A key challenge in Integrated Sensing and Communications (ISAC), especially in Full-Duplex (FD) Dual-Functional Radar-Communication (DFRC) systems, is self-interference (SI) caused by signal leakage from the transmitter to the receiver, impairing sensing tasks. Reconfigurable Intelligent Surface (RIS) can manipulate signal reflections with minimal power, making them promising for enhancing 6G communication, yet its potential for SI mitigation in DFRC systems is underexplored. This paper proposes a novel RIS-enabled approach for SI elimination in mono-static full-duplex DFRC systems. Our method decomposes the RIS function into spatial beam-forming and temporal modulation to ensure sufficient target illumination and orthogonality between reflected and transmitted waveforms, effectively eliminating SI without the need for SI channel information. Simulations confirm the effectiveness of the proposed approach in SI elimination and sensing performance.

Integrated sensing and communication (ISAC) is widely recognized as a pivotal enabling technique for the advancement of future wireless networks. This paper aims to efficiently exploit the inherent sparsity of echo signals for the multi-input-multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) based ISAC system. A novel joint receive echo processing and transmit beamforming design is presented to achieve this goal. Specifically, we first propose a compressive sensing (CS)-assisted estimation approach to facilitate ISAC receive echo processing, which can not only enable accurate recovery of target information, but also allow a substantial reduction in the number of sensing subcarriers to be sampled and processed. Then, based on the proposed CS-assisted processing method, the associated transmit beamforming design is formulated with the objective of maximizing the sum-rate of multiuser communications while satisfying the transmit power budget and ensuring the received signal-to-noise ratio (SNR) for the designated sensing subcarriers. In order to address the formulated non-convex problem involving high-dimensional variables, an effective iterative algorithm employing majorization minimization (MM), fractional programming (FP), and the nonlinear equality alternative direction method of multipliers (neADMM) with closed-form solutions has been developed. Finally, extensive numerical simulations are conducted to verify the effectiveness of the proposed algorithm and the superior performance of the introduced sparsity exploitation strategy.

Integrated sensing and communication (ISAC) is a key enabling technology for future 6G communication systems. Recently emerged symbol-level precoding (SLP) is considered to possess substantial potential for ISAC waveform design, owing to its ability to enhance multi-user communication and radar sensing performances by simultaneously leveraging both tempo-ral and spatial design degrees of freedom (DoFs). Considering the high complexity challenges brought by existing model-driven optimization based SLP design approaches, in this paper we pro-pose a lightweight SLP-Inception-Net and efficient data-driven deep learning algorithm to solve the highly complex SLP design problem. In particular, we propose a phase-based activation function method to guarantee the equality constraints and use the soft loss to ensure the inequality constraints. Simulation results verify that our proposed deep learning algorithm achieves comparable communication and radar sensing performance to the prior optimization-based approaches, while providing a noteworthy lOOO-fold reduction in computational complexity in terms of average execution time.

Integrated sensing and communication (ISAC) is a promising technology in future wireless systems owing to its efficient hardware and spectrum utilization. In this paper, we consider a multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) ISAC system and propose a novel waveform design to provide better radar ranging performance by taking range sidelobe suppression into consideration. In specific, we aim to design the MIMO-OFDM dual-function waveform to minimize its integrated sidelobe level (ISL) while satisfying the quality of service (QoS) requirements of multi-user communications and the transmit power constraint. To achieve a lower ISL, the symbol-level precoding (SLP) technique is employed to fully exploit the degrees of freedom (DoFs) of the waveform design in both temporal and spatial domains. An efficient algorithm utilizing majorization-minimization (MM) framework is developed to solve the non-convex waveform design problem. Simulation results reveal radar ranging performance improvement and demonstrate the benefits of the proposed SLP-based low-range-sidelobe waveform design in ISAC systems.

Dual-function radar-communication (DFRC) systems, which can efficiently utilize the congested spectrum and costly hardware resources by employing one common waveform for both sensing and communication (S&C), have attracted increasing attention. While the orthogonal frequency division multiplexing (OFDM) technique has been widely adopted to support high-quality communications, it also has great potentials of improving radar sensing performance and providing flexible S&C. In this paper, we propose to jointly design the dual-functional transmit signals occupying several subcarriers to realize multi-user OFDM communications and detect one moving target in the presence of clutter. Meanwhile, the signals in other frequency subcarriers can be optimized in a similar way to perform other tasks. The transmit beamforming and receive filter are jointly optimized to maximize the radar output signal-to-interference-plus-noise ratio (SINR), while satisfying the communication SINR requirement and the power budget. An majorization minimization (MM) method based algorithm is developed to solve the resulting non-convex optimization problem. Numerical results reveal the significant wideband sensing gain brought by jointly designing the transmit signals in different subcarriers, and demonstrate the advantages of our proposed scheme and the effectiveness of the developed algorithm.

Symbol-level precoding (SLP), which converts the harmful multi-user interference (MUI) into beneficial signals, can significantly improve symbol-error-rate (SER) performance in multi-user communication systems. While enjoying symbolic gain, however, the complicated non-linear symbol-by-symbol precoder design suffers high computational complexity exponential with the number of users, which is unaffordable in realistic systems. In this paper, we propose a novel low-complexity grouped SLP (G-SLP) approach and develop efficient design algorithms for typical max-min fairness and power minimization problems. In particular, after dividing all users into several groups, the precoders for each group are separately designed on a symbol-by-symbol basis by only utilizing the symbol information of the users in that group, in which the intra-group MUI is exploited using the concept of constructive interference (CI) and the inter-group MUI is also effectively suppressed. In order to further reduce the computational complexity, we utilize the Lagrangian dual, Karush-Kuhn-Tucker (KKT) conditions and the majorization-minimization (MM) method to transform the resulting problems into more tractable forms, and develop efficient algorithms for obtaining closed-form solutions to them. Extensive simulation results illustrate that the proposed G-SLP strategy and design algorithms dramatically reduce the computational complexity without causing significant performance loss compared with the traditional SLP schemes.

Symbol-level precoding (SLP), which can convert the harmful multi-user interference (MUI) into beneficial signals, can significantly improve symbol error rate (SER) performance in multi-user communication systems. While enjoying symbolic gain, however, the complicated non-linear symbol-by-symbol SLP design suffers high computational complexity exponential with the number of users, which is unaffordable in realistic systems. In this paper, we propose a novel low-complexity grouped SLP (G-SLP) approach and develop an efficient design algorithm for a typical max-min fairness problem. This practical G-SLP strategy divides all users into several groups. SLP is utilized for the users within each group to convert intra-group MUI into constructive interference, meanwhile the inter-group MUI is also suppressed. In particular, we first use Lagrangian and Karush-Kuhn-Tucker (KKT) conditions to simplify the G-SLP design problem and then propose an iterative majorization-minimization (MM) based algorithm to solve it. Simulation results illustrate that the proposed G-SLP strategy dramatically reduces the computational complexity without causing significant performance loss compared with the traditional SLP scheme.