Lower Bound on the Capacity of Continuous-Time Wiener Phase Noise Channels

A continuous-time Wiener phase noise channel with an integrate-and-dump multi-sample receiver is studied. A lower bound to the capacity with an average input power constraint is derived, and a high signal-to-noise ratio (SNR) analysis is performed. The capacity pre-log depends on the oversampling factor, and amplitude and phase modulation do not equally contribute to capacity at high SNR.Comment: Extended version of a paper submitted to ISIT 2015. 9 pages and 1 figure. arXiv admin note: text overlap with arXiv:1411.039

Phase Modulation for Discrete-time Wiener Phase Noise Channels with Oversampling at High SNR

A discrete-time Wiener phase noise channel model is introduced in which multiple samples are available at the output for every input symbol. A lower bound on the capacity is developed. At high signal-to-noise ratio (SNR), if the number of samples per symbol grows with the square root of the SNR, the capacity pre-log is at least 3/4. This is strictly greater than the capacity pre-log of the Wiener phase noise channel with only one sample per symbol, which is 1/2. It is shown that amplitude modulation achieves a pre-log of 1/2 while phase modulation achieves a pre-log of at least 1/4.Comment: To appear in ISIT 201

Informational Divergence Approximations to Product Distributions

The minimum rate needed to accurately approximate a product distribution based on an unnormalized informational divergence is shown to be a mutual information. This result subsumes results of Wyner on common information and Han-Verd\'{u} on resolvability. The result also extends to cases where the source distribution is unknown but the entropy is known

Upper Bound on the Capacity of Discrete-Time Wiener Phase Noise Channels

A discrete-time Wiener phase noise channel with an integrate-and-dump multi-sample receiver is studied. An upper bound to the capacity with an average input power constraint is derived, and a high signal-to-noise ratio (SNR) analysis is performed. If the oversampling factor grows as $\text{SNR}^\alpha$ for $0\le \alpha \le 1$, then the capacity pre-log is at most $(1+\alpha)/2$ at high SNR.Comment: 5 pages, 1 figure. To be presented at IEEE Inf. Theory Workshop (ITW) 201