We obtain the Shannon capacity of a fading channel with channel side information at the transmitter and receiver, and at the receiver alone. The optimal power adaptation in the former case is "water-pouring" in time, analogous to water-pouring in frequency for time-invariant frequency-selective fading channels. Inverting the channel results in a large capacity penalty in severe fading.

The information rate of finite-state source/channel models can be accurately estimated by sampling both a long channel input sequence and the corresponding channel output sequence, followed by a forward sum–product recursion on the joint source/channel trellis. This method is extended to compute upper and lower bounds on the information rate of very general channels with memory by means of finite-state approximations. Further upper and lower bounds can be computed by reduced-state methods.

We describe parallel concatenated codes for communication over binary-input, binary-output hidden Markov channels. We present encoder design techniques and decoder processing modifications that utilize the a priori statistics of the channel and show that the resulting codes allow reliable communication at rates which are above the capacity of a memoryless channel with the same stationary bit error probability as the Markov channel. These codes outperform systems based on the traditional approach of using a channel interleaver to create a channel which is assumed to be memoryless. In addition, we introduce a joint estimation/decoding method that allows the estimation of the parameters of the Markov model when they are not known a priori. I. Introduction Many practical communications channels can be modeled using discrete Markov channels. Such channels are characterized by a set of states S j ; 0 j S \Gamma 1, the matrix of transition probabilities among states, and the list giving th...

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We describe parallel concatenated codes for communication over binary-input, binary-output hidden Markov channels when the parameters of the Markov channel are unknown a priori. Specifically, we develop a joint estimation/decoding method that allows the estimation of the parameters of the model without the need for training sequences. This method involves little or no sacrifice in performance relative to the case where the Markov channel parameters are provided to the receiver as a priori information. Furthermore, we show communication at rates which are above the capacity of a memoryless channel with the same stationary bit error probability as the Markov channel, thereby outperforming systems based on the traditional approach of using a channel interleaver to create a channel which is assumed to be memoryless. I. Introduction Many practical communications channels can be modeled using discrete Markov channels. Such channels are characterized by a set of states S j ; 0 j S \Gamma...

The capacity of time-varying asymptotically block-memoryless channels with causal channel side information (CSI) at the sender and receiver is considered. We obtain a formal weak coding theorem that achieves the maximum expected mutual information. We also obtain a converse theorem showing that this maximum expected mutual information is the highest attainable rate. We apply the coding theorem to determine the capacity and optimal input distribution of intersymbol interference (ISI) time-varying channels, whose capacity cannot be found through traditional decomposition methods. We also apply our coding theorem to flat fading channels with imperfect i.i.d. channel estimates.

1 Information-theoretic limits of fading channels have been discussed extensively in the literature under average power constraints. In this paper, we introduce total energy constraints on transmission and study dynamic power control on a time varying channel. Three types of delay constraints are discussed: no delay constraint, soft delay constraint and strict delay constraint. We consider a block fading channel modeled as a finite state Markov chain and study binary power control schemes for both variable rate and constant rate systems. For variable rate systems, we search for the optimal transmission policy that maximizes the minimum value of the expected sum-of-rates over the total communication window. Under no delay constraints, the optimal policy is extremely selective in that it stipulates transmission only on the best channel state, and the corresponding energy e#ciency (the ratio of average mutual information to energy) serves as an upper bound for all delay constrained cases. Under delay constraints, the optimal policies are less selective and result in threshold rules on both the received signal strength (channel state) and the residual battery energy. Further, the energy e#ciency is observed to increase with the Doppler frequency. For constant rate systems, optimal schemes are obtained to minimize the maximum value of the outage probability. It is observed that the outage probability decreases with the Doppler frequency in the strict delay constraint case. In addition, repetition diversity is also considered under a strict delay constraint. 1 This work is supported in part by the NSF under a CAREER award CCR 9874976 and the New Jersey Commission on Science and Technology under the New Jersey Center for Wireless Communication Technologies. 1 2 Keywords: B...

This thesis addresses the problems of signal processing for image communication and restoration. Significant attention is devoted to developing novel stochastic models for images, investigating the information theoretic performance bounds for them, and designing e#cient learning and inference methods for the proposed models. Unlike the commonly accepted approach in which the design of communication systems is performed by first compressing the data into binary representation and then channel coding it to recover from transmission errors, this thesis advocates the joint source-channel coding solution to the problem. The joint approach potentially leads to significant performance gains in emerging multiuser communication scenarios like digital audio and video broadcast (DAB and DVB) and multicast over wireless and wireline networks, multimedia communication in heterogeneous environments, and situations with uncertainty and fluctuations in the data source or channel parameters as is typic...

We consider a queuing problem in which both the service rate of a finite-buffer queue and its rate of arrivals are functions of the same partially observed Markov chain. Basic performance indices of this device, such as long term throughput and loss rates, are expressed in terms of an invariant measure over a suitable finite-dimensional simplex. In this paper we prove the existence of that invariant measure.