Huawei

About: Huawei is a company organization based out in Shenzhen, China. It is known for research contribution in the topics: Terminal (electronics) & Signal. The organization has 41417 authors who have published 44698 publications receiving 343496 citations. The organization is also known as: Huawei Technologies & Huawei Technologies Co., Ltd..

Design of SCMA Codebooks Based on Golden Angle Modulation

Abstract: In this paper, we propose to use golden angle modulation (GAM) points to construct codebooks for uplink and downlink sparse code multiple access (SCMA) systems. We provide two categories of codebooks with one and two optimization parameters, respectively. The advantages of the proposed design method are twofold: $1)$ the number of optimization variables is independent of codebook and system parameters; and $2)$ it is simple to implement. In the downlink, we use GAM points to build a multidimensional mother constellation for SCMA codebooks, while in the uplink GAM points are directly mapped to user codebooks. The proposed codebooks exhibit good performance with low peak to average power ratio compared to the codebooks proposed in the literature based on constellation rotation and interleaving.

System and method for adaptive tti coexistence with lte

Abstract: System and method embodiments are disclosed to provide mechanisms that allow adaptive transmission time interval (TTI) coexistence in Long Term Evolution (LTE) and fifth generation (5G) cellular systems. In accordance with an embodiment, a method for an adaptive TTI coexistence mechanism includes allocating, by a network controller, a LTE TTI at a first bandwidth. The first bandwidth is smaller than an available system bandwidth and is centered around a carrier frequency at a center of the available system bandwidth. The method further includes broadcasting the first bandwidth in LTE system information messages, allocating adaptive TTIs in the available system bandwidth outside the first bandwidth, and broadcasting adaptive TTI bandwidth partitioning information to adaptive TTI-capable terminals.

Machine learning techniques for quality of transmission estimation in optical networks

Abstract: The estimation of the quality of transmission (QoT) in optical systems with machine learning (ML) has recently been the focus of a large body of research. We discuss the sources of inaccuracy in QoT estimation in general; we propose a taxonomy for ML-aided QoT estimation; we briefly review ML-aided optical performance monitoring, a tightly related topic; and we review and compare all recently published ML-aided QoT articles.

RepChain: A Reputation-Based Secure, Fast, and High Incentive Blockchain System via Sharding

Abstract: In today’s blockchain system, designing a secure and high throughput blockchain on par with a centralized payment system is a difficult task. Sharding is one of the most worthwhile emerging technologies for improving the system throughput while maintain high-security level. However, previous sharding-related designs have two main limitations. First, the security and throughput of their random-based sharding system are not high enough as they did not leverage the heterogeneity among validators. Second, to design an incentive mechanism that promotes cooperation could incur a huge overhead on their system. In this article, we propose RepChain , a reputation-based secure and fast blockchain system via sharding, which also provides high incentive to stimulate node cooperation. RepChain utilizes reputation to explicitly characterize the heterogeneity among the validators and lay the foundation for the incentive mechanism. We propose a new double-chain architecture—a transaction chain and a reputation chain. For the transaction chain, an efficient Raft-based synchronous consensus has been presented. For the reputation chain, the synchronous Byzantine fault tolerance consensus that combines collective signing has been utilized to prevent the attack on both reputation score and the related transaction blocks. It supports a high throughput transaction chain with moderate generation speed. Moreover, we propose a reputation-based sharding and leader selection scheme. To analyze the security of RepChain , we propose a recursive formula to calculate the epoch security within only $\mathcal {O}(km^{2})$ time. Furthermore, we implement and evaluate RepChain on the Amazon Web Service platform. The results show our solution can enhance both throughout and security level of the existing sharding-based blockchain system.

Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning

Abstract: Interactive recommender system (IRS) has drawn huge attention because of its flexible recommendation strategy and the consideration of optimal long-term user experiences. To deal with the dynamic user preference and optimize accumulative utilities, researchers have introduced reinforcement learning (RL) into IRS. However, RL methods share a common issue of sample efficiency, i.e., huge amount of interaction data is required to train an effective recommendation policy, which is caused by the sparse user responses and the large action space consisting of a large number of candidate items. Moreover, it is infeasible to collect much data with explorative policies in online environments, which will probably harm user experience. In this work, we investigate the potential of leveraging knowledge graph (KG) in dealing with these issues of RL methods for IRS, which provides rich side information for recommendation decision making. Instead of learning RL policies from scratch, we make use of the prior knowledge of the item correlation learned from KG to (i) guide the candidate selection for better candidate item retrieval, (ii) enrich the representation of items and user states, and (iii) propagate user preferences among the correlated items over KG to deal with the sparsity of user feedback. Comprehensive experiments have been conducted on two real-world datasets, which demonstrate the superiority of our approach with significant improvements against state-of-the-arts.