• Journal of the Institute of Convergence Signal Processing
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• v.12 no.2
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• pp.128-132
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• 2011

Least mean square adaptive algorithms have played important role in radar, sonar, speech processing, and mobile communication. In mobile communication area, the convergence rate of a LMS algorithm is quite important. However, LMS algorithms have slow and non-uniform convergence rate problem For overcoming these shortcomings, various variable step LMS adaptive algorithms have been studied in recent years. Most of these recent LMS algorithms have used complex variable step methods to get a rapid convergence. But complex variable step methods need a high computational complexity. Therefore, the main merits such as the simplicity and the robustness in a LMS algorithm can be eroded. The proposed deterministic variable step LMS algorithm is based upon a simple deterministic function for the step update so that the simplicity of the proposed algorithm is obtained and the fast convergence is still maintainable.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.2
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• pp.42-46
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• 2018

Adaptive signal processing is quite important in various signal and communication environments. In adaptive signal processing methods since the least mean square(LMS) algorithm is simple and robust, it is used everywhere. As the step is varied in the variable step(VS) LMS algorithm, the fast convergence speed and the small excess mean square error can be obtained. Various variable step LMS algorithms are researched for better performances. But in some of variable step LMS algorithms the computational complexity is quite large for better performances. The fixed step LMS algorithm with a low computational complexity merit and the variable step LMS algorithm with a fast convergence merit are combined in the proposed sporadic step algorithm. As the step is sporadically updated, the performances of the variable step LMS algorithm can be maintained in the low update rate using Fibonacci sequence. The performances of the proposed variable step LMS algorithm are proved in the adaptive equalizer.

• The Journal of the Acoustical Society of Korea
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• v.28 no.3
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• pp.245-250
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• 2009

In communications and signal processing area, a number of least mean square adaptive algorithms have been used because of simplicity and robustness. However the LMS algorithm is known to have slow and non-uniform convergence. Various variable step size LMS adaptive algorithms have been introduced and researched to speed up the convergence rate. A variable step size LMS algorithm using the error difference for updating the step size is proposed. Compared with other algorithms, simulation results show that the proposed LMS algorithm has a fast convergence. The theoretical performance of the proposed algorithm is also analyzed for the steady state.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.12A
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• pp.1836-1842
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• 2000

이 논문은 레일레이 감쇄 채널 환경에서 IMT-2000 파일럿 심볼 구조의 W-CDMA 시스템 역방향 링크의 채널 추정에 관한 LMS 적용형 알고리즘 성능을 WMSA(Weighted Multi-Slot Averaging)(K=1,2,3), 일정 추정이득 (Constant estimation gain) 및 RLS 알고리즘 성능과 비교 분석하였다. 이 논문의 모형은 IMT-2000 3GPP 규격의 W-CDMA 채널 구조, 변조 및 파일럿 패턴을 이용하였다. 파일럿 심볼 위치의 채널추정은 LMS 알고리즘을 이용하고 데이터 심볼 위치의 채널보상은 선형 보간으로 수행하였다. 저속 도플러 주파수에서는 WMSA(K=1,2,3) 성능이 일정 추정이득, RLS 및 LMS 적응형 알고리즘 성능보다 우수하며, WMSA(K=1) 성능의 경우 일정 추정이득, RLS 및 LMS 적응형 알고리듬 성능과는 큰 차이가 없다. 그리고 LMS 알고리즘 성능은 WMSA(K=1) 성능과 매우 비슷한 결과를 얻었다. 그러나 도플러 주파수가 고속화될수록 LMS 알고리즘의 성능이 WMSA(K=1), 일정 추정이득 및 RLS 알고리즘 성능보다 우수함을 확인하였다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.4C
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• pp.305-311
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• 2008

In this paper, we proposed an algorithm for adaptive noise cancellation (ANC) using the variable step size normalized least mean square (VSSNLMS) in real-time automobile environment. As a basic algorithm for ANC, the LMS algorithm has been used for its simplicity. However, the LMS algorithm has problems of both convergence speed and estimation accuracy in real-time environment. In order to solve these problems, the VSSLMS algorithm for ANC is considered in nonstationary environment. By computer simulation using real-time data acquisition system(USB 6009), VSSNLMS algorithm turns out to be more effective than the LMS algorithm in both convergence speed and estimation accuracy.

