Analysis of duopoly price competition between WLAN providers

Proceedings of the IEEE International Conference on Communications, 2009, p. 1-5With the rapid development of wireless Internet services, several WLAN service providers may coexist in one public hotspot to compete for the same group of customers, leading to an inevitable price competition. The charged price and the provisioned packet loss at each provider are major factors in determining users' demands and behaviors, which in turn will affect providers' revenue and social welfare. In this paper, we set up a novel game model to analyze a duopoly price competition. We first show the users' demands are distributed between providers according to a Wardrop Equilibrium and then prove the existence of a Nash equilibrium on providers' charged prices. Through analysis, we further find that in Nash equilibrium state the social welfare is very close to its maximal value in cooperative situation. Furthermore, the providers' aggregate revenues also do not decrease when the users have high sensitivity about the charged prices. Thus the competitive duopoly WLAN market can still run in an efficient way even in the absence of complex regulation schemes. ©2009 IEEE.published_or_final_versio

Auction and Game-based Spectrum Sharing in Cognitive Radio Networks

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Machine Learning Approach for Spectrum Sharing in the Next Generation Cognitive Mesh Network

Nowadays, there is an unexpected explosion in the demand for wireless network resources. This is due to the dramatic increase in the number of the emerging web-based services. For wireless computer network, limited bandwidth along with the transmission quality requirements for users, make quality of service (QoS) provisioning a very challenging problem. To overcome spectrum scarcity problem, Federal Communications Commission (FCC) has already started working on the concept of spectrum sharing where unlicensed users (secondary users, SUs) can share the spectrum with licensed users (primary users, PUs), provided they respect PUs rights to use spectrum exclusively. Cognitive technology presents a revolutionary wireless communication where users can exploit the spectrum dynamically. The integration of cognitive technology capability into the conventional wireless networks is perhaps the significant promising architectural upgrade in the next generation of wireless network that helps to solve spectrum scarcity problem. In this work, we propose integrating cognitive technology with wireless mesh network to serve the maximum number of SUs by utilizing the limited bandwidth efficiently. The architecture for this network is selected first. In particular, we introduce the cluster-based architecture, signaling protocols, spectrum management scheme and detailed algorithms for the cognitive cycle. This new architecture is shown to be promising for the cognitive network. In order to manage the transmission power for the SUs in the cognitive wireless mesh network, a dynamic power management framework is developed based on machine learning to improve spectrum utilization while satisfying users requirements. Reinforcement learning (RL) is used to extract the optimal control policy that allocates spectrum and transmission powers for the SUs dynamically. RL is used to help users to adapt their resources to the changing network conditions. RL model considers the spectrum request arrival rate of the SUs, the interference constraint for the PUs, the physical properties of the channel that is selected for the SUs, PUs activities, and the QoS for SUs. In our work, PUs trade the unused spectrum to the SUs. For this sharing paradigm, maximizing the revenue is the key objective of the PUs, while that of the SUs is to meet their requirements and obtain service from the rented spectrum. However, PUs have to maintain their QoS when trading their spectrum. These complex conflicting objectives are embedded in our machine learning model. The objective function is defined as the net revenue gained by PUs from renting some of their spectrum. We use a machine learning to help the PUs to make a decision about the optimal size and price of the offered spectrum for trading. The trading model considers the QoS for PUs and SUs, traffic load at the PUs, the changes in the network conditions, and the revenues of the PUs. Finally, we integrate all the mechanisms described above to build a new cognitive network where users can interact intelligently with network conditions

