194 research outputs found
Artificial intelligence for localisation of ultra-wide bandwidth (UWB) sensor nodes
Full text linkIn this chapter, we have designed an NB classifier for a UWB-based localization system. With the help of NB classifier and RMSE, the data are classified into three categories: high, medium, and low accuracy. ROCs are plotted to show the effec-tiveness of the NB classifier. As our developed technique obtains more than 90% classification accuracy, we have tested it into two different environments: LOS and partial NLOS conditions. Furthermore, to test the accuracy, small-sized and medium-sized rooms were used. From our measurements, it is observed that the accuracy of the developed NB classifier is dependent upon the environment. For LOS and NLOS envi-ronments, the accuracy are around 97% and 87.38%, respectively. Our future research will concentrate on technique that can further improve the localization classification and improve the positioning accuracy of the IP
Deep Q-Network based coverage hole detection for future wireless networks
No full textIn this chapter, we suggest an effective way of discovering a coverage hole with the help of UAV and ML. The main purpose is to take different parameters from the radio environment and detect the coverage hole efficiently and autonomously. The simulation results show that the proposed method is successful in detecting the coverage hole. Further research for this proposed method can be extended in to many directions. For example, the UAV has detected only a single objective or only one coverage hole in this simulation. If there are more than one coverage hole in a complex radio environment then we have to consider multi-objective RL and consider additional constraints such as UAV charging stations and obstacles, e.g., MBS, trees, buildings. Also, the simulation of such complex radio environment needs to urban scenarios with multi obstacles avoidance techniques considering the speed of the UAV. Apart from these, we can also consider an on-demand UAV base station (tethered or untethered UAV) to provide coverage and capacity to a coverage hole or poor network service area. Based on the traffic requirement and available wireless backhaul, UAVs can act as a base station at the same time while flying to the coverage hole area in a shortest distance
Control data separation and its implications on backhaul networks
No full textFuture cellular systems need to cope with a huge amount of data and diverse service requirements in a flexible, sustainable, green and efficient way with minimal signalling overhead. This calls for network densification, a short length wireless link, efficient and proactive control signalling and the ability to switch off the power consuming devices when they are not in use. In this direction, the conventional alwayson service and worst-case design approach has been identified as the main source of inefficiency, and a paradigm shift towards adaptive and on-demand systems is seen as a promising solution. However, the conventional radio access network (RAN) architecture limits the achievable gains due to the tight coupling between network and data access points, which in turn imposes strict coverage and signalling requirements irrespective of the spatio-temporal service demand, channel conditions or mobility profiles
EEG-based biometrics: Effects of template ageing
Full text linkThis chapter discusses the effects of template ageing in EEG-based biometrics. The chapter also serves as an introduction to general biometrics and its main tasks: Identification and verification. To do so, we investigate different characterisations of EEG signals and examine the difference of performance in subject identification between single session and cross-session identification experiments. In order to do this, EEG signals are characterised with common state-of-the-art features, i.e. Mel Frequency Cepstral Coefficients (MFCC), Autoregression Coefficients, and Power Spectral Density-derived features. The samples were later classified using various classifiers, including Support Vector Machines and k-Nearest Neighbours with different parametrisations. Results show that performance tends to be worse for crosssession identification compared to single session identification. This finding suggests that temporal permanence of EEG signals is limited and thus more sophisticated methods are needed in order to characterise EEG signals for the task of subject identificatio
Machine learning-based affect detection within the context of human-horse interaction
Full text linkThis chapter focuses on the use of machine learning techniques within the field of affective computing, and more specifically for the task of emotion recognition within the context of human-horse interaction. Affective computing focuses on the detection and interpretation of human emotion, an application that could significantly benefit quantitative studies in the field of animal assisted therapy. The chapter offers a thorough description, an experimental design, and experimental results on the use of physiological signals, such as electroencephalography (EEG), electrocardiography (ECG), and electromyography (EMG) signals, for the creation and evaluation of machine learning models for the prediction of the emotional state of an individual during interaction with horses
A machine learning driven solution to the problem of perceptual video quality metrics