• The Journal of the Acoustical Society of Korea
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• v.35 no.4
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• pp.280-287
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• 2016

In this paper, we propose a normalization algorithm that can be applied to adaptive filters for multi-channel active noise control. The FxLMS (Filtered-x Least Mean Square) algorithm for the single-channel active noise control can be normalized in the same way as the NLMS (Normalized Least Mean Square) algorithm, whereas in case of the multi-channel active noise control, the single-channel normalization for the FxLMS algorithm cannot be extended to the normalization for the multi-channel FxLMS algorithm straightforwardly. First, we adopt a generalized normalization algorithm for the multi-channel FxLMS algorithm based on the principle of minimal disturbance and then, proposed a normalized algorithm considering only diagonal elements to avoid computation for matrix inversion. We carried out performance comparisons of the proposed algorithm with other algorithms without normalization. It is shown that the proposed algorithm presents better convergence characteristics under non-stationary environments.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.6
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• pp.95-101
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• 2009

Among the adaptive noise cancellers (ANC), the least mean square (LMS) algorithm has probably become the most popular algorithm because of its robustness, good tracking properties, and simplicity of implementation. However, it has non-uniform convergence and a trade-off between the rate of convergence and excess mean square error (EMSE). To overcome these shortcomings, a number of variable step size least mean square (VSSLMS) algorithms have been researched for years. These LMS algorithms use a complex variable step method approach for rapid convergence but need high computational complexity. A variable step approach can impair the simplicity and robustness of the LMS algorithm. The proposed instantaneous gain control (IGC) algorithm uses the instantaneous gain value of the original signal and the noise signal. As a result, the IGC algorithm can reduce computational complexity and maintain better performance.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10c
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• pp.280-282
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• 1999

적응 횡단선 심볼간의 채널에 발생하는 상호 심볼간 간섭을 억압하기 위해 LMS 알고리즘을 사용한다. 이 알고리즘은 원하는 응답과 실제 출력간의 차인 에러를 이용하여 탭 가중치 조절 메카니즘을 통해 탭 가중치를 갱신함으로서 효과적으로 간섭을 제거하였다. 본 논문은 상호 심볼간 간섭을 효율적으로 억압해온 기존의 LMS알고리즘에 다계층 퍼셉트론 신경망을 조합 한 새로운 LMSBP 알고리즘을 제안하였으며, 제안된 알고리즘을 토해 탭 가중치 갱신이 보다 효율적으로 이루어짐을 알 수 있다. 시뮬레이션 결과를 통해 제안된 알고리즘의 평균 자승 에러의 수렴 특성이 LMS 알고리즘을 이용한 수렴특성보다 우월하다는 것을 나타내었다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.7C
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• pp.627-634
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• 2007

When a higher-order modulation scheme (16QAM or 64QAM) is applied to the communications system using the nonlinear high power amplifier (HPA), the performance can be degraded by the nonlinear distortion of the HPA. The nonlinear distortion can be compensated by the adaptive nonlinear Volterra equalizer using the low-complexity LMS algorithm at the receiver. However, the LMS algorithm shows very slow convergence performance. So, in this paper, the parallel M-band discrete wavelet transformed LMS algorithm is proposed in order to improve the convergence speed. Throughout the computer simulations, it is shown that the convergence performance of the proposed method is superior to that of the conventional time-domain and transform-domain LMS algorithms.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.5
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• pp.549-554
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• 2014

In the paper, we propose a new Signed LMS(Least Mean Square) algorithm for ICS(Interference Cancellation System). The proposed Signed LMS algorithm improved performances by adjusting step size values. At the convergence of 1000 iteration state, the MSE(Mean Square Error) performance of the proposed Signed LMS algorithm with step size of 0.067 is about 3 ~ 18 dB better than the conventional LMS, CMA algorithm. And the proposed Signed LMS algorithm requires 500 ~ 4000 less iterations than the and LMS and CMA algorithms at MSE of -25dB.