High-Efficiency FCME-Based Noise Power Estimation for Long-Term and Wide-Band Spectrum Measurements
Statistics in terms of spectrum occupancy are useful for efficient and smart dynamic spectrum sharing, and the statistics can be obtained by long-term and wide-band spectrum measurements. In this paper, we investigate noise floor (NF) estimation for energy detection (ED)-based long-term and wide-band spectrum measurements since the NF estimation heavily affects the ED performance and eventually the accuracy of the statistics in terms of spectrum occupancy. Specifically, we address the following NF estimation problems simultaneously for the first time in the spectrum measurement field: (1) slow time-varying property of the NF, (2) frequency dependency of the NF, (3) the NF estimation in the presence of the signal, and (4) the computational cost of the NF estimation. Firstly, we apply Forward consecutive mean excision (FCME) algorithm-based NF estimation to deal with the above three problems ((1), (2) and (3)) successfully. Second, we propose and apply an NF level change detection on top of the FCME algorithm-based NF estimation to deal with the fourth problem. The proposed NF level change detection exploits the slow time-varying property of the NF. Specifically, only if the significant NF level change is detected, the FCME algorithm-based NF estimation is performed to reduce the redundant NF estimations. In numerical evaluations, we show the efficiency and the validity of the NF level change detection for the NF estimation problems, and compare the NF estimation performance with the method without the NF level change detection.