Exploiting Residual Resources to Support High Throughput with Resource Allocation

TL;DR

This paper proposes a resource allocation strategy that exploits residual wireless network resources to significantly improve the throughput for non-real-time traffic, using predictions of average residual bandwidth and channel gains.

Contribution

It introduces a novel optimization framework for resource allocation based on predicted residual resources and develops an online policy for practical implementation.

Findings

Achieves high throughput for NRT traffic with residual resources.

Demonstrates the effectiveness of prediction-based resource allocation.

Shows significant gains in supporting high request arrival rates.

Abstract

Residual radio resources are abundant in wireless networks due to dynamic traffic load, which can be exploited to support high throughput for serving non-real-time (NRT) traffic. In this paper, we investigate how to achieve this by resource allocation with predicted time-average rate, which can be obtained from predicted average residual bandwidth after serving real-time traffic and predicted average channel gains of NRT mobile users. We show the connection between the statistics of their prediction errors. We formulate an optimization problem to make a resource allocation plan within a prediction window for NRT users that randomly initiate requests, which aims to fully use residual resources with ensured quality of service (QoS). To show the benefit of knowing the contents to be requested and the request arrival time in advance, we consider two types of NRT services, video on demand and video on reservation. The optimal solution is obtained, and an online policy is developed that can transmit according to the plan after instantaneous channel gains are available. Simulation and numerical results validate our analysis and show a dramatic gain of the proposed method in supporting high arrival rate of NRT requests with given tolerance on QoS.