Cellular mobile networks have enabled several new use cases with their rapid development, and they are now considered for mission-critical and even military applications with 5G, their latest commercially available version. However, it is of utmost importance that communication channels operate in a secure and reliable manner for these purposes. Smart jammers pose a significant threat due to their accessibility, strong potential for causing service disruptions, and their difficulty in detection. This issue can be reduced by hopping between frequencies, making it harder for a malicious actor to disrupt communication. In this study, the handover mechanism, which is a supported feature in all commercial devices according to 3GPP standards, is evaluated as a frequency hopping technique in terms of feasibility and performance at preventing the attack. The proposed method is proven effective, as it can switch frequencies every second while keeping data interruptions below 75 ms for 99% of the time.

We propose a deep neural network-based method to recover dynamic online trajectories from offline handwritten Japanese kanji character images. It is a challenging task since Japanese kanji characters consist of multiple strokes. Our proposed model has three main components: Convolutional Neural Network-based encoder, Long Short-Term Memory Network-based decoder with an attention layer, and Gaussian Mixture Model (GMM). The encoder focuses on feature extraction while the decoder refers to the extracted features and generates time-sequences of GMM parameters. The attention layer is the key component for trajectory recovery. The GMM provides robustness to style variations so that the proposed model does not overfit to training samples. In the experiments, the proposed method is evaluated by both visual verification and handwritten character recognition. This is the first attempt to use online recovered trajectories to help improve offline handwriting recognition performance. Although the visual verification reveals some problems, the recognition experiments demonstrate the effect of trajectory recovery in improving offline handwritten character recognition accuracy when online recognition of the recovered trajectories are combined.

This paper presents an attempt to recognize online isolated handwritten Japanese characters as well as text lines by Recurrent Neural Networks (RNNs). Although there are some successful studies on online Chinese handwriting recognition using RNNs, it is difficult to achieve high accuracy for Japanese due to many different types of characters such as kanji and kana. Moreover, training RNNs usually requires a large number of samples for each class of character. Hence, we apply five different transformation operations on the original samples to generate artificial samples with various deformations. For online handwritten Japanese text, we use the Kondate database, but it does not cover the whole Japanese character set. Thus, we generate random text lines using the sentences from corpora and isolated characters from the Nakayosi and Kuchibue databases. Besides, we employ a robust process with some preprocessing steps and the state-of-the-art online features to extract invariant features from handwritten patterns because the handwritten samples are merged from the different databases collected on different devices with various resolutions. For the recognition model, we have implemented the different Bidirectional Long Short-Term Memory Networks (BLSTM networks) for isolated character classification (sequence classification task) and handwritten text recognition (transcription task). The best model of the sequence classification task achieves an accuracy of 97.91% on Nakayosi and 97.74% on Kuchibue. The best model of transcription task performs a character recognition rate of 86.31% on Kondate and 83.15% on the generated text lines.

This paper presents the results of the VOHTR 2018 competition on Vietnamese Online Handwritten Text Recognition. The goal of this competition is to evaluate and compare recent online handwritten text recognition systems on Vietnamese online handwritten text which contains many delayed strokes caused by the diacritic marks. Besides, the general objective is to encourage the studies on Vietnamese online handwritten text recognition based on the large Vietnamese handwriting database collected from 200 writers. In this competition, we introduce three tasks consisting of word recognition (task 1), text-line recognition (task 2) and paragraph recognition (task 3) which are described in details. Subsequently, we describe the evaluation metrics and give comparative results of competitors along with the brief descriptions of the respective methods.

This paper presents our attempts to collect and analyze unconstrained Vietnamese online handwriting text patterns by pen-based computers. Totally, our database contains over 120,000 strokes from more than 140,000 characters, which is one of the largest Vietnamese online handwriting pattern databases currently. For building and analyzing our database, we made a collection tool, a line segmentation tool, and a delayed stroke detection tool. Moreover, we investigated some statistical information from personal information of writers. In order to solve the unconstrained handwriting recognition problem, we conducted experiments using Bidirectional Long Short-Term Memory (BLSTM) networks. BLSTM network is architecture of Recurrent Neural Network (RNN) and applied recently for many related problems. The performance of BLSTM network on our database is nearly 80% of accuracy even though this database contains many delayed strokes. In near future, we are going to avail our database for research purposes, as it would be the fundamental for the handwriting recognition research.

In this paper we study the performance enhancement of a downlink LTE-Advanced system with a combination of the multi-user MIMO and carrier aggregation transmission techniques. Radio resource management for the systems with the combined features are proposed, and the system performance is evaluated. Extensive simulations with various traffic load condition from full buffer with fixed number of UEs to finite buffer with bursty traffic were considered in order to find out when and how these two features can support each other to improve the overall performance gain of the LTE-Advanced system. It is shown that MU-MIMO transmission technique can help to enhance the cell-edge performance of the CA system by 10%-40% depending on traffic load.

