Load Balancing in Downlink LTE Self-Optimizing Networks

Lobinger, Andreas; Stefański, Szymon; Jansen, Thomas; Balan, Irina

doi:10.1109/vetecs.2010.5493656

Cited by 194 publications

(117 citation statements)

References 4 publications

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“…The key idea of load balancing [2] is to shift the load of overloaded cells to less loaded adjacent cells by changing the virtual cell borders. The respective UEs are handed over with a load balancing handover command after the virtual cell borders have been changed.…”

Section: Load Balancing and Handover Parameters Optimization Algmentioning

confidence: 99%

“…In these simulations a constant bitrate traffic (CBR) of 256 kBit/s and a Quality of Service (QoS) of 100% of that rate have been defined. In [3] a mathematical framework for SON investigations on the downlink is defined and in [2] a detailed description of the used load balancing algorithm is provided.…”

Section: Load Balancing and Handover Parameters Optimization Algmentioning

confidence: 99%

“…Based on previous work [1] [2][5], we extend the optimization by a combined use case. We present the interactions of the two SON algorithms and show an example of a coordination system.…”

mentioning

confidence: 99%

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Coordinating Handover Parameter Optimization and Load Balancing in LTE Self-Optimizing Networks

Lobinger

Stefański²,

Jansen

et al. 2011

2011 IEEE 73rd Vehicular Technology Conference (VTC Spring)

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Abstract-In this paper we present simulation results of a self-optimizing network in a long-term-evolution (LTE) mobile communication system that uses two optimizing algorithms at the same time: load balancing (LB) and handover parameter optimization (HPO). Based on previous work [1] [2][5], we extend the optimization by a combined use case. We present the interactions of the two SON algorithms and show an example of a coordination system. The coordination system for self optimization observes system performance and controls the SON algorithms. As both SON algorithms deal with the handover decision itself, not only interactions, but also conflicts in the observation and control of the system are to be expected and are observed. The example of a coordination system here is not the optimal solution covering all aspects, but rather a working solution that shows equal performance to the individual algorithms or in the best case combining the strengths of the algorithms and achieving even better performance; although as localized gain, in time and area.

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Section: Load Balancing and Handover Parameters Optimization Algmentioning

confidence: 99%

Section: Load Balancing and Handover Parameters Optimization Algmentioning

confidence: 99%

See 1 more Smart Citation

Coordinating Handover Parameter Optimization and Load Balancing in LTE Self-Optimizing Networks

Lobinger

Stefański²,

Jansen

et al. 2011

2011 IEEE 73rd Vehicular Technology Conference (VTC Spring)

Self Cite

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show abstract

“…Many papers have focused on load balancing by optimizing the handoff threshold [3] and [4]. The algorithm followed in these papers was that of offloading the overloaded cells into its neighboring sectors by adjusting the handoff threshold in a coordinated manner so as to push the users lying near the boundary of the sectors to be associated to its less crowded neighbors.…”

Section: A Self-optimized Load Balancingmentioning

confidence: 99%

Hybrid spectrum allocation scheme in wireless cellular networks

George

Aliu

Imran

2011

2011 7th International Wireless Communications and Mobile Computing Conference

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Abstract-Mobile services have seen a major upswing driven by the bandwidth hungry applications thus leading to higher data rate requirements on the wireless networks. Spectrum being the most precious resource in the wireless industry is of keen interest. Various spectrum assignment and frequency reuse schemes have been proposed in literature. However in future networks, dynamic schemes that adapt to spatio-temporal variation in the environment are desired. We thus present a hybrid spectrum assignment scheme which adapts its allocation strategies depending on user distribution in the system. Results show that the proposed dynamic spectrum assignment strategy improves spectrum utilization thereby providing a higher data rate for the users.

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“…SON functionalities were included in the standardization of LTE [4], [5]. LTE self-optimizing applications focus mainly on resource and bandwidth allocation [13], Inter-Cell Interference Coordination [14] [15] and load balancing [16] [17].…”

Section: Introductionmentioning

confidence: 99%

Self Organizing Networks: A Reinforcement Learning approach for self-optimization of LTE Mobility parameters

Tiwana

2014

Automatika

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Original scientific paperWith the evolution of broadband mobile networks towards LTE and beyond, the support for the Internet and Internet based services is growing. Self Organizing Network (SON) functionalities intend to optimize the network performance for the improved user experience while at the same time reducing the network operational cost. This paper proposes a Reinforcement Learning (RL) based framework to improve throughput of the mobile users. The problem of spectral efficiency maximization is modeled as co-operative Multi-Agent control problem between the neighbouring eNodeBs (eNBs). Each eNB has an associated agent that dynamically changes the outgoing Handover Margin (HM) to its neighbouring cells. The agent uses the RL technique of Fuzzy Q-Learning (FQL) to learn the optimal mobility parameter i.e., HM value. The learning framework is designed to operate in an environment with the variations in traffic, user positions and propagation conditions. Simulation results have shown the proposed approach improves the network capacity and user experiences in terms of throughput. Key words: Handover Margin, LTE, Reinforcement Learning, Fuzzy Q-Learning, SONSamoorganizirajuće mreže: Podržano učenje za optimizaciju LTE mobilnosti. Razvoj širokopojasne mobilne mreže prema LTE mrežama uvjetuje pojačani rast internetskih servisa i usluga. Samoorganizirajuće mreže namijenjene su optimizaciji performansi mreže s ciljem poboljšanja korisnikovog zadovoljstva i smanjenja troškova rada. U radu se predlaže pristup zasnovan na podržanom učenju kako bi se popravila propusnost mobilnog korisnika. Problem maksimizacije spektralne učinkovitosti modelira se kao kooperativni više agentski problem upravljanje izme u susjednihčvorova (eNBs). Svakičvor ima pridruženog agenta koji dinamički mijenja marginu primopredaje prema susjednimćelijama. Agent koristi tehniku neizrazitog Q učenja (FQL) kako bi naučio optimizirati parametre mreže. Učenje je organizirano za rad u uvjetima raznovrsnog prometa, korisničkih položaja i uvjeta propagacije. Simulacijski rezultati pokazuju kako predloženi pristup poboljšava kapacitet mreže i korisnički doživljaj u smislu propusnosti mreže.

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Load Balancing in Downlink LTE Self-Optimizing Networks

Cited by 194 publications

References 4 publications

Coordinating Handover Parameter Optimization and Load Balancing in LTE Self-Optimizing Networks

Coordinating Handover Parameter Optimization and Load Balancing in LTE Self-Optimizing Networks

Hybrid spectrum allocation scheme in wireless cellular networks

Self Organizing Networks: A Reinforcement Learning approach for self-optimization of LTE Mobility parameters

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