China Mobile Research Institute

About: China Mobile Research Institute is a based out in . It is known for research contribution in the topics: MIMO & Wireless network. The organization has 579 authors who have published 542 publications receiving 13897 citations.

Convolutional Neural Network for Retinal Blood Vessel Segmentation

Abstract: This paper proposes a CNN (Convolutional neural network) based blood vessel segmentation algorithm. Each pixel with its neighbors of the fundus image is checked by the CNN. The preliminary segmentation results of fundus images were refined by a two stages binarization and a morphological operation successively. The algorithm was tested on DRIVE dataset. While the specificity is 0.9603, sensitivity is 0.7731, which is very close to that of manual annotation. The sensitivity is 2% better than the ones found in current studies. The CNN based algorithm improves the segmentation of blood vessels performance significantly.

SCMA: A Promising Non-Orthogonal Multiple Access Technology for 5G Networks

Abstract: Sparse code multiple access (SCMA) is a code domain non-orthogonal multiple-access technique introduced for future 5G wireless networks. This paper describes the basic ideas of SCMA, the SCMA codebook design, the encoder and decoder, as well as the SCMA enabled transmission schemes for different application scenarios, including uplink grant-free contention-based transmission, downlink multi-user superposition, and downlink open-loop CoMP for ultra-dense networks even with moving users. We shall show from design principles and application examples that SCMA can resolve some major issues of current wireless systems and establish itself as a strong candidate for 5G networks.

Software-Defined Edge Computing (SDEC): Principle, Open IoT System Architecture, Applications, and Challenges

Abstract: Edge computing is a bridge for realizing the convergence between physical space and cyber space in the Internet of Things (IoT) paradigm. Large numbers of physical objects produce a huge amount of data that needs to be efficiently processed in the edge side. This situation urgently requires novel ideas and framework in the design and management of edge computing to improve and enhance its performance. In this article, we propose an approach and principle of software-defined edge computing (SDEC) from the perspective of cyber-physical mapping, where the ultimate goal is to achieve a highly automatic and intelligent edge computing system. The SDEC can also help realize flexible management and intelligent collaboration among various edge hardware resources and services by way of software. To this end, we design an SDEC-based open IoT system architecture which decouples upper level IoT applications from the underlying physical edge resources and builds dynamically reconfigurable smart edge services. The software-definition mechanism of the SDEC platform is proposed to introduce the detailed processes that the underlying physical devices are defined in the form of software. We also describe an illustrative application case about smart factory to present the practical effectiveness of the proposed scheme. Finally, we outline several challenges which are worthy of in-depth study and research. The SDEC paradigm can share, reuse, recombine, and reconfigure edge resources and services so that the overall service capability of the edge side can be improved.

Network Energy Consumption Assessment of Conventional Mobile Services and Over-the-Top Instant Messaging Applications

Abstract: The rapid growth in the energy consumption of mobile networks has become a major concern for mobile operators. Today’s mobile networks’ usage is dominated by over-the-top (OTT) applications, and operators are keen to determine the network energy consumed by these OTT applications. With a recent shift in user behavior toward a preference for instant messaging (IM) applications over conventional mobile services, operators are interested in exploring what impact OTT IM applications such as WeChat will have on the energy consumption of a network when compared with a corresponding conventional mobile service. Here, we present for the first time energy assessment models for mobile services based on real network and service measurements to address this need. Using WeChat as an OTT IM application example, our results show that WeChat consumes more network energy than conventional mobile services for both light users and heavy text users due to the network signaling energy overhead. In comparison, for heavy voice users, WeChat consumes less network energy since voice messages are first recorded and then sent in packet bursts. Our findings provide a quantitative analysis of the energy consumption of mobile services, which should be valuable for mobile operators and OTT application developers to improve the energy-efficiency of mobile applications and services.

A Digital Twin Approach for Self-optimization of Mobile Networks

Abstract: Most of the methods in operators' current 5G networks use expert knowledge assisted by machine learning algorithms to generate optimization decisions. However, these methods are inadaptive to the dynamic changes of high-dimensional network states, thus the result is often suboptimal. Reinforcement learning can better cope with high-dimensional network state space and parameter space. However, when applied to real network, challenges arise such as difficult to obtain data samples, time-consuming and risky to explore real networks during model training. To solve these problems, this paper proposes a combined approach of expert knowledge, reinforcement learning and digital twin for the self-optimization of mobile networks. By constructing a digital twin of the current network, the future network state is predicted based on which optimization decisions are generated by expert knowledge and reinforcement learning respectively, and then input into the digital twin. Digital twin simulates their rewards and decides a final action for execution. Simulation results have confirmed that the proposed scheme can achieve higher rewards than either expert knowledge or reinforcement learning, and can avoid negative impact on real network performance. This paper also describes several potential application scenarios for the proposed approach in 6G networks and discusses key issues for future research.