Signaling Design for Noncoherent Distributed Integrated Sensing and Communication Systems

The ultimate goal of enabling sensing through the cellular network is to obtain coordinated sensing of an unprecedented scale, through distributed integrated sensing and communication (D-ISAC). This, however, introduces challenges related to synchronization and demands new transmission methodologies. In this paper, we propose a transmit signal design framework for D-ISAC systems, where multiple ISAC nodes cooperatively perform sensing and communication without requiring phase-level synchronization. The proposed framework employing orthogonal frequency division multiplexing (OFDM) jointly designs downlink coordinated multi-point (CoMP) communication signals and multi-input multi-output (MIMO) radar signals, leveraging both collocated and distributed MIMO radars to estimate angle-of-arrival (AOA) and time-of-flight (TOF) from all possible multi-static measurements for target localization. To design the optimal D-ISAC transmit signal, we use the target localization Cram\'er-Rao bound (CRB) as the sensing performance metric and the signal-to-interference-plus-noise ratio (SINR) as the communication performance metric. Then, an optimization problem is formulated to minimize the localization CRB while maintaining a minimum SINR requirement for each communication user. Moreover, we present three distinct transmit signal design approaches, including optimal, orthogonal, and beamforming designs, which reveal trade-offs between ISAC performance and computational complexity. Unlike single-node ISAC systems, the proposed D-ISAC designs involve per-subcarrier sensing signal optimization to enable accurate TOF estimation, which contributes to the target localization performance. Numerical simulations demonstrate the effectiveness of the proposed designs in achieving flexible ISAC trade-offs and efficient D-ISAC signal transmission.

Federated Learning Strategies for Coordinated Beamforming in Multicell ISAC

We propose two cooperative beamforming frameworks based on federated learning (FL) for multi-cell integrated sensing and communications (ISAC) systems. Our objective is to address the following dilemma in multicell ISAC: 1) Beamforming strategies that rely solely on local channel information risk generating significant inter-cell interference (ICI), which degrades network performance for both communication users and sensing receivers in neighboring cells; 2) conversely centralized beamforming strategies can mitigate ICI by leveraging global channel information, but they come with substantial transmission overhead and latency that can be prohibitive for latency-sensitive and source-constrained applications. To tackle these challenges, we first propose a partially decentralized training framework motivated by the vertical federated learning (VFL) paradigm. In this framework, the participating base stations (BSs) collaboratively design beamforming matrices under the guidance of a central server. The central server aggregates local information from the BSs and provides feedback, allowing BSs to implicitly manage ICI without accessing the global channel information. To make the solution scalable for densely deployed wireless networks, we take further steps to reduce communication overhead by presenting a fully decentralized design based on the horizontal federated learning (HFL). Specifically, we develop a novel loss function to control the interference leakage power, enabling a more efficient training process by entirely eliminating local channel information exchange. Numerical results show that the proposed solutions can achieve significant performance improvements comparable to the benchmarks in terms of both communication and radar information rates.

Multi-scale Vehicle Localization In Heterogeneous Mobile Communication Networks

Low-latency and high-precision vehicle localization plays a significant role in enhancing traffic safety and improving traffic management for intelligent transportation. However, in complex road environments, the low latency and high precision requirements could not always be fulfilled due to the high complexity of localization computation. To tackle this issue, we propose a road-aware localization mechanism in heterogeneous networks (HetNet) of the mobile communication system, which enables real-time acquisition of vehicular position information, including the vehicular current road, segment within the road, and coordinates. By employing this multi-scale localization approach, the computational complexity can be greatly reduced while ensuring accurate positioning. Specifically, to reduce positioning search complexity and ensure positioning precision, roads are partitioned into low-dimensional segments with unequal lengths by the proposed singular point (SP) segmentation method. To reduce feature-matching complexity, distinctive salient features (SFs) are extracted sparsely representing roads and segments, which can eliminate redundant features while maximizing the feature information gain. The Cram\'er-Rao Lower Bound (CRLB) of vehicle positioning errors is derived to verify the positioning accuracy improvement brought from the segment partition and SF extraction. Additionally, through SF matching by integrating the inclusion and adjacency position relationships, a multi-scale vehicle localization (MSVL) algorithm is proposed to identify vehicular road signal patterns and determine the real-time segment and coordinates. Simulation results show that the proposed multi-scale localization mechanism can achieve lower latency and high precision compared to the benchmark schemes.

