Performance comparison of LMS and RLS adaptive array on high speed train delivered from High Altitude Platforms

Zakia, Irma; Tjondronegoro, Suhartono; Iskandar, Iskandar; Kurniawan, Adit

doi:10.1109/icoict.2013.6574544

Cited by 4 publications

(2 citation statements)

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“…Firstly, it is not restricted to the RLS algorithm, so it can be implemented to any adaptive filter algorithm, such as least mean squares (LMS) and Kalman. Secondly, even though we assume a single user SIMO beamforming framework similar to the method shown in White, et al [4], Zakia, et al [5], and Rappaport [11], the generalization to the MIMO case is straightforward as long as the Doppler shift of all channel pairs is assumed to be equal. Finally, the purpose of the adaptive filter is not only beamforming, as assumed here, but also channel tracking, like the one used in Zakia, et al [6].…”

Section: Introductionmentioning

confidence: 99%

“…Faletti, et al [2] have implemented the multiple-input multiple-output (MIMO) beamforming recursive least-squares (RLS) algorithm, while examples of single-input multiple-output (SIMO) are given by White, et al in [4] and Zakia, et al In [5], the recursive LS (RLS) MIMO channel estimation algorithm for Rician fading was proposed by Zakia, et al [6] as a generalization to the Rayleigh fading case given in Karami [7]. Both papers present closed-form solutions on the tracking performance in terms of mean square error (MSE).…”

Section: Introductionmentioning

confidence: 99%

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Performance Improvement of LeastSquares Adaptive Filter for High-Speed Train Communication Systems

Zakia

Kurniawan

2017

J. ICT Res. Appl.

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Abstract. The downlink communication channel from high-altitude platform (HAP) to high-speed train (HST) in the Ka-band is a slowly time-varying Rician distributed flat fading channel with 10-25 dB Rician K factor. In this respect, the received signal is mainly affected by the Doppler shift of the line-of-sight (LOS) link. In order to increase receiver performance, we propose to firstly compensate the Doppler shift of the received signal before least-squares (LS) adaptive filtering is pursued. Implementing the proposed method requires a priori knowledge of the time-varying phase of the LOS component. This is justified since signalling between the train and the controller exists such that the train velocity and location are predictable. Implementing the proposed method to the recursive LS (RLS) received beamforming algorithm shows reduction of mean square error (MSE) and bit error rate (BER).

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Section: Introductionmentioning

confidence: 99%