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Multimedia Broadcast Multicast Service (MBMS)-dedicated cell is a Long Term Evolution (LTE) based broadcast transmission mode with both high transmission efficiency and good compatibility with LTE unicast systems. However, since the physical design of Cell Acquisition Subframe (CAS), which is the control part of an MBMS-dedicated cell transmission period, does not take into account the long delay spread channels in Medium Tower Medium Power (MTMP) and High Tower High Power (HTHP) cells, receivers would probably suffer from multipath aliasing errors in practical transmission. This paper analyzes the impact of aliasing errors on channel estimation (CHE) accuracy and proposes an Aliasing-Elimination CHE algorithm for CAS reception. In the proposed algorithm, the pre-decoded Physical Broadcast Channel (PBCH) is reconstructed and utilized to generate a complete multipath distribution within the entire OFDM symbol length. By using this full-symbol length multipath distribution, the proposed algorithm eliminates aliasing errors effectively. Simulation results and complexity analysis show that the proposed CHE algorithm achieves a significant performance improvement compared to conventional CHE algorithms in long delay spread channels, with acceptable increment in complexity.

Extended Kalman filter (EKF) is one of the most widely used Bayesian estimation methods in the optimal control area. Recent works on mobile robot control and transportation systems have applied various EKF methods, especially for localization. However, it is difficult to obtain adequate and reliable process-noise and measurement-noise models due to the complex and dynamic surrounding environments and sensor uncertainty. Generally, the default noise values of the sensors are provided by the manufacturer, but the values may frequently change depending on the environment. Thus, this paper mainly focuses on designing a highly accurate trainable EKF-based localization framework using inertial measurement units (IMUs) for the autonomous ground vehicle (AGV) with dead reckoning, with the goal of fusing it with a laser imaging, detection, and ranging (LiDAR) sensor-based simultaneous localization and mapping (SLAM) estimation for enhancing the performance. Convolution neural networks (CNNs), backward propagation algorithms, and gradient descent methods are implemented in the system to optimize the parameters in our framework. Furthermore, we develop a unique cost function for training the models to improve EKF accuracy. The proposed work is general and applicable to diverse IMU-aided robot localization models.

With the proliferation and diversification of mobile communication services, the current mobile broadband networks face severe challenges. The progressive video service that will make up the majority of future network traffic has more demands for transmission bandwidth, network capacity, and management flexibility. Moreover, denser connectivity, higher reliability, lower latency, and better interaction impose some great challenges to existing networks. In order to meet these growing demands, a promising approach is to integrate a broadcast network with a cellular network. In this article, we briefly set forth the latest progress on the cellular network and the broadcast network, respectively. Then the logic architecture of the integrated network is given to clarify the functional partitioning. In addition, the physical architecture of the integrated network is presented to enable coordination in different aspects. Some feasible strategies are discussed and several promising technologies are presented.

Broad-band absorption bleaching in erbium-doped fiber by in-band narrow-linewidth pumping is reported. For the first time, spectra of absorption bleaching of erbium-doped fiber is measured and the refractive index changes inferred through the Kramers–Kronig relations in this true two-level system in order to understand its operation in the external cavity of a laser.