arXiv subjects

Chang Wen Chen

Publications and source records attributed to Chang Wen Chen.

77 records · Page 5Linked to original sources

arXiv2016

Resource Allocation in Dynamic TDD Heterogeneous Networks under Mixed Traffic

Recently, Dynamic Time Division Duplex (TDD) has been proposed to handle the asymmetry of traffic demand between DownLink (DL) and UpLink (UL) in Heterogeneous Networks (HetNets). However, for mixed traffic consisting of best effort traffic and soft Quality of Service (QoS) traffic, the resource allocation problem has not been adequately studied in Dynamic TDD HetNets. In this paper, we focus on such problem in a two-tier HetNet with co-channel deployment of one Macro cell Base Station (MBS) and multiple Small cell Base Stations (SBSs) in hotspots. Different from existing work, we introduce low power almost blank subframes to alleviate MBS-to-SBS interference which is inherent in TDD operation. To tackle the resource allocation problem, we propose a two-step strategy. First, from the view point of base stations, we propose a transmission protocol and perform time resource allocation by formulating and solving a network capacity maximization problem under DL/UL traffic demands. Second, from the view point of User Equipments (UEs), we formulate their resource allocation as a Network Utility Maximization (NUM) problem. An efficient iterative algorithm is proposed to solve the NUM problem. Simulations show the advantage of the proposed algorithm in terms of network throughput and UE QoS satisfaction level.

cs.NI↗

arXiv2016

Storytelling of Photo Stream with Bidirectional Multi-thread Recurrent Neural Network

Visual storytelling aims to generate human-level narrative language (i.e., a natural paragraph with multiple sentences) from a photo streams. A typical photo story consists of a global timeline with multi-thread local storylines, where each storyline occurs in one different scene. Such complex structure leads to large content gaps at scene transitions between consecutive photos. Most existing image/video captioning methods can only achieve limited performance, because the units in traditional recurrent neural networks (RNN) tend to "forget" the previous state when the visual sequence is inconsistent. In this paper, we propose a novel visual storytelling approach with Bidirectional Multi-thread Recurrent Neural Network (BMRNN). First, based on the mined local storylines, a skip gated recurrent unit (sGRU) with delay control is proposed to maintain longer range visual information. Second, by using sGRU as basic units, the BMRNN is trained to align the local storylines into the global sequential timeline. Third, a new training scheme with a storyline-constrained objective function is proposed by jointly considering both global and local matches. Experiments on three standard storytelling datasets show that the BMRNN model outperforms the state-of-the-art methods.

cs.CV↗

arXiv2016

Network Morphism

We present in this paper a systematic study on how to morph a well-trained neural network to a new one so that its network function can be completely preserved. We define this as \emph{network morphism} in this research. After morphing a parent network, the child network is expected to inherit the knowledge from its parent network and also has the potential to continue growing into a more powerful one with much shortened training time. The first requirement for this network morphism is its ability to handle diverse morphing types of networks, including changes of depth, width, kernel size, and even subnet. To meet this requirement, we first introduce the network morphism equations, and then develop novel morphing algorithms for all these morphing types for both classic and convolutional neural networks. The second requirement for this network morphism is its ability to deal with non-linearity in a network. We propose a family of parametric-activation functions to facilitate the morphing of any continuous non-linear activation neurons. Experimental results on benchmark datasets and typical neural networks demonstrate the effectiveness of the proposed network morphism scheme.

cs.LG↗

arXiv2015

Attribute-Based Multi-Dimensional Scalable Access Control For Social Media Sharing

Media sharing is an extremely popular paradigm of social interaction in online social networks (OSNs) nowadays. The scalable media access control is essential to perform information sharing among users with various access privileges. In this paper, we present a multi-dimensional scalable media access control (MD-SMAC) system based on the proposed scalable ciphertext policy attribute-based encryption (SCP-ABE) algorithm. In the proposed MD-SMAC system, fine-grained access control can be performed on the media contents encoded in a multi-dimensional scalable manner based on data consumers' diverse attributes. Through security analysis, we show that the proposed MC-SMAC system is able to resist collusion attacks. Additionally, we conduct experiments to evaluate the efficiency performance of the proposed system, especially on mobile devices.

cs.MM↗

arXiv2015

Service Provisioning and Profit Maximization in Network-assisted Adaptive HTTP Streaming

Adaptive HTTP streaming with centralized consideration of multiple streams has gained increasing interest. It poses a special challenge that the interests of both content provider and network operator need to be deliberately balanced. More importantly, the adaptation strategy is required to be flexible enough to be ported to various systems that work under different network environments, QoE levels, and economic objectives. To address these challenges, we propose a Markov Decision Process (MDP) based network-assisted adaptation framework, wherein cost of buffering, significant playback variation, bandwidth management and income of playback are jointly investigated. We then demonstrate its promising service provisioning and maximal profit for a mobile network in which fair or differentiated service is required.

cs.NI↗