scholarly journals BRNN-LSTM for Initial Access in Millimeter Wave Communications

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1505
Author(s):  
Adel Aldalbahi ◽  
Farzad Shahabi ◽  
Mohammed Jasim
Keyword(s):  
Millimeter Wave ◽  
Short Term Memory ◽  
Mobile Station ◽  
Base Station ◽  
Time Step ◽  
Directional Transmission ◽  
Long Short Term Memory ◽  
Power And Energy ◽  
Learning Schemes ◽  
Millimeter Wave Communications

The use of beamforming technology in standalone (SA) millimeter wave communications results in directional transmission and reception modes at the mobile station (MS) and base station (BS). This results in initial beam access challenges, since the MS and BS are now compelled to perform spatial search to determine the best beam directions that return highest signal levels. The high number of signal measurements here prolongs access times and latencies, as well as increasing power and energy consumption. Hence this paper proposes a first study on leveraging deep learning schemes to simplify the beam access procedure in standalone mmWave networks. The proposed scheme combines bidirectional recurrent neural network (BRNN) and long short-term memory (LSTM) to achieve fast initial access times. Namely, the scheme predicts the best beam index for use in the next time step once a MS accesses the network, e.g., transition from sleep to active (or idle) modes. The scheme eliminates the need for beam scanning, thereby achieving ultra-low access times and energy efficiencies as compared to existing methods.

scholarly journals Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3517 ◽  
Author(s):  
Anh Ngoc-Lan Huynh ◽  
Ravinesh C. Deo ◽  
Duc-Anh An-Vo ◽  
Mumtaz Ali ◽  
Nawin Raj ◽  
...  
Keyword(s):  
Deep Learning ◽  
Solar Radiation ◽  
Short Term Memory ◽  
Global Solar Radiation ◽  
Multiple Time ◽  
Short Term ◽  
Time Step ◽  
Term Memory ◽  
Multiple Time Step ◽  
Long Short Term Memory

This paper aims to develop the long short-term memory (LSTM) network modelling strategy based on deep learning principles, tailored for the very short-term, near-real-time global solar radiation (GSR) forecasting. To build the prescribed LSTM model, the partial autocorrelation function is applied to the high resolution, 1 min scaled solar radiation dataset that generates statistically significant lagged predictor variables describing the antecedent behaviour of GSR. The LSTM algorithm is adopted to capture the short- and the long-term dependencies within the GSR data series patterns to accurately predict the future GSR at 1, 5, 10, 15, and 30 min forecasting horizons. This objective model is benchmarked at a solar energy resource rich study site (Bac-Ninh, Vietnam) against the competing counterpart methods employing other deep learning, a statistical model, a single hidden layer and a machine learning-based model. The LSTM model generates satisfactory predictions at multiple-time step horizons, achieving a correlation coefficient exceeding 0.90, outperforming all of the counterparts. In accordance with robust statistical metrics and visual analysis of all tested data, the study ascertains the practicality of the proposed LSTM approach to generate reliable GSR forecasts. The Diebold–Mariano statistic test also shows LSTM outperforms the counterparts in most cases. The study confirms the practical utility of LSTM in renewable energy studies, and broadly in energy-monitoring devices tailored for other energy variables (e.g., hydro and wind energy).

A review on mmWave based energy efficient RoF system for next generation mobile communication and broadband systems

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Parvin Kumar ◽  
Sanjay Kumar Sharma ◽  
Shelly Singla ◽  
Varun Gupta ◽  
Abhishek Sharma
Keyword(s):  
Wireless Communication ◽  
Optical Fiber ◽  
Millimeter Wave ◽  
Mobile Communication ◽  
Energy Efficient ◽  
Mobile Station ◽  
Base Station ◽  
Electrical Signal ◽  
Next Generation ◽  
Efficient System

