SOC Algorithm-Based Framework for Spectrum Occupancy Completion

This paper investigates the completion of continuous spectrum occupancy in space-frequency domain. Most of the existing studies adopt the traditional scheme, namely field strength completion followed by feature extraction. However, the existence of non-ideal sampling data challenges the recovery of spectrum occupancy array in accuracy. In this paper, we first develop a spectrum occupancy completion framework that implements feature extraction and spectrum occupancy completion sequentially, which reduces the impact of non-ideal data on completion. Then, to further improve the completion accuracy, a semantic-optional-based completion (SOC) algorithm is proposed, which divides the continuous spectrum occupancy data into discrete and multi-class spectrum semantic data according to the spectrum occupancy level. Specifically, the completion of spectrum occupancy is realized by specific semantic extraction, spectrum semantic completion and spectrum semantic recovery. Further, the real-world measured data are simulated to demonstrate the effectiveness of our proposed framework and SOC. The experimental results show that, compared with the low-rank completion algorithm and spatial interpolation algorithm, the proposed SOC has higher completion accuracy at more than 93% frequency points.