Sum Rate Enhancement using Machine Learning for Semi-Self Sensing Hybrid RIS-Enabled ISAC in THz Bands

This paper proposes a novel semi-self sensing hybrid reconfigurable intelligent surface (SS-HRIS) in terahertz (THz) bands, where the RIS is equipped with reflecting elements divided between passive and active elements in addition to sensing elements. SS-HRIS along with integrated sensing and communications (ISAC) can help to mitigate the multipath attenuation that is abundant in THz bands. In our proposed scheme, sensors are configured at the SS-HRIS to receive the radar echo signal from a target. A joint base station (BS) beamforming and HRIS precoding matrix optimization problem is proposed to maximize the sum rate of communication users while maintaining satisfactory sensing performance measured by the Cramer-Rao bound (CRB) for estimating the direction of angles of arrival (AoA) of the echo signal and thermal noise at the target. The CRB expression is first derived and the sum rate maximization problem is formulated subject to communication and sensing performance constraints. To solve the complex non-convex optimization problem, deep deterministic policy gradient (DDPG)-based deep reinforcement learning (DRL) algorithm is proposed, where the reward function, the action space and the state space are modeled. Simulation results show that the proposed DDPG-based DRL algorithm converges well and achieves better performance than several baselines, such as the soft actor-critic (SAC), proximal policy optimization (PPO), greedy algorithm and random BS beamforming and HRIS precoding matrix schemes. Moreover, it demonstrates that adopting HRIS significantly enhances the achievable sum rate compared to passive RIS and random BS beamforming and HRIS precoding matrix schemes.

$SE(3)$-Based Trajectory Optimization and Target Tracking in UAV-Enabled ISAC Systems

This paper introduces a novel approach to enhance the performance of UAV-enabled integrated sensing and communication (ISAC) systems. By integrating uniform planar arrays (UPAs) and modeling the UAV as a rigid body using $SE(3)$, the study addresses key challenges in existing ISAC frameworks, such as rigid-body dynamics and trajectory design. We propose a target tracking scheme based on extended Kalman filtering (EKF) in $SE(3)$ and trajectory optimization from a control signal design perspective, leveraging the conditional Posterior Cramer-Rao bound (CPCRB) to optimize performance. Numerical results demonstrate the effectiveness of the proposed method in improving target tracking and trajectory optimization for a UAV-enabled MIMO-OFDM ISAC system.

Joint Optimization of Geometric and Probabilistic Constellation Shaping for OFDM-ISAC Systems

6G communications systems are expected to integrate radar-like sensing capabilities enabling novel use cases. However, integrated sensing and communications (ISAC) introduces a trade-off between communications and sensing performance because the optimal constellations for each task differ. In this paper, we compare geometric, probabilistic and joint constellation shaping for orthogonal frequency division multiplexing (OFDM)-ISAC systems using an autoencoder (AE) framework. We first derive the constellation-dependent detection probability and propose a novel loss function to include the sensing performance in the AE framework. Our simulation results demonstrate that constellation shaping enables a dynamic trade-off between communications and sensing. Depending on whether sensing or communications performance is prioritized, geometric or probabilistic constellation shaping is preferred. Joint constellation shaping combines the advantages of geometric and probabilistic shaping, significantly outperforming legacy modulation formats.

Optimal User and Target Scheduling, User-Target Pairing, and Low-Resolution Phase-Only Beamforming for ISAC Systems

We investigate the joint user and target scheduling, user-target pairing, and low-resolution phase-only beamforming design for integrated sensing and communications (ISAC). Scheduling determines which users and targets are served, while pairing specifies which users and targets are grouped into pairs. Additionally, the beamformers are designed using few-bit constant-modulus phase shifts. This resource allocation problem is a nonconvex mixed-integer nonlinear program (MINLP) and challenging to solve. To address it, we propose an exact mixed-integer linear program (MILP) reformulation, which leads to a globally optimal solution. Our results demonstrate the superiority of an optimal joint design compared to heuristic stage-wise approaches, which are highly sensitive to scenario characteristics.

Integrated Long-range Sensing and Communications in Multi Target Scenarios using CP-OFDM

6G communication systems promise to deliver sensing capabilities by utilizing the orthogonal frequency division multiplexing (OFDM) communication signal for sensing. However, the cyclic prefix inherent in OFDM systems limits the sensing range, necessitating compensation techniques to detect small, distant targets like drones. In this paper, we show that state-of-the-art coherent compensation methods fail in scenarios involving multiple targets, resulting in an increased noise floor in the radar image. Our contributions include a novel multi target coherent compensation algorithm and a generalized signal-to-interference-and-noise ratio for multiple targets to evaluate the performance. Our algorithm achieves the same detection performance at long distances requiring only 3.6% of the radio resources compared to classical OFDM radar processing. This enables resource efficient sensing at long distances in multi target scenarios with legacy communications-only networks.

