Transmit power control and data rate enhancement in cognitive radio network using computational intelligence

Underutilized radio frequencies are the chief apprehension in advance radio communication. The radio recourses are sparse and costly and their efficient allocation has become a challenge. Cognitive radio networks are the ray of hope. Cognitive radio networks use dynamic spectrum access technique to opportunistically retrieve and share the licensed spectrum. The licensed users are called primary users and the users that opportunistically access the licensed spectrum all called secondary users. The proposed system is a feedback system that work on demand and supply concept, in which secondary receivers senses the vacant spectrum and shares the information with the secondary transmitters. The secondary transmitters adjust their transmission parameters of transmit power and data rate in such a way that date rate is maximized. Two methods of spectrum access using frequency division multiple access (FDMA) and Time division multiple access (TDMA) are discussed. Interference temperature limit and maximum achievable capacity are the constraints that regulate the entire technique. The aim of the technique is to control the transmitter power according to the data requirements of each secondary user and optimizing the resources like bandwidth, transmit power using machine learning and feed forward back propagation deep neural networks making full use of the network capacity without hampering the operation of primary network.

Neighbor-Aware Non-Orthogonal Multiple Access Scheme for Energy Harvesting Internet of Things

In a non-orthogonal multiple access (NOMA) environment, an Internet of Things (IoT) device achieves a high data rate by increasing its transmission power. However, excessively high transmission power can cause an energy outage of an IoT device and have a detrimental effect on the signal-to-interference-plus-noise ratio of neighbor IoT devices. In this paper, we propose a neighbor-aware NOMA scheme (NA-NOMA) where each IoT device determines whether to transmit data to the base station and the transmission power at each time epoch in a distributed manner with the consideration of its energy level and other devices’ transmission powers. To maximize the aggregated data rate of IoT devices while keeping an acceptable average energy outage probability, a constrained stochastic game model is formulated, and the solution of the model is obtained using a best response dynamics-based algorithm. Evaluation results show that NA-NOMA can increase the average data rate up to 22% compared with a probability-based scheme while providing a sufficiently low energy outage probability (e.g., 0.05).

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

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.

PR40_Designing Integrated Silicon Photonics Flexible High-Data Rate Transceivers (NNX16AD14G-Y1)

We study a transceiver architecture, which is based on an innovative design which generates a flexible number of communication streams from a single laser. This approach can achieve reductions in size, weight, and energy consumption, and improvements on link performance and bandwidth compared to both RF communications and existing optical technologies Summary of a Project Outcomes report of research funded by NASA.

PR41_Breadboard Study of a Flexible High-Data Rate Transceiver Architecture (NNX16AD14G-Y2)

We experimentally study the breadboard implementation of an optical high-data rate transceiver architecture, which is based on an innovative design which generates a flexible number of communication streams from a single laser. Summary of a Project Outcomes report of research funded by NASA.

The LHCb vertex locator upgrade — the detector calibration overview

The Vertex Locator (VELO) is a silicon tracking detector in the spectrometer of the Large Hadron Collider beauty (LHCb) experiment. LHCb explores and investigates CP violation phenomena in b- and c- hadron decays and is one of the experiments operating on the Large Hadron Collider (LHC) at CERN. After run 1 and run 2 of LHC data taking (2011–2018), the LHCb detectors are being modernized within the LHCb upgrade I program. The upgrade aims to adjust the spectrometer to readout at full LHC 40 MHz frequency, which requires radical changes to the technologies currently used in LHCb. The hardware trigger is removed, and some of the detectors replaced. The VELO changes its tracking technology and silicon strips are replaced by 55 μm pitch silicon pixels. The readout chip for the VELO upgrade is the VeloPix ASIC. The number of readout channels increases to over 40 million, and the hottest ASIC is expected to produce the output data rate of 15 Gbit/s. New conditions challenge the software and the hardware side of the readout system and put special attention on the detector monitoring. This paper presents the upgraded VELO design and outlines the software aspects of the detector calibration in the upgrade I. An overview of the challenges foreseen for the upgrade II is given.

Multistate Active Combined Power and Message/Data Rate Adaptive Decentralized Congestion Control Mechanisms for Vehicular Ad Hoc Networks

Vehicular ad hoc networks (VANETs) have emerged in time to reduce on-road fatalities and provide efficient information exchange for entertainment-related applications to users in a well-organized manner. VANETs are the most instrumental elements in the Internet of Things (IoT). The objective lies in connecting every vehicle to every other vehicle to improve the user’s quality of life. This aim of continuous connectivity and information exchange leads to the generation of more information in the medium, which could congest the medium to a larger extent. Decentralized congestion control (DCC) techniques are specified to reduce medium congestion and provide various safety applications. This article presents two DCC mechanisms that adapt message rate and data rate combined with transmit power control mechanism. These mechanisms are developed under multi-state active design proposed by the standard. The proposed methods deliver better performance over other mechanisms in terms of power, channel load, and channel utilization using real-time-based scenarios by simulation in SUMO.