Institute for Systems Research

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Author: search.filters.author.Alajaji, FadySubject: search.filters.subject.detection

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    Quantization of Memoryless and Gauss-Markov Sources Over Binary Markov Channels
    (1994) Phamdo, N.; Alajaji, Fady; Farvardin, Nariman; ISR
    Joint source-channel coding for stationary memoryless and Gauss- Markov sources and binary Markov channels is considered. The channel is an additive-noise channel where the noise process is an M-th order Markov chain. Two joint source-channel coding schemes are considered. The first is a channel-optimized vector quantizer - optimized for both source and channel. The second scheme consists of a scalar quantizer and a maximum a posteriori detector. In this scheme, it is assumed that the scalar quantizer output has residual redundancy that can be exploited by the maximum a posteriori detector to combat the correlated channel noise. These two schemes are then compared against two schemes which use channel interleaving. Numerical results show that the proposed schemes outperform the interleaving schemes. For very noisy channels with high noise correlation, gains of 4 to 5 dB in signal-to-noise ratio are possible.
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    Strong Converse, Feedback Channel Capacity and Hypothesis Testing
    (1994) Chen, Po-Ning; Alajaji, Fady; ISR
    In light of recent results by Verdu ad Han on channel capacity, we examine three problems: the strong converse condition to the channel coding theorem, the capacity of arbitrary channels with feedback and the Neyman-Pearson hypothesis testing type-II error exponent. It is first remarked that the strong converse condition holds if and only is the sequence of normalized channel information densities converges in probability to a constant. Examples illustrating this condition are also provided. A general formula for the capacity of arbitrary channels with output feedback is then obtained. Finally, a general expression for the Neyman-Pearson type-II exponent based on arbitrary observations subject to a constant bound on the type-I error probability is derived.
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    Detection of Binary Sources Over Discrete Channels with Additive Markov Noise
    (1994) Alajaji, Fady; Phamdo, N.; Farvardin, Nariman; Fuja, Tom E.; ISR
    We consider the problem of directly transmitting a binary source with an inherent redundancy over a binary channel with additive stationary ergodic Markov noise. Out objective is to design an optimum receiver which fully utilizes the source redundancy in order to combat the channel noise.

    We investigate the problem of detecting a binary iid non-uniform source transmitted across the Markov channel. Two maximum a posteriori (MAP) formulations are considered: a sequence MAP detection and an instantaneous MAP detection. The two MAP detection problems are implemented using a modified version of the Viterbi decoding algorithm and a recursive algorithm. Necessary and sufficient conditions under which the sequence MAP detector becomes useless as well as simulation results are presented. A comparison between the performance of the proposed system with that of a (substantially more complex) traditional tandem source-channel coding scheme exhibits a better performance for the proposed scheme at relatively high channel bit error rates.

    The same detection problem is then analyzed for the case of a binary symmetric Markov source. Analytical and simulation results show the existence of a "mismatch" between the source and the channel. This mismatch is reduced by the use of a rate-one convolutional encoder. Finally, the detection problem is generalized for the case of a binary non-symmetric Markov source.