Zhaoming Lu

A History-Based Handover Prediction for LTE Systems

Handover prediction has been considered an effective technology for improvement of LTE Handover performance. Although not a few techniques have been proposed to achieve this goal including handover preparation based on cross-layer optimization and mobility prediction, the fact is that their gains are not usually so high as their cost. To overcome such weaknesses, this paper proposes a simple handover prediction technique which is based on a novel user mobility model to approximate simulation the laws of user mobility actions. We develop a user mobility database to assist the mobility prediction based on the user mobility history records. The simulation results show that the proposed technique minimizes the number of handovers and lower the ping-pang rate in LTE system.

A Novel Self-Optimizing Handover Mechanism for Multi-service Provisioning in LTE-Advanced

The 3GPP is defining the next generation radio access network which introduces advanced self- optimization to reduce the operational efforts and complexity. It is also of interest to minimize operational effort by introducing self-optimizing mechanisms. Optimization of the handover parameters is an important goal of LTE-Advanced. In this paper, a new self- optimizing handover algorithm based on the estimation of the number of cell-boundary crossings is proposed. The algorithm adjusts the handover parameters, such as Time- to-Trigger, measurement interval, and hysteresis, by comparing the number of cell-boundary crossings and the number of handovers UE performed in a period. To optimize the handover performance further, an adaptive L3 filter based on the UE’s velocity is proposed in this paper. A typical application scenario of this algorithm is high/medium speed environment under multi-service provisioning. The simulation results shows that the algorithms proposed in this paper have a better performance in the rate of ping-pong handovers and the average number of handovers.

Joint Optimization of Quality of Experience and Power Consumption in OFDMA Multicell Networks

Quality of experience (QoE) and power consumption are two important considerations of OFDMA multicell networks. In this letter, we propose a fair QoE-based radio resource allocation (FQRA) for OFDMA multicell networks. Different from the previous work, the multiuser QoE per power consumption is introduced as the performance metric. A novel utility function, which can characterize the fairness of users QoE and power consumption jointly, is proposed. Then a multiuser FQRA problem is formulated. Due to the nonconvex form of optimization, a two-step solution is devised in which the subchannel allocation and power allocation are performed iteratively. Simulation results show that our method can significantly improve the QoE per power consumption of cells and outperform the state-of-art schemes.

Flexible Resource Allocation for Joint Optimization of Energy and Spectral Efficiency in OFDMA Multi-Cell Networks

The radio resource allocation problem is studied, aiming to jointly optimize the energy efficiency (EE) and spectral efficiency (SE) of downlink OFDMA multi-cell networks. Different from existing works on either EE or SE optimization, a novel EE-SE tradeoff (EST) metric, which can capture both the EST relation and the individual cells preferences for EE or SE performance, is introduced as the utility function for each base station (BS). Then the joint EE-SE optimization problem is formulated, and an iterative subchannel allocation and power allocation algorithm is proposed. Numerical results show that the proposed algorithm can exploit the EST relation flexibly and optimize the EE and SE simultaneously to meet diverse EE and SE preferences of individual cells.

Energy-efficient resource optimization in OFDMA-based dense femtocell networks

Femtocells have attracted growing attentions in academia, industry, and standardization forums in recent years. In this paper, we investigate a distributed radio resource allocation scheme to maximize the energy-efficiency in uplink OFDMA dense femtocell networks. We model the subchannel allocation and power control problem as a non-cooperation game. Due to the fact that joint subchannel allocation and power control problem is NP-hard, for decreasing the complexity, we decompose the resource allocation problem into two subproblems. A distributed subchannel allocation algorithm and an optimal power allocation algorithm are proposed to resolve the resource allocation problem. Simulation results show the proposed algorithm has better performance in terms of throughput and energy-efficiency.

