Joint Environmental Sensing and Data Demodulation Using OFDM Signals

‍ ‍In next-generation wireless communication networks， the Integrated Sensing and Communications （ISAC） are regarded as a paradigm shift for improving resource utilization. ISAC achieves the integration of sensing and communication through spectrum sharing， hardware sharing， and signal sharing， so as to obtain the orientation， distance， speed， and other state information of target objects while conducting the information transmission. Recently， with the development of communication technologies， ISAC holds great potential in many spectrum and cost limited scenarios by realizing dual/multiple functions besides communication， such as autonomous vehicles networking， collaborative sensing， etc. In order to further improve resource utilization and system integration， integrated waveform design and signal processing becomes one of the main tasks of ISAC. As a fundamental process of signal processing， channel estimation is regarded as a basic ISAC process， which is prerequisite for beamforming， precoding and high-quality data transmission， as well as a mathematical basis for environment sensing and parameter estimation. Therefore， accurate channel estimation plays an important role for both data transition and environmental sensing. However， a large number of polit is required in OFDM-based system to estimate the channel， which results in a low spectrum efficiency， especially in dynamic scenarios. In order to solve this problem， an efficient joint channel sensing and data demodulation scheme based on OFDM signals is designed and implemented in this paper. First， an OFDM-based ISAC signal without periodic pilots is designed， after that， an iterative algorithm for joint channel estimation and data detection is proposed， reconstructing the sensing signal using data. Finally， in order to further improve the channel estimation accuracy in high dynamic scenarios， a data-assisted time-varying channel tracking algorithm based on Kalman filtering technique is designed. Simulation results show that the proposed algorithm can improve the performance of channel estimation and data demodulation significantly using only a few iterations. The results also validate the assistance between sensing and communication. Moreover， the maximum effective spectral efficiency of the proposed scheme under certain conditions is also counted by Monte Carlo experiments， and the result shows that the proposed method can improve the efficiency， robustness and utility in high dynamic ISAC scenarios compared with traditional methods. Finally， the paper also verifies the target sensing result in multipath environment through numerical experiments， which shows that the designed integrated signal the algorithm in this paper can improve the communication and sensing performance in ISAC system.