From Immune Cells to Self-Organizing Ultra-Dense Small Cell Networks

Klessig, Henrik; Ohmann, David; Reppas, Andreas I.; Hatzikirou, Haralampos; Abedi, Majid; Simsek, Meryem; Fettweis, Gerhard

doi:10.1109/jsac.2016.2544638

Cited by 30 publications

(17 citation statements)

References 38 publications

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“…Contributions [84]- [88] in this special issue address the topic of energy efficiency in dense networks. Contribution [84] presents a framework for self-organizing cells that autonomously activate or deactivate in response to traffic demands.…”

Section: A Dense Networkmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Energy-Efficient Techniques for 5G Networks and Challenges Ahead

Buzzi

Chih‐Lin

Klein

et al. 2016

IEEE J. Select. Areas Commun.

711

397

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Abstract-After about a decade of intense research, spurred by both economic and operational considerations, and by environmental concerns, energy efficiency has now become a key pillar in the design of communication networks. With the advent of the fifth generation of wireless networks, with millions more base stations and billions of connected devices, the need for energy-efficient system design and operation will be even more compelling. This survey provides an overview of energy-efficient wireless communications, reviews seminal and recent contribution to the state-of-the-art, including the papers published in this special issue, and discusses the most relevant research challenges to be addressed in the future.

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Section: A Dense Networkmentioning

confidence: 99%

“…Contribution [84] presents a framework for self-organizing cells that autonomously activate or deactivate in response to traffic demands. Network deployment in response to traffic conditions is also analyzed in [85].…”

Section: A Dense Networkmentioning

confidence: 99%

A Survey of Energy-Efficient Techniques for 5G Networks and Challenges Ahead

Buzzi

Chih‐Lin

Klein

et al. 2016

IEEE J. Select. Areas Commun.

711

397

View full text Add to dashboard Cite

show abstract

“…A key distinction between third-party deployments and operator-led deployments is that, in the former case, the small cells deployments are unplanned; that is, the location in which small cells are deployed is not originally defined by the operator's network planning procedures. However, although small cells are deployed autonomously by third-parties, the operator retains remote management via core network and the use of Self-Organizing Networks (SON) [23,24]. Therefore, to contextualize the proposed UDN planning and optimization framework in a realistic setting, we consider a case study for UDN in a high-density urban settlement.…”

Section: Deployment Scenariomentioning

confidence: 99%

A Planning and Optimization Framework for Ultra Dense Cellular Deployments

González

Mutafungwa

Haile

et al. 2017

Mobile Information Systems

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To accommodate the ever-expanding wireless data traffic volumes, mobile network operators are complementing their macrocellular networks by deploying low-power base stations (or small cells) to offload traffic from congested macrocells and to reuse spectrum. To that end, Ultra Dense Network (UDN) deployments provide means to aggressively reuse spectrum, thus providing significant enhancements in terms of system capacity. However, these deployments entail several challenges, including the increased complexity in network planning and optimization. In this paper, we propose a versatile optimization framework for planning UDN deployments. The planning and optimization framework is underpinned by metrics that consider scalability in terms of number of users, cost of densification, and fairness. The proposed methodology is evaluated using a real-world UDN planning case. The numerical results expose a number of interesting insights, including the impact of different bandwidth allocation strategies and spatial service demand distribution on the performance of various network topologies. Specifically, we provide a performance comparison of the optimized UDN topologies versus random (unplanned), regular grid, and heuristically derived UDN topologies. This comparison further underlines the need for flexible network planning and optimization frameworks as different operator performance metrics of interest may require different radio access networks configurations.

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“…Dense small cell networks were investigated in many works with different scenarios. For examples, in [11], authors developed a model of an artificial immune system that automatically turns on/off depending on traffic demands. Assuming stochastic traffic arrivals, an optimization-based scheme for the EE resource management in pico-transceiver Hetnets was proposed in [12].…”

Section: A Related Workmentioning

confidence: 99%

Energy-Efficient Resource Allocation for OFDMA Heterogeneous Networks

Tran

et al. 2019

IEEE Trans. Commun.

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We proposed several energy-efficient resource allocation algorithms for the downlink of an orthogonal frequencydivision-multiple-access (OFDMA) based femtocell heterogeneous networks (HetNets). Heterogeneous QoS and fairness in rate are investigated in the proposed resource allocation problem. A dense deployment of femtocells in the coverage area of a central macrocell is considered and energy usage of both femtocell and macrocell users are optimized simultaneously. We aim to maximize the weighted sum of the individual energy efficiencies (WSEEMax) and the network energy efficiency (NEEMax) while satisfying the following: (1) minimum throughput for delaysensitive (DS) users, (2) fairness constraint for delay-tolerant (DT) users, (3) required constraints of OFDMA systems. The problem is formulated in three different forms: mixed 0-1 integer programming formulation, time-sharing formulation and sparsity-inducing formulation. The proposed resource block (RB) and power optimization problems are combinatorial and highly non-convex due to the fractional form of the objective function, the integer constraint of OFDMA RBs and non-affine fairness. We adopt the successive convex approximation (SCA) approach and transform the problems into a sequence of convex subproblems. With the proposed algorithms, we show that the overall joint RB and power allocation schemes converge to suboptimal solutions. Numerical examples confirm the merits of the proposed algorithms.

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From Immune Cells to Self-Organizing Ultra-Dense Small Cell Networks

Cited by 30 publications

References 38 publications

A Survey of Energy-Efficient Techniques for 5G Networks and Challenges Ahead

A Survey of Energy-Efficient Techniques for 5G Networks and Challenges Ahead

A Planning and Optimization Framework for Ultra Dense Cellular Deployments

Energy-Efficient Resource Allocation for OFDMA Heterogeneous Networks

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