Energy-saving resource allocation scheme with QoS provisioning in OFDMA femtocell networks

In this paper, the resource allocation problem is studied in downlink femtocell networks to minimize the energy consumption of femtocell base stations (FBSs), provide delay-aware quality-of-service (QoS) guarantees for femtocell users and limit the cross-tier interference. Specifically, by integrating the concept of effective capacity, users delay-aware QoS requirements are characterized by the QoS exponent and minimum effective capacity constraints. The problem of minimizing transmit power of FBSs is formulated as a mixed integer programming problem and is decomposed into two subproblems in order to reduce the complexity. Accordingly, a suboptimal subchannel allocation algorithm and a QoS-driven optimal power allocation algorithm are proposed respectively. Simulation results demonstrate that the proposed algorithms can use the lowest transmit power to satisfy diverse delay-aware QoS requirements of femtocell users, which show great advantages in energy saving and QoS provisioning.

QoE-Based Reduction of Handover Delay for Multimedia Application in IEEE 802.11 Networks

Wireless local area network (WLAN) is becoming increasingly important due to the explosive growth of mobile data traffic. The user in some hot spot areas is usually covered with multiple access points (APs). However, the subsequent challenge is the users devices need to handover between these APs as the user moves, and it introduces large latency during the handoff process. For multimedia application, quality of experience (QoE) is an essential measurement for network evaluation. But the large handover latency which gives rise to an interruption will greatly degrade the users QoE to an intolerable extent. Therefore a neighbor list based proactive handoff scheme which has sufficient low handover latency will reduce service disruptions. The proposed scheme exploits the active scan based on information of neighboring APs, received signal strength indicator (RSSI), and variable bitrate video coding (VBR) to reduce handover delay and service interruption. Based on the analysis and simulation, it can be seen that the proposed scheme can decrease the handover time efficiently and increase QoE.

Modeling the TXOP Sharing Mechanism of IEEE 802.11ac Enhanced Distributed Channel Access in Non-Saturated Conditions

IEEE 802.11ac standard, adopting a downlink multi-user multiple-input and multiple-output (DL-MU-MIMO) scheme, can significantly improve the performance of wireless local area networks (WLANs). To support the DL-MU-MIMO technology, a transmission opportunity (TXOP) sharing mechanism has been proposed to allow an access point (AP) to communicate with multiple users simultaneously. In this letter, we present an analytical model based on Markov chains for a non-saturated IEEE 802.11ac enhanced distributed channel access (EDCA) network, which supports the TXOP sharing mechanism. The analytical model computes the 802.11ac EDCA throughput, in the assumption of ideal channel conditions. Extensive simulation and analysis results show that the analytical model can accurately predict the throughput of the 802.11ac networks under non-saturated operation.

Open Wireless Network Architecture in Radio Access Network

Continuous evolution of wireless protocol and the increasing unmanaged deployment of small cells (e.g. Pico-cell, femto-cell or Wi-Fi APs) have raised an urgent need to update mobile network architecture. To support such evolvability, we design the Open Wireless Network (OWN), a novel evolved architecture for radio access networks, including UMTS, 802.11 WLAN, etc. The main contribution of this paper is that it separates radio access networks into control plane and data plane, and introduces a unified controller for different radio access networks. The data plane consists of soft base stations and access points (BS/AP). The LTE base station and Wi-Fi access point have been realized on our GPP-based platform. A cloud-based controller works as the control plane. In this paper, the OWN operating system (OWN OS) is designed for the controller to perform the corresponding control functions, and it provides open application interfaces (APIs) for developing customed network management applications. Our current system has already supported remote system configuration, QoE-oriented wireless resource management, mobility management and load balancing in heterogeneous networks, etc. We believe that OWN is a promising architecture to make the wireless network central controlled, QoE-oriented, feasible to evolve and more open to operators and vendors.

Video Affective Content Recognition Based on Film Grammars and Fuzzy Evaluation

Affective content analysis is an unsolved technical problem in sophisticated video retrieval and high-level applications. In order to recognize emotion types of video scenes, a new algorithm, which composes of two sub-models, is proposed. Firstly, the sub-models of two low-level features extraction are built up based on the film grammars. Secondly, a classification sub-model of scene emotion is presented. This sub-model includes two functions: fuzzy relation matrix computed by fuzzy membership functions which are built from a large number of fuzzy experiments, and affective type decision function based on the maximizing decision making of fuzzy evaluation. Experimental results show the proposed algorithm is feasible and achieves a high recognition accuracy which exceeds 80 percent.