Search

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.

Considering the inefficient resource allocation (RA) and high quality of service (QoS) requirement in vehicular communications, this paper proposes two dynamic RA algorithms, spectral clustering based greedy (SCGR) algorithm and multi-agent deep reinforcement learning (DRL) algorithm, to maximize both the sum capacity of the vehicle-to-infrastructure (V2I) uplinks and the total energy efficiency (EE) of the vehicle-to-vehicle (V2V) links by assigning the proper power and resource block (RB) to each V2V link in spatial modulation (SM) multiple-input multiple-output (SM-MIMO) vehicular system. For the SCGR algorithm, the spectral clustering (SC) scheme is first utilized to group the V2V links for the suitable RBs. Then, the optimal power is distributed to each V2V link by the greedy (GR) algorithm. For the DRL algorithm, a decentralized model-free network, improved multi-agent deep Q fully connected neural network (IDQFN), is developed to simultaneously find the best power allocation (PA) and RB assignment (RBA). Moreover, the SM technology is exploited to convey the information through the V2I and V2V links and improve the system capacity. Numerical results reveal that the proposed SCGR and IDQFN RA schemes outperform the typical RA algorithms, and the IDQFN scheme achieves better EE than the SCGR scheme, while the SCGR algorithm obtains the optimal average bit error rate (ABER) performance.