Recently proliferated deep learning-based semantic communications (DLSC) focus on how transmitted symbols efficiently convey a desired meaning to the destination. However, the sensitivity of neural models and the openness of wireless channels cause the DLSC system to be extremely fragile to various malicious attacks. This inspires us to ask a question: “Can we further exploit the advantages of transmission efficiency in wireless semantic communications while also alleviating its security disadvantages?”. Keeping this in mind, we propose SemEntropy, a novel method that answers the above question by exploring the semantics of data for both adaptive transmission and physical layer encryption. Specifically, we first introduce semantic entropy, which indicates the expectation of various semantic scores regarding the transmission goal of the DLSC. Equipped with such semantic entropy, we can dynamically assign informative semantics to Orthogonal Frequency Division Multiplexing (OFDM) subcarriers with better channel conditions in a fine-grained manner. We also use the entropy to guide semantic key generation to safeguard communications over open wireless channels. By doing so, both transmission efficiency and channel security can be simultaneously improved. Extensive experiments over various benchmarks show the effectiveness of the proposed SemEntropy. We discuss the reason why our proposed method benefits secure transmission of DLSC, and also give some interesting findings, e.g., SemEntropy can keep the semantic accuracy remain 95% with 60% less transmission.

Moving beyond just interconnected devices, the increasing interplay between communication and computation has fed the vision of real-time networked control systems. To obtain timely situational awareness, IoT devices continuously sample computation-intensive status updates, generate perception tasks and offload them to edge servers for processing. In this sense, the timeliness of information is considered as one major contextual attribute of status updates. In this paper, we derive the closed-form expressions of timeliness of information for computation offloading at both edge tier and fog tier, where two-stage tandem queues are exploited to abstract the transmission and computation process. Moreover, we exploit the statistical structure of Gauss-Markov process, which is widely adopted to model temporal dynamics of system states, and derive the closed-form expression for process-related timeliness of information. The obtained analytical formulas explicitly characterize the dependency among task generation, transmission and execution, which can serve as objective functions for system optimization. Based on the theoretical results, we formulate a computation offloading optimization problem at edge tier, where the timeliness of status updates is minimized among multiple devices by joint optimization of task generation, bandwidth allocation, and computation resource allocation. An iterative solution procedure is proposed to solve the formulated problem. Numerical results reveal the intertwined relationship among transmission and computation stages, and verify the necessity of factoring in the task generation process for computation offloading strategy design.

Unmanned Aerial Vehicles (UAVs) are deployed as digital eyes in the sky to collect and deliver streams of valuable data about a monitored area. To enable autonomous decision making at UAVs, multi-access edge computing (MEC) technology is employed to combat the limited battery and computational capability at UAVs. By offloading computation-intensive tasks to edge server, UAVs can obtain real-time status updates of the observed area by continuous data sampling and processing. The timeliness of obtained status updates is vital to the perception ability at UAVs, which can be characterized by the concept of age of information. In this article, we investigate an age-aware computation offloading scheme in asynchronous MEC systems, where the task generation time instants are heterogeneous among UAVs. The formulated age minimization problem involves binary task offloading variables and continuous computation resource allocation variables, and falls in the category of a stochastic mixed-integer program. Considering the randomness in sequential task generation at each UAV and the time-varying workload at MEC server, we propose a deep reinforcement learning-based solution framework to directly work on the hybrid discrete-continuous action space in the formulated problem. Simulation results show that our proposed scheme outperforms existing strategies in obtaining status updates in a timely manner.

In this letter, we develop a three-dimensional (3-D) irregular-shaped semi-ellipsoid channel model for massive multiple-input multiple-output (MIMO) vehicle-to-vehicle (V2V) communication in street scattering environments. The geometric model is irregular-shaped due to the scattering environments cut by vertical structures along the roadside. With employing a uniform planar array (UPA), we first characterize the geometrical relationships between transmitter and receiver constrained by different boundaries of scattering regions in detail. Then we derive the closed-form expressions for the probability density functions (PDFs) of the angle of departure (AoD) and joint time of arrival (ToA)/AoD in azimuth and elevation planes. Moreover, we discuss the impacts of the street width and large-scale antenna array on channel statistical properties. Numerical results show that our proposed model can capture characteristics of massive MIMO V2V channels while in close agreement with the prior reported results, thereby demonstrating the efficiency of the proposed model that can provide guidance for system design and performance evaluation of massive MIMO V2V communication in street scattering environments.

This letter provides a novel hierarchical-block channel estimation scheme for massive multiple-input multiple-output (MIMO) systems based on Bayesian learning frameworks. Specifically, considering the spatial non-stationarity of massive MIMO channels introduced by a large-scale antenna array, we first exploit a hierarchical-block prior to characterize the delay-domain sparse channel structure across the antenna array and then develop an iterative Bayesian learning scheme to infer the channel vector as well as its hyperparameters associated with the hierarchical-block prior. Meanwhile, the Cramer-Rao bound (CRB) is derived as a reference line. To evaluate our proposed scheme, we modify the standardized 3GPP channel model with birth-death process to capture the spatial non-stationarity of practical massive MIMO channels. Simulation results demonstrate that our proposed scheme can recover channels effectively and attain a significant performance improvement over comparison methods.

The implementation of joint source-channel coding (JSCC) schemes using deep learning has accelerated the development of semantic communication research. Existing JSCC schemes based on deep learning (DL) are trained on a fixed signal-to-noise ratio (SNR); however, these trained models are not designed for scenarios in which the SNR is dynamic. Therefore, a novel semantic adaptive model for semantic communication—called joint source-channel coding with adaptive models (AMJSCC)—that has a semantic adaptive model selection (SAMS) module is proposed. The joint source-channel encoding (JSCE) model and the joint source-channel decoding (JSCD) model adapt according to both real-time channel conditions and system available computational power resources. Furthermore, residual networks with different layers are investigated to further improve the accuracy of information recovery. Simulation results demonstrate that our model can achieve higher recovery similarity and is more robust and adaptive to the SNR and communication resources. Meanwhile, compared to the state-of-the-art deep JSCC methods, it reduces storage space and communication resource consumption.