Energy-Efficient Design in Heterogeneous Cellular Networks Based on Large-Scale User Behavior Constraints

Chen

IEEE Trans. Veh. Technol.

et al. 2018

Abstract-Heterogeneous cellular networks (HCNs) are capable of meeting the explosive mobile-traffic demands. However, the conventional base station (BS) deployment strategy is unsuitable for supporting the often unpredictable non-uniform mobiletraffic demands, as governed by the large-scale user behavior (LUB). This results in the inefficient exploitation of the system's resources. In this paper, we develop an analytical framework for characterizing the achievable energy-spectral-efficiency (ESE) of HCNs, which explicitly quantifies the relationship between the network's ESE and the randomly time-varying LUBs as well as other network deployment parameters. Specifically, we model the quantitative impact of the geographical mobiletraffic intensity, the load migration factor, the users' required service rate and the per-tier BS densities on the achievable ESE of network, while considering the area-spectral-efficiency requirements. Importantly, a closed-form ESE expression is derived, which enables us to explicitly analyze the properties of the network's ESE. Furthermore, the optimal LUB-aware BS deployment strategy is proposed for maximizing the ESE under specific outage constraints. Using numerical simulations, we verify the accuracy of the analytical ESE expression and quantify the impact of several relevant system parameters on the achievable ESE. Furthermore, we evaluate the achievable ESE performance of the network under diverse time-varying LUB scenarios. Our work therefore provides valuable insights for designing future ultra-dense HCNs.

Section: B Related Workmentioning

confidence: 99%

Energy-Spectral-Efficiency Analysis and Optimization of Heterogeneous Cellular Networks: A Large-Scale User-Behavior Perspective

Chen

IEEE Trans. Veh. Technol.

et al. 2018

“…This also means that the optimum η j is the corresponding optimum SINR value of the worst user in the jth cell. In addition, the exact solution of problem (4) is not straightforwardly obtained due to the coupling between optimization variables in problem (4). Notice that in this reformulation, the optimization of beamforming vectors, W , and the optimization of power allocation, p and η can be decoupled into two subproblems [16].…”

Section: Energy Efficient Multicast Transmissionmentioning

confidence: 99%

“…In [4], the heterogeneity of large-scale user behavior was leveraged to improve the EE in heterogeneous cellular networks. Recently, the authors in [5], [6] investigated the energy efficient coordinated precoding design for homogenous or heterogenous communication networks.…”

mentioning

confidence: 99%

Energy Efficient Coordinated Beamforming Design in Multi-Cell Multicast Networks

Huang

Jin

et al. 2015

IEEE Commun. Lett.

In this letter, we study energy efficient physical layer multicasting in multi-cell networks where each base station is equipped with multiple antennas, and transmits a common message using a single beamformer to multiple users in the same cell. The goal of our coordinated beamforming design is to maximize the worst case system energy efficiency while guaranteeing that received signal-to-interference-plus-noise ratio (SINR) at each user is above a predetermined threshold. The considered optimization problem is in a non-convex fractional form and hard to tackle. We propose to first transform the original problem into a tractable optimization problem which is then solved by developing an iterative optimization algorithm with provable convergence. Numerical results further validate the effectiveness of our proposed algorithm and show that the developed algorithm converges to a stable point in a limited number of iterations.Index Terms-Physical multicasting, energy efficiency maximization, fractional programming, beamforming and power allocation, second order conic programming.

2016 IEEE Globecom Workshops (GC Wkshps)

“…From [11], the ASE (in bit/s/Hz/m 2 ) of a K-tier HetNet is the summation of the ASE offered by each tier, i.e., ASE of all tiers=…”

Section: B Area Spectral Efficiency and Energy Efficiencymentioning

confidence: 99%

“…In contrast, the ASE of macrocells increases almost linearly with the SINR threshold, since such transition does not occur. Following (11), the ASE of the HetNet (as the summation of per-tier ASE) also first increases with the SINR threshold and then slowly decreases after reaching a maximum value. Given the parameters in Table I and above, the spatial power consumption is approximately 1 W/m 2 , and the EE is thus equal to the overall ASE.…”

Section: B Area Spectral Efficiencymentioning

confidence: 99%

On the Performance of Multi-Tier Heterogeneous Networks under LoS and NLoS Transmissions

Wang

Chu

Ding

et al. 2016

Abstract-Heterogeneous networks (HetNets) with a multi-tier structure have been considered as a promising method to provide high quality of service to mobile users. The dense deployment of small-cell base stations (BSs) implies short distances between BSs and users. It is therefore likely that users will see line-of-sight (LoS) links from its serving BS and even nearby interfering BSs, which has not been considered in performance analysis for multi-tier HetNets yet. In this paper, we study a dense multi-tier HetNet with LoS and non-line-of-sight (NLoS) transmissions based on a multislope path loss model. The spatial locations of BSs of any given network tier and those of mobile users are modeled as independent spatial Poisson point processes (SPPPs). We derive the expression of downlink coverage probability for the multi-tier HetNet, based on which we calculate the area spectral efficiency (ASE) and energy efficiency (EE) of the HetNet. Our analytical results demonstrate that in an extremely dense HetNet, both the ASE and EE of the HetNet will drop quickly with further increase of the small-cell density due to the dominance of LoS interfering small-cell links.