Statistical and Deterministic RCS Characterization for ISAC Channel Modeling

In this study, we perform a statistical analysis of the radar cross section (RCS) for various test targets in an indoor factory at \(25\)-\(28\) GHz, with the goal of formulating parameters that may be used for target identification and other sensing applications for future wireless systems. The analysis is conducted based on measurements in monostatic and bistatic configurations for bistatic angles of \(20^\circ\), \(40^\circ\), and \(60^\circ\), which are functions of transmitter-receiver (T-R) and target positions, via accurate \(3\)dB beamwidth of \(10^\circ\) in both azimuth and elevation planes. The test targets include unmanned aerial vehicles, an autonomous mobile robot, and a robotic arm. We utilize parametric statistical distributions to fit the measured RCS data. The analysis reveals that the \textit{lognormal and gamma distributions} are effective in modeling the RCS of the test targets over different reflecting points of the target itself, i.e. when target is in motion. Additionally, we provide a framework for evaluating the deterministic bistatic RCS of a rectangular sheet of laminated wood, due to its widespread use in indoor hotspot environments. Novel deterministic and statistical RCS models are evaluated, incorporating dependencies on the bistatic angle, T-R distance (\(2\)m -\(10\)m) and the target. The results demonstrate that some proposed RCS models accurately fit the measured data, highlighting their applicability in bistatic configurations.

Beamforming Design for Secure RIS-Enabled ISAC: Passive RIS vs. Active RIS

The forthcoming sixth-generation (6G) communications standard is anticipated to provide integrated sensing and communication (ISAC) as a fundamental service. These ISAC systems present unique security challenges because of the exposure of information-bearing signals to sensing targets, enabling them to potentially eavesdrop on sensitive communication information with the assistance of sophisticated receivers. Recently, reconfigurable intelligent surfaces (RISs) have shown promising results in enhancing the physical layer security of various wireless communication systems, including ISAC. However, the performance of conventional passive RIS (pRIS)-enabled systems are often limited due to multiplicative fading, which can be alleviated using active RIS (aIRS). In this paper, we consider the problem of beampattern gain maximization in a secure pRIS/aRIS-enabled ISAC system, subject to signal-to-interference-plus-noise ratio constraints at communication receivers, and information leakage constraints at an eavesdropping target. For the challenging non-convex problem of joint beamforming design at the base station and the pRIS/aRIS, we propose a novel successive convex approximation (SCA)-based method. Unlike the conventional alternating optimization (AO)-based methods, in the proposed SCA-based approach, all of the optimization variables are updated simultaneously in each iteration. The proposed method shows significant performance superiority for pRIS-aided ISAC system compared to a benchmark scheme using penalty-based AO method. Moreover, our simulation results also confirm that aRIS-aided system has a notably higher beampattern gain at the target compared to that offered by the pRIS-aided system for the same power budget. We also present a detailed complexity analysis and proof of convergence for the proposed SCA-based method.

Reinforcement Learning for Enhancing Sensing Estimation in Bistatic ISAC Systems with UAV Swarms

This paper introduces a novel Multi-Agent Reinforcement Learning (MARL) framework to enhance integrated sensing and communication (ISAC) networks using unmanned aerial vehicle (UAV) swarms as sensing radars. By framing the positioning and trajectory optimization of UAVs as a Partially Observable Markov Decision Process, we develop a MARL approach that leverages centralized training with decentralized execution to maximize the overall sensing performance. Specifically, we implement a decentralized cooperative MARL strategy to enable UAVs to develop effective communication protocols, therefore enhancing their environmental awareness and operational efficiency. Additionally, we augment the MARL solution with a transmission power adaptation technique to mitigate interference between the communicating drones and optimize the communication protocol efficiency. Moreover, a transmission power adaptation technique is incorporated to mitigate interference and optimize the learned communication protocol efficiency. Despite the increased complexity, our solution demonstrates robust performance and adaptability across various scenarios, providing a scalable and cost-effective enhancement for future ISAC networks.

