617 research outputs found

    Coverage Performance Analysis of Reconfigurable Intelligent Surface-aided Millimeter Wave Network with Blockage Effect

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    In order to solve spectrum resource shortage and satisfy immense wireless data traffic demands, millimeter wave (mmWave) frequency with large available bandwidth has been proposed for wireless communication in 5G and beyond 5G. However, mmWave communications are susceptible to blockages. This characteristic limits the network performance. Meanwhile, reconfigurable intelligent surface (RIS) has been proposed to improve the propagation environment and extend the network coverage. Unlike traditional wireless technologies that improve transmission quality from transceivers, RISs enhance network performance by adjusting the propagation environment. One of the promising applications of RISs is to provide indirect line-of-sight (LoS) paths when the direct LoS path between transceivers does not exist. This application makes RIS particularly useful in mmWave communications. With effective RIS deployment, the mmWave RIS-aided network performance can be enhanced significantly. However, most existing works have analyzed RIS-aided network performance without exploiting the flexibility of RIS deployment and/or considering blockage effect, which leaves huge research gaps in RIS-aided networks. To fill the gaps, this thesis develops RIS-aided mmWave network models considering blockage effect under the stochastic geometry framework. Three scenarios, i.e., indoor, outdoor and outdoor-to-indoor (O2I) RIS-aided networks, are investigated. Firstly, LoS propagation is hard to be guaranteed in indoor environments since blockages are densely distributed. Deploying RISs to assist mmWave transmission is a promising way to overcome this challenge. In the first paper, we propose an indoor mmWave RIS-aided network model capturing the characteristics of indoor environments. With a given base station (BS) density, whether deploying RISs or increasing BS density to further enhance the network coverage is more cost-effective is investigated. We present a coverage calculation algorithm which can be adapted for different indoor layouts. Then, we jointly analyze the network cost and coverage probability. Our results indicate that deploying RISs with an appropriate number of BSs is more cost-effective for achieving an adequate coverage probability than increasing BSs only. Secondly, for a given total number of passive elements, whether fewer large-scale RISs or more small-scale RISs should be deployed has yet to be investigated in the presence of the blockage effect. In the second paper, we model and analyze a 3D outdoor mmWave RIS-aided network considering both building blockages and human-body blockages. Based on the proposed model, the analytical upper and lower bounds of the coverage probability are derived. Meanwhile, the closed-form coverage probability when RISs are much closer to the UE than the BS is derived. In terms of coverage enhancement, we reveal that sparsely deployed large-scale RISs outperform densely deployed small-scale RISs in scenarios of sparse blockages and/or long transmission distances, while densely deployed small-scale RISs win in scenarios of dense blockages and/or short transmission distances. Finally, building envelope (the exterior wall of a building) makes outdoor mmWave BS difficult to communicate with indoor UE. Transmissive RISs with passive elements have been proposed to refract the signal when the transmitter and receiver are on the different side of the RIS. Similar to reflective RISs, the passive elements of a transmissive RIS can implement phase shifts and adjust the amplitude of the incident signals. By deploying transmissive RISs on the building envelope, it is feasible to implement RIS-aided O2I mmWave networks. In the third paper, we develop a 3D RIS-aided O2I mmWave network model with random indoor blockages. Based on the model, a closed-form coverage probability approximation considering blockage spatial correlation is derived, and multiple-RIS deployment strategies are discussed. For a given total number of RIS passive elements, the impact of blockage density, the number and locations of RISs on the coverage probability is analyzed. All the analytical results have been validated by Monte Carlo simulation. The observations from the result analysis provide guidelines for the future deployment of RIS-aided mmWave networks

    Active RIS Assisted Rate-Splitting Multiple Access Network: Spectral and Energy Efficiency Tradeoff

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    With the increasing demand of high data rate and massive access in both ultra-dense and industrial Internet-of-things networks, spectral efficiency (SE) and energy efficiency (EE) are regarded as two important and inter-related performance metrics for future networks. In this paper, we investigate a novel integration of rate-splitting multiple access (RSMA) and reconfigurable intelligent surface (RIS) into cellular systems to achieve a desirable tradeoff between SE and EE. Different from the commonly used passive RIS, we adopt reflection elements with active load to improve a newly defined metric, called resource efficiency (RE), which is capable of striking a balance between SE and EE. This paper focuses on the RE optimization by jointly designing the base station (BS) transmit precoding and RIS beamforming (BF) while guaranteeing the transmit and forward power budgets of the BS and RIS, respectively. To efficiently tackle the challenges for solving the RE maximization problem due to its fractional objective function, coupled optimization variables, and discrete coefficient constraint, the formulated nonconvex problem is solved by proposing a two-stage optimization framework. For the outer stage problem, a quadratic transformation is used to recast the fractional objective into a linear form, and a closed-form solution is obtained by using auxiliary variables. For the inner stage problem, the system sum rate is approximated into a linear function. Then, an alternating optimization (AO) algorithm is proposed to optimize the BS precoding and RIS BF iteratively, by utilizing the penalty dual decomposition (PDD) method. Simulation results demonstrate the superiority of the proposed design compared to other benchmarks

