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In this paper, we propose a multi-roadside unit (RSU) assisted mobile crowd computing framework for intelligently connected vehicle (ICV) networks, where vehicles within RSUs’ coverage act as workers to provide their computation and communication resources for computing resource limited vehicle user equipments (VUEs). Physical (PHY) layer security is used to secure computation task offloading and results feedback in time-varying vehicular channels. Artificial noise (AN) assisted adaptive wiretap coding is adopted to enhance the security of offloading links. With PHY security, the intended receiver can decode secret message while eavesdropper cannot. A modified exhaustive two-dimensional (2D) search algorithm is proposed to optimize transmission rate and secrecy rate in an effective secrecy throughput maximization problem, and a multi-agent twin delayed deep deterministic policy gradient algorithm (MATD3) is utilized to assign VUEs’ tasks without a central controller, where a reward function is defined according to the computing costs, including execution time, energy consumption, and price paid for computing. Finally, simulations verify the effectiveness of the proposed framework.

With the deployment of the fifth generation (5G) in many countries, people start to think about what the next-generation of wireless communications will be. The current communication technologies are already approaching the Shannon physical capacity limit with advanced encoding (decoding) and modulation techniques. On the other hand, artificial intelligence (AI) plays an increasingly important role in the evolution from traditional communication technologies to the future. Semantic communication is one of the emerging communication paradigms, which works based on its innovative “semantic-meaning passing” concept. The core of semantic communication is to extract the “meanings” of sent information at a transmitter, and with the help of a matched knowledge base (KB) between a transmitter and a receiver, the semantic information can be “interpreted” successfully at a receiver. Therefore, semantic communication essentially is a communication scheme based largely on AI. In this article, an overview of the latest deep learning (DL) and end-to-end (E2E) communication based semantic communications will be given and open issues that need to be tackled will be discussed explicitly.

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