2021/10/06 by Yijia Guo, Wenkun Wen, Guo, Yijia +5
Computer Science · Engineering · Mathematics · #FOS: Computer and information sciences #FOS: Electrical engineering #Full-Duplex Wireless Communications #Information Theory (cs.IT) #Power Line Communications and Noise #Signal Processing (eess.SP) #Ultra-Wideband Communications Technology #cs.IT #eess.SP #electronic engineering #information engineering #math.IT
paper · pdf · doi:10.48550/arxiv.2110.02515
5 pages, 7 figures, to appear in IEEE Wireless Communications Letters
arxiv created 2021/10/06 · openalex publication_date 2021/10/06 · arxiv updated 2021/10/07 · openalex created_date 2021/10/11 · openalex updated_date 2026/07/28
As a forerunner in 5G technologies, Narrowband Internet of Things (NB-IoT) will be inevitably coexisting with the legacy Long-Term Evolution (LTE) system. Thus, it is imperative for NB-IoT to mitigate LTE interference. By virtue of the strong temporal correlation of the NB-IoT signal, this letter develops a sparsity adaptive algorithm to recover the NB-IoT signal from legacy LTE interference, by combining K-means clustering and sparsity adaptive matching pursuit (SAMP). In particular, the support of the NB-IoT signal is first estimated coarsely by K-means clustering and SAMP algorithm without sparsity limitation. Then, the estimated support is refined by a repeat mechanism. Simulation results demonstrate the effectiveness of the developed algorithm in terms of recovery probability and bit error rate, compared with competing algorithms.