International Journal of Antennas and Propagation

Volume 2015 (2015), Article ID 686783, 9 pages

http://dx.doi.org/10.1155/2015/686783

## Energy-Efficient User Association Strategy for Hyperdense Heterogeneous Networking in the Fifth Generation Systems

^{1}The Key Laboratory of Universal Wireless Communications for Ministry of Education, Beijing University of Posts and Telecommunications, Beijing 100876, China^{2}Huawei Technologies Co., Ltd., Beijing 100085, China

Received 18 April 2015; Accepted 3 June 2015

Academic Editor: Lei Yang

Copyright © 2015 Lei Li 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

Redesigning user association strategies to improve energy efficiency (EE) has been viewed as one of the promising shifting paradigms for the fifth generation (5G) cellular networks. In this paper, we investigate how to optimize users’ association to enhance EE for hyper dense heterogeneous networking in the 5G cellular networks, where the low-power node (LPN) much outnumbers the high-power node (HPN). To characterize that densely deployed LPNs would undertake a majority of high-rate services, while HPNs mainly support coverage; the EE metric is defined as average weighted EE of access nodes with the unit of bit per joule. Then, the EE optimization objective function is formulated and proved to be nonconvex. Two mathematical transformation techniques are presented to solve the nonconvex problem. In the first case, the original problem is reformulated as an equivalent problem involving the maximization of a biconcave function. In the second case, it is equivalent to a concave minimization problem. We focus on the solution of the biconcave framework, and, by exploiting the biconcave structure, a novel iterative algorithm based on dual theory is proposed, where a partially optimal solution can be achieved. Simulation results have verified the effectiveness of the proposed algorithm.

#### 1. Introduction

To provide universal high-data coverage and a seamless user experience, it is anticipated that the fifth generation (5G) cellular networks have extreme base station (BS) density and heterogeneity. Besides, the core networks can reach unprecedented levels of flexibility and intelligence [1], which allows the cellular strategies designed towards high efficiency with moderate complexity. Meanwhile the issue on improving energy efficiency (EE) has gained big momentum due to the increasing awareness of environmental protection and cost-efficiency. Energy-aware design and planning are motivated by the fact that wireless networks are responsible for a fraction between 0.2 and 0.4 percent of total carbon dioxide emissions [2], and this value is expected to grow due to the ever-increasing number of subscribers.

To curtail expenditures and improve EE performance, cloud radio access networks (C-RANs) are by now recognized as a promising system structure evolution for the 5G cellular networks [3]. The densely deployed remote radio heads (RRHs) operate as soft relay by compressing and forwarding the received signals from UEs to the centralized baseband unit (BBU) pool. Then, the centralized large-scale cooperative processing, such as the joint decompression and decoding schemes, can proceed in the BBUs. Since RRHs are mainly deployed to provide high capacity in special zones, to guarantee backward compatibility with the existing cellular systems and support seamless coverage, high-power nodes (HPNs) are still critical in C-RANs. With the help of HPNs, the multiple heterogeneous radio networks can be converged, and all system control signals are delivered wherein. Consequently, HPNs should be incorporated into C-RANs, and thus heterogeneous cloud radio access networks (H-CRANs) are proposed in [4, 5], to take full advantage of both HetNets and C-RANs. The abovementioned advantages and challenges for cellular networks request a redesign of user association strategies [6]. Therefore, this paper focuses on how to design user association strategies to improve EE for the hyperdense heterogeneous networking with a mass of low-power nodes (LPNs) and some HPNs.

Traditionally, users are associated with the BS providing the maximum received signal reference power (RSRP), which imposes a heavy burden on the tower-mounted macro BSs. To make the most of the dense low-power infrastructure, mobile users are actively pushed onto small BSs by using* biases* in [7]. Considering the fairness among users, load balancing between macrocells and small cells is investigated in [8, 9]. In [8], the metric of cell selection is changed from the signal strength to the* average throughput*. Based on this metric, handover will happen once it brings positive gain of network throughput. In [9], the received signal to interference and noise ratios (SINRs) at users from BSs are multiplied by the designed factors to make small cells more attractive than macrocells. To control the bias towards improving throughput and enhancing users’ fairness during association, a -fairness network utility function is formulated and optimized in [10]. The user association problem in conjunction with the almost blank subframe (ABSF) technique is considered in heterogeneous networks (HetNets) in [11], and the optimal ABSF density is proved to be the proportion of vulnerable users in total users. All aforementioned works have a network utility maximization objective that was adopted with log-utility to obtain network-wide proportional fairness [12], but few of them focus on the EE performance of cellular networks.

