Graph-Based User Satisfaction-Aware Fair Resource Allocation in OFDMA Femtocell Networks

Meng, Yun; Li, Jiandong; Li, Hongyan; Liu, Peng

doi:10.1109/tvt.2014.2341250

Cited by 26 publications

(12 citation statements)

References 11 publications

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“…Thus, the resource allocation problem can now be formulated as

({boldf}^{opt}, {boldp}^{opt}) = \underset{boldf \in scriptℳ, boldp \in boldP}{arg max} U_{0}

where U 0 is the system utility function. If we take into account the user traffic demand, without loss of the generality, the form of the satisfaction of user i can be defined as

u_{i} = ({, \begin{matrix} \frac{R_{i}}{D_{i}}, R_{i} < D_{i} \\ 1, otherwise \end{matrix})

where R i denotes the achievable data rate for the user i and D i is the required transmission rate. Then, from the system optimisation point of view, we can have

U_{0} = \sum_{i \in scriptN} u_{i}

and problem can be transformed into

\begin{matrix} (P 1) : \arg \max \sum_{i = 1}^{N} u_{i} (p_{i}, p_{- i}, f_{i}, f_{- i}) \\ subject to \\ (a) f_{i} \in {1, 2, \dots, M}, \forall i \in N \\ (b) p_{i} \in {λ_{1} P_{i, \max}, λ_{2} P_{i, \max}, \dots, λ_{L} P_{<...>} \end{matrix}

…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

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Demand‐aware resource allocation for ultra‐dense small cell networks: an interference‐separation clustering‐based solution

Qiu

et al. 2016

Trans. Emerging Tel. Tech.

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In this paper, we present a novel clustering-based resource allocation framework for downlink transmission in ultra-dense small cell networks. Specifically, we first model a combinatorial optimisation problem that jointly considers subchannel and power allocation and user traffic demand in terms of a large-scale network scenario. Unfortunately, the huge communication overhead and computational complexity make it challenging for traditional centralised/distributed solutions. To address this issue, we propose an interference-separation clustering-based scheme to divide the massive small cells into smaller groups with different priorities, which reduces the network scale. Different from the existing cluster construction scheme, the proposed clustering method effectively avoids the inter-cluster interference through coordination. Then, for a given cluster configuration, we formulate the distributed resource allocation problem as a local interaction game where the utility of each player comprises not only its own profits but also the interests of neighbours. We prove the existence of Nash equilibrium for the formulated game and design a hierarchical learning algorithm to achieve the Nash equilibrium, which only needs local information exchange. Finally, simulation results validate that the proposed solution outperforms some other existing approaches and is more suitable for large-scale networks.

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“…Thus, the resource allocation problem can now be formulated as

({boldf}^{opt}, {boldp}^{opt}) = \underset{boldf \in scriptℳ, boldp \in boldP}{arg max} U_{0}

where U 0 is the system utility function. If we take into account the user traffic demand, without loss of the generality, the form of the satisfaction of user i can be defined as

u_{i} = ({, \begin{matrix} \frac{R_{i}}{D_{i}}, R_{i} < D_{i} \\ 1, otherwise \end{matrix})

where R i denotes the achievable data rate for the user i and D i is the required transmission rate. Then, from the system optimisation point of view, we can have

U_{0} = \sum_{i \in scriptN} u_{i}

and problem can be transformed into

\begin{matrix} (P 1) : \arg \max \sum_{i = 1}^{N} u_{i} (p_{i}, p_{- i}, f_{i}, f_{- i}) \\ subject to \\ (a) f_{i} \in {1, 2, \dots, M}, \forall i \in N \\ (b) p_{i} \in {λ_{1} P_{i, \max}, λ_{2} P_{i, \max}, \dots, λ_{L} P_{<...>} \end{matrix}

…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

“…where U 0 is the system utility function. If we take into account the user traffic demand, without loss of the generality, the form of the satisfaction of user i can be defined as [34]…”

Section: Definitionmentioning

confidence: 99%

Demand‐aware resource allocation for ultra‐dense small cell networks: an interference‐separation clustering‐based solution

Qiu

et al. 2016

Trans. Emerging Tel. Tech.

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show abstract

“…. Motivated by the graph coloring for spectrum allocation problems [4], we define the experienced interference level as following:…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

Load-Aware Dynamic Spectrum Access for Small-Cell Networks: A Graphical Game Approach

Wang

Chen

et al. 2016

IEEE Trans. Veh. Technol.

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Abstract-In this letter, we investigate the problem of dynamic spectrum access for small cell networks, using a graphical game approach. Compared with existing studies, we take the features of different cell loads and local interference relationship into account. It is proved that the formulated spectrum access game is an exact potential game with the aggregate interference level as the potential function, and Nash equilibrium (NE) of the game corresponds to the global or local optima of the original optimization problem. A lower bound of the achievable aggregate interference level is rigorously derived. Finally, we propose an autonomous best response learning algorithm to converge towards its NE. It is shown that the proposed game-theoretic solution converges rapidly and its achievable performance is close to the optimum solution.Index Terms-5G networks, small cell networks, dynamic spectrum access, potential game.

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“…Centralized approaches for resource allocation [ 4 , 5 , 6 , 7 ] require some central authority to maintain complete knowledge of the environment through frequent information exchange with small cell base stations (SBSs). However, such requirements may not be desirable when considering randomly deployed large numbers of small cells, due to the computational complexity of the centralized solution [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Marginal Contribution-Based Distributed Subchannel Allocation in Small Cell Networks

Shah

Lim

Tan

2018

Sensors

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The paper presents a game theoretic solution for distributed subchannel allocation problem in small cell networks (SCNs) analyzed under the physical interference model. The objective is to find a distributed solution that maximizes the welfare of the SCNs, defined as the total system capacity. Although the problem can be addressed through best-response (BR) dynamics, the existence of a steady-state solution, i.e., a pure strategy Nash equilibrium (NE), cannot be guaranteed. Potential games (PGs) ensure convergence to a pure strategy NE when players rationally play according to some specified learning rules. However, such a performance guarantee comes at the expense of complete knowledge of the SCNs. To overcome such requirements, properties of PGs are exploited for scalable implementations, where we utilize the concept of marginal contribution (MC) as a tool to design learning rules of players’ utility and propose the marginal contribution-based best-response (MCBR) algorithm of low computational complexity for the distributed subchannel allocation problem. Finally, we validate and evaluate the proposed scheme through simulations for various performance metrics.

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Graph-Based User Satisfaction-Aware Fair Resource Allocation in OFDMA Femtocell Networks

Cited by 26 publications

References 11 publications

Demand‐aware resource allocation for ultra‐dense small cell networks: an interference‐separation clustering‐based solution

Demand‐aware resource allocation for ultra‐dense small cell networks: an interference‐separation clustering‐based solution

Load-Aware Dynamic Spectrum Access for Small-Cell Networks: A Graphical Game Approach

Marginal Contribution-Based Distributed Subchannel Allocation in Small Cell Networks

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