Channel prediction for OFDMA using mixtures of experts

Article type: Research Article

Authors: Senevirathna, H.M.S.B. | Yamashita, K.^{; *}

Affiliations: Intelligent Information Communication Lab, Department of Electrical and Informationc Systems, Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Sakai, Osaka, 599-8531, Japan

Correspondence: [*] Corresponding author. Tel.: +81 72 254 9258; Fax: +81 72 254 9907; E-mail: yamashita@eis.osakafu-u.ac.jp

Abstract: Channel prediction is the key requirement in adaptive transmission techniques such as adaptive modulation, adaptive coding and adaptive power control. This paper presents a novel self organizing map (SOM) based channel predictor for the downlink of an orthogonal frequency-division multiple access (OFDMA) system. The proposed predictor uses a Kalman trained-SOM backed mixtures of experts (ME) modular neural network. The performance of the predictor is evaluated on an OFDMA system with a system delay where a channel prediction is needed.

DOI: 10.3233/KES-2006-10302

Journal: International Journal of Knowledge-based and Intelligent Engineering Systems, vol. 10, no. 3, pp. 193-200, 2006