Spectrum Prediction for Cognitive Radio System Based on Optimally Pruned Extreme Learning Machine

2014 ◽  
Vol 536-537 ◽  
pp. 430-436 ◽  
Author(s):  
Ling Yang ◽  
Na Lv ◽  
Zhen Xing Xu
Keyword(s):  
Cognitive Radio ◽  
Extreme Learning Machine ◽  
Predictive Performance ◽  
Prediction Algorithm ◽  
Good Precision ◽  
Secondary Users ◽  
Spectrum Prediction ◽  
Radio System ◽  
Novel Approach ◽  
Learning Machine

The Cognitive Radio (CR) technology is an efficient solution to spectrum scarcity by share the spectrum with the secondary users on a non-interfering basis. The spectrum prediction can rationalize the spectrum allocation based on previous information about the spectrum evolution in time. Against previous spectrum prediction algorithm lack of timeliness and accuracy, this paper proposes a novel approach for spectrum prediction based on Optimally Pruned Extreme Learning Machine (OP-ELM) which improved the original Extreme Learning Machine (ELM) algorithm. This method not only takes the advantage of the ELM extremely fast speed and good precision, but also more robust and generic with additional steps compared with ELM. In order to compare its comprehensive properties to other algorithms, some experiments were designed. The results show that the predictive performance of this new algorithm is more satisfaction than others in spectrum prediction problem.

scholarly journals Trip Travel Time Forecasting Based on Selective Forgetting Extreme Learning Machine

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Zhiming Gui ◽  
Haipeng Yu
Keyword(s):  
Travel Time ◽  
Extreme Learning Machine ◽  
Road Networks ◽  
Time Estimation ◽  
Learning Ability ◽  
Prediction Algorithm ◽  
Travel Time Estimation ◽  
Trace Data ◽  
Learning Machine ◽  
Travel Time Forecasting

Travel time estimation on road networks is a valuable traffic metric. In this paper, we propose a machine learning based method for trip travel time estimation in road networks. The method uses the historical trip information extracted from taxis trace data as the training data. An optimized online sequential extreme machine, selective forgetting extreme learning machine, is adopted to make the prediction. Its selective forgetting learning ability enables the prediction algorithm to adapt to trip conditions changes well. Experimental results using real-life taxis trace data show that the forecasting model provides an effective and practical way for the travel time forecasting.

scholarly journals 5G cognitive radio system design with new algorithm asynchronous spectrum sensing

2021 ◽  
Vol 10 (4) ◽  
pp. 2046-2054
Author(s):  
Mohammed Mehdi Saleh ◽  
Ahmed A. Abbas ◽  
Ahmed Hammoodi
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Energy Detection ◽  
Primary User ◽  
Spectrum Access ◽  
Secondary Users ◽  
Radio System ◽  
Wireless Applications ◽  
Fifth Generation ◽  
Time Required

Due to the rapid increase in wireless applications and the number of users, spectrum scarcity, energy consumption and latency issues will emerge, notably in the fifth generation (5G) system. Cognitive radio (CR) has emerged as the primary technology to address these challenges, allowing opportunist spectrum access as well as the ability to analyze, observe, and learn how to respond to environmental 5G conditions. The CR has the ability to sense the spectrum and detect empty bands in order to use underutilized frequency bands without causing unwanted interference with legacy networks. In this paper, we presented a spectrum sensing algorithm based on energy detection that allows secondary user SU to transmit asynchronously with primary user PU without causing harmful interference. This algorithm reduced the sensing time required to scan the whole frequency band by dividing it into n sub-bands that are all scanned at the same time. Also, this algorithm allows cognitive radio networks (CRN) nodes to select their operating band without requiring cooperation with licensed users. According to the BER, secondary users have better performance compared with primary users.

scholarly journals Joint Beamforming and Artificial Noise Optimization for Secure Transmissions in MISO-NOMA Cognitive Radio System with SWIPT

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1948
Author(s):  
Carla E. Garcia ◽  
Mario R. Camana ◽  
Insoo Koo
Keyword(s):  
Cognitive Radio ◽  
Mobile Networks ◽  
Multiple Access ◽  
Relaxation Method ◽  
Spectrum Efficiency ◽  
Artificial Noise ◽  
Power Splitting ◽  
Secondary Users ◽  
Space Division Multiple Access ◽  
Radio System

