Qiaoni Han

Hierarchical-Game-Based Uplink Power Control in Femtocell Networks

Femtocells are viewed as a promising option for mobile operators to improve coverage and provide high-data-rate services in a cost-effective manner. However, cross-tier interference mitigation is considered to be one of the major challenges. Distributed-game-based power control approaches are effective for interference management in wireless networks. In this paper, we focus on the optimal power allocation for uplink transmission in two-tier femtocell networks and take into account the different service requirements and design objectives of macrocell user equipment (MUE) and femtocell user equipment (FUE) devices. The framework of hierarchical game with a multiple-leader-multiple-follower model is adopted to investigate the uplink power allocation problem. By using hierarchical game, on one hand, the utilities of both MUE and FUE devices are maximized; on the other hand, the uplink protection of MUE devices is highlighted. To obtain the game equilibrium (GE) distributively, we develop the iterative power update rules for both MUE and FUE devices. Moreover, given channel uncertainty, a robust hierarchical game is formulated and solved distributively. For implementations, the running process of the proposed hierarchical games is analyzed in detail. Finally, numerical results show the convergence of the hierarchical games without and with channel uncertainty. Moreover, it is also demonstrated that, in either case, the GE is unique and that the game is effective.

Pricing-based spectrum leasing in cognitive radio networks

According to the property rights model of cognitive radio, primary users who own the spectrum resource have the right to lease part of the spectrum to secondary users (SUs) in exchange for appropriate profit. In this study, the authors propose a pricing-based spectrum leasing framework between one primary user and multiple SUs. In this scenario, the primary user attempts to maximise its utility by setting the price of spectrum, the selected SUs have the right to decide their power levels to help the primary users transmission, aiming to obtain corresponding spectrum access time. Then, the spectrum leasing problem can be cast into a Stackelberg game, which jointly maximises the utilities of primary user and SUs. Moreover, the authors design a joint relay selection and admission control algorithm, which can select the proper SUs and protect all the active SUs’ performance. Numerical results show that the proposed pricing-based spectrum leasing framework is effective, and the performance of the primary user and the SUs is both improved, compared with the traditional mechanism without cooperation.

Backhaul-Aware User Association and Resource Allocation for Energy-Constrained HetNets

Growing attention has been paid to renewable- or hybrid-energy-powered heterogeneous networks (HetNets). In this paper, focusing on backhaul-aware joint user association and resource allocation for this type of HetNets, we formulate an online optimization problem to maximize the network utility reflecting proportional fairness. Since user association and resource allocation are tightly coupled not only on resource consumption of the base stations (BSs) but in the constraints of their available energy and backhaul as well, the closed-form solution is quite difficult to obtain. Thus, we solve the problem distributively by employing certain decomposition methods. Specifically, at first, by adopting the primal decomposition method, we decompose the original problem into a lower level resource-allocation problem for each BS and a higher level user-association problem. For the optimal resource allocation, we prove that a BS either assigns equal normalized resources or provides an equal long-term service rate to its served users. Then, the user-association problem is solved by the Lagrange dual decomposition method, and a completely distributed algorithm is developed. Moreover, applying results of the subgradient method, we demonstrate the convergence of the proposed distributed algorithm. Furthermore, to efficiently and reliably apply the proposed algorithm to the future wireless networks with an extremely dense BS deployment, we design a virtual user association and resource allocation scheme based on the software-defined networking architecture. Finally, numerical results validate the convergence of the proposed algorithm and the significant improvement on network utility, load balancing, and user fairness.

Power Control Based on Maximum Power Adaptation in Two-Tier Femtocell Networks

In recent years, femtocells are receiving considerable attention in mobile communication as a cost-effective means of improving indoor coverage and capacity. A significant technical challenge in the deployment of a large number of femtocells is the management of interference from the underlay of femtocells onto the overlay of macrocell. In this paper, a reasonable and effective interference suppression scheme based on the adaptive adjustment of femtocell users’ maximum transmission power is proposed. The highlight of the scheme is the joint design of macrocell users’ uplink communication protection and femtocell users’ optimal power allocation. The scheme restricts the cross-tier interference at macrocell base station below a given threshold and ensures the optimization of femtocell users’ power allocation at each adjustment phase. Last, admission control is also considered, aiming to exploit the network resources more effectively. Simulation results show the superiority of the proposed scheme over the scheme based on the Signal-to-Interference-Plus-Noise Ratio adaptation. We also give some reference on utility function selection by setting different coefficients in the utility function, and show the effectiveness of admission control in both fixed and random network topologies.

Bandwidth allocation for cooperative relay networks based on Nash bargaining solution

This paper is concerned with the bandwidth allocation problem for cooperative relay networks. The relay takes the roles of not only forwarding the data originated from the users but also of transmitting its own data to the access point. We focus on the interesting questions of when and how the users and the relay can both benefit from the cooperation by bandwidth allocation for relaying among the users. The bandwidth allocation problem is formulated in this paper as a Nash bargaining problem, and then the bandwidth allocation algorithm can be given on the basis of the sub-gradient method. Simulation results illustrate that users and the relay can both obtain more profits through cooperation. Copyright ©2011 John Wiley & Sons, Ltd.

