No abstract

Abstract-This paper introduces an expectation-maximization (EM) algorithm within a wavelet domain Bayesian framework for semi-blind channel estimation of multiband OFDM based UWB communications. A prior distribution is chosen for the wavelet coefficients of the unknown channel impulse response in order to model a sparseness property of the wavelet representation. This prior yields, in maximum a posteriori estimation, a thresholding rule within the EM algorithm. We particularly focus on reducing the number of estimated parameters by iteratively discarding "unsignificant" wavelet coefficients from the estimation process. Simulation results using UWB channels issued from both models and measurements show that under sparsity conditions, the proposed algorithm outperforms pilot based channel estimation in terms of mean square error and bit error rate and enhances the estimation accuracy with less computational complexity than traditional semi-blind methods.

The recent adoption of Ultra-WideBand (UWB) technology for applications involving short-range very high datarate wireless communication necessitates efficient allocation of system resources (bits, power etc.) because of the stringent spectral mask constraints. The Multiband-OFDM(MB-OFDM) based UWB proposal permits usage of adaptive resource allocation over different sub-carriers so as to efficiently respond to the frequency-selective nature of the UWB channel.This paper proposes a new discrete bit-allocation methodology that results in minimizing power consumption for a fixed throughput and which appears to converge to the optimal solution faster than most known optimal discrete bit-loading methods. With peakpower-constraint and channel gain factor given for a sub-carrier, the sufficient condition for determining maximum permissible number of bits is also established. While earlier bit-loading algorithms have mostly targeted the ADSL 1 -type systems, UWB application scenario is targeted in this paper to seek the total energy improvement factor with respect to a non-adaptive UWB system. However, the allocation methodology can be used for any multi-carrier based system.

SUMMARYUltra wideband (UWB) communications involve very sparse channels, since the bandwidth increase results in a better time resolution. This property is used here to propose an efficient algorithm jointly estimating the channel and the transmitted symbols. More precisely, this paper introduces an expectation-maximisation (EM) algorithm within a wavelet domain Bayesian framework for semi-blind channel estimation of multiband orthogonal frequency-division multiplexing (MB-OFDM) based UWB communications. A prior distribution is chosen for the wavelet coefficients of the unknown channel impulse response (CIR) in order to model a sparseness property of the wavelet representation. This prior yields, in maximum a posteriori (MAP) estimation, a thresholding rule within the EM algorithm. We particularly focus on reducing the number of estimated parameters by iteratively discarding 'insignificant' wavelet coefficients from the estimation process. Simulation results using UWB channels issued from both models and measurements show that under sparsity conditions, the proposed algorithm outperforms pilot based channel estimation in terms of mean square error (MSE) and bit error rate (BER). Moreover, the estimation accuracy is improved, while the computational complexity is reduced, when compared to traditional semi-blind methods.

No abstract