A High-Efficient Algorithm of Mobile Load Balancing in LTE System

Yang, Ying; Li, Pengfei; Chen, Xiaohui; Wang, Weidong

doi:10.1109/vtcfall.2012.6398873

Cited by 30 publications

(25 citation statements)

References 11 publications

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“…The proposed scheme model the BS satisfaction as a utility per BS. The authors in [23] proposed an algorithm which uses the load difference between neighbouring cells to dynamically adjust the cell specific offset between neighbouring cells (OCN). In [24], Handover Parameters Adjustment for Conflict Avoidance (HPACA) is proposed.…”

Section: Mobility Load Balancingmentioning

confidence: 99%

Mobility robustness self-organizing network handover scheme for LTE-Advanced

Shukry

Fahmy

2016

2016 33rd National Radio Science Conference (NRSC)

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In this paper, we present a new adaptive handover algorithm for LTE-Advanced system. This algorithm ensures the User Equipment (UE) requirements and the Quality of Service (QoS) expectations. The proposed algorithm relies on the dynamic adjustment of the handover parameters settings based on the velocity of the UE. In order to reduce the conflict between mobility robustness optimization and load balancing the handover decision takes into consideration the channel conditions and the load of the neighbor Evolved Nodes B (eNBs). The simulation results show the superiority of our proposed algorithm in terms of throughput and handover failure rate.

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Section: Mobility Load Balancingmentioning

confidence: 99%

Mobility robustness self-organizing network handover scheme for LTE-Advanced

Shukry

Fahmy

2016

2016 33rd National Radio Science Conference (NRSC)

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“…In this algorithm as shown in Fig.2, MLB is achieved by tuning any of these two parameters in Picocells proposed in [13] [14] [15]: handover offset (HOM) and the transmit power (TXP). The former is adjusted to make the user access to the new target node successfully, whereas the latter is adjusted to make the user to be served successfully by the new target node.…”

Section: Self-tuning Schemementioning

confidence: 99%

Study on Based Reinforcement Q-Learning for Mobile Load Balancing Techniques in LTE-A HetNets

Tang

Chen

et al. 2014

2014 IEEE 17th International Conference on Computational Science and Engineering

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Mobile-broadband traffic has experienced a large increase and the network has continuously expanded over the past few years. Picocells are envisioned to cope with such a demand of capacity in network environments. Since those small cells are low-cost nodes, a thorough deployment is not typically performed, particularly in LTE-A HetNets. As a result, the matching between traffic demand and network resources is rarely optimal. In this paper, several common load balancing algorithms are studied and compared to solve localized congestion problems. In particular, these techniques are implemented by reinforcement Q-Learning algorithm that forecasts load status for every node, and combined with the related concepts of self-organization network which is the current research focus to adaptive parameters so that improve network performance.

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“…In a wireless cellular network, the load is almost uneven and time-varying in different cells because of the user mobility [7]. It may increase the call blocking rate and call dropping rate, decrease the network's throughput in a heavy-loaded cell, and reduce the radio resource utilization in a light-loaded cell [7].…”

Section: Introductionmentioning

confidence: 99%

“…It may increase the call blocking rate and call dropping rate, decrease the network's throughput in a heavy-loaded cell, and reduce the radio resource utilization in a light-loaded cell [7]. Thus, how to use SONs to resolve the load balancing problem is also an important issue.…”

Section: Introductionmentioning

confidence: 99%

An adaptive energy saving mechanism for LTE-A self-organizing HetNets

Hsu

Wang

2015

2015 Seventh International Conference on Ubiquitous and Future Networks

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Since the information and communication technology industry is one of contributors to global warming, how to utilize self-organizing networks (SONs), which can simplify network management, to achieve energy saving over future cellular networks has been a significant issue. We propose an adaptive energy saving mechanism (AES) for LTE-A self-organizing heterogeneous networks (HetNets). The proposed AES is designed for multi-hop cellular networks, in which each cell has an enhanced Node B (eNB) and multiple relay nodes (RNs). The AES uses two-level multi-threshold load management for each RN under different eNBs (inter-cell level) and for each RN within the same eNB (intra-cell level) so as to reduce the congestion in hot spot eNBs and RNs. In addition, the AES can dynamically switch an RN between active and sleep modes to maximize the number of sleep RNs for adaptive energy saving. It can also dynamically change an RN's coverage area to reduce energy consumption and to increase radio resource utilization. Besides, the AES adopts a neural network predictor to forecast the loading of each RN to determine whether it is appropriate to switch an RN to sleep mode. Simulation results show that with slightly sacrificing average throughput (1.16% lower) and radio interface delay (1.4% higher), the proposed AES's percentage of sleep RNs is from 0.28 to 0.19 under the percentage of active UEs from 0.7 to 1. Comparing with a representative related work, reinforcement learning (RL), the proposed AES's average energy consumption is 26.44% lower than RL's.

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A High-Efficient Algorithm of Mobile Load Balancing in LTE System

Cited by 30 publications

References 11 publications

Mobility robustness self-organizing network handover scheme for LTE-Advanced

Mobility robustness self-organizing network handover scheme for LTE-Advanced

Study on Based Reinforcement Q-Learning for Mobile Load Balancing Techniques in LTE-A HetNets

An adaptive energy saving mechanism for LTE-A self-organizing HetNets

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