To achieve continuous massive data transmission with significantly reduced data payload, the users can adopt semantic communication techniques to compress the redundant information by transmitting semantic features instead. However, current works on semantic communication mainly focus on high compression ratio, neglecting the wireless channel effects including dynamic distortion and multi-user interference, which significantly limit the fidelity of semantic communication. To address this, this paper proposes a diffusion model (DM)-based channel enhancer (DMCE) for improving the performance of multi-user semantic communication, with the DM learning the particular data distribution of channel effects on the transmitted semantic features. In the considered system model, multiple users (such as road cameras) transmit semantic features of multi-source data to a receiver by applying the joint source-channel coding (JSCC) techniques, and the receiver fuses the semantic features from multiple users to complete specific tasks. Then, we propose DMCE to enhance the channel state information (CSI) estimation for improving the restoration of the received semantic features. Finally, the fusion results at the receiver are significantly enhanced, demonstrating a robust performance even under low signal-to-noise ratio (SNR) regimes, enabling the generation of effective object segmentation images. Extensive simulation results with a traffic scenario dataset show that the proposed scheme can improve the mean Intersection over Union (mIoU) by more than 25% at low SNR regimes, compared with the benchmark schemes.

In the Integrated Sensing and Communication (ISAC) system, the pilot signal has high sensing performance due to its good autocorrelation and high transmit power. However, the equally spaced pilot signal reduces the maximum unambiguous range and velocity compared with the OFDM signal with continuous resources, limiting the sensing performance of base station (BS). Additionally, BS fails to estimate the distances and velocities of multiple targets in coherent signals. To address these problems, we propose a pilot design scheme with coprime and periodic stepping values for pilot indices. Theoretical analysis and simulation results demonstrate that the proposed pilot signal does not reduce the maximum unambiguous range and velocity, which can be processed by the smoothing algorithm and the multiple signal classification (MUSIC) algorithm to accurately estimate the distances and velocities of multiple targets in coherent signals.

The utilization of millimeter-wave in vehicle-to-vehicle (V2V) communications can ensure high system capacity. However, in dense high-mobility environment, V2V communications will encounter severe resource collisions and require fre-quent beam training resulting in substantial signaling overhead. To address the above issues, we study the joint optimization problem of beam management and resource allocation in the millimeter-wave V2V communication system. Specifically, we propose a dual-layer deep reinforcement learning (DRL) archi-tecture that combines beam management and resource allocation into two interconnected tasks. Leveraging this dual-layer DRL architecture, we obtain a solution that involves interactive work between a communication module and an adaptive learning module. This approach is able to collect channel state information in real time and adapt to the ever-changing environment sufficiently. Simulation results show that the joint optimization scheme exhibits fast convergence, improves the effective achievable rate, and reduces the signaling overhead.

In this paper, we propose a joint single-base localization and communication enhancement scheme for the uplink (UL) integrated sensing and communications (ISAC) system with asynchronism, which can achieve accurate single-base localization of user equipment (UE) and significantly improve the communication reliability despite the existence of timing offset (TO) due to the clock asynchronism between UE and base station (BS). Our proposed scheme integrates the CSI enhancement into the multiple signal classification (MUSIC)-based AoA estimation and thus imposes no extra complexity on the ISAC system. We further exploit a MUSIC-based range estimation method and prove that it can suppress the time-varying TO-related phase terms. Exploiting the AoA and range estimation of UE, we can estimate the location of UE. Finally, we propose a joint CSI and data signals-based localization scheme that can coherently exploit the data and the CSI signals to improve the AoA and range estimation, which further enhances the single-base localization of UE. The extensive simulation results show that the enhanced CSI can achieve equivalent bit error rate performance to the minimum mean square error (MMSE) CSI estimator. The proposed joint CSI and data signals-based localization scheme can achieve decimeter-level localization accuracy despite the existing clock asynchronism and improve the localization root mean square error (RMSE) by about 6 dB compared with the maximum likelihood esimation (MLE)-based benchmark method.

In this paper, we propose a novel Kalman Filter (KF)-based uplink (UL) joint communication and sensing (JCAS) scheme, which can significantly reduce the range and location estimation errors due to the clock asynchronism between the base station (BS) and user equipment (UE). Clock asynchronism causes time-varying time offset (TO) and carrier frequency offset (CFO), leading to major challenges in uplink sensing. Unlike existing technologies, our scheme does not require knowing the location of the UE in advance, and retains the linearity of the sensing parameter estimation problem. We first estimate the angle-of-arrivals (AoAs) of multipaths and use them to spatially filter the CSI. Then, we propose a KF-based CSI enhancer that exploits the estimation of Doppler with CFO as the prior information to significantly suppress the time-varying noise-like TO terms in spatially filtered CSIs. Subsequently, we can estimate the accurate ranges of UE and the scatterers based on the KF-enhanced CSI. Finally, we identify the UE’s AoA and range estimation and locate UE, then locate the dumb scatterers using the bi-static system. Simulation results validate the proposed scheme. The localization root mean square error of the proposed method is about 20 dB lower than the benchmarking scheme.

Joint communication and sensing (JCS) has become a promising technology for mobile networks because of its higher spectrum and energy efficiency. Up to now, the prevalent fast Fourier transform (FFT)-based sensing method for mobile JCS networks is on-grid based, and the grid interval determines the resolution. Because the mobile network usually has limited consecutive OFDM symbols in a downlink (DL) time slot, the sensing accuracy is restricted by the limited resolution, especially for velocity estimation. In this paper, we propose a multiple signal classification (MUSIC)-based JCS system that can achieve higher sensing accuracy for the angle of arrival, range, and velocity estimation, compared with the traditional FFT-based JCS method. We further propose a JCS channel state information (CSI) enhancement method by leveraging the JCS sensing results. Finally, we derive a theoretical lower bound for sensing mean square error (MSE) by using perturbation analysis. Simulation results show that in terms of the sensing MSE performance, the proposed MUSIC-based JCS outperforms the FFT-based one by more than 20 dB. Moreover, the bit error rate (BER) of communication demodulation using the proposed JCS CSI enhancement method is significantly reduced compared with communication using the originally estimated CSI.

The joint communication and sensing (JCS) system can provide higher spectrum efficiency and load saving for 6G machine-type communication (MTC) applications by merging necessary communication and sensing abilities with unified spectrum and transceivers. In order to suppress the mutual interference between the communication and radar-sensing signals to improve the communication reliability and radar-sensing accuracy, we propose a novel code-division orthogonal frequency-division multiplex (CD-OFDM) JCS MTC system, where MTC users can simultaneously and continuously conduct communication and sensing with each other. We propose a novel CD-OFDM JCS signal and corresponding successive-interference-cancelation-based signal processing technique that obtains code-division multiplex gain, which is compatible with the prevalent orthogonal frequency-division multiplex (OFDM) communication system. To model the unified JCS signal transmission and reception process, we propose a novel unified JCS channel model. Finally, the simulation and numerical results are shown to verify the feasibility of the CD-OFDM JCS MTC system and the error propagation performance. We show that the CD-OFDM JCS MTC system can achieve not only more reliable communication but also comparably robust radar sensing compared with the precedent OFDM JCS system, especially in a low signal-to-interference-and-noise ratio regime.