Joint interference management and handover optimization in LTE small cells network

Capdevielle, Véronique; Feki, Afef; Sorsy, Elom

doi:10.1109/icc.2012.6364783

Cited by 10 publications

(11 citation statements)

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“…In fact, we allow {E t (i, n)} to be any arbitrary sequence for any n and any i. This is a fundamental difference to the stochastic MAB based mobility solutions [10], [11]. Note that E t (i, n) may include additional energy consumptions if the UE decides to switch from one SBS to another.…”

Section: B Non-stochastic Energy Consumption Modelmentioning

confidence: 99%

“…Mobility management in a heterogeneous network with high-velocity users is considered in [10], where the solution uses stochastic MAB theory to learn the optimal cell range expansion parameter. In [11], the authors propose a stochastic MAB-based interference management algorithm, which improves the handover performance by reducing the effective interference.…”

Section: Introductionmentioning

confidence: 99%

“…• We explicitly add the handover cost to the utility function to model the additional energy consump- [5], [7], [6], [8] [10], [11] This work tions due to handovers, and thus force the optimal solution to minimize frequent handovers.…”

Section: Introductionmentioning

confidence: 99%

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A Non-Stochastic Learning Approach to Energy Efficient Mobility Management

Shen

Tekin

Schaar

2016

IEEE J. Select. Areas Commun.

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Energy efficient mobility management is an important problem in modern wireless networks with heterogeneous cell sizes and increased nodes densities. We show that optimization-based mobility protocols cannot achieve long-term optimal energy consumption, particularly for ultra-dense networks (UDN). To address the complex dynamics of UDN, we propose a non-stochastic online-learning approach which does not make any assumption on the statistical behavior of the small base station (SBS) activities. In addition, we introduce handover cost to the overall energy consumption, which forces the resulting solution to explicitly minimize frequent handovers. The proposed Batched Randomization with Exponential Weighting (BREW) algorithm relies on batching to explore in bulk, and hence reduces unnecessary handovers. We prove that the regret of BREW is sublinear in time, thus guaranteeing its convergence to the optimal SBS selection. We further study the robustness of the BREW algorithm to delayed or missing feedback. Moreover, we study the setting where SBSs can be dynamically turned on and off.We prove that sublinear regret is impossible with respect to arbitrary SBS on/off, and then develop a novel learning strategy, called ranking expert (RE), that simultaneously takes into account the handover cost and the availability of SBS. To address the high complexity of RE, we propose a contextual ranking expert (CRE) algorithm that only assigns experts in a given context. Rigorous regret bounds are proved for both RE and CRE with respect to the best expert. Simulations show that not only do the proposed mobility algorithms greatly reduce the system energy consumption, but they are also robust to various dynamics which are common in practical ultra-dense wireless networks.C. Shen is with the

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Section: B Non-stochastic Energy Consumption Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Non-Stochastic Learning Approach to Energy Efficient Mobility Management

Shen

Tekin

Schaar

2016

IEEE J. Select. Areas Commun.

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“…Lobinger et al investigated the coordination of handover parameter optimization and load balance in LTE SON [8]. Capdevielle et al investigated the joint interference management and handover optimization in LTE small cells networks [9]. They focus on two challenges for LTE small cell deployment, intercell interference management, and mobility management.…”

Section: Related Workmentioning

confidence: 99%

Data-Driven Handover Optimization in Next Generation Mobile Communication Networks

Lin

Casanova

Fatty

2016

Mobile Information Systems

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Network densification is regarded as one of the important ingredients to increase capacity for next generation mobile communication networks. However, it also leads to mobility problems since users are more likely to hand over to another cell in dense or even ultradense mobile communication networks. Therefore, supporting seamless and robust connectivity through such networks becomes a very important issue. In this paper, we investigate handover (HO) optimization in next generation mobile communication networks. We propose a data-driven handover optimization (DHO) approach, which aims to mitigate mobility problems including too-late HO, too-early HO, HO to wrong cell, ping-pong HO, and unnecessary HO. The key performance indicator (KPI) is defined as the weighted average of the ratios of these mobility problems. The DHO approach collects data from the mobile communication measurement results and provides a model to estimate the relationship between the KPI and features from the collected dataset. Based on the model, the handover parameters, including the handover margin and time-to-trigger, are optimized to minimize the KPI. Simulation results show that the proposed DHO approach could effectively mitigate mobility problems.

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“…Fig. 1 illustrates a prospective network architecture for MTC applications where a heterogeneous network (HetNet) consisting of multiple small cells is deployed in an area of interest [2] [3]. These cells are overlapped in design to prevent blind spots and achieve seamless handover.…”

Section: Introductionmentioning

confidence: 99%

Hidden Markov Model Based Machine Learning for mMTC Device Cell Association in 5G Networks

Balapuwaduge

2019

ICC 2019 - 2019 IEEE International Conference on Communications (ICC)

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Massive machine-type communication (mMTC) is expected to play a pivotal role in emerging 5G networks. Considering the dense deployment of small cells and the existence of heterogeneous cells, an MTC device can discover multiple cells for association. Under traditional cell association mechanisms, MTC devices are typically associated with an eNodeB with highest signal strength. However, the selected eNodeB may not be able to handle mMTC requests due to network congestion and overload. Therefore, reliable cell association would provide a smarter solution to facilitate mMTC connections. To enable such a solution, a hidden Markov model (HMM) based machine learning (ML) technique is proposed in this paper to perform optimal cell association. As such, we consider MTC devices with network-assisted decision-making capabilities for selecting the most appropriate eNodeB for data transmission. The proposed HMM based ML technique focuses mainly on the reliability and availability of network resources. Correspondingly, two schemes are developed based on the classical reliability function and the next probable state of the HMM. Based on simulations under various configurations, we demonstrate the advantage of the proposed schemes over a random cell selection scheme.

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Joint interference management and handover optimization in LTE small cells network

Cited by 10 publications

References 0 publications

A Non-Stochastic Learning Approach to Energy Efficient Mobility Management

A Non-Stochastic Learning Approach to Energy Efficient Mobility Management

Data-Driven Handover Optimization in Next Generation Mobile Communication Networks

Hidden Markov Model Based Machine Learning for mMTC Device Cell Association in 5G Networks

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