Opportunistic Spectrum Access: Does Maximizing Throughput Minimize File Transfer Time?

TL;DR

This paper investigates whether maximizing long-term throughput in Opportunistic Spectrum Access also minimizes file transfer time, revealing that the two objectives may not align, especially for small files, and proposes policies to optimize transfer time.

Contribution

It introduces a mathematical framework for minimizing file transfer time in heterogeneous channels, extending to dynamic policies and online algorithms with regret bounds.

Findings

Maximizing throughput does not always minimize transfer time for small files.

Proposed static and dynamic policies improve file transfer time over throughput-maximizing policies.

Numerical simulations validate the analytical results.

Abstract

The Opportunistic Spectrum Access (OSA) model has been developed for the secondary users (SUs) to exploit the stochastic dynamics of licensed channels for file transfer in an opportunistic manner. Common approaches to design channel sensing strategies for throughput-oriented applications tend to maximize the long-term throughput, with the hope that it provides reduced file transfer time as well. In this paper, we show that this is not correct in general, especially for small files. Unlike prior delay-related works that seldom consider the heterogeneous channel rate and bursty incoming packets, our work explicitly considers minimizing the file transfer time of a single file consisting of multiple packets in a set of heterogeneous channels. We formulate a mathematical framework for the static policy, and extend to dynamic policy by mapping our file transfer problem to the stochastic shortest path problem. We analyze the performance of our proposed static optimal and dynamic optimal policies over the policy that maximizes long-term throughput. We then propose a heuristic policy that takes into account the performance-complexity tradeoff and an extension to online implementation with unknown channel parameters, and also present the regret bound for our online algorithm. We also present numerical simulations that reflect our analytical results.