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Hong Kong, Hong Kong

China Mobile Communications Corporation is a Chinese state-owned telecommunication company that provides mobile voice and multimedia services through its nationwide mobile telecommunications network. The company is the largest mobile telecommunications company by market capitalization today, and it was named as such in March 2011. China Mobile Limited is listed on both the NYSE and the Hong Kong stock exchange.As of December 2014, China Mobile is the world's largest mobile phone operator by subscribers with about 806 million.The ARPU for the company stays at 90 Renminbi since 2004. Wikipedia.


The Pontryagin Maximum Principle is one of the most important results in optimal control, and provides necessary conditions for optimality in the form of a mixed initial/terminal boundary condition on a pair of differential equations for the system state and its conjugate costate. Unfortunately, this mixed boundary value problem is usually difficult to solve, since the Pontryagin Maximum Principle does not give any information on the initial value of the costate. In this paper, we explore an optimal control problem with linear and convex structure and derive the associated dual optimization problem using convex duality, which is often much easier to solve than the original optimal control problem. We present that the solution to the dual optimization problem supplements the necessary conditions of the Pontryagin Maximum Principle, and elaborate the procedure of constructing the optimal control and its corresponding state trajectory in terms of the solution to the dual problem. © 2011 Elsevier Ltd. All rights reserved.


Chen X.,Nanjing University of Aeronautics and Astronautics | Chen X.,China Mobile | Yuen C.,Singapore University of Technology and Design
IEEE Transactions on Signal Processing | Year: 2014

In this paper, we address the problem of interference alignment (IA) over MIMO interference channels with limited channel state information (CSI) feedback based on quantization codebooks. Due to limited feedback and, hence, imperfect IA, there are residual interferences across different links and different data streams. As a result, the performance of IA is greatly related to the CSI accuracy (namely number of feedback bits) and the number of data streams (namely transmission mode). In order to improve the performance of IA, it makes sense to optimize the system parameters according to the channel conditions. Motivated by this, we first give a quantitative performance analysis for IA under limited feedback and derive a closed-form expression for the average transmission rate in terms of feedback bits and transmission mode. By maximizing the average transmission rate, we obtain an adaptive feedback allocation scheme, as well as a dynamic mode selection scheme. Furthermore, through asymptotic analysis, we obtain several clear insights on the system performance and provide some guidelines on the system design. Finally, simulation results validate our theoretical claims and show that obvious performance gain can be obtained by adjusting feedback bits dynamically or selecting transmission mode adaptively. © 2014 IEEE.


Iterative detection and decoding (IDD) method based on soft interference cancellation and minimum-mean squared-error filtering (SIC-MMSE) has received considerable attention in recent years due to its good performance-complexity tradeoff for coded multiple-input multiple-output (MIMO) systems. The Gaussianity of the a priori and a posteriori log-likelihood ratios (LLRs) computed at the constitute stages of the SIC-MMSE iterative receiver is a presumption for IDD to work. In this letter, the Gaussianity assumption is first shown to be not tight for high rate coded MIMO systems and thus leads to poor performance (for high rate coded MIMO systems). Then a non-linear companding based transformation method is incorporated into the SIC-MMSE iterative receiver to alleviate the non-Gaussianity of the a priori and a posteriori LLRs due to the imperfection of the (high-rate) code and per-stream approximation. Analytical and numerical results show that the proposed transformed SIC-MMSE iterative receiver achieves significant performances gains over the conventional one for coded MIMO systems, in particular, high rate coded ones with even lower computational complexity. © 2012 IEEE.


The present disclosure relates to the field of mobile communication technology, and provides a method for implementing discontinuous reception and a base state. The method includes the steps of: receiving, by a first base station, a discontinuous reception DRX configuration recommendation reported by UE; and transmitting, by the first base station, the DRX configuration recommendation or DRX configuration parameters configured by the first base station for the UE in accordance with the DRX configuration recommendation to a second base station when the UE is switched from the first base station to the second base station.


A method using drive test data for propagation model calibration includes: step 1, obtaining original drive test data; step 2, selecting the data from the drive test data according to predefined conditions as effective drive test data; and step 3, extracting the effective drive test data to form a data file used for propagation model calibration. An apparatus using drive test data for propagation model calibration includes: a drive test data obtaining module, configured to obtain the drive test data in the regions to be calibrated; an effective drive test data generation module, configured to generate effective drive test data from the drive test data according to predefined conditions; and a data file generation module, configured to extract the effective drive test data to form a data file used for propagation model calibration. The present invention utilizes drive test data of existing networks to largely decrease the CW test work and reduce the network building cost. It is believed that the calibrated model can relatively accurately reflect the propagation characteristics in the field. Furthermore, base stations can be optimally allocated.

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