Genetic Algorithm Based Equalizer for Ultra-Wideband Wireless Communication Systems

Abstract

Ultra Wideband Communications Novel Trends Book 3 square error (MMSE) equalizer receiver for UWB communication systems. Parihar et al in two different papers, Parihar et al. (2005) and Parihar et al. (2007) gave thorough analysis of linear and non-linear equalizers for DS-UWB systems considering two different modulation techniques, binary phase shift keying (BPSK) and 4-ary bi-orthogonal keying (4BOK). Known channel state information (CSI) has been assumed in previous work but practically this is not feasible, because the wireless environment is always changing. Channel estimation is of particular importance in future broadband wireless networks since high data-rate transmissions lead to severe frequency-selective channel fading, which necessitates the use of channel estimation/equalization techniques to combat significant the ISI Sun & Li (2007). Lots of research work has been done on channel estimation techniques using both the training based and blind approaches. In Sato & Ohtsuki (2005), Mielczarek et al. (2003) and Chu et al. (2008), the pilot-aided channel estimation were carried out. Sato and Ohtsuki in Sato & Ohtsuki (2005) used data-aided approach based on using known pilot symbols to estimate the channel impulse response. The sliding window (SW) and successive cancellation (SC) algorithms were proposed in Mielczarek et al. (2003). Chu et al. also proposed a pilot-channel-assisted log-likelihood-ratio selective combining (PCA-LLR-SC) scheme for UWB systems in Chu et al. (2008). In another set of data-aided approaches based on maximum likelihood (ML) scheme, Wang, Xu, Ji & Zhang (2008) proposes a ML approach to channel estimation using a data-aided simplified ML channel estimation algorithm. In Lottici et al. (2002), Lottici et al. proposed data-aided (DA) and non-data aided (NDA) scenarios based on the ML criterion. Frequency domain channel estimation were reported in Takanashi et al. (2008) where an iterative frequency domain channel estimation technique was proposed for multiple-input multiple-output (MIMO)-UWB communication systems. The genetic algorithm (GA) works on the Darwinian principle of natural selection called "survival of the fittest". GA possesses an intrinsic flexibility and freedom to choose desirable optima according to design specifications. GA presumes that the potential solution of any problem is an individual and can be represented by a set of parameters regarded as the genes of a chromosome and can be structured by a string of values in binary form Man et al. (1999). GA is a well studied and effective search technique used in lots of work in CDMA communication systems as can be found in Erguin & Hacioglu (2000); Yen & Hanzo (2001) and Al-Sawafi (2004). In Erguin & Hacioglu (2000), a hybrid approach that employs a GA and multistage detector for the multiuser detection in CDMA system was proposed. Yen and Hanzo in Yen & Hanzo (2001) applied GA as a joint channel estimation and multiuser symbol detection in synchronous CDMA systems. A micro GA was developed in Al-Sawafi (2004) as a mul...

Adaptive Equalization for UWB communication System based on ANFIS

“…Considering a UWB model which transmits a Binary Phase-shift keying (BPSK) modulated signal [13][14]: Furthermore, the received signal through a matched filter of an impulse response p(-t) followed by a DFE is given by [3],…”

Filtering and Detection of Ultra-Wideband Chaotic Radio Pulses with a Matched Frequency-Selective Circuit

“…A negative effect of incoherent summation is that the signal at the receiving point acts as an interference on itself, which raises the question of developing various methods for equalizing the channel response. Actually, equalization is also a kind of digital filtering [32][33][34][35][36][37][38][39][40][41].…”

Uni-Cycle Genetic Algorithm as an Adaptation Engine for Wireless Channel Equalizers

“…Giving consideration to equalization, Humaidi et al [6] introduced an integrated learning algorithm by hybridizing the standard Least Mean Square (LMS) with the GA; the algorithm is capable of tuning both Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) filters. The GA-based equalizer for a wireless communication system that incorporates direct sequence ultra-wideband (DS-UWB) signal spreading has been considered in [7]. In [8], GA was used to estimate the coefficients of high-order IIR filters by applying a time-frequency fitness function.…”