Sparse Regression Codes for Integrated Passive Sensing and Communications

We propose a novel integrated sensing and communication (ISAC) system, where the base station (BS) passively senses the channel parameters using the information carrying signals from a user. To simultaneously guarantee decoding and sensing performance, the user adopts sparse regression codes (SPARCs) with cyclic redundancy check (CRC) to transmit its information bits. The BS generates an initial coarse channel estimation of the parameters after receiving the pilot signal. Then, a novel iterative decoding and parameter sensing algorithm is proposed, where the correctly decoded codewords indicated by the CRC bits are utilized to improve the sensing and channel estimation performance at the BS. In turn, the improved estimate of the channel parameters lead to a better decoding performance. Simulation results show the effectiveness of the proposed iterative decoding and sensing algorithm, where both the sensing and the communication performance are significantly improved with a few iterations. Extensive ablation studies concerning different channel estimation methods and number of CRC bits are carried out for a comprehensive evaluation of the proposed scheme.

Network-level ISAC: Performance Analysis and Optimal Antenna-to-BS Allocation

A cooperative architecture is proposed for integrated sensing and communication (ISAC) networks, incorporating coordinated multi-point (CoMP) transmission along with multi-static sensing. We investigate how the allocation of antennas-to-base stations (BSs) affects cooperative sensing and cooperative communication performance. More explicitly, we balance the benefits of geographically concentrated antennas, which enhance beamforming and coherent processing, against those of geographically distributed antennas, which improve diversity and reduce service distances. Regarding sensing performance, we investigate three localization methods: angle-of-arrival (AOA)-based, time-of-flight (TOF)-based, and a hybrid approach combining both AOA and TOF measurements, for critically appraising their effects on ISAC network performance. Our analysis shows that in networks having N ISAC nodes following a Poisson point process, the localization accuracy of TOF-based methods follow a \ln^2 N scaling law (explicitly, the Cram\'er-Rao lower bound (CRLB) reduces with \ln^2 N). The AOA-based methods follow a \ln N scaling law, while the hybrid methods scale as a\ln^2 N + b\ln N, where a and b represent parameters related to TOF and AOA measurements, respectively. The difference between these scaling laws arises from the distinct ways in which measurement results are converted into the target location. In terms of communication performance, we derive a tractable expression for the communication data rate, considering various cooperative region sizes and antenna-to-BS allocation strategy. It is proved that higher path loss exponents favor distributed antenna allocation to reduce access distances, while lower exponents favor centralized antenna allocation to maximize beamforming gain.

On the Fundamental Trade-Offs of Time-Frequency Resource Distribution in OFDMA ISAC

Integrated sensing and communications (ISAC) is widely recognized as a pivotal and emerging technology for the next-generation mobile communication systems. However, how to optimize the time-frequency domain radio resource distribution for both communications and sensing, especially in scenarios where conflicting priorities emerge, becomes a crucial and challenging issue. In response to this problem, we first formulate the theoretical relationship between frequency domain subcarrier distribution and the range Cram\'er-Rao bound (CRB), and time domain sensing symbol distribution and the velocity CRB, as well as between subcarrier distribution and achievable communication rates in narrowband systems. Based on the derived range and velocity CRB expressions, the subcarrier and sensing symbol distribution schemes with the optimal and the worst sensing performance are respectively identified under both single-user equipment (single-UE) and multi-UE orthogonal frequency-division multiple access (OFDMA) ISAC systems. Furthermore, it is demonstrated that the impact of subcarrier distribution on achievable communication rates in synchronous narrowband OFDMA ISAC systems is marginal. This insight reveals that the constraints associated with subcarrier distribution optimization for achievable rates can be released. To substantiate our analysis, we present simulation results that demonstrate the performance advantages of the proposed distribution schemes.