Recently, attentions have been also paid to the energy-efficient design of user association strategies [13, 14]. A distributed association strategy is developed to minimize the total power cost of heterogeneous cloud cellular networks in [13]. An energy-efficient user association problem is studied from a population game-theoretic perspective to minimize power consumption in [14]. However, the researches on energy-efficient user associations are still much limited and insufficient. One of the open issues is how to define EE for different networks. Ordinarily, EE metrics are mainly designed either to minimize the network power consumption under quality of service (QoS) constraints or to maximize the ratio of the network throughput to the area power consumption. However, for hyperdense heterogeneous networking in the 5G cellular networks, with the loads continuing to be transferred from macrocells to small cells, the roles of macro BSs and small BSs are gradually distinguished [15]. High-power macrocells are mainly responsible for coverage, while a large number of low-power small cells undertake a majority of high-rate services. It indicates that power is consumed for both providing coverage and enhancing network capacity. Such distinction should be reflected in the EE metric. Therefore, the EE metric may need to be redesigned for above characteristics.

Another important issue is how to solve nonconvex EE optimization problems. In [16], the weighted EE is proved to be quasiconcave, and a bisection based resource allocation (RA) strategy is proposed accordingly. In [17], to monotonically increase EE, a quasi-distributed iterative RA algorithm is proposed for heterogeneous orthogonal frequency division multiplexing (OFDM) systems. In [18], an equivalent model for the nonconvex EE optimization object function is researched and solved by an iterative algorithm. These aforementioned works mainly focus on the design of energy-efficient RA strategies and suggest that there is no common solution for nonconvex optimization problems. In this paper, two mathematical transformation techniques will be presented to solve the nonconvex energy-efficient association strategy.

Contributions of this paper are summarized as follows. First, the EE performance of HPNs and LPNs is distinguished, and a new EE metric, that is,* average weighted EE of access nodes*, with the unit of bit per joule is proposed. Second, an EE optimization objective function is formulated to optimize user association strategy under the constraints of backhaul capacity, users’ data rate, and the maximum transmit power of BSs, which is proved to be neither convex nor quasiconvex. Third, two mathematical transformation techniques are introduced. In the first case, the nonconvex original problem is reformulated as a biconcave maximization framework. In the second case, the original problem is equivalent to a concave minimization framework. Furthermore, the relationship between two reformulated problems is presented. Fourth, an iterative algorithm based on dual theory and properties of the biconcavity is proposed to solve the equivalent biconcave maximization problem. Simulation results have verified the effectiveness of the iterative algorithm and suggest that transferring loads from HPNs to LPNs can improve the network EE.

The remainder of the paper is organized as follows. Section 2 gives the system model and formulates the problem. In Section 3, two equivalent transformation techniques are presented, respectively; then the optimization algorithm for the first equivalent problem is introduced. In Section 4, simulation results are presented, followed by some conclusions drawn in Section 5.

#### 2. System Model and Problem Formulation

A general K-tier downlink radio network is considered, which can apply to a H-CRAN network or a dense HetNet. All deployed access nodes are called a BS for simplicity. Then, denote by the set of all BSs in K-tiers and the set of all users. As shown in Figure 1, users are allowed to be associated with multiple BSs at the same time. The association indicator for the th user and the th BS is represented as , where , , , and . Denote by the average spectral efficiency (SE) on the radio link between the th user and the th BS, and assume that the system has the knowledge of during association (similar assumptions can be found in [7–11], in which the received average SINRs at users from BSs for different radio resources are fixed during association; in addition, can be easily obtained in H-CRANs, wherein HPNs can achieve all system control signals in a centralized way). The data rate requirement of the th user is represented as , and the th BS is responsible for . Denote by and the total frequency band and maximum transmit power of the th BS, respectively. Then, the th BS needs to assign the user with frequency band, which accounts for power consumption. Based on above assumptions, it is easy to verify that if the requirement of the th user is satisfied, the achieved data rate is . The total transmit power and throughput in the th BS are written as and , respectively.