Robust Wideband Spectrum Sensing under Interferers
Wideband spectrum sensing significantly reduces the sensing time of detecting unoccupied frequency bands. As a result, the throughput of secondary user network increases. However, the appearance of interferers (detrimentally strong signals) severely degrades wideband spectrum sensing performance of current cognitive radio (CR) prototypes. Reasons of such degradation includes intermodulation (IMD) interference and power leakage in power spectrum density (PSD) estimation.
In this project, we aim to design a CR receiver architecture and algorithm for wideband spectrum sensing with interferers. The following two techniques are under investigation for robust sensing: (1) Interferers identification that quickly identifies dominant interferers in a wideband spectrum and feedforwards interferers’ frequencies to other system modules, i.e., reconfigurable RF notch filters and DSP, in order to suppress interferers’ impacts. The technique requires low ADC sampling rate, and it features low computational complexity. The saving comes from utilizing a-priori knowledge of interferers’ bands and the sparsity of dominant interferers. (2) Adaptive IMD compensation: once the dominant interferers have been identified, our system decodes these signals and digitally reconstructs IMD interference. An algorithm is developed to adaptively compute the optimal compensation weight and mitigate IMD from distorted signal. Simulation results demonstrate a significant improvement in spectrum sensing performance with IMD compensation.
Staff
- Primary Investigators: Danijela Čabrić
- Students: Han Yan
Selected publications
- H. Yan and D. Cabric, "Digitally Enhanced Inter-modulation Distortion Compensation in Wideband Spectrum Sensing", Asilomar Conference on Signals, Systems, and Computers, Nov. 2016.
