An Optimal Resource Allocation with Joint Carrier Aggregation in 4G-LTE

TL;DR

This paper proposes a convex optimization framework for optimal resource allocation across multiple carriers in 4G-LTE, ensuring fairness and minimum QoS guarantees for users with diverse traffic types.

Contribution

It introduces a novel convex optimization model for joint carrier aggregation in 4G-LTE, with a distributed algorithm for optimal resource allocation considering fairness and QoS.

Findings

The optimization problem is proven convex, ensuring tractability.

The distributed algorithm effectively allocates resources among multiple carriers.

Simulation results demonstrate improved resource utilization and fairness.

Abstract

In this paper, we introduce a novel approach for optimal resource allocation from multiple carriers for users with elastic and inelastic traffic in fourth generation long term evolution (4G-LTE) system. In our model, we use logarithmic and sigmoidal-like utility functions to represent the user applications running on different user equipments (UE)s. We use utility proportional fairness policy, where the fairness among users is in utility percentage of the application running on the mobile station. Our objective is to allocate the resources to the users optimally from multiple carriers. In addition, every user subscribing for the mobile service is guaranteed to have a minimum quality-of-service (QoS) with a priority criterion. Our rate allocation algorithm selects the carrier or multiple carriers that provide the minimum price for the needed resources. We prove that the novel resource allocation optimization problem with joint carrier aggregation is convex and therefore the optimal solution is tractable. We present a distributed algorithm to allocate the resources optimally from multiple evolved NodeBs (eNodeB)s. Finally, we present simulation results for the performance of our rate allocation algorithm.