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Due to the flexibility of drones, it is promising to use them in Search and Rescue (SAR) operations for intelligently searching for lost humans over large areas. However, the limited computing resources of drones pose challenges when deploying deep neural network models. To address this problem, we design a lightweight human recognition model for drones, combining the Ghost Module and the Mobilenetv3 Block. The Ghost Module generates more feature maps with fewer parameters, and the squeeze-and-excitation (SE) attention module in the Mobilenetv3 Block greatly improves recognition accuracy. Furthermore, recognizing that relying solely on the human recognition model offers limited assistance in SAR operations, we propose a collaborative recognition mode between the drone and the rescue command center (RCC). In this collaborative recognition mode, we design an offloading model for deployment on the drone. The offloading model learns from the middle layer perception features of the lightweight recognition model and selectively offloads the vision taken by the drone to the RCC. The recognition results provided by the RCC are used to update the parameters of the offloading model. We introduce a reinforcement learning algorithm, a dual-buffer-based proximal policy optimization algorithm (DBPPO), to train the offloading model with the goal of maximizing accuracy and recall while minimizing the offloading ratio. Specifically, we incorporate an additional data buffer for training the Actor network in the PPO algorithm in a supervised manner, with supervised training interspersed throughout the PPO training process. Eventually, experiments comparing different methods demonstrate the effectiveness of the lightweight recognition model and the collaborative recognition mode in SAR operations.

Integrated sensing and communication (ISAC) is regarded as one of the key technologies for future systems due to its high spectrum efficiency and low hardware cost. However, high integration of sensing and communication also brings abundant challenges, e.g., waveform design, signal processing and fundamental performance limits, etc. In this work, we focus on the spatial-temporal delay analysis of sensory data transmission in ISAC networks. Specifically, the spatial distribution of sensing infrastructures (SIs) and fusion centers (FCs) are modeled by Matern cluster process. The Laplace transform of the interference experienced by SIs and FCs is quantificated and the service capability of sensory data transmission is obtained. With the sensory data flow characterized by successful sensing probability, the upper bound of network-layer queuing delay is analyzed via stochastic network calculus. Furthermore, we propose an optimal power allocation strategy to minimize the transmission delay, which balances sensing quality and transmission latency.

In this article, we study the joint platoon partition, power control and spectrum allocation for platoon communication in cellular vehicle-to-everything (V2X) networks with uncertain channel parameters. A distributionally robust (DR) optimization problem is formulated to maximize the vehicle-to-infrastructure (V2I) capacity whilst guaranteeing the reliability of intra-platoon communication. For solving the DR problem, a statistical-based approach is developed to learn the distributional ambiguity set from historical uncertain channels. Then, based on it, a data-driven equivalent transformation approach is proposed to transform the probabilistic vehicle-to-vehicle (V2V) reliability requirement into a deterministic semidefinite expression. Considering that the resource allocation is built on the platoon partition result, the formulated problem is decomposed into a resource allocation problem and a platoon partition problem. Then, a low-complexity two-stage wireless resource allocation strategy is proposed for V2X networks, which contains power control and spectrum matching. After that, the platoon partition is transformed into a specific Markov chain design problem. An exploration-consolidation learning algorithm is proposed to obtain the optimal platoon partition according to the designed Markov transition rate. Finally, simulation results verify that the proposed algorithm is superior to other existing schemes.