Bandwidth allocation in wireless networks employing social distance aware utility functions

IEEE Trans. Veh. Technol.

et al. 2017

Abstract-In this paper, a novel social network-aware user association in wireless small cell networks with underlaid deviceto-device (D2D) communication is investigated. The proposed approach exploits social strategic relationships between user equipments (UEs) and their physical proximity to optimize the overall network performance. This problem is formulated as a matching game between UEs and their serving nodes (SNs) in which, an SN can be a small cell base station (SCBS) or an important UE with D2D capabilities. The problem is cast as a many-to-one matching game in which UEs and SNs rank one another using preference relations that capture both the wireless aspects (i.e., received signal strength, traffic load, etc.) and users' social ties (e.g., UE proximity and social distance). Due to the combinatorial nature of the network-wide UE-SN matching, the problem is decomposed into a dynamic clustering problem in which SCBSs are grouped into disjoint clusters based on mutual interference. Subsequently, an UE-SN matching game is carried out per cluster. The game under consideration is shown to belong to a class of matching games with externalities arising from interference and peer effects due to users social distance, enabling UEs and SNs to interact with one another until reaching a stable matching. Simulation results show that the proposed social-aware user association approach yields significant performance gains, reaching up to 26%, 24%, and 31% for 5-th, 50-th and 95-th percentiles for UE throughputs, respectively, as compared to the classical social-unaware baseline.

Section: A Ue and Sn Utilitiesmentioning

confidence: 99%

Dynamic Clustering and User Association in Wireless Small-Cell Networks With Social Considerations

Ashraf

Bennis

IEEE Trans. Veh. Technol.

et al. 2017

“…To calculate the utility of SCBS i ∈ N , we incorporate the social distance of each UE m with respect to SCBS i [19]. For the given matching η, the utility of an SCBS i is the sum of utilities of its associated UEs m ∈ L i .…”

Section: B Socially-aware Utility Of Scbsmentioning

confidence: 99%

Exploring social networks for optimized user association in wireless small cell networks with device-to-device communications

Ashraf

Bennis

2014 IEEE Wireless Communications and Networking Conference Workshops (WCNCW)

et al. 2014

Abstract-In this paper, we propose a novel social network aware approach for user association in wireless small cell networks. The proposed approach exploits social relationships between user equipments (UEs) and their physical proximity to optimize the network throughput. We formulate the problem as a matching game between UEs and their serving nodes (SNs). In our proposed game, the serving node can be a small cell base station (SCBS) or an important node with device-todevice capabilities. In this game, the SCBSs and UEs maximize their respective utility functions capturing both the spatial and social structures of the network. We show that the proposed game belongs to the class of matching games with externalities. Subsequently, we propose a distributed algorithm using which the SCBSs and UEs interact and reach a stable matching. We show the convergence of the proposed algorithm and study the properties of the resulting matching. Simulation results show that the proposed socially-aware user association approach can efficiently offload traffic while yielding a significant gain reaching up to 63% in terms of data rates as compared to the classical (social-unaware) approach.

“…In addition to the conventional physical layer metrics to optimize the SCN's performance, modern UEs can offer a versatile range of information from higher network layers that could help to reap the prospective gains of D2D deployments in SCNs. Such additional information, referred to as context information, may include the data extracted from online social networks [20]- [23], the history of a user's throughput [24], prediction of a user's location [25], or the delay-throughput tolerance of the applications [26]. The work in [20] develops an analytical model for the epidemic information spreading among mobile users of an ad hoc network.…”

Section: Introductionmentioning

confidence: 99%

“…In [21], the resource allocation in wireless LAN is defined as an optimization problem, taking the notion of social distance into account. The authors in [22] and [23] extend the work in [21] by introducing new utility functions which again account for the social distance of users, extracted from the social graph. Existing context-aware works [20]- [26] are mostly tailored to macrocell networks, are based on centralized approaches, and do not address the SCN or D2D over SCN challenges.…”

Section: Introductionmentioning

confidence: 99%

“…Existing context-aware works [20]- [26] are mostly tailored to macrocell networks, are based on centralized approaches, and do not address the SCN or D2D over SCN challenges. In addition, although the use of social networks has been demonstrated to be useful to improve wireless systems, most existing works such as [20]- [23] are based solely on the physical aspects of the social network, e.g., centrality measures. Such notion of social context is insufficient to capture common interests.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Context-Aware Small Cell Networks: How Social Metrics Improve Wireless Resource Allocation

Semiari

IEEE Trans. Wireless Commun.

Valentin

et al. 2015

Abstract-In this paper, a novel approach for optimizing and managing resource allocation in wireless small cell networks (SCNs) with device-to-device (D2D) communication is proposed. The proposed approach allows to jointly exploit both the wireless and social context of wireless users for optimizing the overall allocation of resources and improving traffic offload in SCNs. This context-aware resource allocation problem is formulated as a matching game in which user equipments (UEs) and resource blocks (RBs) rank one another, based on utility functions that capture both wireless and social metrics. Due to social interrelations, this game is shown to belong to a class of matching games with peer effects. To solve this game, a novel, selforganizing algorithm is proposed, using which UEs and RBs can interact to decide on their desired allocation. The proposed algorithm is then proven to converge to a two-sided stable matching between UEs and RBs. The properties of the resulting stable outcome are then studied and assessed. Simulation results using real social data show that clustering of socially connected users allows to offload a substantially larger amount of traffic than the conventional context-unaware approach. These results show that exploiting social context has high practical relevance in saving resources on the wireless links and on the backhaul.