Device-to-device (D2D) communications underlying cellular networks enabled by unmanned aerial vehicles (UAV) have been regarded as promising techniques for next-generation communications. To mitigate the strong interference caused by the line-of-sight (LoS) air-to-ground channels, we deploy a reconfigurable intelligent surface (RIS) to rebuild the wireless channels. A joint optimization problem of the transmit power of UAV, the transmit power of D2D users and the RIS phase configuration are investigated to maximize the achievable rate of D2D users while satisfying the quality of service (QoS) requirement of cellular users. Due to the high channel dynamics and the coupling among cellular users, the RIS, and the D2D users, it is challenging to find a proper solution. Thus, a RIS softmax deep double deterministic (RIS-SD3) policy gradient method is proposed, which can smooth the optimization space as well as reduce the number of local optimizations. Specifically, the SD3 algorithm maximizes the reward of the agent by training the agent to maximize the value function after the softmax operator is introduced. Simulation results show that the proposed RIS-SD3 algorithm can significantly improve the rate of the D2D users while controlling the interference to the cellular user. Moreover, the proposed RIS-SD3 algorithm has better robustness than the twin delayed deep deterministic (TD3) policy gradient algorithm in a dynamic environment.

The structure of key-value data is a typical data structure generated by mobile devices. The collection and analysis of the data from mobile devices are critical for service providers to improve service quality. Nevertheless, collecting raw data, which may contain various personal information, would lead to serious personal privacy leaks. Local differential privacy (LDP) has been proposed to protect privacy on the device side so that the server cannot obtain the raw data. However, existing mechanisms assume that all keys are equally sensitive, which cannot produce high-precision statistical results. A utility-improved data collection framework with LDP for key-value formed mobile data is proposed to solve this issue. More specifically, we divide the key-value data into sensitive and non-sensitive parts and only provide an LDP-equivalent privacy guarantee for sensitive keys and all values. We instantiate our framework by using a utility-improved key value-unary encoding (UKV-UE) mechanism based on unary encoding, with which our framework can work effectively for a large key domain. We then validate our mechanism which provides better utility and is suitable for mobile devices by evaluating it in two real datasets. Finally, some possible future research directions are envisioned.

Audio Video Coding Standard (AVS) is a second-generation source coding standard and the first standard for audio and video coding in China with independent intellectual property rights. Its performance has reached the international standard. Its coding efficiency is 2 to 3 times greater than that of MPEG-2. This technical solution is more simple, and it can greatly save channel resource. After more than ten years’ development, AVS has achieved great success. The latest version of the AVS audio coding standard is ongoing and mainly aims at the increasing demand for low bitrate and high quality audio services. The paper reviews the history and recent develop-ment of AVS audio coding standard in terms of basic features, key techniques and performance. Finally, the future development of AVS audio coding standard is discussed.