    RF Wireless Power and Data Transfer : Experiment-driven Analysis and Waveform Design

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    The brisk deployment of the fifth generation (5G) mobile technology across the globe has accelerated the adoption of Internet of Things (IoT) networks. While 5G provides the necessary bandwidth and latency to connect the trillions of IoT sensors to the internet, the challenge of powering such a multitude of sensors with a replenishable energy source remains. Far-field radio frequency (RF) wireless power transfer (WPT) is a promising technology to address this issue. Conventionally, the RF WPT concepts have been deemed inadequate to deliver wireless power due to the undeniably huge over-the-air propagation losses. Nonetheless, the radical decline in the energy requirement of simple sensing and computing devices over the last few decades has rekindled the interest in RF WPT as a feasible solution for wireless power delivery to IoT sensors. The primary goal in any RF WPT system is to maximize the harvested direct current (DC) power from the minuscule incident RF power. As a result, optimizing the receiver power efficiency is pivotal for an RF WPT system. On similar lines, it is essential to minimize the power losses at the transmitter in order to achieve a sustainable and economically viable RF WPT system. In this regard, this thesis explores the system-level study of an RF WPT system using a digital radio transmitter for applications where alternative analog transmit circuits are impractical. A prototype test-bed comprising low-cost software-defined radio (SDR) transmitter and an off-the-shelf RF energy-harvesting (EH) receiver is developed to experimentally analyze the impact of clipping and nonlinear amplification at the digital radio transmitter on digital baseband waveform. The use of an SDR allows leveraging the test-bed for the research on RF simultaneous wireless information and power transfer (SWIPT); the true potential of this technology can be realized by utilizing the RF spectrum to transport data and power together. The experimental results indicate that a digital radio severely distorts high peak-to-average power ratio (PAPR) signals, thereby reducing their average output power and rendering them futile for RF WPT. On similar lines, another test-bed is developed to assess the impact of different waveforms, input impedance mismatch, incident RF power, and load on the receiver power efficiency of an RF WPT system. The experimental results provide the foundation and notion to develop a novel mathematical model for an RF EH receiver. The parametric model relates the harvested DC power to the power distribution of the envelope signal of the incident waveform, which is characterized by the amplitude, phase and frequency of the baseband waveform. The novel receiver model is independent of the receiver circuit’s matching network, rectifier configuration, number of diodes, load as well as input frequency. The efficacy of the model in accurately predicting the output DC power for any given power-level distribution is verified experimentally. Since the novel receiver model associates the output DC power to the parameters of the incident waveform, it is further leveraged to design optimal transmit wave-forms for RF WPT and SWIPT. The optimization problem reveals that a constant envelope signal with varying duty cycle is optimal for maximizing the harvested DC power. Consequently, a pulsed RF waveform is optimal for RF WPT, whereas a continuous phase modulated pulsed RF signal is suitable for RF SWIPT. The superior WPT performance of pulsed RF waveforms over multisine signals is demonstrated experimentally. Similarly, the pulsed phase-shift keying (PSK) signals exhibit superior receiver power efficiency than other communication signals. Nonetheless, varying the duty-cycle of pulsed PSK waveform leads to an efficiency—throughput trade-off in RF SWIPT. Finally, the SDR test-bed is used to evaluate the overall end-to-end power efficiency of different digital baseband waveforms through wireless measurements. The results indicate a 4-PSK modulated signal to be suitable for RF WPT considering the overall power efficiency of the system. The corresponding transmitter, channel and receiver power efficiencies are evaluated as well. The results demonstrate the transmitter power efficiency to be lower than the receiver power efficiency

    On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

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    Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden

    Cognitive Decay And Memory Recall During Long Duration Spaceflight

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    This dissertation aims to advance the efficacy of Long-Duration Space Flight (LDSF) pre-flight and in-flight training programs, acknowledging existing knowledge gaps in NASA\u27s methodologies. The research\u27s objective is to optimize the cognitive workload of LDSF crew members, enhance their neurocognitive functionality, and provide more meaningful work experiences, particularly for Mars missions.The study addresses identified shortcomings in current training and learning strategies and simulation-based training systems, focusing on areas requiring quantitative measures for astronaut proficiency and training effectiveness assessment. The project centers on understanding cognitive decay and memory loss under LDSF-related stressors, seeking to establish when such cognitive decline exceeds acceptable performance levels throughout mission phases. The research acknowledges the limitations of creating a near-orbit environment due to resource constraints and the need to develop engaging tasks for test subjects. Nevertheless, it underscores the potential impact on future space mission training and other high-risk professions. The study further explores astronaut training complexities, the challenges encountered in LDSF missions, and the cognitive processes involved in such demanding environments. The research employs various cognitive and memory testing events, integrating neuroimaging techniques to understand cognition\u27s neural mechanisms and memory. It also explores Rasmussen\u27s S-R-K behaviors and Brain Network Theory’s (BNT) potential for measuring forgetting, cognition, and predicting training needs. The multidisciplinary approach of the study reinforces the importance of integrating insights from cognitive psychology, behavior analysis, and brain connectivity research. Research experiments were conducted at the University of North Dakota\u27s Integrated Lunar Mars Analog Habitat (ILMAH), gathering data from selected subjects via cognitive neuroscience tools and Electroencephalography (EEG) recordings to evaluate neurocognitive performance. The data analysis aimed to assess brain network activations during mentally demanding activities and compare EEG power spectra across various frequencies, latencies, and scalp locations. Despite facing certain challenges, including inadequacies of the current adapter boards leading to analysis failure, the study provides crucial lessons for future research endeavors. It highlights the need for swift adaptation, continual process refinement, and innovative solutions, like the redesign of adapter boards for high radio frequency noise environments, for the collection of high-quality EEG data. In conclusion, while the research did not reveal statistically significant differences between the experimental and control groups, it furnished valuable insights and underscored the need to optimize astronaut performance, well-being, and mission success. The study contributes to the ongoing evolution of training methodologies, with implications for future space exploration endeavors

