7,622 research outputs found
Offloading in Software Defined Network at Edge with Information Asymmetry: A Contract Theoretical Approach
The proliferation of highly capable mobile devices such as smartphones and
tablets has significantly increased the demand for wireless access. Software
defined network (SDN) at edge is viewed as one promising technology to simplify
the traffic offloading process for current wireless networks. In this paper, we
investigate the incentive problem in SDN-at-edge of how to motivate a third
party access points (APs) such as WiFi and smallcells to offload traffic for
the central base stations (BSs). The APs will only admit the traffic from the
BS under the precondition that their own traffic demand is satisfied. Under the
information asymmetry that the APs know more about own traffic demands, the BS
needs to distribute the payment in accordance with the APs' idle capacity to
maintain a compatible incentive. First, we apply a contract-theoretic approach
to model and analyze the service trading between the BS and APs. Furthermore,
other two incentive mechanisms: optimal discrimination contract and linear
pricing contract are introduced to serve as the comparisons of the anti adverse
selection contract. Finally, the simulation results show that the contract can
effectively incentivize APs' participation and offload the cellular network
traffic. Furthermore, the anti adverse selection contract achieves the optimal
outcome under the information asymmetry scenario.Comment: 10 pages, 9 figure
Breaking the Economic Barrier of Caching in Cellular Networks: Incentives and Contracts
In this paper, a novel approach for providing incentives for caching in small
cell networks (SCNs) is proposed based on the economics framework of contract
theory. In this model, a mobile network operator (MNO) designs contracts that
will be offered to a number of content providers (CPs) to motivate them to
cache their content at the MNO's small base stations (SBSs). A practical model
in which information about the traffic generated by the CPs' users is not known
to the MNO is considered. Under such asymmetric information, the incentive
contract between the MNO and each CP is properly designed so as to determine
the amount of allocated storage to the CP and the charged price by the MNO. The
contracts are derived by the MNO in a way to maximize the global benefit of the
CPs and prevent them from using their private information to manipulate the
outcome of the caching process. For this interdependent contract model, the
closed-form expressions of the price and the allocated storage space to each CP
are derived. This proposed mechanism is shown to satisfy the sufficient and
necessary conditions for the feasibility of a contract. Moreover, it is shown
that the proposed pricing model is budget balanced, enabling the MNO to cover
all the caching expenses via the prices charged to the CPs. Simulation results
show that none of the CPs will have an incentive to choose a contract designed
for CPs with different traffic loads.Comment: Accepted for publication at Globecom 201
NOMA based resource allocation and mobility enhancement framework for IoT in next generation cellular networks
With the unprecedented technological advances witnessed in the last two decades, more devices are connected to the internet, forming what is called internet of things (IoT). IoT devices with heterogeneous characteristics and quality of experience (QoE) requirements may engage in dynamic spectrum market due to scarcity of radio resources. We propose a framework to efficiently quantify and supply radio resources to the IoT devices by developing intelligent systems. The primary goal of the paper is to study the characteristics of the next generation of cellular networks with non-orthogonal multiple access (NOMA) to enable connectivity to clustered IoT devices. First, we demonstrate how the distribution and QoE requirements of IoT devices impact the required number of radio resources in real time. Second, we prove that using an extended auction algorithm by implementing a series of complementary functions, enhance the radio resource utilization efficiency. The results show substantial reduction in the number of sub-carriers required when compared to conventional orthogonal multiple access (OMA) and the intelligent clustering is scalable and adaptable to the cellular environment. Ability to move spectrum usages from one cluster to other clusters after borrowing when a cluster has less user or move out of the boundary is another soft feature that contributes to the reported radio resource utilization efficiency. Moreover, the proposed framework provides IoT service providers cost estimation to control their spectrum acquisition to achieve required quality of service (QoS) with guaranteed bit rate (GBR) and non-guaranteed bit rate (Non-GBR)
A Signaling-based Incentive Mechanism for Device-to-Device Content Sharing in Cellular Networks
In this letter, we model the Device-to-device (D2D)
content sharing problem as a labor market where the base station
(BS) acts as the principal and content providers serve as agents.
A signaling-based content-sharing incentive (SCSI) mechanism is
designed to encourage candidate content providers to participate
in content sharing, and the optimal strategy for each content
provider is derived to maximize their utility (monetary profit)
while guaranteeing a non-negative utility for the BS. Simulation
results show that the proposed SCSI mechanism can increase the
content provider’s utility and participating enthusiasm in D2D
content sharing
Game Theoretic Approaches to Massive Data Processing in Wireless Networks
Wireless communication networks are becoming highly virtualized with
two-layer hierarchies, in which controllers at the upper layer with tasks to
achieve can ask a large number of agents at the lower layer to help realize
computation, storage, and transmission functions. Through offloading data
processing to the agents, the controllers can accomplish otherwise prohibitive
big data processing. Incentive mechanisms are needed for the agents to perform
the controllers' tasks in order to satisfy the corresponding objectives of
controllers and agents. In this article, a hierarchical game framework with
fast convergence and scalability is proposed to meet the demand for real-time
processing for such situations. Possible future research directions in this
emerging area are also discussed
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