Data rate-based grouping using machine learning to improve the aggregate throughput of IEEE 802.11ah multi-rate IoT networks

2020 ◽  
Author(s):  
Miriyala Mahesh ◽  
Badarla Sri Pavan ◽  
V.P. Harigovindan
Keyword(s):  
Machine Learning ◽  
Data Rate ◽  
Ieee 802.11Ah

scholarly journals Machine Learning-Assisted Adaptive Modulation for Optimized Drone-User Communication in B5G

Drones ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 128
Author(s):  
Sudheesh Puthenveettil Gopi ◽  
Maurizio Magarini ◽  
Saeed Hamood Alsamhi ◽  
Alexey V. Shvetsov
Keyword(s):  
Machine Learning ◽  
High Speed ◽  
Degrees Of Freedom ◽  
Adaptive Modulation ◽  
Data Rate ◽  
Practical Implementation ◽  
Modulation Scheme ◽  
Error Performance ◽  
Wireless Broadcast ◽  
Atmospheric Disturbances

The fundamental issue for Beyond fifth Generation (B5G) is providing a pervasive connection to heterogeneous and various devices in smart environments. Therefore, Drones play a vital role in the B5G, allowing for wireless broadcast and high-speed communications. In addition, the drone offers several advantages compared to fixed terrestrial communications, including flexible deployment, robust Line of Sight (LoS) connections, and more design degrees of freedom due to controlled mobility. Drones can provide reliable and high data rate connectivity to users irrespective of their location. However, atmospheric disturbances impact the signal quality between drones and users and degrade the system performance. Considering practical implementation, the location of drones makes the drone–user communication susceptible to several environmental disturbances. In this paper, we evaluate the performance of drone-user connectivity during atmospheric disturbances. Further, a Machine Learning (ML)-assisted algorithm is proposed to adapt to a modulation technique that offers optimal performance during atmospheric disturbances. The results show that, with the algorithm, the system switches to a lower order modulation scheme during higher rain rate and provides reliable communication with optimized data rate and error performance.

scholarly journals Performance Evaluation of Machine Learning-Based Channel Equalization Techniques: New Trends and Challenges

2022 ◽  
Vol 2022 ◽  
pp. 1-14
Author(s):  
Shahzad Hassan ◽  
Noshaba Tariq ◽  
Rizwan Ali Naqvi ◽  
Ateeq Ur Rehman ◽  
Mohammed K. A. Kaabar
Keyword(s):  
Machine Learning ◽  
Wireless Communication ◽  
Communication Systems ◽  
Short Term Memory ◽  
Classification Problem ◽  
Channel Equalization ◽  
Data Rate ◽  
Wireless Channel ◽  
Support Vector ◽  
Functional Link

Wireless communication systems have evolved and offered more smart and advanced systems like ad hoc and sensor-based infrastructure fewer networks. These networks are evaluated with two fundamental parameters including data rate and spectral efficiency. To achieve a high data rate and robust wireless communication, the most significant task is channel equalization at the receiver side. The transmitted data symbols when passing through the wireless channel suffer from various types of impairments, such as fading, Doppler shifts, and Intersymbol Interference (ISI), and degraded the overall network performance. To mitigate channel-related impairments, many channel equalization algorithms have been proposed for communication systems. The channel equalization problem can also be solved as a classification problem by using Machine Learning (ML) methods. In this paper, channel equalization is performed by using ML techniques in terms of Bit Error Rate (BER) analysis and comparison. Radial Basis Functions (RBFs), Multilayer Perceptron (MLP), Support Vector Machines (SVM), Functional Link Artificial Neural Network (FLANN), Long-Short Term Memory (LSTM), and Polynomial-based Neural Networks (NNs) are adopted for channel equalization.

Data Rate-Based Grouping to Resolve Performance Anomaly of Multi-Rate IEEE 802.11ah IoT Networks

2020 ◽  
Vol 2 (4) ◽  
pp. 166-170 ◽  
Author(s):  
Miriyala Mahesh ◽  
Badarla Sri Pavan ◽  
V. P. Harigovindan
Keyword(s):  
Data Rate ◽  
Performance Anomaly ◽  
Ieee 802.11Ah

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Mathematics for Machine Learning

2020 ◽  
Author(s):  
Marc Peter Deisenroth ◽  
A. Aldo Faisal ◽  
Cheng Soon Ong
Keyword(s):  
Machine Learning
Sign in / Sign up

Export Citation Format

Share Document