Simulation Results of User Behavior-Aware Scheduling Based on Time-Frequency Resource Conversion

TL;DR

This paper introduces a new scheduling approach for LTE networks using time-frequency resource conversion, proposing algorithms that improve QoS and revenue while addressing high-dimensional optimization challenges.

Contribution

It develops polynomial-time algorithms for TFRC-based scheduling, including offline and online methods, to enhance network capacity and service quality.

Findings

Proposed algorithms outperform traditional methods in QoS provisioning.

TFRC-based scheduling increases network revenue.

Algorithms are computationally efficient and scalable.

Abstract

Integrating time-frequency resource conversion (TFRC), a new network resource allocation strategy, with call admission control can not only increase the cell capacity but also reduce network congestion effectively. However, the optimal setting of TFRC-oriented call admission control suffers from the curse of dimensionality, due to Markov chain-based optimization in a high-dimensional space. To address the scalability issue of TFRC, in [1] we extend the study of TFRC into the area of scheduling. Specifically, we study downlink scheduling based on TFRC for an LTE-type cellular network, to maximize service delivery. The service scheduling of interest is formulated as a joint request, channel and slot allocation problem which is NP-hard. An offline deflation and sequential fixing based algorithm (named DSFRB) with only polynomial-time complexity is proposed to solve the problem. For practical online implementation, two TFRC-enabled low-complexity algorithms, modified Smith ratio algorithm (named MSR) and modified exponential capacity algorithm (named MEC), are proposed as well. In this report, we present detailed numerical results of the proposed offline and online algorithms, which not only show the effectiveness of the proposed algorithms but also corroborate the advantages of the proposed TFRC-based schedule techniques in terms of quality-of-service (QoS) provisioning for each user and revenue improvement for a service operator.