26 research outputs found
A Comprehensive Survey of Potential Game Approaches to Wireless Networks
Potential games form a class of non-cooperative games where unilateral
improvement dynamics are guaranteed to converge in many practical cases. The
potential game approach has been applied to a wide range of wireless network
problems, particularly to a variety of channel assignment problems. In this
paper, the properties of potential games are introduced, and games in wireless
networks that have been proven to be potential games are comprehensively
discussed.Comment: 44 pages, 6 figures, to appear in IEICE Transactions on
Communications, vol. E98-B, no. 9, Sept. 201
Grant-Free Power Allocation for Ultra-Dense Internet of Things Environments: A Mean-Field Perspective
Grant free access, in which each Internet of Things (IoT) device delivers its
packets through a randomly selected resource without spending time on
handshaking procedures, is a promising solution for supporting the massive
connectivity required for IoT systems. In this paper, we explore grant free
access with multi packet reception capabilities, with an emphasis on ultra low
end IoT applications with small data sizes, sporadic activity, and energy usage
constraints. We propose a power allocation scheme that integrates the IoT
device's traffic and energy budget by using a stochastic geometry framework and
meanfield game theory to model and analyze mutual interference among active IoT
devices.We also derive a Markov chain model to capture and track the IoT
device's queue length and derive the successful transmission probability at
steady state. Simulation results illustrate the optimal power allocation
strategy and show the effectiveness of the proposed approach.Comment: Submitted to Journal of Network and Computer Application
Game Theory for Multi-Access Edge Computing:Survey, Use Cases, and Future Trends
Game theory (GT) has been used with significant success to formulate, and either design or optimize, the operation of many representative communications and networking scenarios. The games in these scenarios involve, as usual, diverse players with conflicting goals. This paper primarily surveys the literature that has applied theoretical games to wireless networks, emphasizing use cases of upcoming multiaccess edge computing (MEC). MEC is relatively new and offers cloud services at the network periphery, aiming to reduce service latency backhaul load, and enhance relevant operational aspects such as quality of experience or security. Our presentation of GT is focused on the major challenges imposed by MEC services over the wireless resources. The survey is divided into classical and evolutionary games. Then, our discussion proceeds to more specific aspects which have a considerable impact on the game's usefulness, namely, rational versus evolving strategies, cooperation among players, available game information, the way the game is played (single turn, repeated), the game's model evaluation, and how the model results can be applied for both optimizing resource-constrained resources and balancing diverse tradeoffs in real edge networking scenarios. Finally, we reflect on lessons learned, highlighting future trends and research directions for applying theoretical model games in upcoming MEC services, considering both network design issues and usage scenarios
Multilevel Pricing Schemes in a Deregulated Wireless Network Market
Typically the cost of a product, a good or a service has many components.
Those components come from different complex steps in the supply chain of the
product from sourcing to distribution. This economic point of view also takes
place in the determination of goods and services in wireless networks. Indeed,
before transmitting customer data, a network operator has to lease some
frequency range from a spectrum owner and also has to establish agreements with
electricity suppliers. The goal of this paper is to compare two pricing
schemes, namely a power-based and a flat rate, and give a possible explanation
why flat rate pricing schemes are more common than power based pricing ones in
a deregulated wireless market. We suggest a hierarchical game-theoretical model
of a three level supply chain: the end users, the service provider and the
spectrum owner. The end users intend to transmit data on a wireless network.
The amount of traffic sent by the end users depends on the available frequency
bandwidth as well as the price they have to pay for their transmission. A
natural question arises for the service provider: how to design an efficient
pricing scheme in order to maximize his profit. Moreover he has to take into
account the lease charge he has to pay to the spectrum owner and how many
frequency bandwidth to rent. The spectrum owner itself also looks for
maximizing its profit and has to determine the lease price to the service
provider. The equilibrium at each level of our supply chain model are
established and several properties are investigated. In particular, in the case
of a power-based pricing scheme, the service provider and the spectrum owner
tend to share the gross provider profit. Whereas, considering the flat rate
pricing scheme, if the end users are going to exploit the network intensively,
then the tariffs of the suppliers (spectrum owner and service provider)
explode.Comment: This is the last draft version of the paper. Revised version of the
paper accepted by ValueTools 2013 can be found in Proceedings of the 7th
International Conference on Performance Evaluation Methodologies and Tools
(ValueTools '13), December 10-12, 2013, Turin, Ital
Wireless Resource Management in Industrial Internet of Things
Wireless communications are highly demanded in Industrial Internet of Things (IIoT) to realize the vision of future flexible, scalable and customized manufacturing. Despite the academia research and on-going standardization efforts, there are still many challenges for IIoT, including the ultra-high reliability and low latency requirements, spectral shortage, and limited energy supply. To tackle the above challenges, we will focus on wireless resource management in IIoT in this thesis by designing novel framework, analyzing performance and optimizing wireless resources. We first propose a bandwidth reservation scheme for Tactile Internet in the local area network of IIoT. Specifically, we minimize the reserved bandwidth taking into account the classification errors while ensuring the latency and reliability requirements. We then extend to the more challenging long distance communications for IIoT, which can support the global skill-set delivery network. We propose to predict the future system state and send to the receiver in advance, and thus the delay experienced by the user is reduced. The bandwidth usage is analysed and minimized to ensure delay and reliability requirements. Finally, we address the issue of energy supply in IIoT, where Radio frequency energy harvesting (RFEH) is used to charge unattended IIoT low-power devices remotely and continuously. To motivate the third-party chargers, a contract theory-based framework is proposed, where the optimal contract is derived to maximize the social welfare
Learning for Cross-layer Resource Allocation in the Framework of Cognitive Wireless Networks
The framework of cognitive wireless networks is expected to endow wireless devices with a cognition-intelligence ability with which they can efficiently learn and respond to the dynamic wireless environment. In this dissertation, we focus on the problem of developing cognitive network control mechanisms without knowing in advance an accurate network model. We study a series of cross-layer resource allocation problems in cognitive wireless networks. Based on model-free learning, optimization and game theory, we propose a framework of self-organized, adaptive strategy learning for wireless devices to (implicitly) build the understanding of the network dynamics through trial-and-error.
The work of this dissertation is divided into three parts. In the first part, we investigate a distributed, single-agent decision-making problem for real-time video streaming over a time-varying wireless channel between a single pair of transmitter and receiver. By modeling the joint source-channel resource allocation process for video streaming as a constrained Markov decision process, we propose a reinforcement learning scheme to search for the optimal transmission policy without the need to know in advance the details of network dynamics.
In the second part of this work, we extend our study from the single-agent to a multi-agent decision-making scenario, and study the energy-efficient power allocation problems in a two-tier, underlay heterogeneous network and in a self-sustainable green network. For the heterogeneous network, we propose a stochastic learning algorithm based on repeated games to allow individual macro- or femto-users to find a Stackelberg equilibrium without flooding the network with local action information. For the self-sustainable green network, we propose a combinatorial auction mechanism that allows mobile stations to adaptively choose the optimal base station and sub-carrier group for transmission only from local payoff and transmission strategy information.
In the third part of this work, we study a cross-layer routing problem in an interweaved Cognitive Radio Network (CRN), where an accurate network model is not available and the secondary users that are distributed within the CRN only have access to local action/utility information. In order to develop a spectrum-aware routing mechanism that is robust against potential insider attackers, we model the uncoordinated interaction between CRN nodes in the dynamic wireless environment as a stochastic game. Through decomposition of the stochastic routing game, we propose two stochastic learning algorithm based on a group of repeated stage games for the secondary users to learn the best-response strategies without the need of information flooding