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.

Adults on the Autism Spectrum Face Significant Challenges Accessing Health Care Services

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by difficulties with social skills, verbal and non-verbal communication, repetitive behaviours and atypical sensory processing. Individuals on the autism spectrum face a higher prevalence of health conditions and have a higher mortality rate than the general population. There is a critical need to understand adults’ experiences on the autism spectrum when accessing health care services to understand how to improve accessibility to health care for these individuals. The purpose of this scoping review was to investigate how adults on the autism spectrum access and experience health care services. Four databases, including Embase, MEDLINE, CINAHL, and PsycInfo, were systematically searched for literature exploring how individuals on the autism spectrum access and experience health care. Results were extracted and categorized into five determinants based on the accessibility framework described by Levesque et al. (2013). Results indicated adults on the autism spectrum experience numerous barriers when accessing health care services. The dimension of access most frequently cited was the appropriateness of care, followed by the acceptability of care. It is essential to explore access as it is often conceptualized as the availability and affordability of services; however, results of this study indicate the broader experience of quality care provision and acceptance of the individual are important in understanding the complex experience individuals on the autism spectrum face. Based on these findings, there is a need to provide comprehensive education and clinical practice guidelines for health care providers to help reduce barriers to providing appropriate care for adults on the autism spectrum. Efforts to destigmatize intrapersonal and extra-personal perceptions of individuals on the autism spectrum will help overcome the barriers that affect care acceptability. Further research must understand how to design and implement strategies to maximize health service access for adults on the autism spectrum.

Enhanced Dynamic Spectrum Access in UAV Wireless Networks for Post-Disaster Area Surveillance System: A Multi-Player Multi-Armed Bandit Approach

Modern wireless networks are notorious for being very dense, uncoordinated, and selfish, especially with greedy user needs. This leads to a critical scarcity problem in spectrum resources. The Dynamic Spectrum Access system (DSA) is considered a promising solution for this scarcity problem. With the aid of Unmanned Aerial Vehicles (UAVs), a post-disaster surveillance system is implemented using Cognitive Radio Network (CRN). UAVs are distributed in the disaster area to capture live images of the damaged area and send them to the disaster management center. CRN enables UAVs to utilize a portion of the spectrum of the Electronic Toll Collection (ETC) gates operating in the same area. In this paper, a joint transmission power selection, data-rate maximization, and interference mitigation problem is addressed. Considering all these conflicting parameters, this problem is investigated as a budget-constrained multi-player multi-armed bandit (MAB) problem. The whole process is done in a decentralized manner, where no information is exchanged between UAVs. To achieve this, two power-budget-aware PBA-MAB) algorithms, namely upper confidence bound (PBA-UCB (MAB) algorithm and Thompson sampling (PBA-TS) algorithm, were proposed to realize the selection of the transmission power value efficiently. The proposed PBA-MAB algorithms show outstanding performance over random power value selection in terms of achievable data rate.