BER performance evaluation for MC-CDMA systems in Nakagami- m fading

Bit Error Rate Analysis for MC-CDMA Systems in Nakagami-"Equation missing" Fading Channels

“…This is due to the inadequate assumption of a Gaussian MAI model when the number of active users is small (K < 10). In [8], we have compared the analytical results for EGC receivers using the proposed AGA technique and the method proposed in [8]. It was shown that the two analytical methods give quite the same analysis results from which the accuracy of the presented analysis method was further demonstrated.…”

“…It was shown that the two analytical methods give quite the same analysis results from which the accuracy of the presented analysis method was further demonstrated. However, the method presented in [8] requires that the fading channels on all subcarriers have the same fading parameters.…”

“…Multicarrier code division multiple access (MC-CDMA), which efficiently combines CDMA with orthogonal frequency division multiplexing (OFDM), has gained considerable attention as a promising multiple access technique for future mobile communications [1,2,3,4,5,6,7,8]. MC-CDMA is a spread spectrum technique where the signal is spread in the frequency domain.…”

“…Nakagami fading channels have received considerable attention in the study of various aspects of wireless systems [11,12] Nakagami distribution provides a more general and versatile way to model wireless channels [13]. The authors investigated the BER performance of MC-CDMA with an EGC receiver and Nakagami-m fading channels [8] with the same fading parameter m on different subcarriers. Although usually the correlation of fading channels amongst subcarriers cannot be ignored, it can be reduced with a properly designed frequency interleaver.…”

BER performance of MC-CDMA systems with MMSE in Nakagami-m fading

“…It's easy to show that xn is gamma distributed RV with parameters m and On' Then the characteristic function (CF) of xn and y can be respectively given by (fix" (s)=(1-s0)mr m (16) and q}y (s)= rr:)I-sO)mr m (17) By changing the variables in (15) and using the alternative representation of the Q-function [15], [16] Q(x)= � f "/ 2 exp ( -� 2 2 t e (x? :O) (18) 7r 0 2sm e r the average BER can be derived as Similarly to [8] and [13], when using (19) to evaluate the average BER, it is still necessary to take the "semi-analytic" approach whereby a sample average of (19) is taken where each evaluation is performed by generating a random selection of the fading amplitudes {fJ k,n } of all the K -1 interfering users and then numerically evaluates the integral in (19).…”