Game-based Optimization Research Articles

Stackelberg Game for User Clustering and Power Allocation in Millimeter Wave-NOMA Systems

In this paper, the joint design of user clustering and power allocation is investigated in a downlink non-orthogonal multiple access-based millimeter wave (mm-wave-NOMA) system. To reduce the system overhead, hybrid precoding techniques are adopted at the base station by using the channel state information of cluster heads (CHs) only. In order to maximize the sum rate of the system, Stackelberg game-based optimization problems are formulated for two cases with different quality of some targets: Case 1 focuses on improving the data rates of CHs; and Case 2 aims to increase the rates of cluster members when the CHs’ data rates are caped. With the aid of coalitional game theory, a low complexity algorithm is proposed to dynamically allocate users into different clusters. Then, the optimal power allocation coefficients of the users in each cluster are obtained by the derived closed-form expressions. The properties of the proposed joint algorithms are analyzed in terms of Stackelberg equilibrium, the complexity, the convergence and the stability. The simulation results demonstrate that: 1) the proposed algorithms can significantly improve the sum rate and reduce the outage probability of the mm-wave-NOMA system and 2) the application of NOMA in mm-wave systems is capable of achieving promising gains over conventional orthogonal multiple access-based frameworks in both cases.

EdgeCare: Leveraging Edge Computing for Collaborative Data Management in Mobile Healthcare Systems

With the wide application of mobile healthcare systems, the total amount of healthcare data is ever increasing rapidly as users interact with healthcare service providers frequently. This leads to a challenging task to manage healthcare data. Existing work mainly pay attention to centralized and blockchain-based mechanisms. But they cannot adapt to the increasing amount of global healthcare data and suffer from complex application challenges, respectively. Decentralized and collaborative data management assisted by edge computing exhibits major advantages in improving overall system performance. We present a secure and efficient data management system named as EdgeCare for mobile healthcare systems. Local authorities are established to schedule edge servers for processing healthcare data and facilitating data trading. A hierarchical architecture with collaboration is designed for feasible implementation of EdgeCare. After that, we investigate secure data uploading and sharing in the system. We use an electronic medical record to show how healthcare data is processed with security considerations. We also conduct the Stackelberg game-based optimization algorithm to approach the optimal incentive mechanism for a data collector and users in the fair decentralized data trading. The numerical results with security analysis are provided to demonstrate that EdgeCare offers effective solutions to protect healthcare data, and support efficient data trading.

A Stackelberg Game-Based Optimization Framework of the Smart Grid With Distributed PV Power Generations and Data Centers

The emergence of cloud computing has established a trend toward building massive, energy-hungry, and geographically distributed data centers. Due to their enormous energy consumption, data centers are expected to have a major impact on the electric power grid by significantly increasing the load at locations where they are built. Dynamic energy pricing policies in the recently proposed smart power grid technology can incentivize the cloud controller to shift the computation load toward data centers in regions with cheaper electricity or with excessive electricity generated by renewable energy sources, e.g., photovoltaic (PV) and wind power. On the other hand, distributed data centers in the cloud also provide opportunities to help the power grid with distributed renewable energy sources to improve robustness and load balancing. To shed some light into these opportunities, this paper considers an interaction system of the smart power grid with distributed PV power generation and the cloud computing system, jointly accounting for the service request dispatch and routing problem in the cloud with the power flow analysis in power grid. The Stackelberg (sequential) game formulation is provided for the interaction system under two different dynamic pricing scenarios: 1) real-time power-dependent pricing; and 2) time-ahead pricing. The two players in the Stackelberg games are the power grid controller that sets the pricing signal and the cloud controller that performs resource allocation among data centers. The objective of the power grid controller is to maximize its own profit and perform load balancing among power buses, i.e., minimizing the power flow from one power bus to the others, whereas the objective of the cloud computing controller is to maximize its own profit with respect to the location-dependent pricing signal. Based on the backward induction method, this paper derives the near-optimal or suboptimal strategies of the two players in Stackelberg game using convex optimization and simulated annealing techniques.