RXs Directions based Codebook Solution for Passive RIS Beamforming

Recently, reconfigurable intelligent surface (RIS) has immensely been deployed to overcome blockage issue and widen coverage for enabling superior performance 6G networks. Mainly, systems use RIS as an assistant to redirect the transmitter (TX) incident signal towards the receiver (RX) by configuring RIS elements amplitudes and phase shifts in a passive beamforming (PBF) process. Channel estimation (CE) based PBF schemes achieve optimal performance, but they need high overhead and time consumption, especially with high number of RIS elements. Codebook (CB) based PBF solutions can be alternatives to overcome these issues by only searching through a limited reflection patterns (RPs) and determining the optimal one based on a predefined metric. However, they consume high power and time relevant to the used CB size. In this work, we propose a direction based PBF (D-PBF) scheme, where we aim to map between the RXs directions and the codebook RPs and store this information in an updated database (DB). Hence, if the matching between a coming RX and a particular RP exists, the proposed scheme will directly select this RP to configure the RIS elements, otherwise, it memorizes this codeword for future searching. Finally, if the matching failed, searching through the memorized RPs will be done to find the optimal one, then updating the DB accordingly. After a time period, which depends on the CB size, the DB will converge, and the D-PBF scheme will need no searching to select the optimal RP. Hence, the proposed scheme needs extremely lower overhead, power, and time comparable to the CE and conventional CB based solutions, while obtaining acceptable performance in terms of effective rate

Millimeter Wave Beamforming Training: A Reinforcement Learning Approach

Beamforming training (BT) is considered as an essential process to accomplish the communications in the millimeter wave (mmWave) band, i.e., 30 ~ 300 GHz. This process aims to find out the best transmit/receive antenna beams to compensate the impairments of the mmWave channel and successfully establish the mmWave link. Typically, the mmWave BT process is highly-time consuming affecting the overall throughput and energy consumption of the mmWave link establishment. In this paper, a machine learning (ML) approach, specifically reinforcement learning (RL), is utilized for enabling the mmWave BT process by modeling it as a multi-armed bandit (MAB) problem with the aim of maximizing the long-term throughput of the constructed mmWave link. Based on this formulation, MAB algorithms such as upper confidence bound (UCB), Thompson sampling (TS), epsilon-greedy (e-greedy), are utilized to address the problem and accomplish the mmWave BT process. Numerical simulations confirm the superior performance of the proposed MAB approach over the existing mmWave BT techniques.   

Millimeter Wave Beamforming Training: A Reinforcement Learning Approach

Beamforming training (BT) is considered as an essential process to accomplish the communications in the millimeter wave (mmWave) band, i.e., 30 ~ 300 GHz. This process aims to find out the best transmit/receive antenna beams to compensate the impairments of the mmWave channel and successfully establish the mmWave link. Typically, the mmWave BT process is highly-time consuming affecting the overall throughput and energy consumption of the mmWave link establishment. In this paper, a machine learning (ML) approach, specifically reinforcement learning (RL), is utilized for enabling the mmWave BT process by modeling it as a multi-armed bandit (MAB) problem with the aim of maximizing the long-term throughput of the constructed mmWave link. Based on this formulation, MAB algorithms such as upper confidence bound (UCB), Thompson sampling (TS), epsilon-greedy (e-greedy), are utilized to address the problem and accomplish the mmWave BT process. Numerical simulations confirm the superior performance of the proposed MAB approach over the existing mmWave BT techniques.   

Позиціонування об’єктів всередині приміщень з використанням видимого світла

Мета. Розробити алгоритм, який дозволить здійснювати позиціонування об’єктів всередині приміщень з використанням видимого світла за допомогою технології Li-Fi. Під час виконання дипломної роботи було напрацьовано математичний апарат та розроблено алгоритм, який призначений для позиціонування обладнання користувача всередині приміщення з використанням видимого світла за допомогою технології Li-Fi відносно прямокутної Декартової системи координат на площині при нульовому піднятті обладнання користувача над підлогою.The purpose of this work. Develop an algorithm that allows indoor positioning using visible light with Li-Fi technology. During the completion of the thesis, a mathematical apparatus and indoor positioning algorithm were developed. The algorithm allows indoor positioning of the user equipment using visible light with Li-Fi technology relative to the Cartesian coordinate system on the plane with zero elevation of the user equipment above the floor

Wireless indoor localisation within the 5G internet of radio light

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonNumerous applications can be enhanced by accurate and efficient indoor localisation using wireless sensor networks, however trade-offs often exist between these two parameters. In this thesis, realworld and simulation data is used to examine the hybrid millimeter wave and Visible Light Communications (VLC) architecture of the 5G Internet of Radio Light (IoRL) Horizon 2020 project. Consequently, relevant localisation challenges within Visible Light Positioning (VLP) and asynchronous sampling networks are identified, and more accurate and efficient solutions are developed. Currently, VLP relies strongly on the assumed Lambertian properties of light sources. However, in practice, not all lights are Lambertian. To support the widespread deployment of VLC technology in numerous environments, measurements from non-Lambertian sources are analysed to provide new insights into the limitations of existing VLP techniques. Subsequently, a novel VLP calibration technique is proposed, and results indicate a 59% accuracy improvement against existing methods. This solution enables high accuracy centimetre level VLP to be achieved with non- Lambertian sources. Asynchronous sampling of range-based measurements is known to impact localisation performance negatively. Various Asynchronous Sampling Localisation Techniques (ASLT) exist to mitigate these effects. While effective at improving positioning performance, the exact suitability of such solutions is not evident due to their additional processes, subsequent complexity, and increased costs. As such, extensive simulations are conducted to study the effectiveness of ASLT under variable sampling latencies, sensor measurement noise, and target trajectories. Findings highlight the computational demand of existing ASLT and motivate the development of a novel solution. The proposed Kalman Extrapolated Least Squares (KELS) method achieves optimal localisation performance with a significant energy reduction of over 50% when compared to current leading ASLT. The work in this thesis demonstrates both the capability for high performance VLP from non- Lambertian sources as well as the potential for energy efficient localisation for sequentially sampled range measurements.Horizon 202