Network level cooperation for resource allocation in future wireless networks

Khan, Manzoor Ahmed; Truong, Cuong; Geithner, Thomas; Sivrikaya, Fikret; Albayrak, Şahin

doi:10.1109/wd.2008.4812916

Cited by 19 publications

(12 citation statements)

References 13 publications

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“…Then, (9) becomes (10). Equation (10) the same as the proportional allocation with only the throughput gain as the utility function in the Nash bargaining game [1].…”

Section: Mathematical Formulationmentioning

confidence: 99%

“…In summary, the above experiment results show that the proposed method can select one or several networks to transmit data and distribute the traffic to them according to their spectral efficiencies and network status. Our scheme is different from the method in the bargaining game [1] or that based on the bankruptcy game [2]. In the bargaining game, HetNets are rational players and they complete the high-speed transmission in a cooperative way.…”

Section: Experiments 3 Three Network Have Different Traffic Load Andmentioning

confidence: 99%

“…Rate allocation over heterogeneous networks (HetNets) can make full use of available resources in different networks and has been studied in [1][2][3][4][5]. In [1,2], cooperative game theory has been applied to rate control and rate allocation at the network level, where the quality of service (QoS) can be guaranteed as long as the required *Correspondence: jjliu@scut.edu.cn School of Electronic and Information Engineering, South China University of Technology, 381 Wushan Road, Guangzhou 510640, Guangdong Province, China transmission rate is satisfied. The non-cooperative game framework of bandwidth allocation in 4G heterogeneous wireless access networks has been discussed in [3].…”

Section: Introductionmentioning

confidence: 99%

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Rate allocation based on spectrum pricing function in collaborative transmission over heterogeneous wireless access networks

Liu

Wang

2012

J Wireless Com Network

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This article studied rate allocation in collaborative transmission over heterogeneous wireless access networks. With the introduced pricing function, which uses the occupied spectrum to describe the cost in data transmission, rate allocation is formulated as a concave optimization problem and the explicit solution has been obtained. Instead of transmitting through all available networks in pursuit of fairness, the proposed rate allocation scheme distributes traffic to available networks in an unbalanced way according to both spectral efficiency and network status. Simulation results have shown that compared with other methods, the new scheme can maximize the total utility gained in collaborative transmission and avoid congestion effectively.

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“…Then, (9) becomes (10). Equation (10) the same as the proportional allocation with only the throughput gain as the utility function in the Nash bargaining game [1].…”

Section: Mathematical Formulationmentioning

confidence: 99%

Section: Experiments 3 Three Network Have Different Traffic Load Andmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Rate allocation based on spectrum pricing function in collaborative transmission over heterogeneous wireless access networks

Liu

Wang

2012

J Wireless Com Network

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“…At the intraoperator level operator's RANs in an area bargain over the requests coming from users that belong to the operator. Intra-operator game is played in order to find an optimal distribution of divisible application bandwidth among RANS, and is based on our previous work [6]. The request is allocated to different RANs, and the utility function of different RANs is set to be the amount of allocated bandwidth above a certain disagreement point.…”

Section: Network-centric Cooperative Resource Allocationmentioning

confidence: 99%

“…However this utility function strengthens the concept of priorities over network technologies set by the operator for different services, which is reflected in the offered bandwidth. The upper bound on the allocation bandwidth is defined by the feasible set [6].…”

Section: Network-centric Cooperative Resource Allocationmentioning

confidence: 99%

Performance Analysis of User-Centric Network Selection and Network-Centric Resource Allocation Approaches in Wireless Networks

Albayrak

Khan

Sivrikaya

et al. 2010

Communications: Wireless in Developing Countries and Networks of the Future

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Abstract. The possible leveraging of high deployment costs, and the possibility to increase revenue have also introduced the concept of network sharing between different operators. On the other hand, the realization of a user-centric paradigm in future heterogeneous wireless networks, which implies free and automatic choice among different available wireless and mobile access networks, will revolutionize future wireless networks. For this innovative concept to materialize a paradigm shift is required from a long-term contractual based service delivery to a shortterm contractual and dynamic service delivery. In this paper we formulate and compare the network-centric resource allocation and and user-centric network selection problems in a multi-operator scenario using two different flavors of game theory, namely bargaining games (for network-centric resource sharing) and multi-attribute auctions (for user-centric interface selection). We also compare the contributed solutions to one another to investigate their performance in terms of efficient resource allocation, call blocking probability, and user satisfaction.

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Nash Bargaining Approach for Fair and Efficient LTE–WiFi Aggregation

2019

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As the demand for mobile traffic is growing, radio frequency bandwidths often become insufficient for guaranteeing specific performance. Utilizing unlicensed 5 GHz broadband bandwidths, the LWA system enables the synergistic use of LTE and WiFi systems based on link aggregation techniques, without critical deterioration of service quality. This provides considerable benefits to the mobile operators when they successfully operate two networks and maintain an optimal balance in managing network resources. This study addresses balancing and fairness for newly emerging LWA systems. In particular, we develop a two-stage LWA access framework based on the Nash bargaining game. The framework further improves fairness and efficiency in the LTE-WiFi resource utilization by incorporating user segmentations on the basis of user characteristics, such as service contract and usage patterns. Extensive experiments using a network simulator with an NS-3 unlicensed band produce good performance in terms of fairness and efficiency. The proposed scheme demonstrates better outcomes compared to the existing methods while concurrently using radio resources in the LWA system in a fair manner. Moreover, our approach presents a simple and fast decision-making process and requires simple user profiles-contract status and traffic generation history. Therefore, the proposed scheme is far lighter and more flexible than traditional approaches that require extensive information exchanges between heterogeneous networks.

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Network level cooperation for resource allocation in future wireless networks

Cited by 19 publications

References 13 publications

Rate allocation based on spectrum pricing function in collaborative transmission over heterogeneous wireless access networks

Rate allocation based on spectrum pricing function in collaborative transmission over heterogeneous wireless access networks

Performance Analysis of User-Centric Network Selection and Network-Centric Resource Allocation Approaches in Wireless Networks

Nash Bargaining Approach for Fair and Efficient LTE–WiFi Aggregation

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