Image Analysis Oriented Integrated Sensing and Communication via Intelligent Reflecting Surface

Integrated sensing and communication (ISAC) provides a promising paradigm for future beyond 5G (B5G) and 6G networks. As an important application of edge intelligence, image analysis (e.g., recognition) at the edge networks has attracted lots of interests. In this paper, we propose an image analysis oriented ISAC, in which the image captured by a wireless image-sensor is transmitted to an edge server for analysis in parallel with the radar sensing. The key challenge of our considered system lies in that the mutual interference between the transmission of the image data and radar sensing degrades both performances of the image analysis and radar sensing. To address this difficulty, we exploit intelligent reflecting surface (IRS) to mitigate the interference. Specifically, taking IRS into consideration, we characterize the radar estimation information rate as the performance metric of the radar sensing under the impact of the offloading transmission of the image data, and then formulate a joint optimization problem of the IRS phase shift, the image resolution and the transmit-power of image-sensor, with the objective of maximizing a system-wise performance that accounts for both the radar estimation information rate and the image analysis accuracy. To solve this problem, we leverage the block coordinate descent to separate the variables into two subgroups. For the subgroup of the image resolution and the transmit-power of image-sensor, we derive their closed-form solutions. For the subgroup of the IRS phase shift, we take the equivalent transformation and propose a two-tier successive convex optimization (SCA) based algorithm to obtain the solution. Simulation results demonstrate the advantage of leveraging IRS for the image analysis oriented ISAC and the effectiveness of our proposed algorithm.