Abstract In today’s scenario, wireless communication is turning into a decisive and leading backbone to access the worldwide network. Therefore, the usage of mobile phones and broadband is rising staggeringly. To satisfy their expulsive needs, it demands increment in data rates while providing higher bandwidth and utilizing optical fiber in wireless communication, and this becomes a worldwide analysis area. Radio over fiber (RoF) system is taken into account as best solution to fulfill these needs. In RoF system, the radio frequency signal operated at millimeter wave (30–300 GHz) is centralized and processed at control station (CS) and also, the CS upconverts this electrical signal to optical domain. By employing optical fiber link, this signal reaches to base station (BS). Then, the received optical signal converts back to electrical domain at the respective BS. Now BS radiates the electrical signal to corresponding mobile station (MS) in commission with the millimeter wave frequency bands. This RoF system is providing massive bandwidth, facilitating large mobility for RF frequency signals, small loss, fast and cost effective setup, wonderful security, and unlicensed spectrum etc. The RoF system introduces microcells structure for BS cells to boost the frequency reuse and needed capacity. It has benefits in terms of ability to fulfill increasing bandwidth demands to cut back the power consumption and the dimensions of the handset devices. This paper firstly explains the overview of existing wireless mobile communication and broadband systems and then, targets the review of RoF system which will become energy efficient system for next generation mobile communication and future broadband systems. This paper also includes the performance degradation and evaluation parameters. Finally, this paper presents the various research opportunities for its implementation zone.

scholarly journals Robust-Extended Kalman Filter and Long Short-Term Memory Combination to Enhance the Quality of Single Point Positioning

2020 ◽  
Vol 10 (12) ◽  
pp. 4335 ◽  
Author(s):  
Truong-Ngoc Tan ◽  
Ali Khenchaf ◽  
Fabrice Comblet ◽  
Pierre Franck ◽  
Jean-Marc Champeyroux ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Short Term Memory ◽  
Single Point ◽  
Base Station ◽  
Base Stations ◽  
Short Term ◽  
Position Accuracy ◽  
Long Short Term Memory ◽  
Position Precision

In the recent years, multi-constellation and multi-frequency have improved the positioning precision in GNSS applications and significantly expanded the range of applications to new areas and services. However, the use of multiple signals presents advantages as well as disadvantages, since they may contain poor quality signals that negatively impact the position precision. The objective of this study is to improve the Single Point Positioning (SPP) accuracy using multi-GNSS data fusion. We propose the use of robust-Extended Kalman Filter (referred to as robust-EKF hereafter) to eliminate outliers. The robust-EKF used in the present work combines the Extended Kalman Filter with the Iterative ReWeighted Least Squares (IRWLS) and the Receiver Autonomous Integrity Monitoring (RAIM). The weight matrix in IRWLS is defined by the MM Estimation method which is a robust statistics approach for more efficient statistical data analysis with high breaking point. The RAIM algorithm is used to check the accuracy of the protection zone of the user. We apply the robust-EKF method along with the robust combination of GPS, Galileo and GLONASS data from ABMF base station, which significantly improves the position accuracy by about 84% compared to the non-robust data combination. ABMF station is a GNSS reception station managed by Météo-France in Guadeloupe. Thereafter, ABMF will refer to the acronym used to designate this station. Although robust-EKF demonstrates improvement in the position accuracy, its outputs might contain errors that are difficult to estimate. Therefore, an algorithm that can predetermine the error produced by robust-EKF is needed. For this purpose, the long short-term memory (LSTM) method is proposed as an adapted Deep Learning-Based approach. In this paper, LSTM is considered as a de-noising filter and the new method is proposed as a hybrid combination of robust-EKF and LSTM which is denoted rEKF-LSTM. The position precision greatly improves by about 95% compared to the non-robust combination of data from ABMF base station. In order to assess the rEKF-LSTM method, data from other base stations are tested. The position precision is enhanced by about 87%, 77% and 93% using the rEKF-LSTM compared to the non-robust combination of data from three other base stations AJAC, GRAC and LMMF in France, respectively.

scholarly journals Multi-Backup Beams for Instantaneous Link Recovery in mmWave Communications

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1145 ◽  
Author(s):  
Adel Aldalbahi
Keyword(s):  
Antenna Arrays ◽  
Mobile Station ◽  
Base Station ◽  
Main Beam ◽  
Spatial Direction ◽  
Multiple Beams ◽  
Beamformer Design ◽  
Recovery Scheme ◽  
Recovery Times ◽  
Millimeter Wave Communications

