The rapid growth in multimedia applications over cellular networks calls for multicast services. Multicast is an efficient means of delivering contents to multiple users while efficiently utilizing network resources. ...
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The rapid growth in multimedia applications over cellular networks calls for multicast services. Multicast is an efficient means of delivering contents to multiple users while efficiently utilizing network resources. Layered streaming, e.g., scalable video coding (SVC) provides an excellent solution to handle channel diversities in wireless multicast. This paper presents a cooperative multicast scheme for scalable video content delivery in D2D-enabled heterogeneous cellular networks (HetNets). To extend the multicast service beyond base stations (BSs), D2D links are used to help relay content for cellular multicast. network coding (NC), implemented through random linear network coding (RLNC) with unequal error protection (UEP) is incorporated in layered contents to enhance reliability and throughput. This paper tries to optimize the multicast scheduling procedures, aiming to assign the optimal modulation and coding schemes (MCSs) for transmissions. The constraints on cache size, backhaul capacity and channel fading are comprehensively considered. Besides, this paper also presents a interference-aware approach for D2D link selection in order to further improve the cooperative multicast. Sufficient numerical results have demonstrated the improvement brought by the proposed scheme significantly on the content delivery efficiency as well as quality of experience (QoE).
Background Capturing sentence semantics plays a vital role in a range of text mining applications. Despite continuous efforts on the development of related datasets and models in the general domain, both datasets and ...
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Background Capturing sentence semantics plays a vital role in a range of text mining applications. Despite continuous efforts on the development of related datasets and models in the general domain, both datasets and models are limited in biomedical and clinical domains. The BioCreative/OHNLP organizers have made the first attempt to annotate 1,068 sentence pairs from clinical notes and have called for a community effort to tackle the Semantic Textual Similarity (BioCreative/OHNLP STS) challenge. Methods We developed models using traditional machine learning and deep learning approaches. For the post challenge, we focus on two models: the Random Forest and the Encoder network. We applied sentence embeddings pre-trained on PubMed abstracts and MIMIC-III clinical notes and updated the Random Forest and the Encoder network accordingly. Results The official results demonstrated our best submission was the ensemble of eight models. It achieved a Person correlation coefficient of 0.8328 - the highest performance among 13 submissions from 4 teams. For the post challenge, the performance of both Random Forest and the Encoder network was improved;in particular, the correlation of the Encoder network was improved by ~13%. During the challenge task, no end-to-end deep learning models had better performance than machine learning models that take manually-crafted features. In contrast, with the sentence embeddings pre-trained on biomedical corpora, the Encoder network now achieves a correlation of ~0.84, which is higher than the original best model. The ensembled model taking the improved versions of the Random Forest and Encoder network as inputs further increased performance to 0.8528. Conclusions Deep learning models with sentence embeddings pre-trained on biomedical corpora achieve the highest performance on the test set. Through error analysis, we find that end-to-end deep learning models and traditional machine learning models with manually-crafted features complement each
Rate-Aware Instantly Decodable network coding (RA-IDNC) was suggested to improve the throughput of future cellular networks. In this work, we propose to improve the traditional RA-IDNC by taking advantage of the heter...
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Rate-Aware Instantly Decodable network coding (RA-IDNC) was suggested to improve the throughput of future cellular networks. In this work, we propose to improve the traditional RA-IDNC by taking advantage of the heterogeneity in cellular networks. We formulate the completion time minimization problem in heterogeneous wireless networks as an optimization problem over an RA-IDNC graph, and prove it is NP-hard. In addition, we propose a heuristic that iteratively minimizes completion time taking into account the users' download rates. Simulations show a reduction of around 31% in average completion time when utilizing our proposed scheme as compared to traditional RA-IDNC.
The abundance of open-source code, coupled with the success of recent advances in deep learning for natural language processing, has given rise to a promising new application of machine learning to source code. In thi...
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The edge removal problem studies the loss in network coding rates that results when a network communication edge is removed from a given network. It is known, for example, that in networks restricted to linear coding ...
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5G wireless is the next step in the evolution of mobile communications with the aim being to provide connectivity for any kind of device and any kind of application. Wireless Body Area networks (WBANs) constitute just...
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ISBN:
(纸本)9781538611548
5G wireless is the next step in the evolution of mobile communications with the aim being to provide connectivity for any kind of device and any kind of application. Wireless Body Area networks (WBANs) constitute just one component of connected healthcare utilising small intelligent physiological sensors either on or implanted in the human body. This contribution examines the 5G technologies that will make a significant contribution to providing secure healthcare-orientated WBANs with improved energy efficiency, interference mitigation and wireless power transfer capability.
This paper considers the problem of data collection in a sensor network using network coding (NC). It proposes a decoding approach, called HAND, which does not require source packets to be supplemented with NC headers...
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ISBN:
(纸本)9781538646588
This paper considers the problem of data collection in a sensor network using network coding (NC). It proposes a decoding approach, called HAND, which does not require source packets to be supplemented with NC headers (with encoding vectors), classically used to decode the network-coded packets. HAND exploits the structure imposed by the communication protocol on the packet headers to estimate the original source packets from the received network-coded packets. network-decoded packets are obtained as the solution of systems of linear equations. The decoding complexity is only one order of magnitude larger than that of classical network decoding.
In this work, it is revealed that an acyclic multicast network that is scalar linearly solvable over Galois Field of two elements, GF(2), is solvable over all higher finite fields. An algorithm which, given a GF(2) so...
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ISBN:
(纸本)9781538647813
In this work, it is revealed that an acyclic multicast network that is scalar linearly solvable over Galois Field of two elements, GF(2), is solvable over all higher finite fields. An algorithm which, given a GF(2) solution for an acyclic multicast network, computes the solution over any arbitrary finite field is presented. The concept of multicast matroid is introduced in this paper. Gammoids and their base-orderability along with the regularity of a binary multicast matroid are used.
In this paper, we address the problem of efficient data transmission in MANETs with high mobility. Our approach combines multipath routing with network coding for efficient and robust communication. Computing efficien...
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In this paper, we present a novel Cluster-based Separate networking coding (CSNC) scheme, as a solution to solve the problem of continuous data collection for WSNs with a mobile BS. By separately encoding a certain nu...
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