Volume 17, Number 1/2/3
Adaptive Wavelet Filtering for Interference Mitigation in 6G-Oriented IIoTNetworks
Authors
Ndidi Nzeako Anyakora and Cajetan M. Akujuobi, Prairie View A&M University, USA
Abstract
Industrial Internet of Things (IIoT) networks supporting mission-critical control and automation require ultra-reliable, low-latency communications (URLLC). Non-stationary interference, including narrowband tones, impulsive bursts, and chirp-like signals, significantly degrades Quality of Service (QoS) by increasing Block Error Rate (BLER), Hybrid Automatic Repeat Request (HARQ) retransmissions, and worst-case latency. Conventional mitigation techniques based on Fast Fourier Transform (FFT) and Short-Time Fourier Transform (STFT) exhibit limited adaptability and insufficient time-frequency resolution for transient interference. This paper presents a system-level integration and cross-layer evaluation of wavelet-based interference mitigation, integrating the Continuous Wavelet Transform (CWT) for multiscale detection, the Stationary Wavelet Transform (SWT) for shift-invariant suppression, and the Median Absolute Deviation (MAD) for adaptive thresholding. Evaluation is conducted using real 5G New Radio (NR) traces from a Firecell testbed, augmented with heterogeneous synthetic interference. Experimental results demonstrate a 3 dB gain in Effective Signal-to-Noise Ratio (ESNR), a 45% reduction in 99th-percentile latency, a 62% reduction in BLER, and a 39% increase in throughput relative to FFTnotch and STFT-based baselines. These results confirm the practical value of wavelet-domain cross-layer interference mitigation for 6G-oriented IIoT deployments.
Keywords
6G, CWT, IIoT , interference mitigation, MAD, non-stationary interference, SWT, URLLC, wavelet filtering