Huaqing Zhang

A Hierarchical Game Approach for Multi-Operator Spectrum Sharing in LTE Unlicensed

Allowing cellular operators to offload data traffic to unlicensed spectrum has the potential to significantly increase the capacity of the cellular network systems. This paper considers the spectrum sharing among multiple cellular operators in the unlicensed spectrum. One of the main challenges for this system is how to control the interference between the cellular users and the unlicensed users in other networks, e.g., Wi-Fi, and the interference among cellular users of different operators. As such, we develop a hierarchical game where there is a Kalai- Smorodinsky bargaining game among leaders and a Stackelberg game between operators and mobile users (MU). Accordingly, multiple operators can negotiate with each other for the revenue obtained from the unlicensed spectrum and use a pricing mechanism to control the interference caused by each MU to other operators and users in other unlicensed networks. Simulation results show that our proposed strategy significantly increases the revenue and utility for both operators and MUs.

Multi-leader multi-follower stackelberg game among Wi-Fi, small cell and macrocell networks

Wi-Fi, small cells and macrocell networks serve users with different advantages and drawbacks. In this paper, we propose a multi-leader multi-follower Stackelberg game between these three types of networks and mobile users. In the multi-leader game, each network first sets the optimal price according to the behaviors of the other networks and the prediction of each mobile users optimal strategies. Subsequently, mobile users choose their optimal network, accordingly. In the proposed game, there exists a Stackelberg equilibrium between the leader level and the follower level, and two Nash Equilibria within leaders and within followers, respectively. This fact differentiates the proposed work from most existing literature. Simulation results show that the proposed approach yields high social welfare at the equilibrium.

Zero-Determinant Strategy for Resource Sharing in Wireless Cooperations

Cooperation in resource sharing among wireless users and network operators has been widely studied in wireless communication. However, because of the limited coordination capability or cheating strategies, each participant of the cooperation may cease its cooperative behavior or duties unilaterally during the resource sharing, resulting in unsatisfying quality of services (QoSs) for all other participants. In this paper, we model the resource sharing among participants as an iterated game. Specifically, we first define the participant who is responsible for maintaining the social welfare as an administrator of cooperation (AoC), and other selfish participants as the regular participants of cooperation (PoCs). Then we consider three scenarios, i.e., with two-player applying discrete strategy, two-player applying continuous strategy, and multi-player applying continuous strategy, Finally, we investigate the power control problem in each of scenarios, and apply the zero-determinant strategies for the AoC to find the maximum social welfare that the AoC can achieve with existence of PoCs. Simulation results show that the high and stable social welfare can be maintained by the the AoC with the proposed zero-determinant algorithm.

A Multi-Leader Multi-Follower Stackelberg Game for Resource Management in LTE Unlicensed

It is known that the capacity of the cellular network can be significantly improved when cellular operators are allowed to access the unlicensed spectrum. Nevertheless, when multiple operators serve their user equipments (UEs) in the same unlicensed spectrum, the inter-operator interference management becomes a challenging task. In this paper, we develop a multi-operator multi-UE Stackelberg game to analyze the interaction between multiple operators and the UEs subscribed to the services of the operators in unlicensed spectrum. In this game, to avoid intolerable interference to the Wi-Fi access point (WAP), each operator sets an interference penalty price for each UE that causes interference to the WAP, and the UEs can choose their sub-bands and determine the optimal transmit power in the chosen sub-bands of the unlicensed spectrum. Accordingly, the operators can predict the possible actions of the UEs and hence set the optimal prices to maximize its revenue earned from UEs. Furthermore, we consider two possible scenarios for the interaction of operators in the unlicensed spectrum. In the first scenario, referred to as the non-cooperative scenario, the operators cannot coordinate with each other in the unlicensed spectrum. A sub-gradient approach is applied for each operator to decide its best-response action based on the possible behaviors of others. In the second scenario, referred to as the cooperative scenario, all operators can coordinate with each other to serve UEs and control the UEs’ interference in the unlicensed spectrum. Simulation results have been presented to verify the performance improvement that can be achieved by our proposed schemes.

Computing Resource Allocation in Three-Tier IoT Fog Networks: A Joint Optimization Approach Combining Stackelberg Game and Matching

Fog computing is a promising architecture to provide economical and low latency data services for future Internet of Things (IoT)-based network systems. Fog computing relies on a set of low-power fog nodes (FNs) that are located close to the end users to offload the services originally targeting at cloud data centers. In this paper, we consider a specific fog computing network consisting of a set of data service operators (DSOs) each of which controls a set of FNs to provide the required data service to a set of data service subscribers (DSSs). How to allocate the limited computing resources of FNs to all the DSSs to achieve an optimal and stable performance is an important problem. Therefore, we propose a joint optimization framework for all FNs, DSOs, and DSSs to achieve the optimal resource allocation schemes in a distributed fashion. In the framework, we first formulate a Stackelberg game to analyze the pricing problem for the DSOs as well as the resource allocation problem for the DSSs. Under the scenarios that the DSOs can know the expected amount of resource purchased by the DSSs, a many-to-many matching game is applied to investigate the pairing problem between DSOs and FNs. Finally, within the same DSO, we apply another layer of many-to-many matching between each of the paired FNs and serving DSSs to solve the FN-DSS pairing problem. Simulation results show that our proposed framework can significantly improve the performance of the IoT-based network systems.