This paper addresses a method to estimate the multi user channel quality indicator (CQI) from the reported rank 1 single user CQI in LTE systems. We investigate the relationship between the multi user CQI and the channel condition. Based on that, we propose an updating mechanism where the estimated multi user CQI is varied according to the reported channel condition i.e. the rank 1 single user CQI. System performance of the proposed scheme is compared with the conventional fixed offset single user CQI estimation. System level simulation results show the efficiency of our proposed scheme with respect to both the enhancement in the system level performance and uplink overhead reduction. Using the statistic metrics derived from the system level simulation, we highlight the importance of having an accurate multi-user CQI estimation. The results illustrate how the multi user CQI information influence the packet scheduling and link adaptation decisions and ultimately the overall system performance.

LTE-Advanced is the evolutionary path from LTE Release 8. It is designed to significantly enhance the performance of LTE Release 8 in terms of higher peak data rates, improved system capacity and coverage, and lower latency. These enhancements allow LTE-Advanced to meet or exceed the IMT-Advanced requirements and are being considered as part of LTE Release 10. In this paper, some of the physical layer enhancement techniques for LTE-Advanced have been studied including carrier aggregation (CA), uplink multi-cluster scheduling, and uplink multi-user multiple-input multiple-output (MU-MIMO) with non-overlapping allocations. A system-level simulation was conducted to investigate the performance gains that can be achieved in uplink CA with multi-cluster scheduling and MU-MIMO. Simulation results show that with proper differentiation between power-limited and non-power-limited LTE-A users, multi-cluster scheduling with CA has similar coverage performance as in Rel'8, but can achieve substantial gains in average user throughput compared with Rel'8. MU-MIMO can further improve the throughput performance, especially when MU-MIMO is combined with multi-cluster scheduling.

In this paper we analyse the downlink performance of a rank adaptive multiple input multiple output (MIMO) system in a busty traffic cellular network. A LTE-Advanced system with multiple component carriers was selected as a study case. To highlight the advantage of using MIMO techniques, we used a single input multiple output (SIMO) system as a baseline for performance comparison. The gain mechanisms of the MIMO system over the SIMO system are investigated and their characteristic at different traffic load conditions are highlighted. The simulation results are used to verified our proposed model to predict the performance of the SIMO and MIMO systems in a bursty traffic network.

With full channel state information (CSI) available, it has been shown that carrier aggregation (CA) in the downlink can significantly improve the data rate experienced at the user equipments (UE) [1], [2], [3], [4]. However, full CSI feedback in all component carriers (CCs) requires a large portion of the uplink bandwidth and the feedback information increases linearly with the number of CCs. Therefore, the performance gain brought by deploying CA could be easily hindered if the amount of CSI feedback is not thoroughly controlled. In this paper we analyze several feedback overhead compression schemes in CA systems. To avoid a major re-design of the feedback schemes, only CSI compression schemes closely elated to the ones specified in LTE-Release 8 and LTE-Release 9 are considered. Extensive simulations at system level were carried out to evaluate the performance of these feedback schemes in various traffic load conditions. Different scenarios were considered to answer the questions of when and why these CSI feedback compression schemes can be applied in CA.

Interference caused by other users in a multi-user environment degrades the system performance significantly. Conventionally, user separation in a multi-user environment is achieved by signal separation in time, frequency or code. The use of multiple elements antenna (MEA) systems can apply separation of the users in space by targeting the transmitted power to the user of interest. Inevitably in that case, the separation is not ideal and there is interference on the other users in the systems. Moreover in a wideband transmission, frequency selective fading causing inter symbol interference (ISI) is a big challenge for point to point wireless communications. In this paper, we study the feasibility of applying the time reversal technique (TR) in multiple input single output (MISO) systems to alleviate the effect of ISI. The ability of TR to focus the signal on the receiver of interest in a multi-user environment is also considered. The studies are supported by measured complex channel impulse responses with 10 MHz bandwidth centered at 2.14 GHz. For an 8/spl times/1 MISO system, using TR the root mean square delay spread is reduced by a factor of 2. To evaluate the capability of the TR in reducing the ISI, simulation of the bit error rate (BER) was made. The irreducible BER of the TR-MISO system was shown to be lower than that of the SISO system by at least an order of magnitude. By using TR and assuming that two users are communicating simultaneously with the same BS the two signals have 17 dB of isolation.

Multiple input multiple output (MIMO) systems promise better performance and significant increases in channel capacity by utilizing the rich scattering environment to obtain diversity gain and spatial multiplexing. Maximum system capacity and performance can be achieved if the instantaneous channel state information (CSI) is known at both sides of the transmission link. In order to have such improvements, the CSI at both link ends must be updated timely. However, the updating process is always subject to non-ideality. We investigate the effect of delay in updating CSI on the MIMO spectral efficiency and performance with system capacity and BER as benchmarks.

We propose a spatio-temporal model for narrowband indoor non line of sight (NLOS) MIMO channel. The model bases on the parameterization from measured 8/spl times/4 MIMO radio channel. Validation of the model performance is made by comparing the estimated capacity and the temporal characteristic with these obtained from measurement. The results show that our model is capable of capturing the essential characteristics of a temporally correlated multipath MIMO radio channel.