Joint Design of Radar Receive Filter and Unimodular ISAC Waveform with Sidelobe Level Control

Integrated sensing and communication (ISAC) has been considered a key feature of next-generation wireless networks. This paper investigates the joint design of the radar receive filter and dual-functional transmit waveform for the multiple-input multiple-output (MIMO) ISAC system. While optimizing the mean square error (MSE) of the radar receive spatial response and maximizing the achievable rate at the communication receiver, besides the constraints of full-power radar receiving filter and unimodular transmit sequence, we control the maximum range sidelobe level, which is often overlooked in existing ISAC waveform design literature, for better radar imaging performance. To solve the formulated optimization problem with convex and nonconvex constraints, we propose an inexact augmented Lagrangian method (ALM) algorithm. For each subproblem in the proposed inexact ALM algorithm, we custom-design a block successive upper-bound minimization (BSUM) scheme with closed-form solutions for all blocks of variable to enhance the computational efficiency. Convergence analysis shows that the proposed algorithm is guaranteed to provide a stationary and feasible solution. Extensive simulations are performed to investigate the impact of different system parameters on communication and radar imaging performance. Comparison with the existing works shows the superiority of the proposed algorithm.

Reduced-latency DL-based Fractional Channel Estimation in OTFS Receivers

In this work, we propose a deep learning (DL)-based approach that integrates a state-of-the-art algorithm with a time-frequency (TF) learning framework to minimize overall latency. Meeting the stringent latency requirements of 6G orthogonal time-frequency space (OTFS) systems necessitates low-latency designs. The performance of the proposed approach is evaluated under challenging conditions: low delay and Doppler resolutions caused by limited time and frequency resources, and significant interpath interference (IPI) due to poor separability of propagation paths in the delay-Doppler (DD) domain. Simulation results demonstrate that the proposed method achieves high estimation accuracy while reducing latency by approximately 55\% during the maximization process. However, a performance trade-off is observed, with a maximum loss of 3 dB at high pilot SNR values.

RiLoCo: An ISAC-oriented AI Solution to Build RIS-empowered Networks

The advance towards 6G networks comes with the promise of unprecedented performance in sensing and communication capabilities. The feat of achieving those, while satisfying the ever-growing demands placed on wireless networks, promises revolutionary advancements in sensing and communication technologies. As 6G aims to cater to the growing demands of wireless network users, the implementation of intelligent and efficient solutions becomes essential. In particular, reconfigurable intelligent surfaces (RISs), also known as Smart Surfaces, are envisioned as a transformative technology for future 6G networks. The performance of RISs when used to augment existing devices is nevertheless largely affected by their precise location. Suboptimal deployments are also costly to correct, negating their low-cost benefits. This paper investigates the topic of optimal RISs diffusion, taking into account the improvement they provide both for the sensing and communication capabilities of the infrastructure while working with other antennas and sensors. We develop a combined metric that takes into account the properties and location of the individual devices to compute the performance of the entire infrastructure. We then use it as a foundation to build a reinforcement learning architecture that solves the RIS deployment problem. Since our metric measures the surface where given localization thresholds are achieved and the communication coverage of the area of interest, the novel framework we provide is able to seamlessly balance sensing and communication, showing its performance gain against reference solutions, where it achieves simultaneously almost the reference performance for communication and the reference performance for localization.

Joint Bistatic Positioning and Monostatic Sensing: Optimized Beamforming and Performance Tradeoff

We investigate joint bistatic positioning (BP) and monostatic sensing (MS) within a multi-input multi-output orthogonal frequency-division system. Based on the derived Cram\'er-Rao Bounds (CRBs), we propose novel beamforming optimization strategies that enable flexible performance trade-offs between BP and MS. Two distinct objectives are considered in this multi-objective optimization problem, namely, enabling user equipment to estimate its own position while accounting for unknown clock bias and orientation, and allowing the base station to locate passive targets. We first analyze digital schemes, proposing both weighted-sum CRB and weighted-sum mismatch (of beamformers and covariance matrices) minimization approaches. These are examined under full-dimension beamforming (FDB) and low-complexity codebook-based power allocation (CPA). To adapt to low-cost hardwares, we develop unit-amplitude analog FDB and CPA schemes based on the weighted-sum mismatch of the covariance matrices paradigm, solved using distinct methods. Numerical results confirm the effectiveness of our designs, highlighting the superiority of minimizing the weighted-sum mismatch of covariance matrices, and the advantages of mutual information fusion between BP and MS.

Joint Near-Field Sensing and Visibility Region Detection with Extremely Large Aperture Arrays

In this paper, we consider near-field localization and sensing with an extremely large aperture array under partial blockage of array antennas, where spherical wavefront and spatial non-stationarity are accounted for. We propose an Ising model to characterize the clustered sparsity feature of the blockage pattern, develop an algorithm based on alternating optimization for joint channel parameter estimation and visibility region detection, and further estimate the locations of the user and environmental scatterers. The simulation results confirm the effectiveness of the proposed algorithm compared to conventional methods.

