Phase-Coherent D-MIMO ISAC: Multi-Target Estimation and Spectral Efficiency Trade-Offs

We investigate distributed multiple-input multiple-output (D-MIMO) integrated sensing and communication (ISAC) systems, in which multiple phase-synchronized access points (APs) jointly serve user equipments (UEs) while cooperatively detecting and estimating multiple static targets. To achieve high-accuracy multi-target estimation, we propose a two-stage sensing framework combining non-coherent and coherent maximum-likelihood (ML) estimation. In parallel, adaptive AP mode-selection strategies are introduced to balance communication and sensing performance: a communication-centric scheme that maximizes downlink spectral efficiency (SE) and a sensing-centric scheme that selects geometrically diverse receive APs to enhance sensing coverage. Simulation results confirm the SE-sensing trade-off, where appropriate power allocation between communication and sensing and larger array apertures alleviate performance degradation, achieving high SE with millimeter-level sensing precision. We further demonstrate that the proposed AP-selection strategy reveals an optimal number of receive APs that maximizes sensing coverage without significantly sacrificing SE.

Semantic Communication for Rate-Limited Closed-Loop Distributed Communication-Sensing-Control Systems

The growing integration of distributed integrated sensing and communication (ISAC) with closed-loop control in intelligent networks demands efficient information transmission under stringent bandwidth constraints. To address this challenge, this paper proposes a unified framework for goal-oriented semantic communication in distributed SCC systems. Building upon Weaver's three-level model, we establish a hierarchical semantic formulation with three error levels (L1: observation reconstruction, L2: state estimation, and L3: control) to jointly optimize their corresponding objectives. Based on this formulation, we propose a unified goal-oriented semantic compression and rate adaptation framework that is applicable to different semantic error levels and optimization goals across the SCC loop. A rate-limited multi-sensor LQR system is used as a case study to validate the proposed framework. We employ a GRU-based AE for semantic compression and a PPO-based rate adaptation algorithm that dynamically allocates transmission rates across sensors. Results show that the proposed framework effectively captures task-relevant semantics and adapts its resource allocation strategies across different semantic levels, thereby achieving level-specific performance gains under bandwidth constraints.

Sparse OFDM Design for Interference and Ambiguity Mitigation in Multi-Static ISAC

The sixth-generation (6G) wireless networks promises the integration of radar-like sensing capabilities into communication infrastructure. In this paper, we investigate a multi-static sensing framework where half-duplex base stations (BSs) are assigned as either transmitter or sensing receiver nodes. We propose a randomized sparse resource allocation scheme based on orthogonal frequency division multiplexing (OFDM) waveform design tailored for the multi-static scenario to simultaneously mitigate inter-BS interference (IBI) and sensing ambiguities. The waveform design also ensures robustness against inter-symbol interference (ISI) and intercarrier interference (ICI) via a judicious choice of subcarrier spacing according to the deployment of BSs. The potential ambiguity caused by sparse signaling is addressed through controlled irregularity in both time and frequency domains, with a negligible noise floor elevation. Simulation results demonstrate the effectiveness and resilience of the proposed design in the presence of multiple targets and clutter.

POLO: Phase-Only Localization in Uplink Distributed MIMO Systems

We propose a low-complexity localization framework for uplink distributed MIMO (D-MIMO) systems, targeting the challenge of minimizing the highly spiky maximum-likelihood (ML) cost function that arises in sparsely deployed phasecoherent access points (APs) with narrowband transmission. In such systems, ML-based localization typically relies on dense grid search, incurring prohibitive computational complexity. To address this, we introduce phase-only localization (POLO), an approach that leverages differential carrier-phase measurements from selected APs to generate a compact set of candidate user positions. The ML cost function is then evaluated only at these candidates, reducing complexity significantly. A key challenge is to devise an AP selection mechanism that reduces the number of candidate points while maintaining reliable coverage. We propose two variants: POLO-I, which selects three APs to provide closed-form candidate positions with low computational cost, and POLO-II, which selects four APs using an alternative strategy that enhances coverage at marginally higher runtime. Comprehensive analytical and simulation results show that POLO achieves a favorable coverage-complexity trade-off, reducing cost by orders of magnitude relative to exhaustive grid search with only marginal loss in coverage. By characterizing this tradeoff under diverse AP configurations, we also provide practical guidelines for selecting between POLO-I and POLO-II depending on latency and coverage requirements.

UNILocPro: Unified Localization Integrating Model-Based Geometry and Channel Charting

In this paper, we propose a unified localization framework (called UNILocPro) that integrates model-based localization and channel charting (CC) for mixed line-of-sight (LoS)/non-line-of-sight (NLoS) scenarios. Specifically, based on LoS/NLoS identification, an adaptive activation between the model-based and CC-based methods is conducted. Aiming for unsupervised learning, information obtained from the model-based method is utilized to train the CC model, where a pairwise distance loss (involving a new dissimilarity metric design), a triplet loss (if timestamps are available), a LoS-based loss, and an optimal transport (OT)-based loss are jointly employed such that the global geometry can be well preserved. To reduce the training complexity of UNILocPro, we propose a low-complexity implementation (called UNILoc), where the CC model is trained with self-generated labels produced by a single pre-training OT transformation, which avoids iterative Sinkhorn updates involved in the OT-based loss computation. Extensive numerical experiments demonstrate that the proposed unified frameworks achieve significantly improved positioning accuracy compared to both model-based and CC-based methods. Notably, UNILocPro with timestamps attains performance on par with fully-supervised fingerprinting despite operating without labelled training data. It is also shown that the low-complexity UNILoc can substantially reduce training complexity with only marginal performance degradation.

