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In many communication situations, the transmitter and the receiver must be designed without a complete knowledge of the probability law governing the channel over which transmission takes place. Various models for such channels and their corresponding capacities are surveyed. Special emphasis is placed on the encoders and decoders which enable reliable communication over these channels. Index Terms-Arbitrarily varying channel, compound channel, deterministic code, finite-state channel, Gaussian arbitrarily varying channel, jamming, MMI decoder, multiple-access channel, randomized code, robustness, typicality decoder, universal decoder, wireless.

New upper and lower bounds are presented on the capacity of the freespace optical intensity channel. This channel is characterized by inputs that are nonnegative (representing the transmitted optical intensity) and by outputs that are corrupted by additive white Gaussian noise (because in free space the disturbances arise from many independent sources). Due to battery and safety reasons the inputs are simultaneously constrained in both their average and peak power. For a fixed ratio of the average power to the peak power the difference between the upper and the lower bounds tends to zero as the average power tends to infinity, and the ratio of the upper and lower bounds tends to one as the average power tends to zero.The case where only an average-power constraint is imposed on the input is treated separately. In this case, the difference of the upper and lower bound tends to 0 as the average power tends to infinity, and their ratio tends to a constant as the power tends to zero.

Abstract-We consider a peak-power-limited single-antenna flat complex-Gaussian fading channel where the receiver and transmitter, while fully cognizant of the distribution of the fading process, have no knowledge of its realization. Upper and lower bounds on channel capacity are derived, with special emphasis on tightness in the high signal-to-noise ratio (SNR) regime. Necessary and sufficient conditions (in terms of the autocorrelation of the fading process) are derived for capacity to grow double-logarithmically in the SNR. For cases in which capacity increases logarithmically in the SNR, we provide an expression for the "pre-log," i.e., for the asymptotic ratio between channel capacity and the logarithm of the SNR. This ratio is given by the Lebesgue measure of the set of harmonics where the spectral density of the fading process is zero. We finally demonstrate that the asymptotic dependence of channel capacity on the SNR need not be limited to logarithmic or double-logarithmic behaviors. We exhibit power spectra for which capacity grows as a fractional power of the logarithm of the SNR.

Abstract-New upper and lower bounds are presented on the capacity of the free-space optical intensity channel. This channel is characterized by inputs that are nonnegative (representing the transmitted optical intensity) and by outputs that are corrupted by additive white Gaussian noise (because in free space the disturbances arise from many independent sources). Due to battery and safety reasons the inputs are simultaneously constrained in both their average and peak power. For a fixed ratio of the average power to the peak power the difference between the upper and the lower bounds tends to zero as the average power tends to infinity, and the ratio of the upper and lower bounds tends to one as the average power tends to zero.The case where only an average-power constraint is imposed on the input is treated separately. In this case, the difference of the upper and lower bound tends to 0 as the average power tends to infinity, and their ratio tends to a constant as the power tends to zero.

No abstract

We study the power versus distortion trade-off for the distributed transmission of a memoryless bi-variate Gaussian source over a two-to-one average-power limited Gaussian multiple-access channel. In this problem, each of two separate transmitters observes a different component of a memoryless bi-variate Gaussian source. The two transmitters then describe their source component to a common receiver via an average-power constrained Gaussian multiple-access channel. From the output of the multiple-access channel, the receiver wishes to reconstruct each source component with the least possible expected squared-error distortion. Our interest is in characterizing the distortion pairs that are simultaneously achievable on the two source components.We present sufficient conditions and necessary conditions for the achievability of a distortion pair. These conditions are expressed as a function of the channel signal-to-noise ratio (SNR) and of the source correlation. In several cases the necessary conditions and sufficient conditions are shown to agree. In particular, we show that if the channel SNR is below a certain threshold, then an uncoded transmission scheme is optimal. We also derive the precise high-SNR asymptotics of an optimal scheme.

A universal decoder for a parametric family of channels is a decoder whose structure depends on the family but not on the individual channel over which transmission takes place, and it yet attains the same random-coding error exponent as the maximum-likelihood receiver tuned to the channel in use. The existence and structure of such decoders is demonstrated under relatively mild conditions of continuity of the channel law with respect to the parameter indexing the family. It is further shown that under somewhat stronger conditions on the family of channels, the convergence of the performance of the universal decoder to that of the optimal decoder is uniform over the set of channels. Examples of families for which universal decoding is demonstrated include the family of finite-state channels and the family of Gaussian intersymbol interference channels. Index Terms-Compound channel, error exponent, finite-state channel, Gilbert-Elliott channel, intersymbol interference, random coding, universal decoding.