Pilot Optimization for OFDM-Based ISAC Signal in Emergency IoT Networks

The advanced Internet of Things (IoT) technique has provided a promising vision in emergency response issues with its convenience in environment cognition and communication. However, the resource-poor emergency rescue scenarios hinder the applications of IoT because of the volume constraints of portable devices. Integrated sensing and communication (ISAC) would be an ideal solution, but still faces technical challenges, such as signal integration and environment adaption. To this end, this work resorts to pilot optimization to design a dual-function ISAC signal for sensing and communication. Specifically, we derive the closed-form expressions of channel estimation and ranging performances in the OFDM system and formulate a weighted optimization to minimize the bit error ratio (BER) and average ranging error (ARE) with varying requirements of communication and ranging guaranteed. On this basis, a particle swarm optimization algorithm is developed to solve the problem, and a channel feedback framework is proposed to facilitate implementation. For emergency environment adaption, we conduct simulations with the SUI channel model to validate the efficiency of our algorithm. Simulation results demonstrate that our approach has a lower BER and ARE than typical pilot schemes under the same signal-to-noise ratio (SNR).