The integration of non-orthogonal multiple access (NOMA) in cognitive radio (CR) networks has demonstrated how to enhance spectrum efficiency and achieve massive connectivity for future mobile networks. However, security is still a challenging issue due to the wireless transmission environment and the broadcast nature of NOMA. Thus, in this paper, we investigate a beamforming design with artificial noise (AN) to improve the security of a multi-user downlink, multiple-input single-output (MISO) NOMA-CR network with simultaneous wireless information and power transfer (SWIPT). To further support power-limited, battery-driven devices, energy-harvesting (EH) users are involved in the proposed network. Specifically, we investigate the optimal AN, power-splitting ratios, and transmission beamforming vectors for secondary users and EH users in order to minimize the transmission power of the secondary network, subject to the following constraints: a minimum signal-to-interference-plus-noise ratio at the secondary users, minimum harvested energy by secondary users and EH users, maximum power at the secondary transmitter, and maximum permissible interference with licensed users. The proposed solution for the challenging non-convex optimization problem is based on the semidefinite relaxation method. Numerical results show that the proposed scheme outperforms the conventional scheme without AN, the zero-forcing-based scheme and the space-division multiple-access-based method.

Spectral quantitative analysis of complex samples based on the extreme learning machine

2016 ◽  
Vol 8 (23) ◽  
pp. 4674-4679 ◽  
Author(s):  
Xi-Hui Bian ◽  
Shu-Juan Li ◽  
Meng-Ran Fan ◽  
Yu-Gao Guo ◽  
Na Chang ◽  
...  
Keyword(s):  
Quantitative Analysis ◽  
Extreme Learning Machine ◽  
Predictive Performance ◽  
Complex Samples ◽  
Learning Machine ◽  
Novel Algorithm

A novel algorithm called the extreme learning machine is introduced for the spectral quantitative analysis of complex samples, which enhances predictive performance.

A novel approach of power allocation for secondary users in cognitive radio networks

2019 ◽  
Vol 75 ◽  
pp. 301-308 ◽  
Author(s):  
Amrit Mukherjee ◽  
Sagarika Choudhury ◽  
Pratik Goswami ◽  
Gezahegn Abdissa Bayessa ◽  
Sumarga K. Sah Tyagi
Keyword(s):  
Cognitive Radio ◽  
Power Allocation ◽  
Cognitive Radio Networks ◽  
Radio Networks ◽  
Secondary Users ◽  
Novel Approach

scholarly journals Sparse Representation-Based Extreme Learning Machine for Motor Imagery EEG Classification

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Qingshan She ◽  
Kang Chen ◽  
Yuliang Ma ◽  
Thinh Nguyen ◽  
Yingchun Zhang
Keyword(s):  
Sparse Representation ◽  
Extreme Learning Machine ◽  
Motor Imagery ◽  
Vital Role ◽  
Superior Performance ◽  
Common Spatial Pattern ◽  
Significant Information ◽  
Feature Representations ◽  
Novel Approach ◽  
Learning Machine

Classification of motor imagery (MI) electroencephalogram (EEG) plays a vital role in brain-computer interface (BCI) systems. Recent research has shown that nonlinear classification algorithms perform better than their linear counterparts, but most of them cannot extract sufficient significant information which leads to a less efficient classification. In this paper, we propose a novel approach called FDDL-ELM, which combines the discriminative power of extreme learning machine (ELM) with the reconstruction capability of sparse representation. Firstly, the common spatial pattern (CSP) algorithm is adopted to perform spatial filtering on raw EEG data to enhance the task-related neural activity. Secondly, the Fisher discrimination criterion is employed to learn a structured dictionary and obtain sparse coding coefficients from the filtered data, and these discriminative coefficients are then used to acquire the reconstructed feature representations. Finally, a nonlinear classifier ELM is used to identify these features in different MI tasks. The proposed method is evaluated on 2-class Datasets IVa and IIIa of BCI Competition III and 4-class Dataset IIa of BCI Competition IV. Experimental results show that our method achieved superior performance than the other existing algorithms and yielded the accuracies of 80.68%, 87.54%, and 63.76% across all subjects in the above-mentioned three datasets, respectively.

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