<p>How LoRa/LoRaWAN performance evaluation in various environmental scenarios has been an active research topic for researchers, and there are many existing works carried out in outdoor scenarios. On top of that, it is necessary to study how LoRa/LoRaWAN performs in indoor environments as one of the fast-growing IoT network mechanisms. However, few studies are found to work on LoRa and LoRaWAN performance evaluation in indoor scenarios. This study focuses on a real-world experiment to understand how LoRa radio signals behave according to its physical layer parameter settings.</p><p>Data is collected through real-world experiments in a campus environment. The experiments for data sample collection were conducted in September 2021 in the Purdue Campus area in West Lafayette, Indiana, United States. LoRa transceivers with the SX1276 module are deployed operating in the 915MHz frequency band on both LoRa RX and TX end nodes in this study. The data transmitted between LoRa transmitter and LoRa receiver is packet-sized (17 bytes) messages. </p><p>For data collection, LoRa module is configured with 36 PHY parameter settings – three spreading factors (7, 9, 11), three signal bandwidths (125kHz, 250kHz, 500kHz), and four coding rates (4/5, 4/6, 4/7, 4/8). Test devices are the Dragino LoRa shields equipped with SX1276 radio modules in 915MHz frequency bands. The experiment is conducted at three different distances – 10m, 20m, and 40m – between LoRa TX node and LoRa RX node in indoor office buildings in Purdue University West Lafayette Campus, US.</p> <p>The RSSI and SNR are measured to characterize the link performance of Lora. The Received Signal Strength Indication (RSSI) and Signal-to-Noise Ratio (SNR) are two Physical level indicators available on wireless radio chips. In addition to them, the LoRa communication reliability is calculated based on the Received Packet Ratio (RPR) out of transmitted packets with different PHY settings at each distance.</p>

As wireless communications continue to permeate many aspects of human life and technology, future generations of communication networks are expected to become increasingly heterogeneous due to an explosion of the number of different types of user devices, a diverse set of available air interfaces, and a large variety of choices for the architecture of the network core.<br>This heterogeneity, coupled with increasingly strict demands on the communication rate, latency, and fidelity demanded by a growing list of services delivered using wireless technologies, requires optimizations across the entire networking stack.<br>Our contribution to this effort considers three key aspects of modern communication systems:<br>First, we present a set of new techniques for multiple-input, multi-output beam alignment specifically suited for unfavorable signal-to-noise ratio regimes like the ones encountered in beamformed millimeter-wave wireless communication links.<br>Second, we present a computationally efficient estimation algorithm for a specific class of aeronautical channels, which applies to systems designed to extend wireless coverage and communication capacity using unmanned aerial vehicles.<br>Third, we present a new class of multi-hop relaying schemes designed to minimize communication latency with applications in the emerging domain of ultra-reliable and low-latency communications.<br>Each of the three problem areas covered in this work is motivated by the demands of a future generation of wireless communication networks and we develop theoretical and/or numerical results outperforming the state of the art.

<div> Interference management is necessary to meet the growth in demand for wireless data services. The problem was studied in previous work by assuming a fixed channel connectivity model, while network topologies tend to change frequently in practice. </div><div><br></div><div>The associations between cell edge mobile terminals and base stations in a wireless interference network that is backed by cooperative communication schemes is investigated and association decisions are identified that are information-theoretically optimal when taking the uplink-downlink average. Then, linear wireless networks are evaluated from a statistical point of view, where the associations between base stations and mobile terminals are fixed and channel fluctuations exist due to shadow fading. Moreover, the considered fading model is formed by having links in the wireless network, each subject independently to erasure with a known probability. </div><div><br></div><div>Throughout the information theoretic analysis, it is assumed that the network topology is known to the cooperating transmitting nodes. This assumption may not hold in practical wireless networks, particularly Ad-Hoc ones, where decentralized mobile nodes form a temporary network. Further, communication in many next generation networks, including cellular, is envisioned to take place over different wireless technologies, similar to the co-existence of Bluetooth, ZigBee, and WiFi in the 2.4 GHz ISM-Band. The competition of these wireless technologies for scarce spectrum resources confines their coexistence. It is hence elementary for collaborative interference management strategies to identify the channel type and index of a wireless signal, that is received, to promote intelligent use of available frequency bands. It is shown that deep learning based approaches can be used to identify interference between the wireless technologies of the 2.4 GHz ISM-Band effectively, which is compulsory for identifying the channel topology. The value of using deep neural network architectures such as CNN, CLDNN, LSTM, ResNet and DenseNet for this problem of Wireless Channel Identification is investigated. Here, the major focus is on minimizing the time, that takes for training, and keeping a high classification accuracy of the different network architectures through band and training SNR selection, Principal Component Analysis (PCA) and different sub-Nyquist sampling techniques. </div><div>Finally, a number theoretic approach for fast discovery of the network topology is proposed. More precisely, partial results on the simulation of the message passing model are utilized to present a model for discovering the network topology. Specifically, the minimum number of communication rounds needed to discover the network topology is examined. Here, a single-hop network is considered that is restricted to interference-avoidance, i.e., a message is successfully delivered if and only if the transmitting node is the only active transmitter connected to its receiving node. Then, the interference avoidance restriction is relaxed by assuming that receivers can eliminate interference emanating from already discovered transmitters. Finally, it is explored how the network size and the number of interfering transmitters per user adjust the sum of observations.</div><div><br></div>