Zero-determinant strategy in cheating management of wireless cooperation

Cooperation of resource sharing among wireless users and network operators has been widely studied in wireless communication. However, during the resource sharing, because of the weak communication signals or cheating strategies, each participant of the cooperation may sometimes stop its cooperative behavior unilaterally. Such behavior causes non-cooperation, resulting in unsatisfying quality of services for all participants. In this paper, we model the resource sharing between two participants as an iterated prisoners dilemma game. Based on the applications of wireless cooperations, we define the participant who is responsible to maintain the high social welfare as the administrator of cooperation (AoC), and the other rational selfish participant as the regular participant of cooperation (PoC). Then, we propose a zero-determinant strategy for the AoC, and find the maximum social welfare that the AoC can maintain regardless of the strategy of PoC. Simulation results show that when the AoC applies the proposed zero-determinant strategy, the high social welfare can be maintained, and both AoC and PoC receive better performances than those of noncooperation.

Equilibrium analysis for zero-determinant strategy in resource management of wireless network

Game theory is a powerful tool to deal with the interaction of decision makers with conflicting interests. However, for certain game models such as Chicken-Dare games, traditional strategies in game theory cannot achieve stable and high social welfare because of the competition between players. In this paper, we suppose one player in the game as an administrator, who concerns about the performance of the whole network, and the other player aims to improve its own utility based on the behavior of its opponent. Then we propose a zero-determinant strategy for the administrator so as to reach an equilibrium where the social welfare is satisfying. Such equilibrium can be widely applied in resource management of wireless network, and simulation results show the correctness and superiority of the proposed strategy, compared with other equilibrium concepts such as the correlated equilibrium.

A Hierarchical Game Framework for Resource Management in Fog Computing

Supporting real-time and mobile data services, fog computing has been considered as a promising technology to overcome long and unpredicted delay in cloud computing. However, as resources in FNs are owned by independent users or infrastructure providers, the ADSSs cannot connect and access data services from the FNs directly, but can only request data service from the DSOs in the cloud. Accordingly, in fog computing, the DSOs are required to communicate with FNs and allocate resources from the FNs to the ADSSs. The DSOs provide virtualized data services to the ADSSs, and the FNs, motivated by the DSOs, provide data services in the physical network. Nevertheless, with fog computing added as the intermediate layer between the cloud and users, there are challenges such as the resource allocation in the virtualized network between the DSOs and ADSSs, the asymmetric information problem between DSOs and ADSSs, and the resource matching from the FNs to the ADSSs in the physical network. In this article, we propose a three-layer hierarchical game framework to solve the challenges in fog computing networks. In the proposed framework, we apply the Stackelberg sub-game for the interaction between DSOs and ADSSs, moral hazard modeling for the interaction between DSOs and FNs, and the student project allocation matching sub-game for the interaction between FNs and ADSSs. The purpose is to obtain stable and optimal utilities for each DSO, FN, and ADSS in a distributed fashion.

Delay-tolerant resource scheduling in large-scale virtualized radio access networks

Network virtualization facilitates radio access network (RAN) sharing by decoupling the physical network infrastructure from the wireless services. This paper considers a scenario in which a virtual network operator (VNO) leases wireless resources from a software-defined networking based virtualized RAN set up by a third-party infrastructure provider (InP). In order to optimize the revenue, the VNO explores jointly the delay tolerance in mobile traffic and the weak load coupling across the base stations (BSs) when making the resource scheduling decisions to serve its mobile users (MUs). The problem faced by the VNO can be straightforwardly transformed to the problem of minimizing the payments to the InP, which is formulated as a finite time horizon constrained Markov decision process (MDP). However, for a large-scale network with a huge number of MUs, the problem solving becomes extremely challenging. Through the dual decomposition approach, we decompose the problem into a series of per-MU MDPs, which can be solved distributedly. Moreover, the independence of channel conditions between a MU and the BSs is expected to further simplify solving each per-MU MDP. The simulations carried out in this paper show that our proposed scheme achieves minimal average payments compared with other existing approaches in literature.

Radio Access Management of U-LTE

Next generation 5G networks will support not only emerging services, but also other services which may be beyond our imagination today. Improving the system capacity remains nevertheless one of the most important targets of 5G. Long Term Evolution (LTE) operation over the unlicensed band (U-LTE) is considered as one promising solution to achieve this target. In this chapter, we introduce the state of the art coexistence technologies of U-LTE with other unlicensed systems. Specifically, we investigate the coexistence of LTE Licensed Assisted Access (LAA; LAA is the preferred mode of operation for U-LTE and was newly released in 2015) with Wi-Fi over the unlicensed band. To this end, we first develop an analytical model to study the performance of the existing Load Based Equipment (LBE) MAC for LAA, identify the fairness issues, and quantify the reservation overhead of the protocol. Next, we propose a hybrid MAC protocol for LAA to maximize the network throughput, by minimizing LAA MAC reservation overhead while ensuring fair spectrum sharing of U-LTE and Wi-Fi. To achieve the best coexistence performance, a two-level renewal process-based model is developed to analyze the proposed MAC and optimize its parameters. Extensive simulations using NS-3 are conducted to validate the analysis and demonstrate the efficiency of the proposed MAC protocol.