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Data collection and processing timely is crucial for mobile crowd integratedsensing, communication, and computation~(ISCC) systems with variousapplications such as smart home and connected cars, which requires numerousintegrated sensing and communication~(ISAC) devices to sense the targets andoffload the data to the base station~(BS) for further processing. However, asthe number of ISAC devices growing, there exists intensive interactions amongISAC devices in the processes of data collection and processing since theyshare the common network resources. In this paper, we consider the environmentsensing problem in the large-scale mobile crowd ISCC systems and propose anefficient waveform precoding design algorithm based on the mean fieldgame~(MFG). Specifically, to handle the complex interactions among large-scaleISAC devices, we first utilize the MFG method to transform the influence fromother ISAC devices into the mean field term and derive theFokker-Planck-Kolmogorov equation, which model the evolution of the systemstate. Then, we derive the cost function based on the mean field term andreformulate the waveform precoding design problem. Next, we utilize the G-proxprimal-dual hybrid gradient algorithm to solve the reformulated problem andanalyze the computational complexity of the proposed algorithm. Finally,simulation results demonstrate that the proposed algorithm can solve theinteractions among large-scale ISAC devices effectively in the ISCC process. Inaddition, compared with other baselines, the proposed waveform precoding designalgorithm has advantages in improving communication performance and reducingcost function.

Mean Field Game-based Waveform Precoding Design for Mobile Crowd Integrated Sensing, Communication, and Computation Systems