Integrated sensing and communication (ISAC) technique has been viewed as a promising component in future network. A major challenge for ISAC systems is that the constraint introduced by sensing functionality will constrain the degrees of freedom in waveform design and results in large multi-user interference (MUI), thus degrading the communication performance. In this paper, we study the employment of RIS in mitigating MUI in ISAC systems. For practical consideration, we investigate joint constant-modulus waveform and discrete RIS phase shift design, with the aim of minimizing MUI under the Cramer-Rao bound (CRB) constraint for direction of arrival (DOA) estimation. An alternating optimization algorithm is proposed to solve the formulated problem, and two schemes are proposed to deal with the discrete RIS phase shifts. Simulation results show that the proposed algorithm can dramatically improve the sum rate, and the performance under moderate phase shift quantization level is close to that under continuous phase shifts.

This letter proposes a sensing-assisted uplink communications framework between a single-antenna user and a full-duplex (FD) base station (BS) against an aerial eavesdropper (AE). To protect the information from being overheard, the BS transmits radar signals to localize and jam AE while receiving uplink signals. The radar signal transmission is divided into detection phase and tracking phase. In detection phase, the BS synthesizes a wide beam to localize the AE under the secrecy rate constraint; while in tracking phase, the BS maximizes the signal-to-interference-plus-noise ratio (SINR) of its received signals under the AE’s SINR constraint while guaranteeing a predefined radar echo signal signal-to-noise ratio (SNR) level. To deal with the self interference, we jointly optimize the radar waveform and receive beamforming vector. An alternating optimization algorithm and a successive convex approximation (SCA) based algorithm are proposed to solve the two formulated problems, respectively. Simulation results verify the effectiveness of the proposed algorithms. They also show that the secrecy rate can be significantly improved with the assistance of BS sensing.

Vehicle-to-infrastructure (V2I) communications have been regarded as an emerging application in next-generation wireless networks. However, guaranteeing high-quality wireless communications in high-mobility scenarios remains a major challenge. In this paper, we investigate the deployment of reconfigurable intelligent surface (RIS) for improving the communication performance of V2I systems. In particular, integrated sensing and communication (ISAC) signals are exploited to facilitate sensing-assisted beamforming. Aiming at maximizing the achievable rate, two deep learning-based predictive beamforming mechanisms are proposed. First, a two-stage beamforming design is devised, where the channel state information (CSI) is estimated based on the echo signals and predicted by a dedicated neural network for time-varying channels. Then, the transmit beamforming vector at the base station (BS) and the reflect beamforming matrix at the RIS are jointly optimized. To further reduce the computational complexities, we develop an end-to-end beamforming design by employing the parameter sharing mechanism and weighted loss function. Simulation results demonstrate that the proposed algorithms can achieve an outstanding data rate that approaches the upper bound exploiting perfect CSI. In particular, the end-to-end design exhibits remarkable robustness against the impact of noise and achieves outstanding sensing-assisted beamforming performance, especially at the low signal-to-noise ratio region.

When applying integrated sensing and communications (ISAC) in future mobile networks, many sensing tasks have low latency requirements, preferably being implemented at terminals. However, terminals often have limited computing capabilities and energy supply. In this paper, we investigate the effectiveness of leveraging the advanced computing capabilities of mobile edge computing (MEC) servers and the cloud server to address the sensing tasks of ISAC terminals. Specifically, we propose a novel three-tier integrated sensing, communication, and computing (ISCC) framework composed of one cloud server, multiple MEC servers, and multiple terminals, where the terminals can optionally offload sensing data to the MEC server or the cloud server. The offload message is sent via the ISAC waveform, whose echo is used for sensing. We jointly optimize the computation offloading and beamforming strategies to minimize the average execution latency while satisfying sensing requirements. In particular, we propose a low-complexity distributed algorithm to solve the problem. Firstly, we use the alternating direction method of multipliers (ADMM) and derive the closed-form solution for offloading decision variables. Subsequently, we convert the beamforming optimization sub-problem into a weighted minimum mean-square error (WMMSE) problem and propose a fractional programming based algorithm. Numerical results demonstrate that the proposed ISCC framework and distributed algorithm significantly reduce the execution latency and the energy consumption of sensing tasks at a lower computational complexity compared to existing schemes. Keywords: Integrated sensing and communication, mobile edge computing, alternating direction method of multipliers, computation offloading, beamforming design.

Deep learning-based semantic communication is an emerging communication method that achieves cooperative transmission between source and channel. The primary objectives of semantic communication are to enhance the efficiency of information transmission and ensure the accurate restoration of semantic content. Recent studies have shown that semantic communication performs well in enhancing transmission rates, especially in low-signal-to-noise ratio environments. However, existing speech semantic communication methods neglect to account for speech perception at the receiver and the complexity of the method, which limits the practical implementation of semantic communication methods. In this article, we propose a perceptually motivated, low-complexity speech semantic communication method. Specifically, we employ an end-to-end communication approach to transmit the source speech and obtain the reconstructed speech at the receiver. To ensure the accurate extraction of semantic information, we present a low-complexity fully convolutional semantic encoder, which increases the accuracy of semantic information extraction and improves transmission efficiency. Considering the sensitivity of human perception, a multiresolution joint loss function has been implemented to enhance the model’s performance and guarantee that the reconstructed speech aligns with the human ear’s auditory perception. Experimental results show that the proposed method performs better on objective and subjective metrics than existing speech transmission methods. Compared with existing neural semantic transmission methods, we improve the transmission efficiency, and the number of symbols needed for transmission is decreased by 60% without compromising the quality of speech. Furthermore, the proposed semantic communication method has a lower complexity and consumes less time to transmit.

This paper presents a novel two-stage method for constructing channel knowledge maps (CKMs) specifically for A2G (Aerial-to-Ground) channels in the presence of non-cooperative interfering nodes (INs). We first estimate the interfering signal strength (ISS) at sampling locations based on total received signal strength measurements and the desired communication signal strength (DSS) map constructed with environmental topology. Next, an ISS map construction network (IMNet) is proposed, where a negative value correction module is included to enable precise reconstruction. Subsequently, we further execute signal-to-interference-plus-noise ratio map construction and IN localization. Simulation results demonstrate lower construction error of the proposed IMNet compared to baselines in the presence of interference.