Semantic communication has been introduced into collaborative perception systems for autonomous driving, offering a promising approach to enhancing data transmission efficiency and robustness. Despite its potential, existing semantic communication approaches predominantly rely on analog transmission models, rendering these systems fundamentally incompatible with the digital architecture of modern vehicle-to-everything (V2X) networks and posing a significant barrier to real-world deployment. To bridge this critical gap, we propose CoDS, a novel collaborative perception framework based on digital semantic communication, designed to realize semantic-level transmission efficiency within practical digital communication systems. Specifically, we develop a semantic compression codec that extracts and compresses task-oriented semantic features while preserving downstream perception accuracy. Building on this, we propose a novel semantic analog-to-digital converter that converts these continuous semantic features into a discrete bitstream, ensuring integration with existing digital communication pipelines. Furthermore, we develop an uncertainty-aware network (UAN) that assesses the reliability of each received feature and discards those corrupted by decoding failures, thereby mitigating the cliff effect of conventional channel coding schemes under low signal-to-noise ratio (SNR) conditions. Extensive experiments demonstrate that CoDS significantly outperforms existing semantic communication and traditional digital communication schemes, achieving state-of-the-art perception performance while ensuring compatibility with practical digital V2X systems.

In the realm of cognitive radio (CR), the soft and hard combinations are commonly applied for the fusion rule of cooperative spectrum sensing (CSS) to detect the primary user (PU) signal for available vacant spectrum resources and allow cooperative secondary users (SUs) to opportunistically access the channel without harmful interference to the PU’s normal communication. However, in the process of submitting the sensing information to the fusion center (FC), the soft combination requires great communication overhead from the SU to the FC, and the hard combination reports only one bit of the local decision resulting in unsatisfactory detection performance. In view of this, in this paper we make an in-depth investigation on the quantization of raw measurement data and propose a quantization-based multi-bit combination method that utilizes the information of each sampling point. On the basis of the proposed multi-bit combination approach, each SU quantifies the information obtained from the local sensing information and transmits the quantified sensing data in a few bits to the FC. Furthermore, we formulate an optimization problem for the error probability of the multi-bit combination to achieve the optimal CSS performance. Finally, numerical simulation results confirm the effectiveness and robustness of the proposed multi-bit combination method, establishing its superiority in various environments, in terms of the overall error probability.

As a promising paradigm for the sixth-generation (6G) networks, task-oriented semantic communication significantly enhances transmission efficiency. However, it faces complex security challenges, particularly the risk of eavesdropping due to the open nature of wireless channels. To address this issue, we propose a secure semantic communication framework that integrates physical-layer secure beamforming (SBF) to safeguard semantic information from eavesdropping. Specifically, we design an SBF network to generate SBF vectors that focus signal beams on legitimate users to enhance signal power while directing designed artificial noise toward potential eavesdroppers to strengthen jamming. To further improve system adaptability across varying channel conditions, we introduce attention-based channel-aware modules that dynamically optimize the encoding, decoding, and beamforming processes based on perceived channel state information. Furthermore, task-oriented artificial noise is employed to degrade the task performance of eavesdroppers more effectively. Finally, a stepwise training strategy with task-specific loss functions is employed to jointly optimize the SBF and semantic modules, maximizing the performance gap in downstream tasks between legitimate users and eavesdroppers. The simulation results demonstrate that the proposed approach effectively maintains task performance for legitimate users while significantly suppressing eavesdroppers, outperforming conventional methods.

In order to solve the shortage of unmanned aerial vehicles (UAVs) spectrum resources, cooperative spectrum sensing (CSS) is applied to identify the underutilized spectrums in cognitive UAV networks (CUAVNs). However, the UAV’s location flexibility makes the detection performance unstable, especially under the fixed sensing delay constraint, and limits or even damages the available spectrum resources, resulting in a significant degradation of spectrum efficiency (SE) and energy efficiency (EE) as well as the increase of energy consumption (EC). In view of this, we develop a CSS framework among minisensing slots in a periodic spectrum sensing in this paper. Further, we are motivated by the sequential detection to take the flexible sensing delay constraint into consideration, with aim of making in-depth analysis on various CSS scenarios for CUAVNs. Concurrently, in order to ensure the performance of CSS, the false alarm probability is used as a limiting condition to change the sensing delay constraint. Finally, simulation results corroborate the correctness and effectiveness of our theoretical analysis and show that the flexible sensing delay constraint is better than the fixed sensing delay constraint, in terms of SE, EE and EC.

Cooperative spectrum sensing (CSS) is the pivotal function of cognitive radio (CR) to prevent harmful interference with primary users (PUs) and identify the available spectrum for improving the spectrum's utilization. However, it opens an opportunity for Byzantine attackers to exploit the decision-making process by sending false sensing decisions, thereby causing degradation of the cognitive radio network (CRN) quality of service but currently there are no specific security protocols for it. For this aim, we formulate a dynamic Byzantine attack model to characterize attack behaviors of the malicious user (MU) in this paper. Based on this dynamic attack model, it is expected that the fusion center (FC) benefit from Byzantine attack to contribute to the CSS performance. To this end, the FC needs to have certain knowledge of attack strategies, then we propose an estimation algorithm for the FC to estimate the MU's attack parameters. Furthermore, the reputation value (RV) is introduced to evaluate the impact of Byzantine attack exploitation on the CSS performance and analyze the conditions of Byzantine attack exploitation. Finally, simulation results show the effectiveness and correctness of Byzantine attack exploitation.