In this paper, a novel link recover scheme is proposed for standalone (SA) millimeter wave communications. Once the main beam between the base station (BS) and the mobile station (MS) is blocked, then a bundle-beam is radiated that covers the spatial direction of the blocked beam. These beams are generated from an analog beamformer design that is composed of parallel adjacent antenna arrays to radiate multiple simultaneous beams, thus creating an analog beamformer of multiple beams. The proposed recovery scheme features instantaneous recovery times, without the need for beam scanning to search for alternative beam directions. Hence, the scheme features reduced recovery times and latencies, as opposed to existing methods.

scholarly journals Cell ID and Angle of Departure Estimation for Millimeter-wave Cellular Systems in Line-of-Sight Dominant Conditions Using Zadoff-Chu Sequence Based Beam Weight

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 335 ◽  
Author(s):  
Yeong Jun Kim ◽  
Yong Soo Cho
Keyword(s):  
Millimeter Wave ◽  
Communication Systems ◽  
Estimation Method ◽  
Mobile Station ◽  
Base Station ◽  
Cellular Systems ◽  
Line Of Sight ◽  
High Attenuation ◽  
Conventional Methods ◽  
Dominant Condition

Millimeter-wave (mmWave) bands is considered for fifth-generation (5G) cellular systems because abundant spectrum is available for mobile broadband communications. In mmWave communication systems, accurate beamforming is important to compensate for high attenuation in the mmWave frequency band and to extend the transmission range. However, with the existing beamformers in mmWave cellular systems, the mobile station (MS) cannot identify the source (base station; BS) of the received beam because there are many neighboring BSs transmitting their training signals, requiring a large overhead. This paper proposes a new beam weight generation method for transmitting (Tx) beamformers at the BS in mmWave cellular systems during a beam training period. Beam weights are generated for Tx beamformers at neighboring BSs, so that a mobile station (MS) can estimate the source (cell ID; CID) and angle of departure (AoD) for each BS in multi-cell environments. A CID and AoD estimation method for mmWave cellular systems in a line-of-sight (LOS) dominant condition is presented using the beam weights generated by Zadoff-Chu sequence. A simulation is conducted in a LOS dominant condition to show that the performances of CID detection and AoD estimation are similar for both the proposed and conventional methods. In the conventional methods, the DFT-based beamforming weight is used for Tx beamformer at the BS and orthogonal matching pursuit (OMP) algorithm is used for AoD estimation at the MS. The proposed method significantly reduces the processing time (1.6–6.25%) required for beam training compared to the conventional method.

scholarly journals Link Recovery Scheme for Multi-Point mmWave Communications

Electronics ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 50
Author(s):  
Adel Aldalbahi
Keyword(s):  
Mobile Station ◽  
Base Station ◽  
Fast Recovery ◽  
Link Failures ◽  
Access Scheme ◽  
Directional Transmission ◽  
Recovery Scheme ◽  
Recovery Times ◽  
Efficient Power ◽  
Recovery Schemes

Directional transmission in millimeter wave (mmWave) communications results in prolonged access times. This is attributed to the increased number of conducted measurements to determine optimum beam directions at the mobile station (MS) and base station (BS) that return the highest received signal levels. Additionally, once these beams are determined and links are established for data-planes, then blockage effects and outages make these links more vulnerable to link failures, resulting in communications drops. Hence, dynamic and fast recovery schemes are required to maintain communications sessions following the beam access stage. In this paper, a novel recovery access scheme is proposed for multi-point mmWave communications based on fog access points (AP). Namely, the scheme leverages diversity and network coding techniques to achieve near-instantaneous recovery times, without the need for beam scanning. The scheme features near-instantaneous data recovery times and efficient power consumption as compared to traditional recovery methods.

scholarly journals Deep Learning for Subtyping and Prediction of Diseases: Long-Short Term Memory

2021 ◽  
Author(s):  
Hayrettin Okut
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Short Term ◽  
Time Step ◽  
Term Memory ◽  
The Neural Network ◽  
Long Short Term Memory ◽  
Sequential Information ◽  
Hidden Layer

