Totally Asynchronous Interference Channels

TL;DR

This paper investigates asynchronous interference channels with multiple users sharing frequency bands, deriving bounds and optimal spectrum division strategies to maximize outage capacity under uncertainty about user count and channel gains.

Contribution

It introduces a novel analysis of asynchronous interference channels, deriving bounds and optimal spectrum partitioning strategies for maximizing outage capacity without user or channel gain knowledge.

Findings

Derived upper and lower bounds on mutual information for up to two users.

Established closed-form achievable rates as code length approaches infinity.

Designed activation probabilities to maximize outage capacity in multi-user scenarios.

Abstract

This paper addresses an interference channel consisting of $\mathbf{n}$ active users sharing $u$ frequency sub-bands. Users are asynchronous meaning there exists a mutual delay between their transmitted codes. A stationary model for interference is considered by assuming the starting point of an interferer's data is uniformly distributed along the codeword of any user. The spectrum is divided to private and common bands each containing $v_{\mathrm{p}}$ and $v_{\mathrm{c}}$ frequency sub-bands respectively. We consider a scenario where all transmitters are unaware of the number of active users and the channel gains. The optimum $v_{\mathrm{p}}$ and $v_{\mathrm{c}}$ are obtained such that the so-called outage capacity per user is maximized. If $\Pr\{\mathbf{n}\leq 2\}=1$, upper and lower bounds on the mutual information between the input and output of the channel for each user are derived using a genie-aided technique. The proposed bounds meet each other as the code length grows to infinity yielding a closed expression for the achievable rates. If $\Pr\{\mathbf{n}>2\}>0$, all users follow a locally Randomized On-Off signaling scheme on the common band where each transmitter quits transmitting its Gaussian signals independently from transmission to transmission. Using a conditional version of Entropy Power Inequality (EPI) and an upper bound on the differential entropy of a mixed Gaussian random variable, lower bounds on the achievable rates of users are developed. Thereafter, the activation probability on each transmission slot is designed resulting in the largest outage capacity.