Yawen Chen

Content-Aware Video QoE Coverage Analysis in Heterogeneous Wireless Networks

AbstractWith quality of experience (QoE) taking a center stage in the evaluation of wireless telecommunication system, optimizing the performance of heterogeneous wireless networks (HWNs) is in need of taking service QoE into consideration. End to end QoE of video services in HWN is determined by not only the network but also the video characteristics. In this paper, a metric termed as QoE coverage is proposed to evaluate the coverage performance of HWNs for video services. First, subjective quality test is performed to improve the existing video quality prediction model, and the expression of QoE coverage is derived by combining video and network parameters. Second, two QoE provisioning schemes, i.e., homogeneous and heterogeneous quality thresholds of HWN are analyzed to show the mathematic properties. Third, a Monte Carlo simulation is performed to show its differences to SINR coverage. Compared with state-of-the-art SINR coverage, QoE coverage takes both physical layer and application layer parameters into consideration and provides more realistic user satisfactory. Results show that QoE coverage can be well maintained by adjusting target QoE thresholds, modulation and coding schemes with different videos.

Resource allocation for OFDMA two-way relay networks with the smart grid

Communication networks for electric system is drawing lots of attention for its indispensable effect in the smart grid. When design an electric dedicated private network in the smart grid, only considering maximum capacity may not be enough. In this paper, we consider an orthogonal frequency division multiple access (OFDMA) two-way relay network in the smart grid. Joint resource allocation is used to get higher spectrum efficiency. A capacity reservation scheme is proposed to guarantee specific kind of service while taking energy efficiency and pollutant emissions into account. Simulation results show that our scheme can improve spectrum efficiency, lower energy cost and significantly reduce pollutant emissions.

Cooperation-enabled energy efficient base station management for dense small cell networks

Dense small cell networks are deployed for future wireless communication to meet the ever-increasing mobile traffic demand. However, network densification will significantly increase the energy budget and lead to energy inefficiency due to the constant operation of network hardware. In this paper, we consider cooperation-enabled dynamic base station (BS) management for downlink dense small cell networks. By introducing two traffic-aware sleep modes, i.e., deep sleep mode and opportunistic sleep mode which are operating in different time and energy consumption scales, the network hardwares are turned to be the resources that can be occupied and released dynamically. Small cell BSs (SBSs) with zero or low load are completely switched off and reside in deep sleep mode during a predefined time interval. At each time slot, SBS dynamically turn some antennas and associated physical components into opportunistic sleep mode according to the short term traffic distribution, and the users are jointly served by the remaining antennas via cooperative transmission. The corresponding sleep mode decision making are presented respectively to find the optimal number of SBS and antennas that should be switched off. Numerical results are then presented to illustrate the superior performance in terms of energy efficiency gain. In summary, the proposed cooperation-aided sleep strategies for dense small cell networks take both traffic features and optimal cooperative transmission into account, and can achieve great energy saving while maintaining required quality of service.

Joint remote radio head activation and beamforming for energy efficient C-RAN.

The cloud radio access network (C-RAN) has emerged as a promising architecture to provide extremely high throughput with fantastic energy efficiency (EE) performance. However, as all the RRHs need to be connected to the baseband unit pool (BBU) through transport links, the transmit power consumption becomes significant, which result in the demands of new researches in energy efficiency optimization. In this paper, we focus on the dynamic remote radio head (RRH) activation and network EE optimization in order to fully reap the benefits brought by Green C-RAN. First, an EE optimization problem that jointly considers RRH activation and group sparse beamforming is formulated, which is hard to solve due to its non-convexity nature. Thus, we utilize the weighted minimal mean square error (WMMSE) method to transfer the non-convex EE problem into a concave-convex fractional program problem. And the Lagrangian theory is exploited to assist the problem analysis and algorithm design. Specifically, the weighted group sparse beamforming algorithm is proposed. In this algorithm, we adopt the mixed l1=lp-norm to induce group sparsity in the beamformers, which corresponds to switching off RRHs. Simulation results will show that the proposed algorithm can significantly improve the EE for C-RAN.

Energy Efficient Clustering and Beamforming for Cloud Radio Access Networks

Cloud radio access network (Cloud-RAN) is recognized as one of the key enabling techniques for 5G due to its advantages in flexibility. In addition, the greatly increased energy efficiency (EE, evaluated by bits/Hz/J) is listed as one main objective when designing 5G wireless network. In this paper, we concentrate on EE optimization through Cloud-RAN enabled flexible multicell cooperative transmission. A joint clustering and beamforming problem for EE optimization is formulated. Due to the combinatorial nature of the clustering process and the non-convexity of the energy efficient beamforming design, the problem is difficult to solve directly. Therefore, we propose a hierarchical iterative framework to solve the problem. The origin optimization problem is decoupled into two subproblems, i.e., energy efficient beamforming problem and energy efficient cluster forming problem. Coalition formation game theory and fractional programming are utilized to obtain the optimal network cluster partition and beamformers respectively. Simulation results demonstrate the superior performance of the proposed algorithms.