An Experimental Multi-Band Channel Characterization in the Upper Mid-Band

The following paper provides a multi-band channel measurement analysis on the frequency range (FR)3. This study focuses on the FR3 low frequencies 6.5 GHz and 8.75 GHz with a setup tailored to the context of integrated sensing and communication (ISAC), where the data are collected with and without the presence of a target. A method based on multiple signal classification (MUSIC) is used to refine the delays of the channel impulse response estimates. The results reveal that the channel at the lower frequency 6.5 GHz has additional distinguishable multipath components in the presence of the target, while the one associated with the higher frequency 8.75 GHz has more blockage. The set of results reported in this paper serves as a benchmark for future multi-band studies in the FR3 spectrum.

Low Dynamic Range for RIS-aided Bistatic Integrated Sensing and Communication

The following paper presents a reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) system model scenario, where a base station communicates with a user, and a bi-static sensing unit, i.e. the passive radar (PR), senses targets using downlink signals. Given that the RIS aids with communication and sensing tasks, this paper introduces new interfering paths that can overwhelm the PR with unnecessarily high power, namely the path interference (PI), \textit{which is itself a combination of two interfering paths, the direct path interference (DPI) and the reflected path interference (RPI)}. For this, we formulate an optimization framework that allows the system to carry on with its ISAC tasks, through analog space-time beamforming at the sensing unit, in collaboration with RIS phase shift and statistical transmit covariance matrix optimization, while minimizing the PI power. As the proposed optimization problem is non-convex, we tailor a block-cyclic coordinate descent (BCCD) method to decouple the non-convex sub-problem from the convex one. A Riemannian conjugate gradient method is devised to generate the RIS and PR space-time beamforming phase shifts per BCCD iteration, while the convex sub-problem is solved via off-the-shelf solvers. Simulation results demonstrate the effectiveness of the proposed solver when compared with benchmarking ones.

Statistical Radar Cross Section Characterization for Indoor Factory Targets

In this work, we statistically analyze the radar cross section (RCS) of different test targets present in an indoor factory (InF) scenario specified by 3rd Generation Partnership Project considering bistatic configuration. The test targets that we consider are drones, humans, quadruped robot and a robotic arm. We consider two drones of different sizes and five human subjects for RCS characterization. For the drones, we measure the RCS when they are are flying over a given point and while they are rotating over the same point. For human subjects, we measure the RCS while standing still, sitting still and walking. For quadruped robot and robotic arm, we consider a continuous random motion emulating different tasks which they are supposed to perfom in typical InF scenario. We employ different distributions, such as Normal, Lognormal, Gamma, Rician, Weibull, Rayleigh and Exponential to fit the measurement data. From the statistical analysis, we gather that Lognormal distribution can fit all the considered targets in the InF scenario.

Fundamental Trade-offs in Quantized Hybrid Radar Fusion: A CRB-Rate Perspective

While recent advancements have highlighted the role of low-resolution analog-to-digital converters (ADCs) in integrated sensing and communication (ISAC) systems, the specific impact of ADC resolution on hybrid radar fusion (HRF) remains relatively unexplored. The uplink (UL) paths in HRF, comprising both direct and reflected signals within the same frequency band, pose unique challenges, particularly given that the reflected signal is often significantly weaker than the direct path, making HRF systems susceptible to ADC resolution. To investigate the influence of quantization and ADC resolution on HRF, we employ the quantized Cram\'er-Rao bound (CRB) as a metric for sensing accuracy. This work derives the quantized CRB specifically for HRF systems and the quantized communication rate. We extend our analysis to obtain lower bounds on the Fisher Information Matrix (FIM) and UL communication rates, which we use to characterize quantized HRF systems. Using these derived bounds, we analyze quantized HRF systems through the lens of CRB-rate boundaries. We obtain the CRB-rate boundary through two optimization problems, where each solution point represents a trade-off boundary between the sensing accuracy and the communication rate. Extensive simulations illustrate the influence of ADC resolution, DR, and various system parameters on the CRB-rate boundary of HRF systems. These results offer critical insights into the design of efficient and high-performance HRF systems.

