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..

Exploiting Temporal Consistency for Real-Time Video Depth Estimation

Abstract: Accuracy of depth estimation from static images has been significantly improved recently, by exploiting hierarchical features from deep convolutional neural networks (CNNs). Compared with static images, vast information exists among video frames and can be exploited to improve the depth estimation performance. In this work, we focus on exploring temporal information from monocular videos for depth estimation. Specifically, we take the advantage of convolutional long short-term memory (CLSTM) and propose a novel spatial-temporal CSLTM (ST-CLSTM) structure. Our ST-CLSTM structure can capture not only the spatial features but also the temporal correlations/consistency among consecutive video frames with negligible increase in computational cost. Additionally, in order to maintain the temporal consistency among the estimated depth frames, we apply the generative adversarial learning scheme and design a temporal consistency loss. The temporal consistency loss is combined with the spatial loss to update the model in an end-to-end fashion. By taking advantage of the temporal information, we build a video depth estimation framework that runs in real-time and generates visually pleasant results. Moreover, our approach is flexible and can be generalized to most existing depth estimation frameworks. Code is available at: https://tinyurl.com/STCLSTM

Multiplexing scheme in a communication system

Abstract: The present invention is related to a method for multiplexing communication resources to multiple users in a communication system having no network resource planning. The method comprises the steps of: generating a generic time-frequency (T-F) mapping pattern (TFPgeneric), generating a set of orthogonal T-F mapping patterns from said generic T-F mapping pattern (TFPgeneric) , performing a random variable cyclic offsetting of said set of orthogonal T-F mapping patterns in each transmission time interval (TTI), and allocating the orthogonal T-F mapping patterns to the one or more users and/or traffic channels in each TTI. The invention also relates to a transmitter for executing said multiplexing method, and a system including such transmitters.

Cooperative Hierarchical Caching and Request Scheduling in a Cloud Radio Access Network

Abstract: In this article, we propose a novel cooperative hierarchical caching framework in a Cloud Radio Access Network (C-RAN), in which a new cloud-cache at Cloud Processing Unit (CPU) is envisioned to bridge the storage-capacity/delay-performance gap between the traditional edge-based and core-based caching paradigms. A delay-cost model is introduced and the cache placement problem is formulated that aims at minimizing the average delay-cost of content delivery in the network. Given the NP-completeness of the cache placement problem, we propose a low-complexity heuristic cache-management strategy comprising of a proactive cache-distribution algorithm and a reactive cache-replacement algorithm. Furthermore, a Cache-Aware Request Scheduling (CARS) algorithm is devised in order to optimize online the tradeoff between content download rate and content access delay. Via extensive numerical simulations—carried out using both real-world YouTube video requests and synthetic content requests—it is demonstrated that the proposed cache-management strategy outperforms traditional caching strategies in terms of cache hit ratio, average content access delay, and backhaul traffic load. Additionally, it is shown that the proposed CARS algorithm achieves superior tradeoff performance over traditional approaches that optimize either users’ rate or access delay alone.

D2D caching vs. small cell caching: Where to cache content in a wireless network?

Abstract: Proximity-based content caching and distribution in wireless networks has been identified as a promising traffic offloading solution for improving the capacity and the quality of experience (QoE) by exploiting content popularity and spatiotemporal request correlation. In this paper, we address the following question: where should popular content be cached in a wireless network? For that, we model a wireless cellular network using stochastic geometry and analyze the performance of two network architectures, namely caching at the mobile device allowing device-to-device (D2D) connectivity and local caching at the radio access network edge (small cells). We provide analytical and numerical results to compare their performance in terms of the cache hit probability, the density of cache-served requests and average power consumption. Our results reveal that the performance of cache-enabled networks with either D2D caching or small cell caching heavily depends on the user density and the content popularity distribution.

Deep Federated Q-Learning-Based Network Slicing for Industrial IoT

Abstract: Fifth generation and beyond networks are envisioned to support multi industrial Internet of Things (IIoT) applications with a diverse quality-of-service (QoS) requirements. Network slicing is recognized as a flagship technology that enables IIoT networks with multiservices and resource requirements by allowing the network-as-infrastructure transition to the network-as-service. Motivated by the increasing IIoT computational capacity, and taking into consideration the QoS satisfaction and private data sharing challenges, federated reinforcement learning (RL) has become a promising approach that distributes data acquisition and computation tasks over distributed network agents, exploiting local computation capacities and agent's self-learning experiences. This article proposes a novel deep RL scheme to provide a federated and dynamic network management and resource allocation for differentiated QoS services in future IIoT networks. This involves IIoT slices resource allocation in terms of transmission power (TP) and spreading factor (SF) according to the slices QoS requirements. Toward this goal, the proposed deep federated Q-learning (DFQL) is reached into two main steps. First, we propose a multiagent deep Q-learning-based dynamic slices TP and SF adjustment process that aims at maximizing self-QoS requirements in term of throughput and delay. Second, the deep federated learning is proposed to learn multiagent self-model and enable them to find an optimal action decision on the TP and the SF that satisfy IIoT virtual network slice QoS reward, exploiting the shared experiences between agents. Simulation results show that the proposed DFQL framework achieves efficient performance compared to the traditional approaches.