Multiaccess Radio Network Enviroments

Charilas, Dimitris E.; Panagopoulous, A D

doi:10.1109/mvt.2010.939107

Cited by 43 publications

(17 citation statements)

References 17 publications

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“…The Lagrangian associated with the optimization problem can be expressed as follows: L x, p, λ (1) , λ (2) , μ, ν (1) , ν (2) = k∈K w k n∈N m∈Mn u k,mn (x k,mn , p k,mn )…”

Section: A Joint Resource Allocation Based On Fractional Subchannel mentioning

confidence: 99%

“…where λ (1) = λ (1) mn ≥ 0 ∀m, n and λ (2) = λ (2) k ≥ 0 ∀k are the sets of Lagrange multipliers corresponding to the physical limitation constraints, and μ = (μ k ∀k), ν (1) = ν (1) k ≥ 0 ∀k , and ν (2) = ν (2) k ≥ 0 ∀k are the sets of Lagrange multipliers corresponding to the required application throughput constraints, respectively. Also, the Lagrange dual function is obtained as follows:…”

Section: A Joint Resource Allocation Based On Fractional Subchannel mentioning

confidence: 99%

“…To accommodate the explosive growth in mobile data traffic, multihoming networks where users with multiple air interfaces can simultaneously transmit data traffic over multiple radio access networks (RANs) such as long-term evolution (LTE) and wireless local area network (WLAN) would become one of the promising solutions [2], [3]. Synergistic utilization of multiple RANs through multihoming access (MHA) not only offers a rich dimension for an increase in the capacity of the networks but also provides further flexibility in choosing appropriate networks according to the needs of users (e.g., voice and video traffic) and in managing how radio resources of the chosen networks are utilized.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Joint Optimization of Radio Resources for Uplink Multihoming Networks

Song

Choi

Han

2018

IEEE Systems Journal

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Multihoming networks in which users with multiple air interfaces can simultaneously transmit data traffic over multiple radio access technologies have greatly garnered attention as a promising solution to address the explosive growth in mobile data traffic. In this paper, we propose joint resource allocation algorithms for uplink multihoming networks. Unlike existing solutions in the literature, the proposed algorithms reflect two conflicting factors (i.e., throughput and monetary cost) affecting user's satisfaction level with respect to allocated radio resources in the process of resource allocation. Depending on the different assumptions of subchannel allocation, different joint resource allocation algorithms are developed where the main difference between the algorithms is whether they can be implemented in a distributed manner or not. Also, a diverse set of user's service types is taken into consideration, which makes the proposed algorithms suitable for an integrated service network. Numerical results demonstrate the validity of the proposed algorithms.Index Terms-Convex optimization, multihoming access (MHA), radio access networks (RANs), radio resource management, user's satisfaction.

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“…The Lagrangian associated with the optimization problem can be expressed as follows: L x, p, λ (1) , λ (2) , μ, ν (1) , ν (2) = k∈K w k n∈N m∈Mn u k,mn (x k,mn , p k,mn )…”

Section: A Joint Resource Allocation Based On Fractional Subchannel mentioning

confidence: 99%

Section: A Joint Resource Allocation Based On Fractional Subchannel mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Joint Optimization of Radio Resources for Uplink Multihoming Networks

Song

Choi

Han

2018

IEEE Systems Journal

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“…A good tutorial on the network selection problem is introduced by Charilas et al in [3]. The authors describe the network selection concept and the basic processes involved in the network selection process (e.g., selecting the decision criteria, collecting the alternatives' values, defining the weights, and ranking the alternatives).…”

Section: Network Selection Conceptmentioning

confidence: 99%

Game Theory-Based Network Selection: Solutions and Challenges

Trestian

Ormond

Muntean

2012

IEEE Commun. Surv. Tutorials

212

114

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In order to cater for the overwhelming growth in bandwidth demand from mobile Internet users operators have started to deploy different, overlapping radio access network technologies. One important challenge in such a heterogeneous wireless environment is to enable network selection mechanisms in order to keep the mobile users Always Best Connected (ABC) anywhere and anytime. Game theory techniques have been receiving growing attention in recent years as they can be adopted in order to model and understand competitive and cooperative scenarios between rational decision makers. This paper presents an overview of the network selection decision problem and challenges, a comprehensive classification of related game theoretic approaches and a discussion on the application of game theory to the network selection problem faced by the next generation of 4G wireless networks.

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“…Particularly, cognitive radio functionalities have been shown as a must to implement such a system. The authors in [7] studied the network selection process in multi-RAT networks by decomposing it and comparing the common approaches for network selection proposed in literature. In [8], the authors compared the data transmission schemes over multi-RATs with single RAT.…”

mentioning

confidence: 99%

A Framework for Distributed Resource Allocation and Admission Control in a Cognitive Digital Home

Mandayam

Reznik

2013

IEEE Trans. Wireless Commun.

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Abstract-The advances in radio design and wireless networking, along with the growth of multimedia home entertainment technologies, is creating the concept of a cognitive digital home requiring spectrum coexistence of various devices and networks of networks. A framework for resource allocation in a Cognitive Digital Home (CDH) with a multiplicity of radio access technologies (RAT) such as cognitive radios and legacy radio devices supporting heterogeneous applications is developed. We consider two channel access models in the CDH for addressing spectrum coexistence of legacy devices: (i) Pessimistic Controllability (PC) Model where the Home Genie node (HG) has no influence over legacy devices, and (ii) Switched RAT (SR) Model where the HG has perfect control of legacy devices. Distributed algorithms for maximizing sum rate and maximizing service capacity are designed using partial dual decomposition techniques. A distributed power control scheme is also designed for efficient use of energy. An admission control scheme based on pricing information obtained from the distributed algorithms is used to improve system feasibility.

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Multiaccess Radio Network Enviroments

Cited by 43 publications

References 17 publications

Joint Optimization of Radio Resources for Uplink Multihoming Networks

Joint Optimization of Radio Resources for Uplink Multihoming Networks

Game Theory-Based Network Selection: Solutions and Challenges

A Framework for Distributed Resource Allocation and Admission Control in a Cognitive Digital Home

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