A Sparsity Adaptive Algorithm to Recover NB-IoT Signal from Legacy LTE Interference

TL;DR

This paper presents a sparsity adaptive algorithm combining K-means clustering and SAMP to effectively recover NB-IoT signals amidst LTE interference, crucial for coexistence in 5G networks.

Contribution

It introduces a novel sparsity adaptive algorithm that leverages clustering and iterative support refinement for NB-IoT signal recovery from LTE interference.

Findings

High recovery probability demonstrated in simulations

Significant improvement in bit error rate

Effective support estimation and refinement

Abstract

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.