The long short-term memory neural network (LSTM) is a type of recurrent neural network (RNN). During the training of RNN architecture, sequential information is used and travels through the neural network from input vector to the output neurons, while the error is calculated and propagated back through the network to update the network parameters. Information in these networks incorporates loops into the hidden layer. Loops allow information to flow multi-directionally so that the hidden state signifies past information held at a given time step. Consequently, the output is dependent on the previous predictions which are already known. However, RNNs have limited capacity to bridge more than a certain number of steps. Mainly this is due to the vanishing of gradients which causes the predictions to capture the short-term dependencies as information from earlier steps decays. As more layers in RNN containing activation functions are added, the gradient of the loss function approaches zero. The LSTM neural networks (LSTM-ANNs) enable learning long-term dependencies. LSTM introduces a memory unit and gate mechanism to enable capture of the long dependencies in a sequence. Therefore, LSTM networks can selectively remember or forget information and are capable of learn thousands timesteps by structures called cell states and three gates.

scholarly journals Efficient Spectrum Occupancy Prediction Exploiting Multidimensional Correlations through Composite 2D-LSTM Models

2020 ◽  
Author(s):  
Mehmet Ali Aygül ◽  
Mahmoud Nazzal ◽  
Mehmet İzzet Sağlam ◽  
Daniel Benevides da Costa ◽  
Hasan Fehmi Ateş ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Mobile Network ◽  
Base Station ◽  
Time Frequency ◽  
Spectrum Occupancy ◽  
Cognitive Radio Systems ◽  
Radio Systems ◽  
Long Short Term Memory ◽  
Occupancy Prediction ◽  
Mobile Network Operators

In cognitive radio systems, identifying spectrum opportunities is fundamental to efficiently use the spectrum. Spectrum occupancy prediction is a convenient way of revealing opportunities based on previous occupancies. Studies have demonstrated that usage of the spectrum has a high correlation over multidimensions which includes time, frequency, and space. Accordingly, recent literature uses tensor-based methods to exploit the multidimensional spectrum correlation. However, these methods share two main drawbacks. First, they are computationally complex. Second, they need to re-train the overall model when no information is received from any base station for any reason. Different than the existing works, this paper proposes a method for dividing the multidimensional correlation exploitation problem into a set of smaller sub-problems. This division is achieved through composite two-dimensional (2D)-long short-term memory (LSTM) models. Extensive experimental results reveal a high detection performance with more robustness and less complexity attained by the proposed method. The real-world measurements provided by one of the leading mobile network operators in Turkey validate these results.

scholarly journals Multimedia Data Modelling Using Multidimensional Recurrent Neural Networks

Symmetry ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 370
Author(s):  
Zhen He ◽  
Shaobing Gao ◽  
Liang Xiao ◽  
Daxue Liu ◽  
Hangen He
Keyword(s):  
Short Term Memory ◽  
Multimedia Data ◽  
Cross Layer ◽  
Data Modelling ◽  
Short Term ◽  
Time Step ◽  
Machine Learning Approach ◽  
Long Short Term Memory ◽  
Hidden States ◽  
Text Images

Modelling the multimedia data such as text, images, or videos usually involves the analysis, prediction, or reconstruction of them. The recurrent neural network (RNN) is a powerful machine learning approach to modelling these data in a recursive way. As a variant, the long short-term memory (LSTM) extends the RNN with the ability to remember information for longer. Whilst one can increase the capacity of LSTM by widening or adding layers, additional parameters and runtime are usually required, which could make learning harder. We therefore propose a Tensor LSTM where the hidden states are tensorised as multidimensional arrays (tensors) and updated through a cross-layer convolution. As parameters are spatially shared within the tensor, we can efficiently widen the model without extra parameters by increasing the tensorised size; as deep computations of each time step are absorbed by temporal computations of the time series, we can implicitly deepen the model with little extra runtime by delaying the output. We show by experiments that our model is well-suited for various multimedia data modelling tasks, including text generation, text calculation, image classification, and video prediction.

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