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

In emergency rescue scenarios, the communication infrastructure is often compromised, and GPS may be denied. Rescue teams employ various Internet of Things (IoT) devices, including unmanned aerial vehicles (UAVs) and ad-hoc network equipment, to ensure communication and positioning. Nevertheless, the scarce resources at the emergency site, along with the limited carrying capacity of UAVs and rescuers, pose challenges in transporting multiple sets of equipment. Therefore, it becomes paramount to develop a portable device that can seamlessly handle both communication and positioning tasks. This study introduces a novel pilot-based dual-function integrated sensing and communication (ISAC) signal, harnessing pilots for channel estimation and ranging. Initially, we provide closed-form expressions that evaluate the performances of communication and ranging within an orthogonal frequency-division multiplexing system. As we confront the complexities of dynamic channel conditions and the evolving needs of communication and ranging during emergency rescue operations, we present an optimization problem for ISAC with weighted considerations. To address this, we devise a particle swarm optimization (PSO)-based time-frequency resources allocation (P-TFRA) algorithm. Additionally, we propose a channel feedback framework that aids periodic optimization. This approach utilizes prior frames to assess the current channel state, guiding the insertion of pilots in subsequent frames. In the final stage, we conduct simulations using the Stanford University Interim channel model to verify the effectiveness of our algorithm in emergency scenarios. The simulations demonstrate that our approach achieves a lower bit error ratio (BER) and average ranging error compared to conventional pilot schemes under the same conditions.