33 research outputs found
Selection Combiner in Time-Varying Amplify Forward Cooperative Communication
This research presents the diversity combining schemes for Multiple Symbol Double Differential Sphere Detection (MSDDSD) in a time-varying amplify-and-forward wireless cooperative communication network. Four diversity combiners, including direct combiner, Maximal Ratio Combiner (MRC), semi MRC and Selection Combiner (SC) are demonstrated and explained in details. A comprehensive error probability and outage probability performance analysis are carried through the flat fading Rayleigh environment for semi MRC and SC. Specifically, error performance analysis is obtained using the PDF for SC detectors. Finally, power allocation expression based on error performance minimization approach is presented for the proposed SC performance optimization. It is observed that the performance analysis matches well with the simulation results. Furthermore, the proposed SC scheme offers better performance among the conventional MRC and direct combiner schemes in the presence of frequency offsets
Learning-based communication system design â autoencoder for (differential) block coded modulation designs and path loss predictions
Shannonâs channel coding theorem states the existence of long random codes that can
make the error probability arbitrarily small. Recently, advanced error-correcting codes
such as turbo and low-density parity-check codes have almost reached the theoretical
Shannon limit for binary additive white Gaussian noise channels. However, designing
optimal high-rate short-block codes with automatic bit-labeling for various wireless networks is still an unsolved problem.
Deep-learning-based autoencoders (AE) have appeared as a potential near-optimal
solution for designing wireless communications systems. We take a holistic approach that
jointly optimizes all the components of the communication networks by performing data-driven end-to-end learning of the neural network-based transmitter and receiver together.
Specifically, to tackle the fading channels, we show that AE frameworks can perform
near-optimal block coded-modulation (BCM) and differential BCM (d-BCM) designs in
the presence and absence of the channel state information knowledge. Moreover, we
focus on AE-based designing of high-rate short block codes with automatic bit-labeling
that are capable of outperforming conventional networks with larger margins as the rate
R increases. We also investigate the BCM and d-BCM from an information-theoretic
perspective.
With the advent of internet-of-things (IoT) networks and the widespread use of small
devices, we face the challenge of limited available bandwidth. Therefore, novel techniques need to be utilized, such as full-duplex (FD) mode transmission reception at the
base station for the full utilization of the spectrum, and non-orthogonal multiple access
(NOMA) at the user-end for serving multiple IoT devices while fulfilling their quality-of-service requirement. Furthermore, the deployment of relay nodes will play a pivotal
role in improving network coverage, reliability, and spectral efficiency for the future 5G
networks. Thus, we design and develop novel end-to-end-learning-based AE frameworks
for BCM and d-BCM in various scenarios such as amplify-and-forward and decode-and-forward relaying networks, FD relaying networks, and multi-user downlink networks.
We focus on interpretability and understand the AE-based BCM and d-BCM from an
information-theoretic perspective, such as the AEâs estimated mutual information, convergence, loss optimization, and training principles. We also determine the distinct properties of AE-based (differential) coded-modulation designs in higher-dimensional space.
Moreover, we also studied the reproducibility of the trained AE framework.
In contrast, large bandwidth and worldwide spectrum availability at mm-wave bands
have also shown a great potential for 5G and beyond, but the high path loss (PL) and
significant scattering/absorption loss make the signal propagation challenging. Highly
accurate PL prediction is fundamental for mm-wave network planning and optimization,
whereas existing methods such as slope-intercept models and ray tracing fall short in
capturing the large street-by-street variation seen in urban cities. We also exploited the
potential benefits of AE framework-based compression capabilities in mm-wave PL prediction. Specifically, we employ extensive 28 GHz measurements from Manhattan Street
canyons and model the street clutters via a LiDAR point cloud dataset and 3D-buildings
by a mesh-grid building dataset. We aggressively compress 3D-building shape information using convolutional-AE frameworks to reduce overfitting and propose a machine
learning (ML)-based PL prediction model for mm-wave propagation.EPSRC-UKRI fundin
Architectures and synchronization techniques for distributed satellite systems: a survey
Cohesive Distributed Satellite Systems (CDSSs) is a key enabling technology for the future of remote sensing and communication missions. However, they have to meet strict synchronization requirements before their use is generalized. When clock or local oscillator signals are generated locally at each of the distributed nodes, achieving exact synchronization in absolute phase, frequency, and time is a complex problem. In addition, satellite systems have significant resource constraints, especially for small satellites, which are envisioned to be part of the future CDSSs. Thus, the development of precise, robust, and resource-efficient synchronization techniques is essential for the advancement of future CDSSs. In this context, this survey aims to summarize and categorize the most relevant results on synchronization techniques for Distributed Satellite Systems (DSSs). First, some important architecture and system concepts are defined. Then, the synchronization methods reported in the literature are reviewed and categorized. This article also provides an extensive list of applications and examples of synchronization techniques for DSSs in addition to the most significant advances in other operations closely related to synchronization, such as inter-satellite ranging and relative position. The survey also provides a discussion on emerging data-driven synchronization techniques based on Machine Learning (ML). Finally, a compilation of current research activities and potential research topics is proposed, identifying problems and open challenges that can be useful for researchers in the field.This work was supported by the Luxembourg National Research Fund (FNR), through the CORE Project COHEsive SATellite (COHESAT): Cognitive Cohesive Networks of Distributed Units for Active and Passive Space Applications, under Grant FNR11689919.Award-winningPostprint (published version