Energy-aware and QoS-aware load balancing for HetNets powered by renewable energy

Cellular networks are now rapidly evolving to renewable heterogeneous network (HetNet) architectures, where a mixture of macro base stations (BSs) and small cell BSs coexist within the cellular network. Particularly, these BSs are partly-powered or self-powered by renewable energy sources (RES). Load awareness has been elevated to a central problem for HetNets, while RES also poses relevant technical challenges. Thus, load balancing for RES powered HetNets is quite challenging. Focusing on this, we study the energy-aware and quality of service (QoS)-aware load balancing for this type of HetNets. With the objective of maximizing network-wide weighted utility of load efficiency in a time duration, where the weight is the corresponding amount of consumed resources, we formulate a user association optimization problem. Based on relaxing, discretization and dual decomposition, an optimal offline algorithm is firstly obtained. Then, distributed and low-complexity suboptimal online algorithms are proposed. Last, given load balancing index and call blocking probability as metrics, numerical results evaluate the performance of the proposed algorithms, which demonstrates the accuracy of the theoretical analysis and provides further insights into the design and operation of the energy-aware load balancing system.

Distributed Control for Charging Multiple Electric Vehicles with Overload Limitation

Severe pollution induced by traditional fossil fuels arouses great attention on the usage of plug-in electric vehicles (PEVs) and renewable energy. However, large-scale penetration of PEVs combined with other kinds of appliances tends to cause excessive or even disastrous burden on the power grid, especially during peak hours. This paper focuses on the scheduling of PEVs charging process among different charging stations and each station can be supplied by both renewable energy generators and a distribution network. The distribution network also powers some uncontrollable loads. In order to minimize the on-grid energy cost with local renewable energy and non-ideal storage while avoiding the overload risk of the distribution network, an online algorithm consisting of scheduling the charging of PEVs and energy management of charging stations is developed based on Lyapunov optimization and Lagrange dual decomposition techniques. The algorithm can satisfy the random charging requests from PEVs with provable performance. Simulation results with real data demonstrate that the proposed algorithm can decrease the time-average cost of stations while avoiding overload in the distribution network in the presence of random uncontrollable loads.

Spectrum leasing based on Nash Bargaining Solution in cognitive radio networks

Cognitive radio is becoming an emerging technology that has the potential of dealing with the stringent requirement and scarcity of the radio spectrum resource. In this paper, we focus on the dynamic spectrum access of cognitive radio networks, in which the primary user (PU) and secondary users (SUs) coexist. In property-rights model, the PU has property of the bandwidth and may decide to lease it to secondary network for a fraction of time in exchange for appropriate remuneration. We propose a cooperative communication-aware spectrum leasing framework, in which, PU selects SUs as cooperative relays to help transmit information, while the selected SUs have the right to decide their payment made for PU in order to obtain a proportional access time to the spectrum. Then, the spectrum leasing scheme is cast into a Nash Bargaining Problem, and the Nash Bargaining Solution (NBS) can be used to fairly and efficiently address the resource allocation between PU and secondary network, enhancing both the utility of PU and secondary network. Numerical results show that spectrum leasing based on NBS is an effective method to improve performance for cognitive radio networks.

Stackelberg game based interference management for two-tier femtocell networks

Femtocell is viewed as a promising option for mobile operators to improve coverage and provide high-data-rate services in a cost-effective manner. This paper considers the uplink interference management problem in a spectrum-sharing femtocell network. Assuming that the macrocell base station (MBS) is rewarded for sharing the spectrum with femtocells by setting a reasonable interference cap (IC) for femtocell users’ (FUEs’) transmissions. Within IC, the FUEs allocate their transmission powers competitively while not introducing much interference to both the macrocell users (MUEs) and other FUEs. A Stackelberg game is formulated to jointly maximize the utility of MBS and the individual utility of FUEs. Specifically, the maximum tolerable interference at the MBS is used as the resource that the leader (MBS) and the followers (FUEs) compete for. Then, the backward induction method is applied to achieve the Stackelberg equilibrium and a distributed power update rule is developed for FUEs. In addition, the implementation protocol is presented, some issues related to the implementations and some future extensions regarding the MUEs’ uplink protection are discussed. Lastly, numerical results demonstrate the performance of our proposed power allocation in detail, and show the effects of varying the number of FBSs and changing other system parameters on the system’s performance.

Matching-based joint uplink and downlink user association for energy-efficient hetnets

We consider the energy efficiency-aware user association problem in heterogeneous networks (HetNets). Since there is a large disparity between the transmit power levels of the macro and small cell base stations, the traditional downlink received signal strength indicator (RSSI) based user association results in extreme load imbalance and high uplink interference, which degrades the spectrum efficiency and energy efficiency, especially in uplink. Focusing on this, we propose energy efficiency-aware user association taking into account both uplink and downlink. However, the formulated non-convex and combinatorial problem cannot be distributively solved by convex optimization techniques or common game-theoretic methods. Thus, we apply the matching theory to develop a distributed user association algorithm, which reaches a stable matching upon convergence. Lastly, by comparing with two downlink-centric user association methods, the proposed algorithm is shown to have a superior performance in terms of energy efficiency, uplink capacity and energy saving.