Deep joint source-channel coding (DeepJSCC) has emerged as a promising paradigm for efficient and robust information transmission. However, its intrinsic characteristics also pose new security challenges, notably an increased vulnerability to eavesdropping attacks. Existing studies on defending against eavesdropping attacks in DeepJSCC, while demonstrating certain effectiveness, often incur considerable computational overhead or introduce performance trade-offs that may adversely affect legitimate users. In this paper, we present DeepGuard, to the best of our knowledge, the first physical-layer defense framework for DeepJSCC against eavesdropping attacks, validated through over-the-air experiments using software-defined radios (SDRs). Considering that existing eavesdropping attacks against DeepJSCC are limited to simulation under ideal channels, we take a step further by identifying and implementing four representative types of attacks under various configurations in orthogonal frequency-division multiplexing systems. These attacks are evaluated over-the-air under diverse scenarios, allowing us to comprehensively characterize the real-world threat landscape. To mitigate these threats, DeepGuard introduces a novel preamble perturbation mechanism that modifies the preamble shared only between legitimate transceivers. To realize it, we first conduct a theoretical analysis of the perturbation’s impact on the signals intercepted by the eavesdropper. Building upon this, we develop an end-to-end perturbation optimization algorithm that significantly degrades eavesdropping performance while preserving reliable communication for legitimate users. We prototype DeepGuard using SDRs and conduct extensive over-the-air experiments in practical scenarios. Extensive experiments demonstrate that DeepGuard effectively mitigates eavesdropping threats while preserving reliable communication for legitimate users. In particular, DeepGuard can reduce the eavesdropper’s reconstruction performance by as much as 29 dB in PSNR and decrease classification accuracy by up to 91% compared with the performance achieved by the legitimate user.

Benefited from the advances of deep learning (DL) techniques, deep joint source-channel coding (JSCC) has shown its great potential to improve the performance of wireless transmission. However, most of the existing works focus on the DL-based transceiver design of the JSCC model, while ignoring the resource allocation problem in wireless systems. In this paper, we consider a downlink resource allocation problem, where a base station (BS) jointly optimizes the compression ratio (CR) and power allocation as well as resource block (RB) assignment of each user according to the latency and performance constraints to maximize the number of users that successfully receive their requested content with desired quality. To solve this problem, we first decompose it into two subproblems without loss of optimality. The first subproblem is to minimize the required transmission power for each user under given RB allocation. We derive the closed-form expression of the optimal transmit power by searching the maximum feasible compression ratio. The second one aims at maximizing the number of supported users through optimal user-RB pairing, which is solved by utilizing bisection search as well as interior-point algorithm. To reduce the computational complexity, we propose a heuristic greedy algorithm to obtain a simplified problem. Then the Bregman alternating direction method of multipliers (BADMM) based algorithm is adopted to decompose the simplified problem into several subproblems that can be computed in parallel. Simulation results validate the effectiveness of the proposed resource allocation methods in terms of the number of satisfied users with given resources. It is also shown that the BADMM-based algorithm can significantly reduce the computational complexity and retain high performance.

The next-generation wireless network is envisioned to support real-time broadband communication (RTBC) to provision services for immersive applications. Such applications (e.g., virtual reality, VR) usually need to simultaneously transmit multi-modal (e.g., visual, audio and haptic) data streams that have different traffic characteristics and transmission requirements, within multiple transmission time intervals (TTIs). In this paper, we formulate an optimization problem of multi-user multiple-input multiple-output (MIMO) precoding within multiple TTIs for multi-modal data transmission. As it is hard to find an optimal solution, we first resort to a novel soft actor-critic (SAC)-based learning approach. Specifically, a lightweight reinforcement learning architecture is employed to learn the adaptive priority weight of each user within multiple TTIs by taking into account its remaining multi-modal data amount and dynamic interaction state. The learned priority weights are then input to an iterative weighted minimum mean-square error (WMMSE) algorithm to adjust the precoder matrix and user transmission rates. With a scalable state design, the proposed algorithm can be tailored to different numbers of potential or active users. We also provide another practical solution to the formulated multi-TTI precoding problem, which transforms the problem into a single-TTI optimization problem by adding the quality-of-service (QoS) constraints into the traditional WMMSE problem and then solves it using the alternating direction method of multipliers (ADMM). Simulation results demonstrate the robustness and efficiency of the proposed algorithms, which show that the SAC-based precoding algorithm can achieve a 50.0% increment in system capacity compared to traditional WMMSE and a significant reduction in time complexity compared to the QoS-constrained WMMSE algorithm.

