Energy-Spectrum Efficiency Tradeoff for Video Streaming over Mobile Ad Hoc Networks

Wang

Wireless Communications and Mobile Computing

et al. 2017

Device-to-device (D2D) communication has been widely studied to improve network performance and considered as a potential technological component for the next generation communication. Considering the diverse users' demand, Quality of Experience (QoE) is recognized as a new degree of user's satisfaction for media service transmissions in the wireless communication. Furthermore, we aim at promoting user's Mean of Score (MOS) value to quantify and analyze user's QoE in the dynamic cellular networks. In this paper, we explore the heterogeneous media service distribution in D2D communications underlaying cellular networks to improve the total users' QoE. We propose a novel media service scheme based on different QoE models that jointly solve the massive media content dissemination issue for cellular networks. Moreover, we also investigate the so-called Media Service Adaptive Update Scheme (MSAUS) framework to maximize users' QoE satisfaction and we derive the popularity and priority function of different media service QoE expression. Then, we further design Media Service Resource Allocation (MSRA) algorithm to schedule limited cellular networks resource, which is based on the popularity function to optimize the total users' QoE satisfaction and avoid D2D interference. In addition, numerical simulation results indicate that the proposed scheme is more effective in cellular network content delivery, which makes it suitable for various media service propagation.

Section: Numerical Resultsmentioning

confidence: 99%

“…Moreover, the heterogeneous media service over D2D communication is a very interesting topic. Meanwhile, it can make a difference on our daily life and provide a high Quality of Experience (QoE) service entertainment [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

QoE-Driven D2D Media Services Distribution Scheme in Cellular Networks

Wang

Wireless Communications and Mobile Computing

et al. 2017

“…However, we still focus on the imbalance characteristic between classes. So far, many measures have been proposed to solve the binary class imbalance problem [10][11][12][13][14][16][17][18][19][20][21]. These measures can be broadly by data level and algorithm level, as shown in Table 1.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Ensemble Method Based on Spatial Characteristics for Classifying Imbalanced Data

Wang¹,

Zhao²,

Gui³

et al. 2017

Scientific Programming

The class imbalance problems often reduce the classification performance of the majority of standard classifiers. Many methods have been developed to solve these problems, such as cost-sensitive learning methods, synthetic minority oversampling technique (SMOTE), and random oversampling (ROS). However, the existing methods still have some problems due to the possible performance loss of useful information and overfitting. To solve the problems, we propose an adaptive ensemble method by using the most advanced feature of self-adaption by considering an average Euclidean distance between test data and training data, where the average distance is calculated by -nearest neighbors (KNN) algorithm. Simulation results are provided to confirm that the proposed method has a better performance than existing ensemble methods.

“…Despite optimality of centralized techniques in resource allocation, finding the analytic formulation and in turn the optimum point is prohibitively difficult due to the non-convexity of the associated optimization problem [16,17]. Furthemore, the resource allocation problem in CoMP is proved to be NP-complete [18].…”

Section: Background and Related Workmentioning

confidence: 99%

On the power allocation strategies in coordinated multi-cell networks using Stackelberg game

Haddadi

Oliaiee

Behroozi

et al. 2016

J Wireless Com Network

In this paper, we study the power allocation problem in multi-cell OFDMA networks, where given the tradeoff between user satisfaction and profit of the service provider, maximizing the revenue of the service provider is also taken into account. Consequently, two Stackelberg games are proposed for allocating proper powers to central and cell-edge users. In our algorithm, assuming the fact that users agree to pay more for better QoS level, the service provider imposes optimum prices for unit-power transmitted to users as they request different levels of QoS. In addition, in order to improve system performance at cell-edge locations, users are divided into two groups based on their distance to the corresponding base-station (BSs): Central users and cell-edge users. The paper also exploits unique features of Coordinated Multi-Point Joint Transmission (CoMP-JT) where coordinated BSs are clustered together statically or dynamically in order to also address the requirements of cell-edge users. After simulating the proposed game with static clustering, a simple dynamic clustering algorithm is introduced for inter-cell coordinated networks where its performance is evaluated through simulations.