Creating and sharing knowledge for telecommunications

A new combination of adaptive channel estimation methods and TORC equalizer in MC-CDMA systems

Zidane, M. ; Dinis, R.

International Journal of Communication Systems Vol. 33, Nº 11, pp. e4429 - e4429, April, 2020.

ISSN (print): 1099-1131
ISSN (online): 1074-5351

Scimago Journal Ranking: 0,34 (in 2020)

Digital Object Identifier: 10.1002/dac.4429

Adaptive systems identification has been widely studied, but most studies have focused on the convergence of these methods. Applications of equalization systems have also received much attention. This paper presents a new combination of adaptive Broadband Radio Access Networks (BRAN) channel identification algorithms for multicarrier code division multiple access (MC‐CDMA) systems downlink equalization. In fifth‐generation (5G) wireless communications, MC‐CDMA is expected to support the associated networks. The BRAN E channel parameters, representing an outdoor scenario normalized for MC‐CDMA systems, are identified using a recursive least mean p th power algorithm with logarithmic transformation (RlogLMP). For validity and test aim, this algorithm is compared with the existing recursive least square (RLS) and least mean square (LMS) algorithms. Moreover, we use the estimated coefficients in the adaptive equalization problem. We give a review of the threshold orthogonality restoring combining (TORC) equalizer, which is coupled with the presented algorithms to counteract channel fading, as evaluated by the bit error rate (BER). Our performance results show that the RlogLMP algorithm can estimate the measured BRAN E channel with good efficiency for various values of the signal‐to‐noise ratio (SNR), as compared with the classical algorithms RLS and LMS. In adaptive equalization problems, the achieved results demonstrate that two thresholds ρ T H in the TORC equalizer minimize the performance degradation, in terms of the BER, of the MC‐CDMA system under multipath channel fading with very good accuracy, especially if the coefficients are estimated with the specific case of the power p in the RlogLMP algorithm.