Waveform Precoding Design for Mobile Crowd ISCC System Using Mean Field Game

Data collection and processing timely is crucial for mobile crowd integrated sensing, communication, and computation (ISCC) systems with various applications such as smart home and connected cars. However, as the number of integrated sensing and communication (ISAC) devices grows, there exist intensive interactions among ISAC devices in the processes of data collection and processing. In this paper, we consider the environment sensing problem in the large-scale mobile crowd ISCC system and propose an efficient waveform precoding design algorithm based on the mean field game (MFG). Specifically, to handle the complex interactions among large-scale ISAC devices, we first employ the MFG method to transform the influence from other ISAC devices into the mean field term. Then, we derive the cost function based on the mean field term and reformulate the waveform precoding design problem based on the MFG. Next, we utilize the G-prox primal-dual hybrid gradient algorithm to solve the reformulated problem. Finally, numerical results demonstrate that the proposed algorithm converges quickly and can reduce energy consumption by about 10% compared with the existing algorithms.