy. The method yields asymptotically valid marginal and calibration-conditional coverage guarantees for each participating agent and supports protocols with one-shot communication. Theoretical analysis presents an adjustment to the coverage variance, governed by an effective sample size expression, which is necessary in the context of weighted conformal prediction, and experiments on synthetic and real datasets show improved calibration quality over state-of-the-art federated conformal baselines.

Rate-Splitting--Inspired Bistatic OFDM-ISAC

Achieving effective uplink bistatic ISAC over an OFDM waveform gives rise to challenging interference structures. These are mostly due to unequal direct- and echo-path contributions and Doppler-induced ICI, rendering orthogonal resource separation and fixed SIC strategies inadequate. To address this problem, we propose a RS-inspired framework where the transmitter splits each communication message into a robust and a supplementary stream, which are jointly superposed over a sensing signal. Furthermore, we present the design of a staged sensing-communication receiver. Based on this framework, we derive tractable per-subcarrier SINR expressions and establish the relation between sensing accuracy and communication reliability based on the Fisher information. Building on these, we formulate a joint power-allocation problem for SE maximization under sensing-performance and power constraints. The resulting non-convex formulation is solved using convex surrogates and fractional programming. Numerical results demonstrate that, compared to NOMA-inspired baselines, the proposed framework provides more effective IFI management and improved robustness to Doppler-induced ICI.

Beam Scheduling for Cross-Layer ISAC: A Deep Reinforcement Learning Approach

Resource allocation in integrated sensing and communication (ISAC) systems needs to be optimized to balance the requirements of the communication and sensing modules considering complicated cross-layer data traffic and queue status in dynamic multi-user environments. This paper studies the beam allocation for cross-layer ISAC that achieves low-latency communication and minimizes sensing parameters estimation error. To handle the complex coupling between practical data buffer dynamics and varying wireless channels, we propose a deep reinforcement learning (DRL)-assisted approach. Rather than relying on explicit channel state information, the DRL-assisted beam allocation reduces feedback overhead by leveraging sensing observations. Simulation results verify that the DRL framework effectively takes buffer status into account and adapts to the wireless environment while allocating resources. The proposed multi-beam scheme improves overall throughput with only modest delay increases. Finally, the DRL-assisted beam management achieves both communication and sensing performance close to that of the genie-aided benchmark with perfect angle-of-departure (AoD) knowledge. These contributions advance the state-of-the-art intelligent resource management for ISAC systems.

Digital Twin-Assisted Measurement Design and Channel Statistics Prediction

Prediction of wireless channels and their statistics is a fundamental procedure for ensuring performance guarantees in wireless systems. Statistical radio maps powered by Gaussian processes (GPs) offer flexible, non-parametric frameworks, but their performance depends critically on the choice of mean and covariance functions. These are typically learned from dense measurements without exploiting environmental geometry. Digital twins (DTs) of wireless environments leverage computational power to incorporate geometric information; however, they require costly calibration to accurately capture material and propagation characteristics. This work introduces a hybrid channel prediction framework that leverages uncalibrated DTs derived from open-source maps to extract geometry-induced prior information for GP prediction. These structural priors are fused with a small number of channel measurements, enabling data-efficient prediction of channel statistics across the entire environment. By exploiting the uncertainty quantification inherent to GPs, the framework supports principled measurement selection by identifying informative probing locations under resource constraints. Through this integration of imperfect DTs with statistical learning, the proposed method reduces measurement overhead, improves prediction accuracy, and establishes a practical approach for resource-efficient wireless channel prediction.

Goal-Oriented Access Optimization for ISAC-Enabled Digital Twins

The digital twins (DTs) of physical systems and environments enable real-time remote tracking, control, and learning, but require low-latency transmission of updates and sensory data to maintain alignment with their physical counterparts. In this context, augmenting sensory data with the network's own integrated sensing and communication (ISAC)capabilities can expand the DT's awareness of the environment by allowing it to precisely non-radar locate measurements from mobile nodes. However, this integration increases the complexity of the communication system, and can only be supported through intelligent resource allocation and access optimization. In this work, we propose a two-step goal-oriented approach to solve this problem: we design a push-based random access in which sensors with a high Value of Information (VoI) inform the network of their access requirements, followed by a pull-based scheduled transmission of the actual sensory data. This design allows to combine the ISAC and reliable transmission requirements and maximize the VoI of the information delivered to the DT, significantly outperforming existing schemes.

Control Plane for Reconfigurable Intelligent Surfaces

Research on reconfigurable intelligent surfaces (RISs) has predominantly focused on purely physical (PHY)-layer aspects, particularly, on how signals are dynamically shaped by a controllable wireless propagation environment. However, integrating RISs as system-level network elements requires the development of an RIS-compatible control plane. In this article, we explore design options for such a control plane across two key dimensions: i) the allocation of spectral resources for the control plane (in- or out-of-band), and ii) the rate selection for the data plane (multiplexing or diversity). While our analysis is necessarily simplified, it reveals the fundamental trade-offs inherent in these design choices, which are crucial for integrating RIS technology into future networks.

Interference Detection and Exploitation for Multi-User Radar Sensing

Integrated sensing and communication is a key feature in next-generation wireless networks, enabling joint data transmission and environmental radar sensing on shared spectrum. In multi-user scenarios, simultaneous transmissions cause mutual interference on overlapping frequencies, leading to spurious target detections and degraded sensing accuracy. This paper proposes an interference detection and exploitation algorithm for sensing using spectrally interleaved orthogonal frequency division multiplexing. A statistically rigorous procedure is introduced to detect interference while controlling the familywise error rate. We propose an algorithm that estimates the angle by exploiting interference, while estimating the delay by avoiding the interference. Numerical experiments demonstrate that the proposed method reliably detects interference, and that the delay and angle estimation error approaches the Cramér-Rao lower bound.