Incentive-Compatible Device-to-Device Communication Mode Selection

With the large expected demand of wireless network communication, Device-to-Device (D2D) communication has been proposed as a promising technology to enhance network performance. The selfish nature of potential D2D users, may impale the performance of D2D-enabled network. We consider a D2D-enabled cellular network framework, which supports both divided and shared D2D modes, under overlay D2D communication. The framework provides a pricing-based Stackelberg game for optimal mode selection and spectrum partitioning. The BS and potential D2D pairs act as the leader and the followers in this game. The BS, who intends to maximize the overall network utility, decides spectrum proportion by a dynamic spectrum partition strategy and service price by primal-dual pricing strategy for D2D communication. Potential D2D pairs then select their communication modes selfishly. We propose the incentive compatible pricing strategy to provide proper incentive for these selfish potential D2D pairs to make optimal choices in mode selection. We compare the performance between divided and shared D2D mode under the proposed framework through simulations and show the advantages of each mode in different scenarios. Our results show that the pricing and spectrum partition strategy effectively prevents selfish potential D2D users from harming the system performance while fully exploits the potential of D2D communications.

Profit-Maximized Femtocell Contract Design

Most service providers offer an unlimited data service plan under a flat, fixed-rate contract to meet the huge demand. However, because service quality and user experience can vary dramatically in wireless communications, such a contract design is unable to provide equal service quality for all users, which greatly limits the profit potential of service providers. As a result, mobile industries look to femtocell technology to improve service quality and increase profit by attracting customers. Meanwhile, differentiated contracts for different types of users also show great potential for profit increase. We investigate unlimited data service plans in terms of enhancements from both femtocell systems and differentiated contracts. The incentive compatibility (IC) issue in differentiated contract design is considered under the overlay macrocell-femtocell system in both split-spectrum and shared-spectrum models. The profits under optimal differentiated contracts, with and without the IC condition are compared to traditional flat fee contracts, and numerical results show that optimal differentiated contracts indeed generate more profits and serve more users.

FEVER: Voting-based Femtocell Cell-Breathing Control

Overlay macrocell-femtocell system aims to increase the system capacity with a low-cost infrastructure. To construct such an infrastructure, we need to solve some existing problems. First, there is a tradeoff between femtocell coverage and overall system thrughput, which we defined as the cell-breathing phenomenon. In light of this, we propose a femtocell downlink cell-breathing control framework to strike a balance between the coverage and data rate. Second, due to the selfish nature of mobile stations, the system information collected from them does not necessarily reflect the true status of the system. Thus, we design FEmtocell Virtual Election Rule (FEVER), a voting based direct mechanism that only requires users to report their channel quality information to the femtocell base station. Not only is it proved to be truthful and has low implementation complexity, but also strikes a balance between efficiency and fairness to meet the different needs. The simulation results verify the enhanced system performance under FEVER mechanism.

Chinese Restaurant Game - A Social Learning Framework

In a social network, agents are intelligent and have the capability to make decisions to maximize their utilities. They can either make wise decisions by taking advantages of other agents’ experiences through learning, or make decisions earlier to avoid competitions from huge crowds. Both these two effects, social learning and negative network externality, play important roles in the decision process of an agent. While there are existing works on either social learning or negative network externality, a general study on considering both effects is still limited. We find that Chinese restaurant process, a popular random process, provides a well-defined structure to model the decision process of an agent under these two effects. By introducing the strategic behavior into the non-strategic Chinese restaurant process, we propose a new game, called Chinese Restaurant Game, to formulate the social learning problem with negative network externality. Through analyzing the proposed Chinese restaurant game, we derive the optimal strategy of each agent and provide a recursive method to achieve the optimal strategy. How social learning and negative network externality influence each other under various settings is studied through simulations. We also illustrate the spectrum access problem in cognitive radio networks as one of the application of Chinese restaurant game. We find that the proposed Chinese restaurant game theoretic approach indeed helps users make better decisions and improves the overall system performance.