    Decoding the Real World: Tackling Virtual Ethnographic Challenges through Data-Driven Methods

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    Switchable wideband receiver frontend for 5G and satellite applications

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    Modern day communication architectures provides the requirement for interconnected devices offering very high data rate (more than 10 Gbps), low latency, and support for multiple service integration across existing communication generations with wideband spectrum coverage. An integrated satellite and 5G architecture switchable receiver frontend is presented in this thesis, consisting of a single pole double throw (SPDT) switch and two low noise amplifiers (LNAs) spanning X-band and K/Ka-band frequencies. The independent X-band LNA (8-12 GHz) has a gain of 38 dB at a centre design frequency of 9.8 GHz, while the K/Ka-band (23-28 GHz) has a gain of 29 GHz at a centre design frequency of 25.4 GHz. Both LNAs are a three-stage cascaded design with separated gate and drain lines for each transistor stage. The broadband high isolation single pole double throw (SPDT) switch based on a 0.15 μm gate length Indium Gallium Arsenide (InGaAs) pseudomorphic high electron transistor (pHEMT) is designed to operate at the frequency range of DC-50 GHz with less than 3 dB insertion loss and more than 40 dB isolation. The switch is designed to improve the overall stability of the system and the gain. A gain of about 25 dB is achieved at 9.8 GHz when the X-band arm is turned on and the K/Ka-band is turned off. A gain of about 23 dB is achieved at 25.4 GHz when the K/Ka-band arm is turned on and the X-band arm is off. This presented switchable receiver frontend is suitable for radar applications, 5G mobile applications, and future broadband receivers in the millimetre wave frequency range

    Designing data-aided demand-driven user-centric architecture for 6G and beyond networks

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    Despite advancements in capacity-enhancing technologies like massive MIMO (multiple input, multiple output) and intelligent reflective surfaces, network densification remains crucial for significant capacity gains in future networks such as 6G. However, network densification increases interference and power consumption. Traditional cellular architectures struggle to minimize these without compromising service quality or capacity, which necessitates a shift to a user-centric radio access network (UC-RAN). The UC-RAN approach offers additional degrees of freedom to ease the spectral-energy efficiency interlock while improving the service quality. However, its increased degrees of freedom make its optimal design and operation more challenging. This dissertation introduces four novel approaches for UC-RAN optimal design and operation. The objectives include mitigating interference, reducing power consumption, ensuring diverse user/vertical service quality, facilitating proactive network operation, risk-aware optimization, adopting an open radio access network, and enabling universal coverage. First, we construct an analytical framework to assess the effects of incorporating Coordinated Multipoint (CoMP) technology into UC-RAN to reduce interference and power consumption. We use stochastic geometry tools to derive expressions for network-wide coverage, spectral efficiency, and energy efficiency as a function of UC-RAN Configuration and Optimization Parameters (COPs), including data base station densities and user-centric service zone sizes. While the analytical framework provides insightful performance analysis that can guide overall system design, it cannot fully capture the dynamics of a UC-RAN system to enable optimal operation. Next, we present a Deep Reinforcement Learning (DRL) based method to dynamically orchestrate the UC-RAN service zone size to satisfy varying application demands of various service verticals during its operation. We define a novel multi-objective optimization problem that fairly optimizes otherwise conflicting key performance indicators (KPIs). DRL's practical adaptation by the industry remains thwarted by the risk it poses to the safe operation of a live network. To address this challenge, we propose a digital twin-enabled approach to enrich the DRL-based optimization framework, ensuring risk-aware COP optimization. We use Open Radio Access Network standards-based simulations to show that the proposed risk-aware DRL framework can maximize system-level KPIs while maintaining safe operational requirements. Lastly, we propose a hybrid model of aerial and terrestrial UC-RAN deployment to ensure universal coverage. We assess the impact of aerial base station parameters on system-level KPIs, providing a quantitative analysis of the advantages of a hybrid over a solely terrestrial UC-RAN. We develop a robust multi-objective function solvable via our DRL-based framework to balance and optimize these KPIs in a hybrid UC-RAN. Our extensive analytical and system-level simulation results suggest that these contributions can foster the much-needed paradigm shift towards demand-driven, elastic, and user-centric architecture in emerging and future cellular networks
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