Game theory for dynamic spectrum sharing cognitive radio

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University on 21 June 2010.‘Game Theory’ is the formal study of conflict and cooperation. The theory is based on a set of tools that have been developed in order to assist with the modelling and analysis of individual, independent decision makers. These actions potentially affect any decisions, which are made by other competitors. Therefore, it is well suited and capable of addressing the various issues linked to wireless communications. This work presents a Green Game-Based Hybrid Vertical Handover Model. The model is used for heterogeneous wireless networks, which combines both dynamic (Received Signal Strength and Node Mobility) and static (Cost, Power Consumption and Bandwidth) factors. These factors control the handover decision process; whereby the mechanism successfully eliminates any unnecessary handovers, reduces delay and overall number of handovers to 50% less and 70% less dropped packets and saves 50% more energy in comparison to other mechanisms. A novel Game-Based Multi-Interface Fast-Handover MIPv6 protocol is introduced in this thesis as an extension to the Multi-Interface Fast-handover MIPv6 protocol. The protocol works when the mobile node has more than one wireless interface. The protocol controls the handover decision process by deciding whether a handover is necessary and helps the node to choose the right access point at the right time. In addition, the protocol switches the mobile nodes interfaces ‘ON’ and ‘OFF’ when needed to control the mobile node’s energy consumption and eliminate power lost of adding another interface. The protocol successfully reduces the number of handovers to 70%, 90% less dropped packets, 40% more received packets and acknowledgments and 85% less end-to-end delay in comparison to other Protocols. Furthermore, the thesis adapts a novel combination of both game and auction theory in dynamic resource allocation and price-power-based routing in wireless Ad-Hoc networks. Under auction schemes, destinations nodes bid the information data to access to the data stored in the server node. The server will allocate the data to the winner who values it most. Once the data has been allocated to the winner, another mechanism for dynamic routing is adopted. The routing mechanism is based on the source-destination cooperation, power consumption and source-compensation to the intermediate nodes. The mechanism dramatically increases the seller’s revenue to 50% more when compared to random allocation scheme and briefly evaluates the reliability of predefined route with respect to data prices, source and destination cooperation for different network settings. Last but not least, this thesis adjusts an adaptive competitive second-price pay-to-bid sealed auction game and a reputation-based game. This solves the fairness problems associated with spectrum sharing amongst one primary user and a large number of secondary users in a cognitive radio environment. The proposed games create a competition between the bidders and offers better revenue to the players in terms of fairness to more than 60% in certain scenarios. The proposed game could reach the maximum total profit for both primary and secondary users with better fairness; this is illustrated through numerical results

Game theory for dynamic spectrum sharing cognitive radio

‘Game Theory’ is the formal study of conflict and cooperation. The theory is based on a set of tools that have been developed in order to assist with the modelling and analysis of individual, independent decision makers. These actions potentially affect any decisions, which are made by other competitors. Therefore, it is well suited and capable of addressing the various issues linked to wireless communications. This work presents a Green Game-Based Hybrid Vertical Handover Model. The model is used for heterogeneous wireless networks, which combines both dynamic (Received Signal Strength and Node Mobility) and static (Cost, Power Consumption and Bandwidth) factors. These factors control the handover decision process; whereby the mechanism successfully eliminates any unnecessary handovers, reduces delay and overall number of handovers to 50% less and 70% less dropped packets and saves 50% more energy in comparison to other mechanisms. A novel Game-Based Multi-Interface Fast-Handover MIPv6 protocol is introduced in this thesis as an extension to the Multi-Interface Fast-handover MIPv6 protocol. The protocol works when the mobile node has more than one wireless interface. The protocol controls the handover decision process by deciding whether a handover is necessary and helps the node to choose the right access point at the right time. In addition, the protocol switches the mobile nodes interfaces ‘ON’ and ‘OFF’ when needed to control the mobile node’s energy consumption and eliminate power lost of adding another interface. The protocol successfully reduces the number of handovers to 70%, 90% less dropped packets, 40% more received packets and acknowledgments and 85% less end-to-end delay in comparison to other Protocols. Furthermore, the thesis adapts a novel combination of both game and auction theory in dynamic resource allocation and price-power-based routing in wireless Ad-Hoc networks. Under auction schemes, destinations nodes bid the information data to access to the data stored in the server node. The server will allocate the data to the winner who values it most. Once the data has been allocated to the winner, another mechanism for dynamic routing is adopted. The routing mechanism is based on the source-destination cooperation, power consumption and source-compensation to the intermediate nodes. The mechanism dramatically increases the seller’s revenue to 50% more when compared to random allocation scheme and briefly evaluates the reliability of predefined route with respect to data prices, source and destination cooperation for different network settings. Last but not least, this thesis adjusts an adaptive competitive second-price pay-to-bid sealed auction game and a reputation-based game. This solves the fairness problems associated with spectrum sharing amongst one primary user and a large number of secondary users in a cognitive radio environment. The proposed games create a competition between the bidders and offers better revenue to the players in terms of fairness to more than 60% in certain scenarios. The proposed game could reach the maximum total profit for both primary and secondary users with better fairness; this is illustrated through numerical results.EThOS - Electronic Theses Online ServiceGBUnited Kingdo