CDMA/OTFS Sensing Outperforms Pure OTFS at the Same Communication Throughput

There is a dearth of publications on the subject of spreading-aided Orthogonal Time Frequency Space (OTFS) solutions, especially for Integrated Sensing and Communication (ISAC), even though Code Division Multiple Access (CDMA) assisted multi-user OTFS (CDMA/OTFS) exhibits tangible benefits. Hence, this work characterises both the communication Bit Error Rate (BER) and sensing Root Mean Square Error (RMSE) performance of CDMA/OTFS, and contrasts them to pure OTFS. Three CDMA/OTFS configurations are considered: Delay Code Division Multiple Access OTFS (Dl-CDMA/OTFS), Doppler Code Division Multiple Access OTFS (Dp-CDMA/OTFS), and Delay Doppler Code Division Multiple Access OTFS (DD-CDMA/OTFS), which harness direct sequence spreading along the delay axis, Doppler axis, and DD domains respectively. For each configuration, the performance of Gold, Hadamard, and Zadoff-Chu sequences is investigated. The results demonstrate that Zadoff-Chu Dl-CDMA/OTFS and DD-CDMA/OTFS consistently outperform pure OTFS sensing, whilst maintaining a similar communication performance at the same throughput. The extra modulation complexity of CDMA/OTFS is similar to that of other OTFS multi-user methodologies, but the demodulation complexity of CDMA/OTFS is lower than that of some other OTFS multi-user methodologies. CDMA/OTFS sensing can also consistently outperform OTFS sensing whilst not requiring any additional complexity for target parameter estimation. Therefore, CDMA/OTFS is an appealing candidate for implementing multi-user OTFS ISAC.

Predictive Target-to-User Association in Complex Scenarios via Hybrid-Field ISAC Signaling

This paper presents a novel and robust target-to-user (T2U) association framework to support reliable vehicle-to-infrastructure (V2I) networks that potentially operate within the hybrid field (near-field and far-field). To address the challenges posed by complex vehicle maneuvers and user association ambiguity, an interacting multiple-model filtering scheme is developed, which combines coordinated turn and constant velocity models for predictive beamforming. Building upon this foundation, a lightweight association scheme leverages user-specific integrated sensing and communication (ISAC) signaling while employing probabilistic data association to manage clutter measurements in dense traffic. Numerical results validate that the proposed framework significantly outperforms conventional methods in terms of both tracking accuracy and association reliability.

Harnessing Rydberg Atomic Receivers: From Quantum Physics to Wireless Communications

The intrinsic integration of Rydberg atomic receivers into wireless communication systems is proposed, by harnessing the principles of quantum physics in wireless communications. More particularly, we conceive a pair of Rydberg atomic receivers, one incorporates a local oscillator (LO), referred to as an LO-dressed receiver, while the other operates without an LO and is termed an LO-free receiver. The appropriate wireless model is developed for each configuration, elaborating on the receiver's responses to the radio frequency (RF) signal, on the potential noise sources, and on the system performance. Next, we investigate the association distortion effects that might occur, specifically demonstrating the boundaries of linear dynamic regions, which provides critical insights into its practical implementations in wireless systems. Extensive simulation results are provided for characterizing the performance of wireless systems, harnessing this pair of Rydberg atomic receivers. Our results demonstrate that they deliver complementary benefits: LO-free systems excel in proximity operations, while LO-dressed systems are eminently suitable for long-distance sensing at extremely low power levels. More specifically, LO-dressed systems achieve a significant signal-to-noise ratio (SNR) gain of approximately 44 dB over conventional RF receivers, exhibiting an effective coverage range extension over conventional RF receivers by a factor of 150. Furthermore, LO-dressed systems support higher-order quadrature amplitude modulation (QAM) at reduced symbol error rates (SER) compared to conventional RF receivers, hence significantly enhancing wireless communication performance.

Pinching Antennas: Principles, Applications and Challenges

Flexible-antenna systems, such as fluid antennas and movable antennas, have been recognized as key enabling technologies for sixth-generation (6G) wireless networks, as they can intelligently reconfigure the effective channel gains of the users and hence significantly improve their data transmission capabilities. However, existing flexible-antenna systems have been designed to combat small-scale fading in non-line-of-sight (NLoS) conditions. As a result, they lack the ability to establish line-of-sight links, which are typically 100 times stronger than NLoS links. In addition, existing flexible-antenna systems have limited flexibility, where adding/removing an antenna is not straightforward. This article introduces an innovative flexible-antenna system called pinching antennas, which are realized by applying small dielectric particles to waveguides. We first describe the basics of pinching-antenna systems and their ability to provide strong LoS links by deploying pinching antennas close to the users as well as their capability to scale up/down the antenna system. We then focus on communication scenarios with different numbers of waveguides and pinching antennas, where innovative approaches to implement multiple-input multiple-output and non-orthogonal multiple access are discussed. In addition, promising 6G-related applications of pinching antennas, including integrated sensing and communication and next-generation multiple access, are presented. Finally, important directions for future research, such as waveguide deployment and channel estimation, are highlighted.

Two-Stage Distributed Beamforming Design in Cell-Free Massive MIMO ISAC Systems

Integrating radio-sensing functionalities into future cell-free (CF) wireless networks promises efficient resource utilization and facilitates the seamless roll-out of applications such as public safety and smart infrastructure. While the beamforming design problem for the CF integrated sensing and communication (ISAC) paradigm has been addressed in the literature, existing methods rely on centralized signal processing, leading to fronthaul load and scalability issues. This paper presents a two-stage beamforming design for the CF ISAC paradigm, aiming to significantly reduce the fronthaul load by distributing the signal processing tasks between the central unit (CU) and the access points (APs). The design optimizes the sum signal-to-interference-plus-noise ratio (SINR) for communication users, subject to per-AP power constraints and signal-to-noise ratio (SNR) requirements for radio-sensing purposes. The resulting optimization problems are non-convex and challenging to solve. To address this, we employ a majorization-minimization (MM) approach, which decomposes the problem into simpler convex subproblems. The results show that the two-stage beamforming design achieves performance comparable to centralized methods while substantially reducing the fronthaul load, thus minimizing data transmission requirements over the fronthaul network. This advancement highlights the potential of the proposed method to enhance the efficiency and scalability of cell-free MIMO ISAC systems.