An effective visual saliency detection method based on maximum entropy random walk

Visual saliency detection (VSD) has been attracting increasing attention due to its wide applications in computer visions. In this paper, a visual saliency detection method based on maximum entropy random walk (MEVSD) is proposed. Gaze wandering over images is modeled as a random walk process on a graph, in which the super-pixels and their similarities are regarded as nodes and edges respectively. The visual salient region is detected based on the stationary distribution of the stochastic process. Compared with the stat-of-the-art methods, MEVSD focuses on globally maximizing the entropy of walking trajectories. A two-tier neighbor scheme of super-pixels is proposed to reduce the computational complexity. Besides, a belief diffusion algorithm on weight matrix is proposed to improve precision rate. Extensive experiments with diverse content of pictures indicate that MEVSD performs well compared with 11 state-of-the-art methods.

Bursty interference-oriented video quality assessment method

The frequent bursty interference leads to wireless throughput variability in future networks, which results in video quality of experience (QoE) degradation. It is highly desirable to be able to predict video quality to meet QoE requirements. There has been a great deal of studies on video quality assessment, but only limited work has been reported for assessing video quality under bursty interference environment. In this paper, we seek to ameliorate this by developing a bursty interference-oriented video quality assessment algorithm. First, a subjective experiment has been carried out and a hysteresis model was proposed by analyzing the experiment data. Simulation result shows that in burst traffic environment, the model has a better correlation with human visual system (HVS) effect. Then we proposed an objective quality assessment algorithm by taking the video color, brightness, motion and other spatial features together with Structural Similarity Index Measurement (SSIM) into consideration, which outperforms Peak Signal Noise Rate (PSNR), Visual Information Fidelity (VIF) and SSIM in bursty environment.

User-centric Clustering and Beamforming for Energy Efficiency Optimization in Cloud-RAN

User-centric and energy efficient are becoming two foremost design principles in the cloud radio access networks (Cloud-RAN). In this paper, we thus consider the problem of how to assign each user to several preferred remote radio heads (RRHs) and design the corresponding beamforming coefficients in a user-centric and energy efficient manner. We formulate this problem as a joint clustering and beamforming optimization problem, with the objective to maximize the energy efficiency (EE) while satisfying the users’ quality of service (QoS) requirement and respecting the RRHs’ transmit power limits. We first transform it into an equivalent parametric subtractive problem using the approach in fractional programming, and then it is cast into a tractable convex optimization problem by introducing a lower bound of the objective function. Finally, the structure of the optimal solution is derived and a two-tier iterative scheme is developed to find the clustering pattern and beamforming coefficients that maximize EE. Specially, we derive a RRH-user association threshold, based on which the RRH clustering pattern and the corresponding beamforming coefficients can be simultaneously determined. Through simulations, we show the superior performance of the proposed user-centric clustering and beamforming scheme in Cloud-RAN.

Dynamic user-centric clustering algorithm based on energy efficiency in Cloud-RAN

This paper studies user-centric cluster scheme in cloud radio access network (Cloud-RAN), where distributed Radio Remote Heads (RRHs) are connected to a centralized Based Band Units (BBUs) pool and all baseband processing is performed via high-bandwidth low-latency backhaul network. In user-centric cluster scheme, BBU schedules multiple RRHs for each user to form the serving cluster, and then BBU pool distributes the users data to the cluster via backhaul link. By taking into account signal-to-interference-and-noise ratio (SINR) constraint for each user and transmit power constraint for per-RRH, we design a cluster scheme aiming at maximizing network energy efficiency (EE). And then weighted minimal mean square error (WMMSE) method are adopted to transform the original problem into a tractable form. Specially, a sparsity-based dynamic user-centric cluster algorithm is proposed, where the cluster for each user can change over time according to network EE performance. Simulation results show that the proposed algorithm can effectively form the serving cluster according to users QoS demand and greatly improve the network energy efficiency.

Energy Efficient Clustering and Beamforming for Cooperative Multicell Networks

Network densification is the most important way to improve the network capacity and hence is widely adopted to handle the ever-increasing mobile traffic demand. However, network densification will make the inter-cell interference severe and also significantly increase the energy budget. Multicell cooperative transmission is an efficient way to mitigate the inter-cell interference and plays an important role in energy efficiency optimization. This paper investigates the energy efficient multicell cooperation strategy for dense wireless networks. Joint cluster forming and beamforming are considered to optimize the energy efficiency (evaluated by bits/Hz/J). The optimization problem is then decoupled into two subproblems, i.e., energy efficient beamforming problem and energy efficient cluster forming problem. The fractional programming and Lagrangian duality theory are used to obtain the optimal beamformer. Coalition formation game theory is exploited to solve the cluster forming problem. The proposed energy efficient clustering and beamforming strategy can provide flexible network service according to spatially uneven traffic and greatly improve the network energy efficiency.