Urban Outdoor Propagation Measurements and Channel Models at 6.75 GHz FR1(C) and 16.95 GHz FR3 Upper Mid-Band Spectrum for 5G and 6G

Global allocations in the upper mid-band spectrum (4-24 GHz) necessitate a comprehensive exploration of the propagation behavior to meet the promise of coverage and capacity. This paper presents an extensive Urban Microcell (UMi) outdoor propagation measurement campaign at 6.75 GHz and 16.95 GHz conducted in Downtown Brooklyn, USA, using a 1 GHz bandwidth sliding correlation channel sounder over 40-880 m propagation distance, encompassing 6 Line of Sight (LOS) and 14 Non-Line of Sight (NLOS) locations. Analysis of the path loss (PL) reveals lower directional and omnidirectional PL exponents compared to mmWave and sub-THz frequencies in the UMi environment, using the close-in PL model with a 1 m reference distance. Additionally, a decreasing trend in root mean square (RMS) delay spread (DS) and angular spread (AS) with increasing frequency was observed. The NLOS RMS DS and RMS AS mean values are obtained consistently lower compared to 3GPP model predictions. Point data for all measured statistics at each TX-RX location are provided to supplement the models and results. The spatio-temporal statistics evaluated here offer valuable insights for the design of next-generation wireless systems and networks.

Sum Secrecy Rate Maximization for Full Duplex ISAC Systems

In integrated sensing and communication (ISAC) systems, the target of interest may \textit{intentionally disguise itself as an eavesdropper}, enabling it to intercept and tap into the communication data embedded in the ISAC waveform. The following paper considers a full duplex (FD)-ISAC system, which involves multiple malicious targets attempting to intercept both uplink (UL) and downlink (DL) communications between the dual-functional radar and communication (DFRC) base station (BS) and legitimate UL/DL communication users (CUs). For this, we formulate an optimization framework that allows maximization of both UL and DL sum secrecy rates, under various power budget constraints for sensing and communications. As the proposed optimization problem is non-convex, we develop a method called Iterative Joint Taylor-Block cyclic coordinate descent (IJTB) by proving essential lemmas that transform the original problem into a more manageable form. In essence, IJTB alternates between two sub-problems: one yields UL beamformers in closed-form, while the other approximates the solution for UL power allocation, artificial noise covariance, and DL beamforming vectors. This is achieved through a series of Taylor approximations that effectively \textit{"convexify"} the problem, enabling efficient optimization. Simulation results demonstrate the effectiveness of the proposed solver when compared with benchmarking ones. Our findings reveal that the IJTB algorithm shows fast convergence, reaching stability within approximately $10$ iterations. In addition, all benchmarks reveal a substantial decline in the sum secrecy rate, approaching zero as the eavesdropper distance reaches $17$ meters, underscoring their vulnerability in comparison to IJTB.

DRIP: A Versatile Family of Space-Time ISAC Waveforms

The following paper introduces Dual beam-similarity awaRe Integrated sensing and communications (ISAC) with controlled Peak-to-average power ratio (DRIP) waveforms. DRIP is a novel family of space-time ISAC waveforms designed for dynamic peak-to-average power ratio (PAPR) adjustment. The proposed DRIP waveforms are designed to conform to specified PAPR levels while exhibiting beampattern properties, effectively targeting multiple desired directions and suppressing interference for multi-target sensing applications, while closely resembling radar chirps. For communication purposes, the proposed DRIP waveforms aim to minimize multi-user interference across various constellations. Addressing the non-convexity of the optimization framework required for generating DRIP waveforms, we introduce a block cyclic coordinate descent algorithm. This iterative approach ensures convergence to an optimal ISAC waveform solution. Simulation results validate the DRIP waveforms' superior performance, versatility, and favorable ISAC trade-offs, highlighting their potential in advanced multi-target sensing and communication systems.