903 research outputs found

    Reliable indoor optical wireless communication in the presence of fixed and random blockers

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    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

    Analysis and Design of Non-Orthogonal Multiple Access (NOMA) Techniques for Next Generation Wireless Communication Systems

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    The current surge in wireless connectivity, anticipated to amplify significantly in future wireless technologies, brings a new wave of users. Given the impracticality of an endlessly expanding bandwidth, there’s a pressing need for communication techniques that efficiently serve this burgeoning user base with limited resources. Multiple Access (MA) techniques, notably Orthogonal Multiple Access (OMA), have long addressed bandwidth constraints. However, with escalating user numbers, OMA’s orthogonality becomes limiting for emerging wireless technologies. Non-Orthogonal Multiple Access (NOMA), employing superposition coding, serves more users within the same bandwidth as OMA by allocating different power levels to users whose signals can then be detected using the gap between them, thus offering superior spectral efficiency and massive connectivity. This thesis examines the integration of NOMA techniques with cooperative relaying, EXtrinsic Information Transfer (EXIT) chart analysis, and deep learning for enhancing 6G and beyond communication systems. The adopted methodology aims to optimize the systems’ performance, spanning from bit-error rate (BER) versus signal to noise ratio (SNR) to overall system efficiency and data rates. The primary focus of this thesis is the investigation of the integration of NOMA with cooperative relaying, EXIT chart analysis, and deep learning techniques. In the cooperative relaying context, NOMA notably improved diversity gains, thereby proving the superiority of combining NOMA with cooperative relaying over just NOMA. With EXIT chart analysis, NOMA achieved low BER at mid-range SNR as well as achieved optimal user fairness in the power allocation stage. Additionally, employing a trained neural network enhanced signal detection for NOMA in the deep learning scenario, thereby producing a simpler signal detection for NOMA which addresses NOMAs’ complex receiver problem

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    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

    Multi-user MIMO beamforming:implementation, verification in L1 capacity, and performance testing

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    Abstract. A certain piece of technology takes a lot of effort, research, and testing to reach the productisation phase. Radio features are implemented in layer 1 (L1) before moving to the hardware implementation phase, where their functioning is tested and verified. The target of the thesis is to implement and verify beamforming based multi-user multiple-input multiple-output (MU-MIMO) in L1 capacity and performance testing (PET) environment. The L1 testing environment mainly focuses on 4G and 5G stand-alone (SA) cases, while the focus of this thesis work is only on 5G SA technology, which features beamforming and MU-MIMO. Beamforming and MU-MIMO have been tested in an end-to-end system but not specifically in L1. The L1 testing provides a deeper analysis of beamforming and MU-MIMO in L1 and aids in problem identification at an early productisation phase, saving both time and money. L1 PET has multiple components that work together for L1 data transmission in both uplink (UL) and downlink (DL) directions and handle the verification of the transmitted data. The main components that play a key role in the implementation of multi-user MIMO beamforming concern frame design setup, message setup for UL and DL using correct channels and interfaces, transmission of the generated data in UL and DL, and message capturing at L1 end (whether correct messages are transmitted or not). For verification purposes, methods such as analysing plots from L1 log results based on comparison with radio specifications are used to determine whether the generated test output is correct or not. Finally, performance metrics, such as error vector magnitude (EVM), UE per transmission time interval (TTI), number of layers per UE, channel quality indicator (CQI), physical resource block (PRB) count, and throughput, are evaluated to assess the capacity and performance correctness of the implemented test setup

    Plastic Optical Fibers as Passive Optical Front-Ends for Visible Light Communication

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    Signal Processing and Learning for Next Generation Multiple Access in 6G

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    Wireless communication systems to date primarily rely on the orthogonality of resources to facilitate the design and implementation, from user access to data transmission. Emerging applications and scenarios in the sixth generation (6G) wireless systems will require massive connectivity and transmission of a deluge of data, which calls for more flexibility in the design concept that goes beyond orthogonality. Furthermore, recent advances in signal processing and learning have attracted considerable attention, as they provide promising approaches to various complex and previously intractable problems of signal processing in many fields. This article provides an overview of research efforts to date in the field of signal processing and learning for next-generation multiple access, with an emphasis on massive random access and non-orthogonal multiple access. The promising interplay with new technologies and the challenges in learning-based NGMA are discussed

    Colosseum as a Digital Twin: Bridging Real-World Experimentation and Wireless Network Emulation

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    Wireless network emulators are being increasingly used for developing and evaluating new solutions for Next Generation (NextG) wireless networks. However, the reliability of the solutions tested on emulation platforms heavily depends on the precision of the emulation process, model design, and parameter settings. To address, obviate or minimize the impact of errors of emulation models, in this work we apply the concept of Digital Twin (DT) to large-scale wireless systems. Specifically, we demonstrate the use of Colosseum, the world's largest wireless network emulator with hardware-in-the-loop, as a DT for NextG experimental wireless research at scale. As proof of concept, we leverage the Channel emulation scenario generator and Sounder Toolchain (CaST) to create the DT of a publicly-available over-the-air indoor testbed for sub-6 GHz research, namely, Arena. Then, we validate the Colosseum DT through experimental campaigns on emulated wireless environments, including scenarios concerning cellular networks and jamming of Wi-Fi nodes, on both the real and digital systems. Our experiments show that the DT is able to provide a faithful representation of the real-world setup, obtaining an average accuracy of up to 92.5% in throughput and 80% in Signal to Interference plus Noise Ratio (SINR).Comment: 15 pages, 21 figures, 1 tabl

    1-D broadside-radiating leaky-wave antenna based on a numerically synthesized impedance surface

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    A newly-developed deterministic numerical technique for the automated design of metasurface antennas is applied here for the first time to the design of a 1-D printed Leaky-Wave Antenna (LWA) for broadside radiation. The surface impedance synthesis process does not require any a priori knowledge on the impedance pattern, and starts from a mask constraint on the desired far-field and practical bounds on the unit cell impedance values. The designed reactance surface for broadside radiation exhibits a non conventional patterning; this highlights the merit of using an automated design process for a design well known to be challenging for analytical methods. The antenna is physically implemented with an array of metal strips with varying gap widths and simulation results show very good agreement with the predicted performance

    Plastic Optical Fibers as Passive Optical Front-Ends for Visible Light Communication

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