Increase in system capacity and data rates can be achieved efficiently in a wireless system by getting the transmitter and receiver closer to each other. Femtocells deployed in the macrocell significantly improve the indoor coverage and provide better user experience. The femtocell base station called as Femtocell Access Point (FAP) is fully user deployed and hence reduces the infrastructure, maintenance and operational cost of the operator while at the same time providing good Quality of Service (QoS) to the end user and high network capacity gains. However, the mass deployment of femtocell faces a number of challenges, among which interference management is of much importance, as the fundamental limits of capacity and achievable data rates mainly depends on the interference faced by the femtocell network. To cope with the technical challenges including interference management faced by the femtocells, researchers have suggested a variety of solutions. These solutions vary depending on the physical layer technology and the specific scenarios considered. Furthermore, the cognitive capabilities, as a functionality of femtocell have also been discussed in this survey. This article summarises the main concepts of femtocells that are covered in literature and the major challenges faced in its large scale deployment. The main challenge of interference management is discussed in detail with its types in femtocells and the solutions proposed over the years to manage interference have been summarised. In addition an overview of the current femtocell standardisation and the future research direction of femtocells have also been provided.

Small cell base stations (SBSs) and multiple antennas are seen as fundamental technologies in the emergence of the next generation [i.e., 5th generation (5G)] of cellular wireless technology. This paper provides a comprehensive survey of literature relating to the applications and challenges associated with using multiple antennas in SBSs. The use of multiple antenna techniques in conventional wireless base stations has undergone much study and is widespread. With heterogeneity in current networks and a furthering of this theme together with greater densification expected in 5G systems, their use in SBSs is at an evolutionary stage. In this paper, unique design challenges associated with size, cost, and performance in SBSs are presented. We present a clear understanding of this increasingly important research area, identifying a clear classification of use and design guidelines. We present a state-of-the-art review of the literature to show how researchers are using and considering the use of multiple antennas in small cells. Attention is given to current generation networks, and with SBSs being a dominant technology necessary for 5G, we also provide insights into the design challenges in such possible future networks.

The posture detection received lots of attention in the fields of human sensing and artificial intelligence. Posture detection can be used for the monitoring health status of elderly remotely by identifying their postures such as standing, sitting and walking. Most of the current studies used traditional machine learning classifiers to identify the posture. However, these methods do not perform well to detect the postures accurately. Therefore, in this study, we proposed a novel hybrid approach based on machine learning classifiers (i. e., support vector machine (SVM), logistic regression (KNN), decision tree, Naive Bayes, random forest, Linear discrete analysis and Quadratic discrete analysis) and deep learning classifiers (i. e., 1D-convolutional neural network (1D-CNN), 2D-convolutional neural network (2D-CNN), LSTM and bidirectional LSTM) to identify posture detection. The proposed hybrid approach uses prediction of machine learning (ML) and deep learning (DL) to improve the performance of ML and DL algorithms. The experimental results on widely benchmark dataset are shown and results achieved an accuracy of more than 98%.

New technological advancements in wireless networks have enlarged the number of connected devices. The unprecedented surge of data volume in wireless systems empowered by artificial intelligence (AI) opens up new horizons for providing ubiquitous data-driven intelligent services. Traditional cloud-centric machine learning (ML)-based services are implemented by centrally collecting datasets and training models. However, this conventional training technique encompasses two challenges: (i) high communication and energy cost and (ii) threatened data privacy. In this article, we introduce a comprehensive survey of the fundamentals and enabling technologies of federated learning (FL), a newly emerging technique coined to bring ML to the edge of wireless networks. Moreover, an extensive study is presented detailing various applications of FL in wireless networks and highlighting their challenges and limitations. The efficacy of FL is further explored with emerging prospective beyond fifth-generation (B5G) and sixth-generation (6G) communication systems. This survey aims to provide an overview of the state-of-the-art FL applications in key wireless technologies that will serve as a foundation to establish a firm understanding of the topic. Lastly, we offer a road forward for future research directions.

This paper presents a first-time review of the open literature focused on the significance of big data generated within nano-sensors and nano-communication networks intended for the future healthcare and biomedical applications. It is aimed toward the development of modern smart healthcare systems enabled with P4, i.e., predictive, preventive, personalized, and participatory capabilities to perform diagnostics, monitoring, and treatment. The analytical capabilities that can be produced from the substantial amount of data gathered in such networks will aid in exploiting the practical intelligence and learning capabilities that could be further integrated with conventional medical and health data leading to more efficient decision making. We have also proposed a big data analytics framework for gathering intelligence, form the healthcare big data, required by futuristic smart healthcare to address relevant problems and exploit possible opportunities in the future applications. Finally, the open challenges, the future directions for researchers in the evolving healthcare domain, are presented.

The atrial fibrillation (AF) is one of the most well-known cardiac arrhythmias in clinical practice, with a prevalence of 1–2% in the community, which can increase the risk of stroke and myocardial infarction. The detection of AF electrocardiogram (ECG) can improve the early detection of diagnosis. In this paper, we have further developed a framework for processing the ECG signal in order to determine the AF episodes. We have implemented machine learning and deep learning algorithms to detect AF. Moreover, the experimental results show that better performance can be achieved with long short-term memory (LSTM) as compared to other algorithms. The initial experimental results illustrate that the deep learning algorithms, such as LSTM and convolutional neural network (CNN), achieved better performance (10%) as compared to machine learning classifiers, such as support vectors, logistic regression, etc. This preliminary work can help clinicians in AF detection with high accuracy and less probability of errors, which can ultimately result in reduction in fatality rate.