Unified and Distributed QoS-Driven Cell Association Algorithms in Heterogeneous Networks

Boostanimehr, Hamidreza; Bhargava, V.K.

doi:10.1109/twc.2014.2371465

Cited by 104 publications

(105 citation statements)

References 30 publications

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“…The outage probability is defined as the probability of , > . From [36], the outage probability of a UE associated with a cell is given as follows:…”

Section: Performance Evaluationmentioning

confidence: 99%

UE‐Initiated Cell Reselection Game for Cell Load Balancing in a Wireless Network

Park

2018

Wireless Communications and Mobile Computing

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A user changes its serving cell if the quality of experience (QoE) provided by the current serving cell is not satisfactory. Since users reselect cells to increase their QoEs selfishly, the system resource efficiency can be deteriorated and a system can be unstable if users are not driven to cooperate appropriately. In this paper, inspired by the minority game (MG) model, we design a UE-initiated cell reselection policy. The MG has a salient characteristic that the number of players who win the game converges to a prespecified value even though players act selfishly without knowing the actions taken by the other players. Using the MG model, we devise a rule by which each UE plays a cell reselection game. We also design a criterion that a system controller uses to determine the result of a game and public information sent by a system controller to induce implicit cooperation among UEs. The simulation results show that compared with noncooperative method the proposed method increases not only the system performance, such as cell load balance index and system utility, but also the performance of UEs in terms of a downlink data rate and an outage probability received from a system.

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“…The outage probability is defined as the probability of , > . From [36], the outage probability of a UE associated with a cell is given as follows:…”

Section: Performance Evaluationmentioning

confidence: 99%

UE‐Initiated Cell Reselection Game for Cell Load Balancing in a Wireless Network

Park

2018

Wireless Communications and Mobile Computing

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“…Hence, the solution space of JOP is |B| |U|·|P| · 2 |S|+|P| , and no computationally efficient solution for JOP exists. Moreover, JOP belongs to a class of assignment problems which are proven to be combinatorial and NP-hard [8], [9].…”

Section: Optimization Problemmentioning

confidence: 99%

SDN-Based Self-Organizing Energy Efficient Downlink/Uplink Scheduling in Heterogeneous Cellular Networks

Moon

Kazmi

et al. 2017

IEICE Trans. Inf. & Syst.

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Seungil MOON†a) , Thant Zin OO †b) , S. M. Ahsan KAZMI †c) , Bang Ju PARK † †d) , Nonmembers, and Choong Seon HONG †e) , Member SUMMARYThe increase in network access devices and demand for high quality of service (QoS) by the users have led to insufficient capacity for the network operators. Moreover, the existing control equipment and mechanisms are not flexible and agile enough for the dynamically changing environment of heterogeneous cellular networks (HetNets). This nonagile control plane is hard to scale with ever increasing traffic demand and has become the performance bottleneck. Furthermore, the new HetNet architecture requires tight coordination and cooperation for the densely deployed small cell base stations, particularly for interference mitigation and dynamic frequency reuse and sharing. These issues further complicate the existing control plane and can cause serious inefficiencies in terms of users' quality of experience and network performance. This article presents an SDN control framework for energy efficient downlink/uplink scheduling in HetNets. The framework decouples the control plane from data plane by means of a logically centralized controller with distributed agents implemented in separate entities of the network (users and base stations). The scheduling problem consists of three sub-problems: (i) user association, (ii) power control, (iii) resource allocation and (iv) interference mitigation. Moreover, these sub-problems are coupled and must be solved simultaneously. We formulate the DL/UL scheduling in HetNet as an optimization problem and use the Markov approximation framework to propose a distributed economical algorithm. Then, we divide the algorithm into three sub-routines for (i) user association, (ii) power control, (iii) resource allocation and (iv) interference mitigation. These sub-routines are then implemented on different agents of the SDN framework. We run extensive simulation to validate our proposal and finally, present the performance analysis.

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“…For macro users, they undergo interference of all femtocells in the community. It is assumed that the macro base station has a transmitting power of Considering spectrum scarcity and increasing capacity demand, full frequency multiplex has become a main feature of a multilayer HetNet [24], but it will lead to severe cross-layer interference, especially when macro users are close to a femtocell. The channel transmission model is a mixed one consisting of path loss and shadow fading with logarithmic normal distribution.…”

Section: System Modelmentioning

confidence: 99%

Game Algorithm for Resource Allocation Based on Intelligent Gradient in HetNet

Dai

2017

Symmetry

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Abstract:In order to improve system performance such as throughput, heterogeneous network (HetNet) has become an effective solution in Long Term Evolution-Advanced (LET-A). However, co-channel interference leads to degradation of the HetNet throughput, because femtocells are always arranged to share the spectrum with the macro base station. In this paper, in view of the serious cross-layer interference in double layer HetNet, the Stackelberg game model is adopted to analyze the resource allocation methods of the network. Unlike the traditional system models only focusing on macro base station performance improvement, we take into account the overall system performance and build a revenue function with convexity. System utility functions are defined as the average throughput, which does not adopt frequency spectrum trading method, so as to avoid excessive signaling overhead. Due to the value scope of continuous Nash equilibrium of the built game model, the gradient iterative algorithm is introduced to reduce the computational complexity. As for the solution of Nash equilibrium, one kind of gradient iterative algorithm is proposed, which is able to intelligently choose adjustment factors. The Nash equilibrium can be quickly solved; meanwhile, the step of presetting adjustment factors is avoided according to network parameters in traditional linear iterative model. Simulation results show that the proposed algorithm enhances the overall performance of the system.

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Unified and Distributed QoS-Driven Cell Association Algorithms in Heterogeneous Networks

Cited by 104 publications

References 30 publications

UE‐Initiated Cell Reselection Game for Cell Load Balancing in a Wireless Network

UE‐Initiated Cell Reselection Game for Cell Load Balancing in a Wireless Network

SDN-Based Self-Organizing Energy Efficient Downlink/Uplink Scheduling in Heterogeneous Cellular Networks

Game Algorithm for Resource Allocation Based on Intelligent Gradient in HetNet

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