Optimal Reference Nodes Deployment for Positioning Seafloor Anchor Nodes

Seafloor anchor nodes, which form a geodetic network, are designed to provide surface and underwater users with positioning, navigation and timing (PNT) services. Due to the non-uniform distribution of underwater sound speed, accurate positioning of underwater anchor nodes is a challenge work. Traditional anchor node positioning typically uses cross or circular shapes, however, how to optimize the deployment of reference nodes for positioning underwater anchor nodes considering the variability of sound speed has not yet been studied. This paper focuses on the optimal reference nodes deployment strategies for time--of--arrival (TOA) localization in the three-dimensional (3D) underwater space. We adopt the criterion that minimizing the trace of the inverse Fisher information matrix (FIM) to determine optimal reference nodes deployment with Gaussian measurement noise, which is positive related to the signal propagation path. A comprehensive analysis of optimal reference-target geometries is provided in the general circumstance with no restriction on the number of reference nodes, elevation angle and reference-target range. A new semi-closed form solution is found to detemine the optimal geometries. To demonstrate the findings in this paper, we conducted both simulations and sea trials on underwater anchor node positioning. Both the simulation and experiment results are consistent with theoretical analysis.

Cooperative ISAC Networks: Opportunities and Challenges

The integration of sensing and communication (ISAC) emerges as a cornerstone technology for the forth upcoming sixth generation era, seamlessly incorporating sensing functionality into wireless networks as a native capability. The main challenges in efficient ISAC are constituted by its limited sensing and communication coverage, as well as severe inter-cell interference. Network-level ISAC relying on multi-cell cooperation is capable of effectively expanding both the sensing and communication (S&C) coverage and of providing extra degrees of freedom (DoF) for realizing increased integration gains between S&C. In this work, we provide new considerations for ISAC networks, including new metrics, the optimization of the DoF, cooperation regimes, and highlight new S&C tradeoffs. Then, we discuss a suite of cooperative S&C architectures both at the task, as well as data, and signal levels. Furthermore, the interplay between S&C at the network level is investigated and promising research directions are outlined.

Intelligent Reflecting Surface Aided AirComp: Multi-Timescale Design and Performance Analysis

The integration of intelligent reflecting surface (IRS) into over-the-air computation (AirComp) is an effective solution for reducing the computational mean squared error (MSE) via its high passive beamforming gain. Prior works on IRS aided AirComp generally rely on the full instantaneous channel state information (I-CSI), which is not applicable to large-scale systems due to its heavy signalling overhead. To address this issue, we propose a novel multi-timescale transmission protocol. In particular, the receive beamforming at the access point (AP) is pre-determined based on the static angle information and the IRS phase-shifts are optimized relying on the long-term statistical CSI. With the obtained AP receive beamforming and IRS phase-shifts, the effective low-dimensional I-CSI is exploited to determine devices' transmit power in each coherence block, thus substantially reducing the signalling overhead. Theoretical analysis unveils that the achievable MSE scales on the order of ${\cal O}\left( {K/\left( {{N^2}M} \right)} \right)$, where $M$, $N$, and $K$ are the number of AP antennas, IRS elements, and devices, respectively. We also prove that the channel-inversion power control is asymptotically optimal for large $N$, which reveals that the full power transmission policy is not needed for lowering the power consumption of energy-limited devices.

Shifting the ISAC Trade-Off with Fluid Antenna Systems

As an emerging antenna technology, a fluid antenna system (FAS) enhances spatial diversity to improve both sensing and communication performance by shifting the active antennas among available ports. In this letter, we study the potential of shifting the integrated sensing and communication (ISAC) trade-off with FAS. We propose the model for FAS-enabled ISAC and jointly optimize the transmit beamforming and port selection of FAS. In particular, we aim to minimize the transmit power, while satisfying both communication and sensing requirements. An efficient iterative algorithm based on sparse optimization, convex approximation, and a penalty approach is developed. The simulation results show that the proposed scheme can attain 33% reductions in transmit power with guaranteed sensing and communication performance, showing the great potential of the fluid antenna for striking a flexible tradeoff between sensing and communication in ISAC systems.