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The optimal decoder achieving the outage capacity under imperfect channel estimation is investigated. First, by searching into the family of nearest neighbor decoders, which can be easily implemented on most practical coded modulation systems, we derive a decoding metric that minimizes the average of the transmission error probability over all channel estimation errors. Next, we specialize our general expression to obtain the corresponding decoding metric for fading MIMO channels. According to the notion of Estimation-induced outage (EIO) capacity introduced in our previous work and assuming a block Rayleigh-fading channel, we characterize the maximal achievable information rates using Gaussian codebooks associated to the proposed decoder. These achievable rates are compared to the rates achieved by the classical mismatched maximum-likelihood (ML) decoder and the ultimate limits given by the EIO capacity. Numerical results show that the derived metric provides significant gains, in terms of achievable EIO rates and bit error rate (BER), in a bit interleaved coded modulation (BICM) framework, without introducing any additional decoding complexity. However, the achievable rates of such metric are still far from the EIO capacity. Index Terms--Decoding, fading channels, information rates, maximum likelihood estimation, MIMO systems.

Ultrawideband (UWB) communications involve very sparse channels, because the bandwidth increase results in a better time resolution. This property is used in this paper to propose an efficient algorithm that jointly estimates the channel and the transmitted symbols. More precisely, this paper introduces an expectation-maximization (EM) algorithm within a wavelet-domain Bayesian framework for semiblind channel estimation of multiband orthogonal frequency division multiplexing based UWB communications. A prior distribution is chosen for the wavelet coefficients of the unknown channel impulse response 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 'insignificant' wavelet coefficients from the estimation process. Simulation results using UWB channels that were issued from both models and measurements show that, under sparseness conditions, the proposed algorithm outperforms pilot-based channel estimation in terms of the mean square error (MSE) and bit error rate (BER). Moreover, the estimation accuracy is improved, whereas the computational complexity is reduced compared with traditional semiblind methods. Index Terms--Bayesian channel estimation, expectation-maximization (EM) algorithm, iterative (turbo) detection, orthogonal frequency-division multiplexing (OFDM), sparse-wavelet-domain representation, ultrawideband (UWB).

Direct Position Determination (DPD) is known as an optimal, single-step technique for localizing co-channel signal sources since it processes the data gathered from all the array receiver elements together. In contrast, the commonly used radio location techniques include two independent stages. First, they estimate some initial parameters like direction, time, time-difference, frequency of arrival, etc., or their combination, and second, they localize signal sources using the triangulation of loci generated by the first stage. This disjoint structure leads to the sub-optimality of conventional localization algorithms. In this paper, we compare the Location root-mean-square-Error Lower Bounds (LELB) for DPD and position finding by DOA (PF-DOA) to prove the superiority of DPD over PF-DOA, which are commonly used for tactical fields or outdoor applications. Moreover, we demonstrate the advantages of DPD for indoor localization applications compared to PF-DOA techniques in terms of localization accuracy. We also introduce the singlegroup-array (SGA) structure for DPD in indoor applications and reveal that it outperforms both the PF-DOA and DPD with a classical multi-group-array (MGA) structure. [ABSTRACT FROM AUTHOR]