Yunjian Jia

Game-Theoretic Hierarchical Resource Allocation for Heterogeneous Relay Networks

Relay nodes (RNs), as important components of heterogeneous networks introduced in Long-Term Evolution-Advanced systems, are employed to improve network capacity and extend cell coverage in a cost-effective manner. In heterogeneous relay networks, there are two types of user access connections: the direct link and the two-hop link. The former is used when a user accesses the base station (BS) directly, whereas the latter, which is composed of a backhaul link and an access link, is used when a user accesses the BS via an RN. For the RNs with in-band wireless backhauls, two-hop links operate in shared spectrum, and thus, resource allocation is a challenging issue to provide sufficient capacity for backhaul links while maintaining fair sharing of resources with normal macro users. In this paper, we develop a hierarchical game based on the Stackelberg model to address the resource allocation in heterogeneous relay networks. The proposed game consists of two subgames, namely, the backhaul-level game (BLG) and the access-level game (ALG), respectively. In the game model, RNs in backhaul links as leaders play the BLG, whereas mobile stations (MSs) in access links as followers play the ALG. By estimating the achievable rates of MSs, the leaders choose their optimal resource-allocation strategies, and then, the followers do the best responses to the leaders strategies. Simulation results demonstrate that our proposed hierarchical game can guarantee the throughput balance between backhaul and access links, and thus, improve the user data rates and the overall system resource utilization, as compared with the resource-allocation schemes that consider backhaul and access links separately.

Iterative Energy-Efficient Stable Matching Approach for Context-Aware Resource Allocation in D2D Communications

Energy efficiency (EE) is critical to fully achieve the huge potentials of device-to-device (D2D) communications with limited battery capacity. In this paper, we consider the two-stage EE optimization problem, which consists of a joint spectrum and power allocation problem in the first stage, and a context-aware D2D peer selection problem in the second stage. We provide a general tractable framework for solving the combinatorial problem, which is NP-hard due to the binary and continuous optimization variables. In each stage, user equipments (UEs) from two finite and disjoint sets are matched in a two-sided stable way based on the mutual preferences. First, the preferences of UEs are defined as the maximum achievable EE. An iterative power allocation algorithm is proposed to optimize EE under a specific match, which is developed by exploiting nonlinear fractional programming and Lagrange dual decomposition. Second, we propose an iterative matching algorithm, which first produces a stable match based on the fixed preferences, and then dynamically updates the preferences according to the latest matching results in each iteration. Finally, the properties of the proposed algorithm, including stability, optimality, complexity, and scalability, are analyzed in detail. Numerical results validate the efficiency and superiority of the proposed algorithm under various simulation scenarios.

A Cluster-Based Energy-Efficient Resource Management Scheme for Ultra-Dense Networks

Ultra-dense networks (UDNs), which can provide extremely high throughput and data rates, have been considered as one of the key techniques for the fifth generation mobile networks. However, it may cause severe inter-cell interference and significant energy consumption due to numerous base stations (BSs) being randomly deployed. To mitigate the interference and boost energy efficiency (EE) of the UDN effectively, we propose a cluster-based energy-efficient resource allocation scheme in this paper. The proposed scheme has two stages: clustering stage and resource allocation stage. In clustering stage, we use a modified K-means algorithm in BS-clustering process to dynamically adjust the number of BS-clusters based on the density of BSs. Then, in each BS cluster, we divide user equipments (UEs) into multiple UE-groups with minimum intra-cluster interference. In this way, the complexity of resource allocation can be greatly reduced. While in resource allocation stage, we design a two-step resource blocks assignment algorithm and an iterative energy efficient power allocation algorithm based on a non-cooperative game. Furthermore, we implement simulations under the realistic broadband channel propagation conditions and the simulation results show that the proposed approach can effectively mitigate the interference and improve the EE of UDN.

Social Network-Based Content Delivery in Device-to-Device Underlay Cellular Networks Using Matching Theory

With the popularity of social network-based services, the unprecedented growth of mobile date traffic has brought a heavy burden on the traditional cellular networks. Device-to-device (D2D) communication, as a promising solution to overcome wireless spectrum crisis, can enable fast content delivery based on user activities in social networks. In this paper, we address the content delivery problem related to optimization of peer discovery and resource allocation by combining both the social and physical layer information in D2D underlay networks. The social relationship, which is modeled as the probability of selecting similar contents and estimated by using the Bayesian nonparametric models, is used as a weight to characterize the impact of social features on D2D pair formation and content sharing. Next, we propose a 3-D iterative matching algorithm to maximize the sum rate of D2D pairs weighted by the intensity of social relationships while guaranteeing the quality of service requirements of both cellular and D2D links simultaneously. Moreover, we prove that the proposed algorithm converges to a stable matching and is weak Pareto optimal, and also provide the theoretical complexity. Simulation results show that the algorithm is able to achieve more than 90% of the optimum performance with a computation complexity 1000 times lower than the exhaustive matching algorithm. It is also demonstrated that the satisfaction performance of D2D receivers can be increased significantly by incorporating social relationships into the resource allocation design.

