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Integrated sensing and communication (ISAC) is a promising technology to improve the band-utilization efficiency via spectrum sharing or hardware sharing between radar and communication systems. Since a common radio resource budget is shared by both functionalities, there exists a tradeoff between the sensing and communication performance. However, this tradeoff curve is currently unknown in ISAC systems with human motion recognition tasks based on deep learning. To fill this gap, this paper formulates and solves a multi-objective optimization problem which simultaneously maximizes the recognition accuracy and the communication data rate. The key ingredient of this new formulation is a nonlinear recognition accuracy model with respect to the wireless resources, where the model is derived from power function regression of the system performance of the deep spectrogram network. To avoid cost-expensive data collection procedures, a primitive-based autoregressive hybrid (PBAH) channel model is developed, which facilitates efficient training and testing dataset generation for human motion recognition in a virtual environment. Extensive results demonstrate that the proposed wireless recognition accuracy and PBAH channel models match the actual experimental data very well. Moreover, it is found that the accuracy-rate region consists of a communication saturation zone, a sensing saturation zone, and a communication-sensing adversarial zone, of which the third zone achieves the desirable balanced performance for ISAC systems.
With the continuous increase of the spectrum and antennas, endogenous sensing is now possible in the fifth generation and future wireless communication systems. However, sensing is a highly complex task for a heterogeneous communication network with
This paper studies the transmit beamforming in a downlink integrated sensing and communication (ISAC) system, where a base station (BS) equipped with a uniform linear array (ULA) sends combined information-bearing and dedicated radar signals to simul
The integrated sensing and communication (ISAC), in which the sensing and communication share the same frequency band and hardware, has emerged as a key technology in future wireless systems. Early works on ISAC have been focused on the design, analy
Realizing edge intelligence consists of sensing, communication, training, and inference stages. Conventionally, the sensing and communication stages are executed sequentially, which results in excessive amount of dataset generation and uploading time
Age-of-information is a novel performance metric in communication systems to indicate the freshness of the latest received data, which has wide applications in monitoring and control scenarios. Another important performance metric in these applicatio