On Interference Management Techniques in LTE Heterogeneous Networks

Simsek, Meryem; Czylwik, Andreas; Bennis, Mehdi

doi:10.1109/icccn.2012.6289281

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…The Fuzzy Q-learning can also be decentralized for HetNets, as studied in [38], which focuses on cross-layer interference mitigation in an OFDMA based HetNet deployment [39], [40]. By the comprehensive comparison among decentralized Q-learning, fuzzy Q-learning, improved Q-learning and expertness-based Q-learning methods, the study in [41] delves into the challenges of interference management solutions.…”

Section: A Machine Learningmentioning

confidence: 99%

Artificial Intelligence-Based Techniques for Emerging Heterogeneous Network: State of the Arts, Opportunities, and Challenges

2015

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Recently, mobile networking systems have been designed with more complexity of infrastructure and higher diversity of associated devices and resources, as well as more dynamical formations of networks, due to the fast development of current Internet and mobile communication industry. In such emerging mobile heterogeneous networks (HetNets), there are a large number of technical challenges focusing on the efficient organization, management, maintenance, and optimization, over the complicated system resources. In particular, HetNets have attracted great interest from academia and industry in deploying more effective solutions based on artificial intelligence (AI) techniques, e.g., machine learning, bio-inspired algorithms, fuzzy neural network, and so on, because AI techniques can naturally handle the problems of large-scale complex systems, such as HetNets towards more intelligent and automatic-evolving ones. In this paper, we discuss the state-of-the-art AI-based techniques for evolving the smarter HetNets infrastructure and systems, focusing on the research issues of self-configuration, self-healing, and self-optimization, respectively. A detailed taxonomy of the related AI-based techniques of HetNets is also shown by discussing the pros and cons for various AI-based techniques for different problems in HetNets. Opening research issues and pending challenges are concluded as well, which can provide guidelines for future research work.INDEX TERMS Artificial intelligence, genetic algorithms, ant colony optimization, self-organization networks, heterogeneous networks.

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Section: A Machine Learningmentioning

confidence: 99%

Artificial Intelligence-Based Techniques for Emerging Heterogeneous Network: State of the Arts, Opportunities, and Challenges

2015

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“…BSs, such as the Home eNodeBs (HeNBs) are implemented recently to get similar cellular network to the User Equipments (UEs). Such BSs in turn are user deployed or operator deployed, in which they can be located in the same area with a probability to distribute the same spectrum [7].…”

Section: Introductionmentioning

confidence: 99%

Traffic Offloading in LTE System Based Heteroge-neous Networks

Mosleh¹

2017

IJEEE

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The continuous growing developments in the traffic of mobile data limits the data throughput and capacity of cellular networks. "Heterogeneous Networks (HetNets)" are efficient solution to realize such demands. However, in HetNets, the congestion on the overloaded cellular network can be increased when the traffic of data is pushed from a cellular network to the Wi-Fi. In practice, offloading the cellular data traffic to a Wireless Local Area Network (WLAN) depending on the signal quality is a broadly deployed method to solve such problem. The use of Device to Device (D2D) communication further enhances the traffic offloading in WLAN systems and helps to obtain better throughput, end-to-end delay and network load. However, the critical offloading potential and its impacts on the whole performance is not totally understood. In this paper, the offloading of Long Term Evolution (LTE) traffic is presented using a WLAN for voice and video applications. A comparison is performed among two WLAN mechanisms; Distributed coordination function (DCF) and Point Coordination Function (PCF). As well, the effect of adding a D2D technology to the PCF is discussed. The WLAN effectively offloaded nodes at their Signal to Interference and Noise Ratio (SINR) becomes more than a specific threshold. Results presented that the PCF mechanism outperforms the DCF one in terms of packet loss ratio, throughput and the maximum load of the entire network. In addition, the use of a D2D serviced in the PCF helps in further reduction in the network load.

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“…More power control algorithms for the HetNets have been proposed for both uplink [15,20] and downlink scenarios [21,22] based on optimization frameworks with objectives of minimizing the transmit power of small cells under a required SINR constraint. In addition, learning based noncooperative power control algorithms [23][24][25] were proposed. In 3GPP Rel-10 standard, enhanced intercell interference coordination (eICIC) technique has been introduced for interference mitigation in the HetNets.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Resource Scheduling for Dual Connectivity in Heterogeneous IoT Cellular Networks

Kim

2016

International Journal of Distributed Sensor Networks

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As massive distributed sensor devices are integrated into Internet for Internet of things (IoT) and generate tremendous data from simple measurement to rich multimedia information, wireless cellular networks like LTE are enforced to deploy more small cells to accommodate data from the countless IoT devices. In 3GPP Rel-12 specification, dual connectivity helps deploying the small cell eNBs by separating a control and data plane to a macro and small cell, respectively. The dual connectivity also improves per-user throughput and mobility robustness. Meanwhile, dynamic TDD configuration in the Rel-12 can enhance radio resource utilization of TDD-based small cells even though intercell interference can be worse than legacy static configuration within a small cell cluster. In this paper, we propose a heterogeneous cellular IoT network architecture using the aforementioned two small cell features, as well as scheduling algorithms for load balancing in the dual connectivity and for dynamic TDD configuration to mitigate interference in the small cell cluster. We evaluate proposed algorithms using LTE system level simulator and show that our approach improves network throughput.

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On Interference Management Techniques in LTE Heterogeneous Networks

Cited by 5 publications

References 8 publications

Artificial Intelligence-Based Techniques for Emerging Heterogeneous Network: State of the Arts, Opportunities, and Challenges

Artificial Intelligence-Based Techniques for Emerging Heterogeneous Network: State of the Arts, Opportunities, and Challenges

Traffic Offloading in LTE System Based Heteroge-neous Networks

Adaptive Resource Scheduling for Dual Connectivity in Heterogeneous IoT Cellular Networks

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