Cooperative Radio Communications for Green Smart Environments
The demand for mobile connectivity is continuously increasing, and by 2020 Mobile and Wireless Communications will serve not only very dense populations of mobile phones and nomadic computers, but also the expected multiplicity of devices and sensors located in machines, vehicles, health systems and city infrastructures. Future Mobile Networks are then faced with many new scenarios and use cases, which will load the networks with different data traffic patterns, in new or shared spectrum bands, creating new specific requirements. This book addresses both the techniques to model, analyse and optimise the radio links and transmission systems in such scenarios, together with the most advanced radio access, resource management and mobile networking technologies. This text summarises the work performed by more than 500 researchers from more than 120 institutions in Europe, America and Asia, from both academia and industries, within the framework of the COST IC1004 Action on "Cooperative Radio Communications for Green and Smart Environments". The book will have appeal to graduates and researchers in the Radio Communications area, and also to engineers working in the Wireless industry. Topics discussed in this book include: ⢠Radio waves propagation phenomena in diverse urban, indoor, vehicular and body environments⢠Measurements, characterization, and modelling of radio channels beyond 4G networks⢠Key issues in Vehicle (V2X) communication⢠Wireless Body Area Networks, including specific Radio Channel Models for WBANs⢠Energy efficiency and resource management enhancements in Radio Access Networks⢠Definitions and models for the virtualised and cloud RAN architectures⢠Advances on feasible indoor localization and tracking techniques⢠Recent findings and innovations in antenna systems for communications⢠Physical Layer Network Coding for next generation wireless systems⢠Methods and techniques for MIMO Over the Air (OTA) testin
Unmanned Aerial Vehicle for Internet of Everything: Opportunities and Challenges
The recent advances in information and communication technology (ICT) have
further extended Internet of Things (IoT) from the sole "things" aspect to the
omnipotent role of "intelligent connection of things". Meanwhile, the concept
of internet of everything (IoE) is presented as such an omnipotent extension of
IoT. However, the IoE realization meets critical challenges including the
restricted network coverage and the limited resource of existing network
technologies. Recently, Unmanned Aerial Vehicles (UAVs) have attracted
significant attentions attributed to their high mobility, low cost, and
flexible deployment. Thus, UAVs may potentially overcome the challenges of IoE.
This article presents a comprehensive survey on opportunities and challenges of
UAV-enabled IoE. We first present three critical expectations of IoE: 1)
scalability requiring a scalable network architecture with ubiquitous coverage,
2) intelligence requiring a global computing plane enabling intelligent things,
3) diversity requiring provisions of diverse applications. Thereafter, we
review the enabling technologies to achieve these expectations and discuss four
intrinsic constraints of IoE (i.e., coverage constraint, battery constraint,
computing constraint, and security issues). We then present an overview of
UAVs. We next discuss the opportunities brought by UAV to IoE. Additionally, we
introduce a UAV-enabled IoE (Ue-IoE) solution by exploiting UAVs's mobility, in
which we show that Ue-IoE can greatly enhance the scalability, intelligence and
diversity of IoE. Finally, we outline the future directions in Ue-IoE.Comment: 21 pages, 9 figure
Cooperative Radio Communications for Green Smart Environments
The demand for mobile connectivity is continuously increasing, and by 2020 Mobile and Wireless Communications will serve not only very dense populations of mobile phones and nomadic computers, but also the expected multiplicity of devices and sensors located in machines, vehicles, health systems and city infrastructures. Future Mobile Networks are then faced with many new scenarios and use cases, which will load the networks with different data traffic patterns, in new or shared spectrum bands, creating new specific requirements. This book addresses both the techniques to model, analyse and optimise the radio links and transmission systems in such scenarios, together with the most advanced radio access, resource management and mobile networking technologies. This text summarises the work performed by more than 500 researchers from more than 120 institutions in Europe, America and Asia, from both academia and industries, within the framework of the COST IC1004 Action on "Cooperative Radio Communications for Green and Smart Environments". The book will have appeal to graduates and researchers in the Radio Communications area, and also to engineers working in the Wireless industry. Topics discussed in this book include: ⢠Radio waves propagation phenomena in diverse urban, indoor, vehicular and body environments⢠Measurements, characterization, and modelling of radio channels beyond 4G networks⢠Key issues in Vehicle (V2X) communication⢠Wireless Body Area Networks, including specific Radio Channel Models for WBANs⢠Energy efficiency and resource management enhancements in Radio Access Networks⢠Definitions and models for the virtualised and cloud RAN architectures⢠Advances on feasible indoor localization and tracking techniques⢠Recent findings and innovations in antenna systems for communications⢠Physical Layer Network Coding for next generation wireless systems⢠Methods and techniques for MIMO Over the Air (OTA) testin