Wireless Network Virtualization With SDN and C-RAN for 5G Networks: Requirements, Opportunities, and Challenges

Wireless network virtualization (WNV) has drawn attention from the researchers ranging from academia to industry as one of the significant technologies in the cellular network communication. It is considered as a pioneer to achieve effective resource utilization with decreased operating expenses and capital expenses by decoupling the networks functionalities of coexisting virtual networks. It facilitates fast deployment of new services and novel technologies. WNV paradigm is in the early stages, and there is a large room for the research community to develop new architectures, systems, and applications. The availability of software-defined networking (SDN) and cloud/centralized radio access network (C-RAN) steers up the hope for the WNV realization. This paper surveys WNV along with the recent developments in SDN and C-RAN technologies. Based on these technologies and WNV concepts, we identify the requirements and opportunities of future cellular networks. We then propose a general architectural framework for the WNV based on SDN. In-depth discussion of challenges and research issues as well as promising approaches for future networks communication improvements are also proposed. Finally, we give several promising candidates of future network services for residential customers and business customers.

A Non-Intrusive Cyber Physical Social Sensing Solution to People Behavior Tracking: Mechanism, Prototype, and Field Experiments

Tracking people’s behaviors is a main category of cyber physical social sensing (CPSS)-related people-centric applications. Most tracking methods utilize camera networks or sensors built into mobile devices such as global positioning system (GPS) and Bluetooth. In this article, we propose a non-intrusive wireless fidelity (Wi-Fi)-based tracking method. To show the feasibility, we target tracking people’s access behaviors in Wi-Fi networks, which has drawn a lot of interest from the academy and industry recently. Existing methods used for acquiring access traces either provide very limited visibility into media access control (MAC)-level transmission dynamics or sometimes are inflexible and costly. In this article, we present a passive CPSS system operating in a non-intrusive, flexible, and simplified manner to overcome above limitations. We have implemented the prototype on the off-the-shelf personal computer, and performed real-world deployment experiments. The experimental results show that the method is feasible, and people’s access behaviors can be correctly tracked within a one-second delay.

Distributed Transmission Scheduling and Power Allocation in CoMP

The performance of wireless networks can be largely enhanced by coordinated multipoint (CoMP). To design an efficient CoMP in multiuser multiple-input multiple-output scenario, conventional transmission scheduling and power allocation are usually performed in a static and centralized manner. In this paper, we focus on dynamic and distributed transmission scheduling and power allocation. We first determine the coordinated base-station sets (defined as CBSs) candidates in each subband by the channel energy (i.e., square Frobenius norm of channel matrix) of each user. Each CBS candidate contains a set of coordination base-stations and edge users. By chordal distance, we can measure the orthogonality between space spanned of users in the same CBS candidate. Then, we propose two heuristic user scheduling algorithms based on channel energy and chordal distance between users to determine the set of users served by each CBS candidate. The first algorithm is based on an open problem, which reveals the philosophy of user scheduling with orthogonalization threshold guarantee. The second one deals with user scheduling by selecting a set of edge users with the largest total channel energy and orthogonalization threshold guarantee. With the total channel energy per CBS candidate, the CBSs and their served edge users can be determined. Then, water-filling power allocation is further applied to CBSs with block diagonalization precoding. Algorithm performance is demonstrated by extensive simulations.

Energy-efficient game-theoretical random access for M2M communications in overlapped cellular networks

The unprecedented growth of machine-to-machine (M2M) devices has brought a heavy burden to traditional cellular networks. In this paper, we focus on the overload problem caused by massive connections of M2M devices in overlapped cellular networks. We formulate the joint base station (BS) selection and power allocation optimization problem for each M2M device as a noncooperative access game. The utility function of each M2M device is described as the success probability of random access weighted by the energy efficiency (EE). We propose an iterative energy-efficient game-theoretical random access algorithm, in which each M2M device searches its optimal strategies in turn until no M2M device is able to improve its individual utility with a unilateral deviation. Numerical results demonstrate that significant performance enhancements on both the delay and energy consumption can be achieved simultaneously.

Interference Cooperation via Distributed Game in 5G Networks

Nash noncooperative power game is an effective method to implement interference cooperation in downlink multiuser multiple-input multiple-output (MU-MIMO). Power equilibrium point of Nash noncooperative power game can achieve a satisfactory tradeoff between self-benefits of Internet of Things (IoT) users and interference between IoT users which largely enhance the edge IoT user throughput. However, either power strategy space, i.e., the enabled range of power allocation for IoT users, or overall BS transmit power in the existing Nash noncooperative power games is generally static. This limits the performance of systems, especially in IoT systems, etc., in 5G. As an effort to address these problems, we design a novel framework of Nash noncooperative game with iterative convergence for downlink MU-MIMO. We first decompose the MU-MIMO into multiple virtual single-antenna transmit-receive pairs with a stream analytical model. Afterwards, based on streams, we propose a noncooperative water-filling power game with pricing (WFPGP) where the power strategy space of each stream can be dynamically determined by iterative water-filling. We derive the sufficient condition for the existence and uniqueness of WFPGP game, in which the verification of the sufficient condition can be executed in a distributed manner. By simulations, we verify the performance of WFPGP compared to other Nash noncooperative games.