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..

Green Cognitive Mobile Networks With Small Cells for Multimedia Communications in the Smart Grid Environment

Abstract: High-data-rate mobile multimedia applications can greatly increase energy consumption, leading to an emerging trend of addressing the “energy efficiency” aspect of mobile networks. Cognitive mobile networks with small cells are important techniques for meeting the high-data-rate requirements and improving the energy efficiency of mobile multimedia communications. However, most existing works do not consider the power grid, which provides electricity to mobile networks. Currently, the power grid is experiencing a significant shift from the traditional grid to the smart grid. In the smart grid environment, only considering energy efficiency may not be sufficient since the dynamics of the smart grid will have significant impacts on mobile networks. In this paper, we study green cognitive mobile networks with small cells in the smart grid environment. Unlike most existing studies on cognitive networks, where only the radio spectrum is sensed, our cognitive networks sense not only the radio spectrum environment but also the smart grid environment, based on which power allocation and interference management for multimedia communications are performed. We formulate the problems of electricity price decision, energy-efficient power allocation, and interference management as a three-stage Stackelberg game. A homogeneous Bertrand game with asymmetric costs is used to model price decisions made by the electricity retailers. A backward induction method is used to analyze the proposed Stackelberg game. Simulation results show that our proposed scheme can significantly reduce operational expenditure and $\hbox{CO}_{2}$ emissions in cognitive mobile networks with small cells for multimedia communications.

Method and system for interference reduction through proximity based transmission mode change

Abstract: The invention relates to a method for transmission mode change in a telecommunication network, said network having a public network comprising at least one public base station (NB) covering a public cell and a private network comprising at least one private base station (PBS) covering a private cell, wherein said at least one private base station has limited access rights for User Equipment (UE:s) in the private network, and where said UE:s are able to also communicate with the public network, comprising the step of: performing a private base station transmission mode change when the private base station is in an interference reduction transmission mode to an active transmission mode when an UE with access rights to said private base station is detected in proximity of said private base station, where a detection of said proximity is based on information being specific for said UE and its relation to said private base station. The invention also relates to a telecommunication network.

Exploiting MIMO antennas in cooperative cognitive radio networks

Abstract: Recently, a new paradigm for cognitive radio networks has been advocated, where primary users (PUs) recruit some secondary users (SUs) to cooperatively relay the primary traffic. However, all existing work on such cooperative cognitive radio networks (CCRNs) operate in the temporal domain. The PU needs to give out a dedicated portion of channel access time to the SUs for transmitting the secondary data in exchange for the SUs' cooperation, which limits the performance of both PUs and SUs. On the other hand, Multiple Input Multiple Output (MIMO) enables transmission of multiple independent data streams and suppression of interference via beam-forming in the spatial domain over MIMO antenna elements to provide significant performance gains. Researches have not yet explored how to take advantage of the MIMO technique in CCRNs. In this paper, we propose a novel MIMO-CCRN framework, which enables the SUs to utilize the capability provided by the MIMO to cooperatively relay the traffic for the PUs while concurrently accessing the same channel to transmit their own traffic. We design the MIMO-CCRN architecture by considering both the temporal and spatial domains to improve spectrum efficiency. Further we provide theoretical analysis for the primary and secondary transmission rate under MIMO cooperation and then formulate an optimization model based on a Stackelberg game to maximize the utilities of PUs and SUs. Evaluation results show that both primary and secondary users achieve higher utility by leveraging MIMO spatial cooperation in MIMO-CCRN than with conventional schemes.

A Non-Stationary IMT-Advanced MIMO Channel Model for High-Mobility Wireless Communication Systems

Abstract: With the recent developments of high-mobility wireless communication systems, e.g., high-speed train (HST) and vehicle-to-vehicle communication systems, the ability of conventional stationary channel models to mimic the underlying channel characteristics has widely been challenged. Measurements have demonstrated that the current standardized channel models, like IMT-Advanced (IMT-A) and WINNER II channel models, offer stationary intervals that are noticeably longer than those in measured HST channels. In this paper, we propose a non-stationary channel model with time-varying parameters, including the number of clusters, the powers, and the delays of the clusters, the angles of departure, and the angles of arrival. Based on the proposed non-stationary IMT-A channel model, important statistical properties, i.e., the local spatial cross-correlation function and local temporal autocorrelation function are derived and analyzed. Simulation results demonstrate that the statistical properties vary with time due to the non-stationarity of the proposed channel model. An excellent agreement is achieved between the stationary interval of the developed non-stationary IMT-A channel model and that of relevant HST measurement data, demonstrating the utility of the proposed channel model.

RS-Forest: A Rapid Density Estimator for Streaming Anomaly Detection

Abstract: Anomaly detection in streaming data is of high interest in numerous application domains. In this paper, we propose a novel one-class semi-supervised algorithm to detect anomalies in streaming data. Underlying the algorithm is a fast and accurate density estimator implemented by multiple fully randomized space trees (RS-Trees), named RS-Forest. The piecewise constant density estimate of each RS-tree is defined on the tree node into which an instance falls. Each incoming instance in a data stream is scored by the density estimates averaged over all trees in the forest. Two strategies, statistical attribute range estimation of high probability guarantee and dual node profiles for rapid model update, are seamlessly integrated into RS Forestto systematically address the ever-evolving nature of data streams. We derive the theoretical upper bound for the proposed algorithm and analyze its asymptotic properties via bias-variance decomposition. Empirical comparisons to the state-of-the-art methods on multiple benchmark datasets demonstrate that the proposed method features high detection rate, fast response, and insensitivity to most of the parameter settings. Algorithm implementations and datasets are available upon request.