2,375 research outputs found
Reliable indoor optical wireless communication in the presence of fixed and random blockers
The advanced innovation of smartphones has led to the exponential growth of internet users which is expected to reach 71% of the global population by the end of 2027. This in turn has given rise to the demand for wireless data and internet devices that is capable of providing energy-efficient, reliable data transmission and high-speed wireless data services. Light-fidelity (LiFi), known as one of the optical wireless communication (OWC) technology is envisioned as a promising solution to accommodate these demands. However, the indoor LiFi channel is highly environment-dependent which can be influenced by several crucial factors (e.g., presence of people, furniture, random users' device orientation and the limited field of view (FOV) of optical receivers) which may contribute to the blockage of the line-of-sight (LOS) link.
In this thesis, it is investigated whether deep learning (DL) techniques can effectively learn the distinct features of the indoor LiFi environment in order to provide superior performance compared to the conventional channel estimation techniques (e.g., minimum mean square error (MMSE) and least squares (LS)). This performance can be seen particularly when access to real-time channel state information (CSI) is restricted and is achieved with the cost of collecting large and meaningful data to train the DL neural networks and the training time which was conducted offline. Two DL-based schemes are designed for signal detection and resource allocation where it is shown that the proposed methods were able to offer close performance to the optimal conventional schemes and demonstrate substantial gain in terms of bit-error ratio (BER) and throughput especially in a more realistic or complex indoor environment.
Performance analysis of LiFi networks under the influence of fixed and random blockers is essential and efficient solutions capable of diminishing the blockage effect is required. In this thesis, a CSI acquisition technique for a reconfigurable intelligent surface (RIS)-aided LiFi network is proposed to significantly reduce the dimension of the decision variables required for RIS beamforming. Furthermore, it is shown that several RIS attributes such as shape, size, height and distribution play important roles in increasing the network performance. Finally, the performance analysis for an RIS-aided realistic indoor LiFi network are presented. The proposed RIS configuration shows outstanding performances in reducing the network outage probability under the effect of blockages, random device orientation, limited receiver's FOV, furniture and user behavior.
Establishing a LOS link that achieves uninterrupted wireless connectivity in a realistic indoor environment can be challenging. In this thesis, an analysis of link blockage is presented for an indoor LiFi system considering fixed and random blockers. In particular, novel analytical framework of the coverage probability for a single source and multi-source are derived. Using the proposed analytical framework, link blockages of the indoor LiFi network are carefully investigated and it is shown that the incorporation of multiple sources and RIS can significantly reduce the LOS coverage blockage probability in indoor LiFi systems
A review of differentiable digital signal processing for music and speech synthesis
The term “differentiable digital signal processing” describes a family of techniques in which loss function gradients are backpropagated through digital signal processors, facilitating their integration into neural networks. This article surveys the literature on differentiable audio signal processing, focusing on its use in music and speech synthesis. We catalogue applications to tasks including music performance rendering, sound matching, and voice transformation, discussing the motivations for and implications of the use of this methodology. This is accompanied by an overview of digital signal processing operations that have been implemented differentiably, which is further supported by a web book containing practical advice on differentiable synthesiser programming (https://intro2ddsp.github.io/). Finally, we highlight open challenges, including optimisation pathologies, robustness to real-world conditions, and design trade-offs, and discuss directions for future research
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Interpretable Machine Learning Architectures for Efficient Signal Detection with Applications to Gravitational Wave Astronomy
Deep learning has seen rapid evolution in the past decade, accomplishing tasks that were previously unimaginable. At the same time, researchers strive to better understand and interpret the underlying mechanisms of the deep models, which are often justifiably regarded as "black boxes". Overcoming this deficiency will not only serve to suggest better learning architectures and training methods, but also extend deep learning to scenarios where interpretability is key to the application. One such scenario is signal detection and estimation, with gravitational wave detection as a specific example, where classic methods are often preferred for their interpretability. Nonetheless, while classic statistical detection methods such as matched filtering excel in their simplicity and intuitiveness, they can be suboptimal in terms of both accuracy and computational efficiency. Therefore, it is appealing to have methods that achieve ``the best of both worlds'', namely enjoying simultaneously excellent performance and interpretability.
In this thesis, we aim to bridge this gap between modern deep learning and classic statistical detection, by revisiting the signal detection problem from a new perspective. First, to address the perceived distinction in interpretability between classic matched filtering and deep learning, we state the intrinsic connections between the two families of methods, and identify how trainable networks can address the structural limitations of matched filtering. Based on these ideas, we propose two trainable architectures that are constructed based on matched filtering, but with learnable templates and adaptivity to unknown noise distributions, and therefore higher detection accuracy. We next turn our attention toward improving the computational efficiency of detection, where we aim to design architectures that leverage structures within the problem for efficiency gains. By leveraging the statistical structure of class imbalance, we integrate hierarchical detection into trainable networks, and use a novel loss function which explicitly encodes both detection accuracy and efficiency. Furthermore, by leveraging the geometric structure of the signal set, we consider using signal space optimization as an alternative computational primitive for detection, which is intuitively more efficient than covering with a template bank. We theoretical prove the efficiency gain by analyzing Riemannian gradient descent on the signal manifold, which reveals an exponential improvement in efficiency over matched filtering. We also propose a practical trainable architecture for template optimization, which makes use of signal embedding and kernel interpolation.