Full text linkThe advent of high-speed internet connections, advanced video coding algorithms, and consumer-grade computers with high computational capabilities has led videostreaming-over-the-internet to make up the majority of network traffic. This effect has led to a continuously expanding video streaming industry that seeks to offer enhanced quality-of-experience (QoE) to its users at the lowest cost possible. Video streaming services are now able to adapt to the hardware and network restrictions that each user faces and thus provide the best experience possible under those restrictions. The most common way to adapt to network bandwidth restrictions is to offer a video stream at the highest possible visual quality, for the maximum achievable bitrate under the network connection in use. This is achieved by storing various pre-encoded versions of the video content with different bitrate and visual quality settings. Visual quality is measured by means of objective quality metrics, such as the Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Visual Information Fidelity (VIF), and others, which can be easily computed analytically. Nevertheless, it is widely accepted that although these metrics provide an accurate estimate of the statistical quality degradation, they do not reflect the viewer’s perception of visual quality accurately. As a result, the acquisition of user ratings in the form of Mean Opinion Scores (MOS) remains the most accurate depiction of human-perceived video quality, albeit very costly and time consuming, and thus cannot be practically employed by video streaming providers that have hundreds or thousands of videos in their catalogues. A recent very promising approach for addressing this limitation is the use of machine learning techniques in order to train models that represent human video quality perception more accurately. To this end, regression techniques are used in order to map objective quality metrics to human video quality ratings, acquired for a large number of diverse video sequences. Results have been very promising, with approaches like the Video Multimethod Assessment Fusion (VMAF) metric achieving higher correlations to useracquired MOS ratings compared to traditional widely used objective quality metrics
An integrated approach for functional decomposition of future RAN
No full textSoftware-defined radio access networks (SD-RAN), dense deployment of small cells with possible macro-overlay for users with high mobility, decoupled signaling and data transmissions, or beyond cellular green generation (BCG2) architecture for enhanced energy efficiency, etc. are some of the very active research themes and most promising technologies for future RAN architecture. In this chapter, we present the idea of an integrated deployment solution for energy efficient cellular networks combining the strengths of the above mentioned themes. While SD-RAN envisions a decoupled centralized control plane and data forwarding plane for flexible control, the BCG2 architecture calls for decoupling coverage from capacity and coverage is provided through always-on low-power signaling node for a larger geographical area; capacity is catered by various on-demand data nodes or small cells for maximum energy efficiency. We identify that a combined approach bringing in both decompositions together can, not only achieve greater benefits, but also facilitates the faster realization of both technologies. We propose the idea and design of a signaling controller which acts as a signaling node to provide always-on coverage, consuming low power, and at the same time also hosts the control plane functions for the SD-RAN through a general purpose processing platform. Phantom cell concept is also a similar idea where a normal macro cell provides interference control to densely deployed small cells, although, our preliminary results show that the proposed integrated architecture has much greater potential of energy savings in comparison to phantom cells as a signaling controller is supposed to consume minimal power in comparison with the normal macro cell BS
Access, Fronthaul and Backhaul Networks for 5G and Beyond
No full textThe widespread use of mobile internet and smart applications has led to an explosive growth in mobile data traffic, which will continue due to the emerging need of connecting people, machines, and applications in an ubiquitous manner through the mobile infrastructure. The efficient and satisfactory operation of all these densely-deployed networks hinges on a suitable backhaul and fronthaul provisioning. The research community is working to provide innovative technologies with extensive performance evaluation metrics along with the required standardisation milestones, hardware and components for a fully deployed network by 2020 and beyond.
Access, Fronthaul and Backhaul Networks for 5G & Beyond provides an overview from both academic and industrial stakeholders of innovative backhaul/fronthaul solutions. Covering a wide spectrum of underlying themes ranging from the recent thrust in edge caching for backhaul relaxation to mmWave-based fronthauling for radio access networks, this book is essential reading for engineers, researchers, designers, architects, technicians, students and service providers in the field of networking, mobile and wireless and computing technologies working towards the deployment of 5G networks
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