Limit Theorems in Hidden Markov Models

In this paper, under mild assumptions, we derive a law of large numbers, a central limit theorem with an error estimate, an almost sure invariance principle and a variant of Chernoff bound in finite-state hidden Markov models. These limit theorems are of interest in certain ares in statistics and information theory. Particularly, we apply the limit theorems to derive the rate of convergence of the maximum likelihood estimator in finite-state hidden Markov models.Comment: 35 page

Generalized PSK in Space Time Coding

A wireless communication system using multiple antennas promises reliable transmission under Rayleigh flat fading assumptions. Design criteria and practical schemes have been presented for both coherent and non-coherent communication channels. In this paper we generalize one dimensional phase shift keying (PSK) signals and introduce space time constellations from generalized phase shift keying (GPSK) signals based on the complex and real orthogonal designs. The resulting space time constellations reallocate the energy for each transmitting antenna and feature good diversity products, consequently their performances are better than some of the existing comparable codes. Moreover since the maximum likelihood (ML) decoding of our proposed codes can be decomposed to one dimensional PSK signal demodulation, the ML decoding of our codes can be implemented in a very efficient way.Comment: 22 pages, 3 figures, submitted to IEEE transactions on communicaton

On Continuous-Time Gaussian Channels

A continuous-time white Gaussian channel can be formulated using a white Gaussian noise, and a conventional way for examining such a channel is the sampling approach based on the Shannon-Nyquist sampling theorem, where the original continuous-time channel is converted to an equivalent discrete-time channel, to which a great variety of established tools and methodology can be applied. However, one of the key issues of this scheme is that continuous-time feedback and memory cannot be incorporated into the channel model. It turns out that this issue can be circumvented by considering the Brownian motion formulation of a continuous-time white Gaussian channel. Nevertheless, as opposed to the white Gaussian noise formulation, a link that establishes the information-theoretic connection between a continuous-time channel under the Brownian motion formulation and its discrete-time counterparts has long been missing. This paper is to fill this gap by establishing causality-preserving connections between continuous-time Gaussian feedback/memory channels and their associated discrete-time versions in the forms of sampling and approximation theorems, which we believe will play important roles in the long run for further developing continuous-time information theory. As an immediate application of the approximation theorem, we propose the so-called approximation approach to examine continuous-time white Gaussian channels in the point-to-point or multi-user setting. It turns out that the approximation approach, complemented by relevant tools from stochastic calculus, can enhance our understanding of continuous-time Gaussian channels in terms of giving alternative and strengthened interpretation to some long-held folklore, recovering "long known" results from new perspectives, and rigorously establishing new results predicted by the intuition that the approximation approach carries

Concavity of Mutual Information Rate for Input-Restricted Finite-State Memoryless Channels at High SNR

We consider a finite-state memoryless channel with i.i.d. channel state and the input Markov process supported on a mixing finite-type constraint. We discuss the asymptotic behavior of entropy rate of the output hidden Markov chain and deduce that the mutual information rate of such a channel is concave with respect to the parameters of the input Markov processes at high signal-to-noise ratio. In principle, the concavity result enables good numerical approximation of the maximum mutual information rate and capacity of such a channel.Comment: 26 page