We demonstrate the performance of all proposed architectures on the task of gravitational wave detection in astrophysics, where matched filtering is the current method of choice. The architectures are also widely applicable to general signal or pattern detection tasks, which we exemplify with the handwritten digit recognition task using the template optimization architecture. Together, we hope the this work useful to scientists and engineers seeking machine learning architectures with high performance and interpretability, and contribute to our understanding of deep learning as a whole
Analytical Models and Artificial Intelligence for Open and Partially Disaggregated Optical Networks
L'abstract è presente nell'allegato / the abstract is in the attachmen
Modulation and Signal Processing for LEO-LEO Optical Inter-satellite Links
We investigate key aspects of coherent optical communications on
inter-satellite links (ISLs) for the next-generation ultra-dense low-Earth
orbit (LEO) constellations. Initially, the suitability of QPSK, 8-QAM, and
16-QAM modulation formats with different symbol rates (28 GBaud, 60 GBaud, and
120 GBaud) and channel coding schemes (oFEC and staircase codes) for intra- and
interorbital connections is evaluated. We provide SNR margins for all
investigated sets and determine unfeasible operating points. We show that sets
with higher-order modulation formats combined with high symbol rates can prove
unfeasible, even for first-neighbor connections. Furthermore, the presence or
absence of optical pre-amplification as well as the choice for a more robust
channel coding technique, such as the oFEC, can be decisive in certain LEO-LEO
links. Next, we characterize the Doppler shift (DS) and its time derivative for
first-neighbor interorbital connections in two different topologies and for
general connections established between any pairs of satellites. Our results
reveal that while the maximum Doppler-generated frequency shift amplitude can
be considerably higher than those typically found in fiber-optic
communications, the time derivative values are significantly lower. Finally, we
address all-digital DS compensation in extreme cases of frequency offset
amplitude and derivative where the typical Mth-power algorithm is not
sufficient. To this end, we propose a filtered version of an existing two-stage
method combining spectral shifts with the Mth-power method. The simulation
results indicate that this approach provides an appropriate solution for all
examined cases.Comment: 13 pages, 7 figures, 6 table
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
Integration of hybrid networks, AI, Ultra Massive-MIMO, THz frequency, and FBMC modulation toward 6g requirements : A Review
The fifth-generation (5G) wireless communications have been deployed in many countries with the following features: wireless networks at 20 Gbps as peak data rate, a latency of 1-ms, reliability of 99.999%, maximum mobility of 500 km/h, a bandwidth of 1-GHz, and a capacity of 106 up to Mbps/m2. Nonetheless, the rapid growth of applications, such as extended/virtual reality (XR/VR), online gaming, telemedicine, cloud computing, smart cities, the Internet of Everything (IoE), and others, demand lower latency, higher data rates, ubiquitous coverage, and better reliability. These higher requirements are the main problems that have challenged 5G while concurrently encouraging researchers and practitioners to introduce viable solutions. In this review paper, the sixth-generation (6G) technology could solve the 5G limitations, achieve higher requirements, and support future applications. The integration of multiple access techniques, terahertz (THz), visible light communications (VLC), ultra-massive multiple-input multiple-output ( μm -MIMO), hybrid networks, cell-free massive MIMO, and artificial intelligence (AI)/machine learning (ML) have been proposed for 6G. The main contributions of this paper are a comprehensive review of the 6G vision, KPIs (key performance indicators), and advanced potential technologies proposed with operation principles. Besides, this paper reviewed multiple access and modulation techniques, concentrating on Filter-Bank Multicarrier (FBMC) as a potential technology for 6G. This paper ends by discussing potential applications with challenges and lessons identified from prior studies to pave the path for future research
Rate-splitting multiple access for non-terrestrial communication and sensing networks
Rate-splitting multiple access (RSMA) has emerged as a powerful and flexible
non-orthogonal transmission, multiple access (MA) and interference management
scheme for future wireless networks. This thesis is concerned with the application of
RSMA to non-terrestrial communication and sensing networks. Various scenarios
and algorithms are presented and evaluated.
First, we investigate a novel multigroup/multibeam multicast beamforming strategy
based on RSMA in both terrestrial multigroup multicast and multibeam satellite
systems with imperfect channel state information at the transmitter (CSIT). The
max-min fairness (MMF)-degree of freedom (DoF) of RSMA is derived and shown
to provide gains compared with the conventional strategy. The MMF beamforming
optimization problem is formulated and solved using the weighted minimum mean
square error (WMMSE) algorithm. Physical layer design and link-level simulations
are also investigated. RSMA is demonstrated to be very promising for multigroup
multicast and multibeam satellite systems taking into account CSIT uncertainty
and practical challenges in multibeam satellite systems.
Next, we extend the scope of research from multibeam satellite systems to satellite-
terrestrial integrated networks (STINs). Two RSMA-based STIN schemes are
investigated, namely the coordinated scheme relying on CSI sharing and the co-
operative scheme relying on CSI and data sharing. Joint beamforming algorithms
are proposed based on the successive convex approximation (SCA) approach to
optimize the beamforming to achieve MMF amongst all users. The effectiveness and
robustness of the proposed RSMA schemes for STINs are demonstrated.
Finally, we consider RSMA for a multi-antenna integrated sensing and communications (ISAC) system, which simultaneously serves multiple communication users
and estimates the parameters of a moving target. Simulation results demonstrate
that RSMA is beneficial to both terrestrial and multibeam satellite ISAC systems by
evaluating the trade-off between communication MMF rate and sensing Cramer-Rao
bound (CRB).Open Acces
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