Accelerated testing method for bit error rate in wireless network

2014 ◽  
Author(s):  
Nan Zhang ◽  
Ning Huang ◽  
Ruiying Li ◽  
Xiaolei Sun
Keyword(s):  
Wireless Network ◽  
Bit Error Rate ◽  
Error Rate ◽  
Accelerated Testing ◽  
Testing Method

scholarly journals The Intelligent Integration of Interactive Installation Art Based on Artificial Intelligence and Wireless Network Communication

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Ze Gao ◽  
Lin Lin
Keyword(s):  
Artificial Intelligence ◽  
Wireless Network ◽  
Bit Error Rate ◽  
Error Rate ◽  
Installation Art ◽  
Communication Delay ◽  
Network Communication ◽  
Positioning Accuracy ◽  
Interactive Installation ◽  
Communication Equipment

With the development of technology and the times, the development of new media technology and interactive installation art has slowly entered the vision of our audience. It is simply “silent art.” The public no longer “retires” like the traditional one, but participates in it and swims with the artists in the world of art. This article is aimed at studying the application of artificial intelligence and wireless network communication to the application of interactive installation art. Through the optimization of various communication equipment and the continuous advancement of various algorithms, we can strengthen the communication and connection between our interactive installation art. This article proposes that with the addition of artificial intelligence and wireless network communication, the interaction between artists and audiences may be more fun, so that we can be more colorful in our lives. The experimental results in this article show that when performing wireless network communication, the communication delay rate of the intelligent algorithm with artificial intelligence is much lower than that of the one without it, which shows that they can better transmit information to the control end. When affected by the outside world, the bit error rate of wireless network communication will increase, however, the artificial intelligence algorithm is added to his impact range, and his bit error rate increase is obviously not so high. In the process of wireless network communication, the improved algorithm is definitely better than the nonimproved algorithm in terms of energy consumption, communication delay, and bit error rate. Through the enhancement of signals and the selection of materials for communication equipment, these are all in continuous progress, and in this respect, are in continuous exploration. Compared with other algorithms, the ml algorithm has improved positioning accuracy by about 70%, 65%, and 30%. Increasing the number of nodes in the transmission signal can greatly reduce the number of hops between nodes, correspondingly reducing the hop distance error, correspondingly reducing the distance estimation error, and improving the positioning accuracy. It can solve the technical barriers of interactive installation art faster.

scholarly journals H∞-dCNN: Enhancing the SNR using Deep Learning Algorithm in Wireless Communication System

2021 ◽  
Author(s):  
C. Priya ◽  
D. Kumutha ◽  
M. Shilpa ◽  
K. Jayanthi ◽  
S. Baskaran
Keyword(s):  
Deep Learning ◽  
Wireless Communication ◽  
Wireless Network ◽  
Bit Error Rate ◽  
Error Rate ◽  
Communication Systems ◽  
Mimo Systems ◽  
Learning Algorithm ◽  
Network Computing ◽  
Deep Learning Algorithm

Abstract In Wireless communication systems, the deep learning-based Convolution Neural Networks (dCNN) is performed to gain a better improvement of Quality of Services(QoS) with higher Signal to Noise Ratio (SNR). Multiple Input and Multiple Output (MIMO) systems are presented for real-time evaluation from various technologies, which has served the purpose of services in improving the communication performance of the physical layer of the wireless network. By increasing the communication throughput by focusing on resource allocation, the overall efficiency was not up to the market due to the network’s dynamic behavior. This article proposes the system in two different stages to express the analytical solution for decreasing the Bit Error Rate(BER). The first is to employ the Hybrid Infinity (H∞) through the channel for better robustness in wireless network computing. Next is to optimize Bit Error Rate (BER) with carrier detection as well as other criteria for improving service quality by analyzing the network behavior using Deep Learning Algorithm. The deep Convolution Neural Network with Hybrid Infinity (H∞ - dCNN) is implemented and evaluates the low BER values with high SNR for the performance of QoS. Thus, H∞ - dCNN is proved and outperform the simulated results with better characteristics by using the Matlab software. Hence, the mathematical expression for the proposed system is noticed that a significant improvement is obtained in terms of BER lesser than 0.6 e-4. It is observed the SNR lesser than 18dB, which is comparatively best than the baseline method.

Bit-Error-Rate Degradation Due to Inter-Channel Crosstalk of Different Signal Format

2019 ◽  
Vol E102.B (5) ◽  
pp. 1000-1004
Author(s):  
Naruki SHINOHARA ◽  
Koji IGARASHI ◽  
Kyo INOUE
Keyword(s):  
Bit Error Rate ◽  
Error Rate

ANALISIS SENSITIVITAS VIDEO MPEG-4 BERDASARKAN STRUKTUR FRAME PADA TRANSMISI DVB-T

2020 ◽  
Vol 25 (2) ◽  
pp. 86-97
Author(s):  
Sandy Suryo Prayogo ◽  
Tubagus Maulana Kusuma
Keyword(s):  
Deep Learning ◽  
Bit Error Rate ◽  
Error Rate ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Signal To Noise ◽  
Structural Similarity Index ◽  
Noise Ratio

DVB merupakan standar transmisi televisi digital yang paling banyak digunakan saat ini. Unsur terpenting dari suatu proses transmisi adalah kualitas gambar dari video yang diterima setelah melalui proses transimisi tersebut. Banyak faktor yang dapat mempengaruhi kualitas dari suatu gambar, salah satunya adalah struktur frame dari video. Pada tulisan ini dilakukan pengujian sensitifitas video MPEG-4 berdasarkan struktur frame pada transmisi DVB-T. Pengujian dilakukan menggunakan simulasi matlab dan simulink. Digunakan juga ffmpeg untuk menyediakan format dan pengaturan video akan disimulasikan. Variabel yang diubah dari video adalah bitrate dan juga group-of-pictures (GOP), sedangkan variabel yang diubah dari transmisi DVB-T adalah signal-to-noise-ratio (SNR) pada kanal AWGN di antara pengirim (Tx) dan penerima (Rx). Hasil yang diperoleh dari percobaan berupa kualitas rata-rata gambar pada video yang diukur menggunakan metode pengukuran structural-similarity-index (SSIM). Dilakukan juga pengukuran terhadap jumlah bit-error-rate BER pada bitstream DVB-T. Percobaan yang dilakukan dapat menunjukkan seberapa besar sensitifitas bitrate dan GOP dari video pada transmisi DVB-T dengan kesimpulan semakin besar bitrate maka akan semakin buruk nilai kualitas gambarnya, dan semakin kecil nilai GOP maka akan semakin baik nilai kualitasnya. Penilitian diharapkan dapat dikembangkan menggunakan deep learning untuk memperoleh frame struktur yang tepat di kondisi-kondisi tertentu dalam proses transmisi televisi digital.

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