Conditional fuzzy clustering for blind channel equalization

Modification of CFCM in The Presence of Heavy AWGN for Bayesian Blind Channel Equalizer

“…This study is an extension of previous work [16] and thus the similarity of the structure of Section 2 and 3 can be found in [16]. Finally, the simulation results including some comparative studies with early work [16] and conclusions are provided in Section 6 and 7, respectively. A general channel equalization system with ISI is illustrated in Fig.…”

“…2. This relationship of desired channel states and channel output states is always valid for the channel that has a one-toone mapping between the channel inputs and outputs [13] and is successfully used in [14]- [16]. Additionally, it can be easily extended with a higher channel order such as p=2, which is evaluated in the experimental section.…”

“…The conditioning aspect of CFCM, which describes a level of involvement of incoming input pattern in the constructed clusters, influences the clustering mechanism and improves the estimation accuracy www.ijacsa.thesai.org of an unknown channel states for blind channel equalization. However, in the presence of heavy AWGN that often arises in a high speed communication channel, the estimation accuracy of CFCM presented in [16] needs the higher level of reliability, even though it is superior to other FCM or GArelated algorithms. This leads to the consideration of the modification of CFCM clustering mechanism, which makes it more robust to the heavy noise.…”

“…Recently, to overcome this weakness, Fuzzy C-Means (FCM), one of the representative clustering algorithms which exhibits shorter processing time than the GA-based methods, has been modified and applied, and the faster convergence speed along with the reliable estimation accuracy in search of the optimal channel output states has been achieved [15] [16]. Especially, the algorithm based on CFCM clearly outperforms the GA and FCM approaches in terms of speed and accuracy [16]. The CFCM was first introduced in [17], and successfully applied to channel equalization problem [16] [18].…”

“…Especially, the algorithm based on CFCM clearly outperforms the GA and FCM approaches in terms of speed and accuracy [16]. The CFCM was first introduced in [17], and successfully applied to channel equalization problem [16] [18]. The conditioning aspect of CFCM, which describes a level of involvement of incoming input pattern in the constructed clusters, influences the clustering mechanism and improves the estimation accuracy www.ijacsa.thesai.org of an unknown channel states for blind channel equalization.…”