Integrated Sensing and Communication from Learning Perspective: An SDP3 Approach

Abstract

Integrated sensing and communication (ISAC) is a promising technology to improve the bandutilization 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 This work was supported by the SUSTech Huawei Cooperation Project under Grant OR1912016.

SPARCS: A Sparse Recovery Approach for Integrated Communication and Human Sensing in mmWave Systems

“…Many of those target the joint communication and sensing waveform design [19] and are mostly oriented to automotive applications to measure distance and velocity of nearby vehicles. In contrast, few works focus on human sensing [17], which is the aim of the present work. All of the above approaches alternate communication and sensing phases according to a time-division scheme, causing significant overhead and channel occupation.…”

“…Integrated sensing and communication. A number of technical works address ISAC systems in next generation 5G/6G cellular networks [17,18] and WLANs [23,26]. Many of those target the joint communication and sensing waveform design [19] and are mostly oriented to automotive applications to measure distance and velocity of nearby vehicles.…”

“…However, ISAC system designs are still very limited, focusing on joint communication and sensing waveform design [19], which would require significant modifications to existing communication protocols and a reduction in the achievable communication data rates. Other approaches [17,18,23,39] need to alternate communication and sensing phases according to a time-division scheme, where regularly spaced, radar-like transmissions are performed during dedicated sensing periods. This is needed to perceive the fine-grained 𝜇D effect of human motion, for which dense and regular sampling of the Channel Impulse Response (CIR) is required, causing significant overhead and channel occupation.…”

The Degrees-of-Freedom in Monostatic ISAC Channels: NLoS Exploitation vs. Reduction

“…In this spirit, novel resource allocation and waveform design approaches were developed [12] for approaching the ISAC performance bounds, leading to attractive integration and coordination gains [3]. More recently, numerous design tradeoffs have been revealed [2], ranging from information theoretical tradeoffs to physical layer tradeoffs [4], and to cross-layer designs [5].…”