Capacity and performance analysis for multi-user system under distributed opportunistic scheduling in a time dependent channel

TL;DR

This paper analyzes the capacity, delay, and quality of service in a multi-user system with distributed opportunistic scheduling under time-dependent channels, deriving scaling laws and proposing accurate approximate models.

Contribution

It derives the expected capacity scaling law for distributed scheduling in time-dependent channels and introduces simplified models for queueing performance analysis.

Findings

Expected capacity scales as $O(\sigma_g\sqrt{2\log K}+\mu_g)$.

Proposed approximate models accurately predict system performance.

Simulation results confirm the models' effectiveness.

Abstract

Consider the problem of a multi-user multiple access channel. While several multi-user coding techniques exist, in practical scenarios, not all users can be scheduled simultaneously. Thus, a key problem is which users to schedule in a given time slot. Under realistic approach for time dependency of the channel, we adopt a distributed scheduling algorithm in which each user, in the beginning of each slot, estimates his channel gain and compares it to a threshold, and if exceeding it the user can transmit. In this work we are interested in the expected capacity of the system and the delay and quality of service of the data accumulated at the users under this scheduling scheme. First we derive the expected capacity under scheduling (distributed and centralized) for this time dependent environment and show that its scaling law is $O(\sigma_g\sqrt{2\log K}+\mu_g)$, were $\sigma_g, \mu_g$ are the good channel parameters (assuming Gaussian capacity approximation, e.g., under MIMO) and $K$ is the number of users. Then we turn to the performance analysis of such system while assuming the users are not necessarily fully backlogged, and focus specifically on the queueing problem and the strong dependence between the queues which leave no alternative but to turn to approximate models for this system. We adopt the celebrated model of Ephremides and Zhu to give new results on the convergence of the probability of collision to its average value (as the number of users grows), and hence for the ensuing system performance metrics, such as throughput and delay. We further utilize this finding to suggest a much simpler approximate model, which accurately describes the system behavior when the number of queues is large. The system performance as predicted by the approximate models shows excellent agreement with simulation results.