RIS-Aided Positioning Under Adverse Conditions: Interference from Unauthorized RIS

Positioning technology, which aims to determine the geometric information of a device in a global coordinate, is a key component in integrated sensing and communication systems. In addition to traditional active anchor-based positioning systems, reconfigurable intelligent surfaces (RIS) have shown great potential for enhancing system performance. However, their ability to manipulate electromagnetic waves and ease of deployment pose potential risks, as unauthorized RIS may be intentionally introduced to jeopardize the positioning service. Such an unauthorized RIS can cause unexpected interference in the original localization system, distorting the transmitted signals, and leading to degraded positioning accuracy. In this work, we investigate the scenario of RIS-aided positioning in the presence of interference from an unauthorized RIS. Theoretical lower bounds are employed to analyze the impact of unauthorized RIS on channel parameter estimation and positioning accuracy. Several codebook design strategies for unauthorized RIS are evaluated, and various system arrangements are discussed. The simulation results show that an unauthorized RIS path with a high channel gain or a delay similar to that of legitimate RIS paths leads to poor positioning performance. Furthermore, unauthorized RIS generates more effective interference when using directional beamforming codebooks compared to random codebooks.

Intrinsic Cramér-Rao Bound based 6D Localization and Tracking for 5G/6G Systems

Localization and tracking are critical components of integrated sensing and communication (ISAC) systems, enhancing resource management, beamforming accuracy, and overall system reliability through precise sensing. Due to the high path loss of the high-frequency systems, antenna arrays are required at the transmitter and receiver sides for beamforming gain. However, beam misalignment may occur, which requires accurate tracking of the six-dimensional (6D) state, namely, 3D position and 3D orientation. In this work, we first address the challenge that the rotation matrix, being part of the Lie group rather than Euclidean space, necessitates the derivation of the ICRB for an intrinsic performance benchmark. Then, leveraging the derived ICRB, we develop two filters-one utilizing pose fusion and the other employing error-state Kalman filter to estimate the UE's 6D state for different computational resource consumption and accuracy requirements. Simulation results validate the ICRB and assess the performance of the proposed filters, demonstrating their effectiveness and improved accuracy in 6D state tracking.

Positioning-Aided Channel Estimation for Multi-LEO Satellite Downlink Communications

We investigate a multi-low Earth orbit (LEO) satellite system that simultaneously provides positioning and communication services to terrestrial user terminals. To address the challenges of channel estimation in LEO satellite systems, we propose a novel two-timescale positioning-aided channel estimation framework, exploiting the distinct variation rates of position-related parameters and channel gains inherent in LEO satellite channels. Using the misspecified Cramer-Rao bound (MCRB) theory, we systematically analyze positioning performance under practical imperfections, such as inter-satellite clock bias and carrier frequency offset. Furthermore, we theoretically demonstrate how position information derived from downlink positioning can enhance uplink channel estimation accuracy, even in the presence of positioning errors, through an MCRB-based analysis. To overcome the constraints of limited link budgets and communication rates associated with single-satellite-based communication, we develop a distributed beamforming strategy for downlink communication. This strategy allows LEO satellites to independently optimize their beamformers using local channel state information, eliminating the need for centralized processing while preserving the advantages of multi-satellite cooperative communication. Theoretical analyses and numerical results confirm the effectiveness of the proposed framework in achieving high-precision downlink positioning under practical imperfections, facilitating uplink channel estimation, and enabling efficient downlink communication.

Modern Base Station Architecture: Enabling Passive Beamforming with Beyond Diagonal RISs

Beamforming plays a crucial role in millimeter wave (mmWave) communication systems to mitigate the severe attenuation inherent to this spectrum. However, the use of large active antenna arrays in conventional architectures often results in high implementation costs and excessive power consumption, limiting their practicality. As an alternative, deploying large arrays at transceivers using passive devices, such as reconfigurable intelligent surfaces (RISs), offers a more cost-effective and energy-efficient solution. In this paper, we investigate a promising base station (BS) architecture that integrates a beyond diagonal RIS (BD-RIS) within the BS to enable passive beamforming. By utilizing Takagi's decomposition and leveraging the effective beamforming vector, the RIS profile can be designed to enable passive beamforming directed toward the target. Through the beamforming analysis, we reveal that BD-RIS provides robust beamforming performance across various system configurations, whereas the traditional diagonal RIS (D-RIS) exhibits instability with increasing RIS size and decreasing BS-RIS separation-two critical factors in optimizing RIS-assisted systems. Comprehensive computer simulation results across various aspects validate the superiority of the proposed BS-integrated BD-RIS over conventional D-RIS architectures, showcasing performance comparable to active analog beamforming antenna arrays.

Optimized Beamforming for Joint Bistatic Positioning and Monostatic Sensing

We investigate the performance tradeoff between \textit{bistatic positioning (BP)} and \textit{monostatic sensing (MS)} in a multi-input multi-output orthogonal frequency division multiplexing scenario. We derive the Cram\'er-Rao bounds (CRBs) for BP at the user equipment and MS at the base station. To balance these objectives, we propose a multi-objective optimization framework that optimizes beamformers using a weighted-sum CRB approach, ensuring the weak Pareto boundary. We also introduce two mismatch-minimizing approaches, targeting beamformer mismatch and variance matrix mismatch, and solve them distinctly. Numerical results demonstrate the performance tradeoff between BP and MS, revealing significant gains with the proposed methods and highlighting the advantages of minimizing the weighted-sum mismatch of variance matrices.