  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, which requires numerous
integrated sensing and communication~(ISAC) devices to sense the targets and
offload the data to the base station~(BS) for further processing. However, as
the number of ISAC devices growing, there exists intensive interactions among
ISAC devices in the processes of data collection and processing since they
share the common network resources. In this paper, we consider the environment
sensing problem in the large-scale mobile crowd ISCC systems 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 utilize the MFG method to transform the influence from
other ISAC devices into the mean field term and derive the
Fokker-Planck-Kolmogorov equation, which models the evolution of the system
state. Then, we derive the cost function based on the mean field term and
reformulate the waveform precoding design problem. Next, we utilize the G-prox
primal-dual hybrid gradient algorithm to solve the reformulated problem and
analyze the computational complexity of the proposed algorithm. Finally,
simulation results demonstrate that the proposed algorithm can solve the
interactions among large-scale ISAC devices effectively in the ISCC process. In
addition, compared with other baselines, the proposed waveform precoding design
algorithm has advantages in improving communication performance and reducing
cost function.
