Wireless Communications and Mobile Computing

Volume 2018, Article ID 8572489, 7 pages

https://doi.org/10.1155/2018/8572489

## Energy Efficiency Maximization of Dynamic CoMP-JT Algorithm in Dense Small Cell Networks

State Key Laboratory of Integrated Service Networks, Xidian University, Xi’an 710071, China

Correspondence should be addressed to Jiandong Li; nc.ude.naidix@ildj

Received 2 April 2018; Revised 13 June 2018; Accepted 21 June 2018; Published 12 July 2018

Academic Editor: Shunqing Zhang

Copyright © 2018 Xuefei Peng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

We firstly formulate the energy efficiency (EE) maximization problem of joint user association and power allocation considering minimum data rate requirement of small cell users (SUEs) and maximum transmit power constraint of small cell base stations (SBSs), which is NP-hard. Then, we propose a dynamic coordinated multipoint joint transmission (CoMP-JT) algorithm to improve EE. In the first phase, SUEs are associated with the SBSs close to them to reduce the loss of power by the proposed user association algorithm, where the associated SBSs of each small cell user (SUE) form a dynamic CoMP-JT set. In the second phase, through the methods of fractional programming and successive convex approximation, we transform the EE maximization subproblem of power allocation for SBSs into a convex problem that can be solved by proposed power allocation optimization algorithm. Moreover, we show that the proposed solution has a much lower computational complexity than that of the optimal solution obtained by exhaustive search. Simulation results demonstrate that the proposed solution has a better performance.

#### 1. Introduction

In future 5G wireless network, EE improvement is arisen as a challenging issue [1]. Therefore, how to promote EE is of great significance. With the explosive increasing data traffic demands of users in 5G wireless cellular network, one of the prospective solutions for satisfying the data rate requirement of users and improving EE is the deployment of low power, low cost, and small coverage range SBSs. Small cell tier is an integral part of 5G heterogeneous cellular network architecture that can provide more opportunities for users to connect to the networks close to them, which will decrease power consumption [2, 3].

However, due to the dense deployment and spectrum sharing of small cells, the interference among small cells becomes a key factor that influences EE of the network [4].

On the one hand, several recent works [5–7] have adopted scheduling schemes to mitigate interference in small cell networks. In [5], the authors studied the performance of different scheduling methods under distinct channel models with considering the intersite distance. In [6], the authors formulated a fast convergent speed algorithm to solve the energy-efficient multijob scheduling function. In [7], dynamic clustering framework of multicell scheduling based on graph was proposed to mitigate intercell interference in dense small cell networks, and channel-aware resource allocation was incorporated in the dynamic clustering framework to provide different levels of tunable quality of service. However, EE optimization for dynamic coordination among SBSs is not considered [5–7].

On the other hand, some studies have discussed multicell CoMP transmission (CoMP) [8–15] of cellular network. CoMP-JT is a promising technique that guarantees data availability at multiple coordinated SBSs transmitted to a user simultaneously to improve the received signal quality of the user and reduce intercell interference, which is proposed in Third Generation Partnership Project (3GPP) LTE-Advanced systems [8]. In [9–11], the authors studied CoMP-JT from the aspects of throughput and coverage probability. However, EE optimization is not considered. In [12], the authors proposed two algorithms to tackle the problem of minimizing backhaul user data transfer by establishing CoMP joint processing beamforming matrix of multicell. In [13, 14], the authors formulated EE maximization problem with global precoding matrix design. However, the beamforming and precoding matrix of global optimization are complex and difficult to realize in practical systems [12–14]. In [15], the authors proposed a distributed algorithm to solve the weighted EE max-min fairness problem for CoMP systems. However, dynamic coordinated transmission among SBSs is not considered.

Unlike existing literature, a dynamic CoMP-JT algorithm considering user association, power allocation, and minimum data rate requirement of SUEs is proposed in this paper to mitigate interference and maximize EE.

**The main contributions of this paper are summarized as follows:**

(i) We utilize dynamic CoMP-JT as an interference management technique to mitigate interference and improve EE, where minimum data rate requirements of SUEs and maximum transmit power constraint of SBSs are considered.

(ii) We propose to solve the NP-hard problem of EE maximization through the proposed dynamic CoMP-JT algorithm. In the first phase, each SUE is associated with SBSs close to them to reduce the loss of power by the proposed user association algorithm. In the second phase, we transform the formulated EE optimization problem into a convex problem by methods of fractional programming and successive convex approximation. Finally, we solve the formulated problem by the proposed power allocation optimization algorithm.

(iii) We analyze the computational complexities of the optimal solution and the proposed solution and compare the performance of near-optimal solution, no-CoMP, and random-CoMP with the proposed solution.

The rest of this paper organized as follows. The system model and problem formulation are given in Section 2. In Section 3, the dynamic CoMP-JT algorithm is proposed. In Section 4, numerical results are given. Finally, the paper is concluded in Section 5.

#### 2. System Model and Problem Formulation

Our system model is depicted in Figure 1. We consider a downlink orthogonal frequency division multiple access (OFDMA) network, where macrocell tier and small cell tier are allocated with orthogonal spectrums. Therefore, there is no cross-tier interference and we focus on the EE analysis of small cell tier, where only the interference between SBSs and SUEs is considered. Assume that SUEs are randomly and uniformly distributed in the coverage region of SBSs. Let denote the set of all SBSs with low transmit power and denote the set of SUEs. We will next derive the optimization problem of EE, where the EE is defined as the transmitted bits per unit energy consumption and equals the ratio of the sum data rate to the total power consumption. We define a vector to represent the power of all SBSs allocate for all SUEs, where denotes the power of SBSs allocated for SUE 1 to SUE U. Let denote a binary vector to represent the association relationship between SBSs and SUEs. Its element is an association indicator decision variable denoting whether SBS is associated with SUE , , if SBS is associated with SUE ; otherwise .