Quadratic Auto-Step Least Mean Square Equalization for High-Data-Rate IR-UWB Wireless Communication Systems

• Published in: IEEE access Vol. 10; pp. 53909 - 53917
• Main Authors: Wang, Gang, Lin, Min, Liu, Qianyun
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive algorithms; adaptive equalization; Algorithms; auto-step least mean square (A-LMS); Bit error rate; Convergence; Equalization; Error correction; Error detection; Indoor environments; Intersymbol interference (ISI); Learning curves; Machine learning; Mean square values; Noise threshold; Nonstationary environments; quadratic; Quadratic equations; Robustness; Signal to noise ratio; Steady-state; Time-domain analysis; Training; Ultra wideband technology; ultra-wideband (UWB); Ultrawideband; Wireless communication systems; Wireless communications
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3176454

High-data-rate impulse radio ultra-wideband (IR-UWB) wireless communication system suffers from serious intersymbol interference (ISI) issues in an indoor multipath environment. This paper proposes an auto-step least mean square time-domain equalization algorithm based on quadratic function (QA-LMS), which outplays the overall convergence speed, steady-state error, signal-to-noise ratio (SNR) threshold and robustness compared to the state of the art relevant to the adaptive step-size algorithms. The proposed algorithm does not need to preset the parameters according to channel conditions. The algorithm converges absolutely fast in the mean square error (MSE) learning curve. To meet the bit error rate (BER) of forward error correction (FEC) code, the algorithm improves the SNR threshold of traditional auto-step LMS (A-LMS) by 4.4 dB in CM1 and 3.6 dB in CM3 of 802.15.3a channel model respectively. The proposed algorithm is stable and robust in nonstationary environment with the advantage of vectorial step-size that based on the gradient of estimated error and high step detection.