SUMMARYSignals transmitted through multiple-input multiple-output (MIMO) wireless channels suffer from multiple-access interference (MAI), multipath propagation and additive noise. Iterative multiuser receiver algorithms mitigate these signal impairments, while offering a good tradeoff between performance and complexity. The receiver presented in this paper performs channel estimation, multiuser detection and decoding in an iterative manner. The estimation of the frequency selective, block-fading channel is initiated with the pilot symbols. In subsequent iterations, soft decisions of all the data symbols are used in an appropriate way to improve the channel estimates. This approach leads to significant improvement of the overall receiver performance, compared to other schemes. The bit-error-rate (BER) performance of the receiver is evaluated by simulations for different parameter setups.

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SUMMARYBEAST is a Bidirectional Efficient Algorithm for Searching code Trees. In this paper, it is used for decoding block codes over a binary-input memoryless channel. If no constraints are imposed on the decoding complexity (in terms of the number of visited nodes during the search), BEAST performs maximumlikelihood (ML) decoding. At the cost of a negligible performance degradation, BEAST can be constrained to perform almost-ML decoding with significantly reduced complexity. The benchmark for the complexity assessment is the number of nodes visited by the Viterbi algorithm operating on the minimal trellis of the code. The decoding complexity depends on the trellis structure of a given code, which is illustrated by three different forms of the generator matrix for the (24, 12, 8) Golay code. Simulation results that assess the error-rate performance and the decoding complexity of BEAST are presented for two longer codes.