Precoding design for the downlink of multiuser multiple-input multiple-output (MU-MIMO) systems is a fundamental problem. In this paper, we aim to maximize the weighted sum rate (WSR) while considering both quality-of-service (QoS) constraints of each user and per-antenna power constraints (PAPCs) in the downlink MU-MIMO system. To solve the problem, we reformulate the original problem to an equivalent problem by using the well-known weighted minimal mean square error (WMMSE) framework, which can be tackled by iteratively solving three subproblems. Since the precoding matrices are coupled among the QoS constraints and PAPCs, we adopt alternating direction method of multipliers (ADMM) to obtain a distributed solution. Simulation results validate the effectiveness of the proposed algorithm.

As three-dimensional (3D) data acquisition devices become increasingly prevalent, the demand for 3D point cloud transmission is growing. In this study, we introduce a semanticaware communication system for robust point cloud classification that capitalizes on the advantages of pre-trained Point-BERT models. Our proposed method comprises four main components: the semantic encoder, channel encoder, channel decoder, and semantic decoder. By employing a two-stage training strategy, our system facilitates efficient and adaptable learning tailored to the specific classification tasks. The results show that the proposed system achieves classification accuracy of over 89% when SNR is higher than 10 dB and still maintains accuracy above 66.6% even at SNR of 4 dB. Compared to the existing method, our approach performs at 0.8% to 48% better across different SNR values, demonstrating robustness to channel noise. Our system also achieves a balance between accuracy and speed, being computationally efficient while maintaining high classification performance under noisy channel conditions. This adaptable and resilient approach holds considerable promise for a wide array of 3D scene understanding applications, effectively addressing the challenges posed by channel noise.

The 5.5th Generation is envisioned to support the Real-time Broadband Communication (RTBC) scenarios, which needs to satisfy the enhanced Mobile Broadband(eMBB) and Ultra-Reliable Low-Latency Communication (uRLLC) services requirements simultaneously. As an essential technique to improve the capacity of systems, pre coding in RTBC faces the challenge of finding the optimal solution over long-term transmission with multimodal streams for the system. To meet these requirements, we propose a soft actor-critic (SAC) based multiple transmission time interval (TTl) intelligent precoding algorithm that optimizes the multi-user precoding scheme by learning the priority weight of each user in the iterative weighted minimum mean-square error (MMSE) algorithm. Considering the remaining multimodal data and dynamic activation state of real-time interaction users, we design a novel and lightweight reinforcement learning architecture scalable to different numbers of potential or active users. Simulation results demonstrate the robustness and superiority of our precoding algorithm, which achieves 50% performance improvement in system capacity compared to the weighted MMSE algorithm.

Benefited from the advances of deep learning (DL) techniques, deep joint source-channel coding (JSCC) has shown its great potential to improve the performance of wireless transmission. However, most of the existing works focus on the DL-based transceiver design of the JSCC model, while ignoring the resource allocation problem in wireless systems. In this paper, we consider a downlink resource allocation problem, where a base station (BS) jointly optimizes the compression ratio (CR) and power allocation as well as resource block (RB) assignment of each user according to the latency and performance constraints to maximize the number of users that successfully receive their requested content with desired quality. To solve this problem, we first decompose it into two subproblems without loss of optimality. The first subproblem is to minimize the required transmission power for each user under given RB allocation. We derive the closed-form expression of the optimal transmit power by searching the maximum feasible compression ratio. The second one aims at maximizing the number of supported users through optimal user-RB pairing, which we solve by utilizing bisection search as well as Karmarkar's algorithm. Simulation results validate the effectiveness of the proposed resource allocation method in terms of the number of satisfied users with given resources.