Provisioning Energy-Efficiency and QoS for Multi-Carrier CoMP with Limited Feedback

TL;DR

This paper develops resource allocation algorithms for multi-carrier CoMP systems with limited feedback, optimizing for spectral efficiency, QoS, and energy efficiency, and demonstrates significant performance improvements through simulations.

Contribution

The paper introduces a unified framework and algorithms for resource allocation in multi-carrier CoMP systems that optimize multiple system objectives with limited feedback.

Findings

Algorithms achieve fast convergence and handle combinatorial complexity.

Simulation results reveal optimal cluster sizes and BS distribution.

UE scheduling and subcarrier assignment greatly enhance system performance.

Abstract

We consider resource allocation (RA) in multi-carrier coordinated multi-point (CoMP) systems with limited feedback, in which a cluster of base stations (BSs), each equipped with multiple antennas, are connect to each other and/or a central processor via backhauls/fronthauls. The main objective of coordinated RA is to select user equipments (UEs) on each subcarrier, dynamically decide upon the cluster size for each subcarrier, and finally partition the feedback resources, provisioned for acquisition of channel direction information (CDI) across all subcarriers, active cells, and selected UEs, in order to maximize the weighted sum utility (WSU). We show how to recast the WSU maximization problem to achieve spectral efficiency, quality-of-service (QoS), and energyefficiency (EE). Specifically, we investigate four instances of WSU to maximize practical system objectives: (i) weighted sum capacity, (ii) weighted sum effective capacity, (iii) weighted sum energy-efficiency (EE), and (iv) weighted sum effective EE. The unified composition of these problems through WSU allows us to use the same set of developed algorithms for all cases. The algorithms have a greedy structure achieving fast convergence, and successfully cope with the huge computational complexity of RA problems, mostly rooted in their combinatorial compositions. Our simulation results shed lights on the network optimization by discovering insights on appropriate cluster-size, distribution of BSs in the cluster, and the number of subcarriers. The proposed UE scheduling and subcarrier assignment are shown to improve the system performance by several orders-of-magnitude.