Fanqin Zhou

Resource Allocation for 5G D2D Multicast Content Sharing in Social-Aware Cellular Networks

As the traffic of local content sharing grows rapidly, device-to-device multicast (D2MD) is introduced into 5G cellular networks, enabling traffic offloading from base stations to device direct transmissions and improved energy and spectrum efficiency. In the content sharing context, the social attributes of mobile users have special concerns to generate effective D2MD groups and D2MD links, and due to reuse of licensed cellular spectrum, the interference between D2MD groups and cellular users should be carefully mitigated. This article presents a D2MD scheme for content sharing in cellular networks by taking into account social and physical attributes in D2MD cluster formation, and jointly optimizing power and channel allocation among D2MD clusters. Simulation results validate the significant throughput gain for D2MD-based content sharing in 5G cellular networks.

Topology-aware based energy-saving mechanism in wireless cellular networks

Reducing the energy consumption (EC) of base station (BS) is one of the major concerns in wireless cellular networks. Additionally, turning off some underutilized BSs during off-peak period and performing effective compensation without delay are the most efficient way to save energy. However, large-scale energy conservation yet remains to be investigated at macro level. In this paper, to solve the problem that long convergence time and poor convergence precision in the large-scale network, we propose a BS topology-aware based energy-saving (ES) model, whose core is cell adjacency graph (CAG) with vertexes and links representing eNodeBs (eNBs) and their neighboring relationship. In addition, we introduce new metrics, predicted energy efficiency (PEE) and quality of compensation (QoC), as the weights of nodes and links respectively. Consequently, the model transforms the ES problem into average weights maximization in CAG. In view of the model presented, centralized and hybrid algorithms are put forward to solve the problem. Compared with classic distributed algorithm, simulation results claim that our hybrid approach achieves the maximization of ES with guaranteed QoC while our centralized approach maximize the PEE.

A load balancing method in downlink LTE network based on load vector minimization

Load balancing is one of the key target of LTE Self-Optimization Network (SON). In this paper, we propose a load balancing method for LTE downlink network, namely Load Vector Minimization based Load Balancing (LVMLB) method. Load Vector (LV) is a vector whose elements are the load values of cells and sorted in descending order. The order of LVs is defined by the lexicographical order. The smaller the LV is, the higher the balance degree of cells load will be. As the LV has a lower bound with total load fixed, the balance degree of cells load would reach a local optimal. On this basis, we design the LVMLB algorithm, trying to get the optimal solutions to load balancing problems, the proof of being optimal will also be given in this paper. Simulation scenarios are set in a square part of Macro-Pico mixed HetNets. Simulation results show that LVMLB outperforms the Cell Region Expansion (or Bias) scheme, increasing the capacities of Macro and Pico tiers at the same time, and improving balance degree of cells load, only sacrificing a little QoS performance.

Benders Decomposition-based video bandwidth allocation in mobile media cloud network

Due to the fast growth of wireless multimedia applications, mobile media cloud network is getting more and more popular. In the architecture of mobile media cloud network, the wireless access points are placed on edge of the cloud to provide media services for the mobile users. The video bandwidth allocation managed by a centralized media cloud directly affect the user’s experiences. In this paper, the problem of the video bandwidth allocation in the mobile media cloud access network is explored. Firstly, this paper formulates the problems in bandwidth allocation in the form of quadratic programming in order to maximize the system revenue on the basis of video bitrates capacity between the user and the Mobile Access Edge Point (MAEP). The optimization model could more vividly explicate the trade-off between the expected bitrates capacity and the allocation fairness of User Equipment (UE). Then this paper subdivides the problem into major and minor ones and proposes an algorithm based on Benders’ Decomposition to deal with it. The optimality of the solution is proved by both theoretical and experimental investigations. The error tolerance is analyzed as the algorithm disavows the trade-off between the convergence time and the system performance. The experiments show that the average computing time and confidence interval of the proposed algorithm are lower than Simplex algorithm by 68% and 94% and Barrier algorithm by 46% and 75% respectively at most. Finally, some conclusions are derived from evaluations on the system performance against various network topologies and different values for parameters of the proposed algorithms.

Self-Organized Cell Outage Detection Architecture and Approach for 5G H-CRAN

An attractive architecture called heterogeneous cloud radio access networks (H-CRAN) becomes one of the important components of 5G networks, which can provide ubiquitous high-bandwidth services with flexible network construction. However, massive access nodes increase the risk of cell outages, leading to negative impact on user-perceived QoS (Quality of Service) and QoE (Quality of Experience). Thus, cell outage management (COM) became a key function proposed in SON (Self-Organized Networks) use cases. Based on COM, cell outage detection (COD) will be resolved before cell outage compensation (COC). Currently few studies concentrate on COD for 5G H-CRAN, and we propose self-organized COD architecture and approach for it. We firstly summarize current COD solutions for LTE/LTE-A HetNets and then introduce self-organized architecture and approach suitable for H-CRAN, which includes COD architecture and procedures, and corresponding key technologies for it. Based on the architecture, we take a use case with handover data analysis using modified LOF (Local Outlier Factor) detection approach to detect outage for different kinds of cells in H-CRAN. Results show that the proposed approach can identify the outage cell effectively.

Energy efficient device-to-device clustering method in wireless communication network

This paper focuses on D2D clustering communication method in the uplink and downlink aiming at saving energy of Mobile Terminals (MTs). The method contains MTs clustering method and cluster head selection methods. The MTs clustering method is based on the distance information of MTs. The cluster head selection method is based on the energy consumption or the residual energy of MTs. And Quality of Service (QoS) is also taken into consideration and evaluated in the simulation. Simulation results verify that compared with non-collaborative case, the proposed methods can save energy by approximately 70% and 48% in the uplink and downlink respectively. Additionally, compared with cluster head selection method based on energy consumption, the method based on MTs residual energy can significantly decrease the number of MTs out of charge during the initial and middle period of the D2D clustering communication.

Utility Maximization for Load Optimization in Cellular/WLAN Interworking Network Based on Generalized Benders Decomposition

Load steering is widely accepted as a key SON function in cellular/WLAN interworking network. To investigate load optimizing from a perspective of system utilization maximization more than just offloading to improve APs’ usage, a utility maximization (UTMAX) optimization model and an ASRAO algorithm based on generalized Benders Decomposition are proposed in this paper. UTMAX is to maximize the sum of logarithmic utility functions of user data rate by jointly optimizing user association and resource allocation. To maintain the flexibility of resource allocation, a parameter is added to the utility function, where smaller means more resources can be allocated to edge users. As a result, it reflects a tradeoff between improvements in user throughput fairness and system total throughput. UTMAX turns out to be a mixed integer nonlinear programming, which is intractable intuitively. So ASRAO is proposed to solve it optimally and effectively, and an optional phase for expediting ASRAO is proposed by using relaxation and approximation techniques, which reduces nearly 10% iterations and time needed by normal ASRAO from simulation results. The results also show UTMAX’s good effects on improving WLAN usage and edge user throughput.