2,057 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
Comparative analysis of energy transfer mechanisms for neural implants
As neural implant technologies advance rapidly, a nuanced understanding of their powering mechanisms becomes indispensable, especially given the long-term biocompatibility risks like oxidative stress and inflammation, which can be aggravated by recurrent surgeries, including battery replacements. This review delves into a comprehensive analysis, starting with biocompatibility considerations for both energy storage units and transfer methods. The review focuses on four main mechanisms for powering neural implants: Electromagnetic, Acoustic, Optical, and Direct Connection to the Body. Among these, Electromagnetic Methods include techniques such as Near-Field Communication (RF). Acoustic methods using high-frequency ultrasound offer advantages in power transmission efficiency and multi-node interrogation capabilities. Optical methods, although still in early development, show promising energy transmission efficiencies using Near-Infrared (NIR) light while avoiding electromagnetic interference. Direct connections, while efficient, pose substantial safety risks, including infection and micromotion disturbances within neural tissue. The review employs key metrics such as specific absorption rate (SAR) and energy transfer efficiency for a nuanced evaluation of these methods. It also discusses recent innovations like the Sectored-Multi Ring Ultrasonic Transducer (S-MRUT), Stentrode, and Neural Dust. Ultimately, this review aims to help researchers, clinicians, and engineers better understand the challenges of and potentially create new solutions for powering neural implants
Potential of machine learning/Artificial Intelligence (ML/AI) for verifying configurations of 5G multi Radio Access Technology (RAT) base station
Abstract. The enhancements in mobile networks from 1G to 5G have greatly increased data transmission reliability and speed. However, concerns with 5G must be addressed. As system performance and reliability improve, ML and AI integration in products and services become more common. The integration teams in cellular network equipment creation test devices from beginning to end to ensure hardware and software parts function correctly. Radio unit integration is typically the first integration phase, where the radio is tested independently without additional network components like the BBU and UE. 5G architecture and the technology that it is using are explained further. The architecture defined by 3GPP for 5G differs from previous generations, using Network Functions (NFs) instead of network entities. This service-based architecture offers NF reusability to reduce costs and modularity, allowing for the best vendor options for customer radio products. 5G introduced the O-RAN concept to decompose the RAN architecture, allowing for increased speed, flexibility, and innovation. NG-RAN provided this solution to speed up the development and implementation process of 5G. The O-RAN concept aims to improve the efficiency of RAN by breaking it down into components, allowing for more agility and customization. The four protocols, the eCPRI interface, and the functionalities of fronthaul that NGRAN follows are expressed further. Additionally, the significance of NR is described with an explanation of its benefits. Some benefits are high data rates, lower latency, improved spectral efficiency, increased network flexibility, and improved energy efficiency. The timeline for 5G development is provided along with different 3GPP releases. Stand-alone and non-stand-alone architecture is integral while developing the 5G architecture; hence, it is also defined with illustrations. The two frequency bands that NR utilizes, FR1 and FR2, are expressed further. FR1 is a sub-6 GHz frequency band. It contains frequencies of low and high values; on the other hand, FR2 contains frequencies above 6GHz, comprising high frequencies. FR2 is commonly known as the mmWave band. Data collection for implementing the ML approaches is expressed that contains the test setup, data collection, data description, and data visualization part of the thesis work. The Test PC runs tests, executes test cases using test libraries, and collects data from various logs to analyze the system’s performance. The logs contain information about the test results, which can be used to identify issues and evaluate the system’s performance. The data collection part describes that the data was initially present in JSON files and extracted from there. The extraction took place using the Python code script and was then fed into an Excel sheet for further analysis. The data description explains the parameters that are taken while training the models. Jupyter notebook has been used for visualizing the data, and the visualization is carried out with the help of graphs. Moreover, the ML techniques used for analyzing the data are described. In total, three methods are used here. All the techniques come under the category of supervised learning. The explained models are random forest, XG Boost, and LSTM. These three models form the basis of ML techniques applied in the thesis. The results and discussion section explains the outcomes of the ML models and discusses how the thesis will be used in the future. The results include the parameters that are considered to apply the ML models to them. SINR, noise power, rxPower, and RSSI are the metrics that are being monitored. These parameters have variance, which is essential in evaluating the quality of the product test setup, the quality of the software being tested, and the state of the test environment. The discussion section of the thesis explains why the following parameters are taken, which ML model is most appropriate for the data being analyzed, and what the next steps are in implementation
Decoupled UL/DL User Association in Wireless-Powered HetNets with Full-Duplex Small Cells
In this paper, we propose two downlink (DL)-uplink (UL) decoupled (DUDe) user association schemes in wireless-powered full-duplex (FD) heterogeneous networks (HetNets). We consider a two-tier HetNet comprising of half-duplex (HD) massive multi-antenna macrocell base stations (MBSs) and dual-antenna FD small cell base stations (SBSs) to support UL and DL transmissions of FD user equipments (UEs). Each FD UE is first associated to one MBS/SBS, based on the mean maximum received power (MMP) scheme or maximum received power (MRP) to harvest energy. During the consecutive data transmission phase, UEs choose to receive DL traffic from the same MBSs/SBSs as that associated with during energy harvesting phase, and send UL traffic through the same/another SBS. Leveraging tools from the stochastic geometry, we develop an analytical framework to analyze the average harvested energy and derive expressions for the UL and DL coverage probabilities of the proposed DUDe user association schemes. Our results show that there is an optimal value for the SBS density in the wireless-powered FD HetNets, at which both DL and UL coverage probabilities are maximized. Moreover, by applying MMPA and MRPA scheme, wireless-powered FD HetNets with DUDe achieves up to and energy efficiency gain over the FD HetNets with DL/UL coupled user association scheme and without wireless power transfer, respectively
Twenty-five years of sensor array and multichannel signal processing: a review of progress to date and potential research directions
In this article, a general introduction to the area of sensor array and multichannel signal processing is provided, including associated activities of the IEEE Signal Processing Society (SPS) Sensor Array and Multichannel (SAM) Technical Committee (TC). The main technological advances in five SAM subareas made in the past 25 years are then presented in detail, including beamforming, direction-of-arrival (DOA) estimation, sensor location optimization, target/source localization based on sensor arrays, and multiple-input multiple-output (MIMO) arrays. Six recent developments are also provided at the end to indicate possible promising directions for future SAM research, which are graph signal processing (GSP) for sensor networks; tensor-based array signal processing, quaternion-valued array signal processing, 1-bit and noncoherent sensor array signal processing, machine learning and artificial intelligence (AI) for sensor arrays; and array signal processing for next-generation communication systems
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
A survey on reconfigurable intelligent surfaces: wireless communication perspective
Using reconfigurable intelligent surfaces (RISs) to improve the coverage and the data rate of future wireless networks is a viable option. These surfaces are constituted of a significant number of passive and nearly passive components that interact with incident signals in a smart way, such as by reflecting them, to increase the wireless system's performance as a result of which the notion of a smart radio environment comes to fruition. In this survey, a study review of RIS-assisted wireless communication is supplied starting with the principles of RIS which include the hardware architecture, the control mechanisms, and the discussions of previously held views about the channel model and pathloss; then the performance analysis considering different performance parameters, analytical approaches and metrics are presented to describe the RIS-assisted wireless network performance improvements. Despite its enormous promise, RIS confronts new hurdles in integrating into wireless networks efficiently due to its passive nature. Consequently, the channel estimation for, both full and nearly passive RIS and the RIS deployments are compared under various wireless communication models and for single and multi-users. Lastly, the challenges and potential future study areas for the RIS aided wireless communication systems are proposed
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