Integrating Phase-Coherent Multistatic Imaging in Downlink D-MIMO Networks

This paper addresses the challenge of integrating multistatic coherent imaging functionalities in the downlink (DL) of a phase-coherent distributed multiple input multiple output (D-MIMO) communication network. During DL, the D-MIMO access points (APs) jointly precode the transmitted signals to maximize the spectral efficiency (SE) at the users (UEs) locations. However, imaging requires that \textit{(i)} a fraction of the APs work as receivers for sensing and \textit{(ii)} the transmitting APs emit AP-specific and orthogonal signals to illuminate the area to be imaged and allow multistatic operation. In these settings, our contribution is twofold. We propose a novel distributed integrated sensing and communication (D-ISAC) system that superposes a purposely designed AP-specific signal for imaging to the legacy UE-specific communication one, with a tunable trade-off factor. We detail both the imaging waveform design according to the \textit{extended orthogonality condition} and the space-frequency precoder design. Then, we propose an optimized selection strategy for the receiving APs, in order to maximize imaging performance under half-duplex constraints. Extensive numerical results prove the feasibility and benefits of our proposal, materializing the potential of joint multistatic imaging and communications in practical D-MIMO deployments.

Sensing with Mobile Devices through Radio SLAM: Models, Methods, Opportunities, and Challenges

The integration of sensing and communication (ISAC) is a cornerstone of 6G, enabling simultaneous environmental awareness and communication. This paper explores radio SLAM (simultaneous localization and mapping) as a key ISAC approach, using radio signals for mapping and localization. We analyze radio SLAM across different frequency bands, discussing trade-offs in coverage, resolution, and hardware requirements. We also highlight opportunities for integration with sensing, positioning, and cooperative networks. The findings pave the way for standardized solutions in 6G applications such as autonomous systems and industrial robotics.

Model-based Neural Data Augmentation for sub-wavelength Radio Localization

The increasing deployment of large antenna arrays at base stations has significantly improved the spatial resolution and localization accuracy of radio-localization methods. However, traditional signal processing techniques struggle in complex radio environments, particularly in scenarios dominated by non line of sight (NLoS) propagation paths, resulting in degraded localization accuracy. Recent developments in machine learning have facilitated the development of machine learning-assisted localization techniques, enhancing localization accuracy in complex radio environments. However, these methods often involve substantial computational complexity during both the training and inference phases. This work extends the well-established fingerprinting-based localization framework by simultaneously reducing its memory requirements and improving its accuracy. Specifically, a model-based neural network is used to learn the location-to-channel mapping, and then serves as a generative neural channel model. This generative model augments the fingerprinting comparison dictionary while reducing the memory requirements. The proposed method outperforms fingerprinting baselines by achieving sub-wavelength localization accuracy, even in NLoS environments. Remarkably, it offers an improvement by several orders of magnitude in localization accuracy, while simultaneously reducing memory requirements by an order of magnitude compared to classical fingerprinting methods.

Near-Field RIS-Assisted Localization Under Mutual Coupling

Reconfigurable intelligent surfaces (RISs) have the potential to significantly enhance the performance of integrated sensing and communication (ISAC) systems, particularly in line-of-sight (LoS) blockage scenarios. However, as larger RISs are integrated into ISAC systems, mutual coupling (MC) effects between RIS elements become more pronounced, leading to a substantial degradation in performance, especially for localization applications. In this paper, we first conduct a misspecified and standard Cram\'er-Rao bound analysis to quantify the impact of MC on localization performance, demonstrating severe degradations in accuracy, especially when MC is ignored. Building on this, we propose a novel joint user equipment localization and RIS MC parameter estimation (JLMC) method in near-field wireless systems. Our two-stage MC-aware approach outperforms classical methods that neglect MC, significantly improving localization accuracy and overall system performance. Simulation results validate the effectiveness and advantages of the proposed method in realistic scenarios.

Cross-layer Integrated Sensing and Communication: A Joint Industrial and Academic Perspective

Integrated sensing and communication (ISAC) enables radio systems to simultaneously sense and communicate with their environment. This paper, developed within the Hexa-X-II project funded by the European Union, presents a comprehensive cross-layer vision for ISAC in 6G networks, integrating insights from physical-layer design, hardware architectures, AI-driven intelligence, and protocol-level innovations. We begin by revisiting the foundational principles of ISAC, highlighting synergies and trade-offs between sensing and communication across different integration levels. Enabling technologies, such as multiband operation, massive and distributed MIMO, non-terrestrial networks, reconfigurable intelligent surfaces, and machine learning, are analyzed in conjunction with hardware considerations including waveform design, synchronization, and full-duplex operation. To bridge implementation and system-level evaluation, we introduce a quantitative cross-layer framework linking design parameters to key performance and value indicators. By synthesizing perspectives from both academia and industry, this paper outlines how deeply integrated ISAC can transform 6G into a programmable and context-aware platform supporting applications from reliable wireless access to autonomous mobility and digital twinning.