All wireless devices expose users to some level of electromagnetic radiation during operation. In many countries, exposure levels are strictly regulated to ensure the safety of consumers. Previous research demonstrates that incorporating exposure constraints into transmit signal design leads to substantial capacity gains over traditional power back-off techniques. This is especially vital for millimeter wave systems, which require large array gains to combat high path losses and are more susceptible to a decrease in transmit power. In this work, we present exposure modeling procedures and exposure-aware transmission schemes for millimeter wave systems. We first develop methods to approximate the characteristic matrix of a quadratic model for two exposure measures in the millimeter wave band: incident power density and surface specific absorption rate (SAR). The proposed models can be calculated with a small number of parameters and can be altered to account for changes in the exposure scenario. Software simulations with half-wave dipole antennas corroborate the accuracy of the exposure models in the millimeter wave band. We then exploit the ability of the model to calculate exposure at any point surrounding the device to develop efficient exposure-aware signaling strategies. Finally, we propose a low-complexity perturbation approach to obtain exposure-compliant beamforming vectors. Analytical and numerical results demonstrate that the proposed exposure-aware signaling techniques outperform power reduction approaches.

This thesis establishes the need for a standardized, interoperable, front end interface to support the development of open RAN technologies, and establishes the viability and desirability of the VITA 49 interface standard as the alternative to other interface technologies. The purpose of this work is to propose a testbed platform for the further development for VITA 49 as a standard frontend interface as other current testbeds are not designed not as well suited to the VITA 49 standard or open RAN architecture. The VITA 49 interface standard provides a packetized interface between the front-end and the digital back-end of a split architecture system in a way that enables hardware interoperability between and within vendor supplies. The VITA 49 Radio Transport standard is ideally appropriate for integration into SDRs [12] due to its flexibility and metadata support. The REDHAWK platform is an integrated development environment which is used to develop a radio system that utilizes a remote radio unit to send and receive signals which transmits it using the VITA 49 protocol to the base band unit for processing. It was found that REDHAWK is better than GNURadio for this purpose, and that VRT technology is a much better than the current CPRI Standard as it provides an open standard, that enables a flexible, scalable interface that enables long-term growth.

In recent years, multiple-input multiple-output (MIMO)systems appear to be very promising since they can provide highdata rates in environments with sucient scattering byexploiting the spatial domain. To design a real MIMO wirelesssystem and predict its performance under certain circumstances,it is necessary to have accurate MIMO wireless channel modelsfor dierent scenarios. This thesis presents dierent models forindoor MIMO radio propagation channels based on 5.2 GHz indoorMIMO channel measurements.The recent research on MIMO radio channel modeling isbriey reviewed in this thesis. The models are categorized intonon-physical and physical models. The non-physical modelsprimarily rely on the statistical characteristics of MIMOchannels obtained from the measured data while the physicalmodels describe the MIMO channel (or its distribution) via somephysical parameters. The relationships between dierent modelsare also discussed.For the narrowband case, a non line-of-sight (NLOS)indoor MIMO channel model is presented. The model is based on aKronecker structure of the channel covariance matrix and thefact that the channel is complex Gaussian. It is extended tothe line-of-sight (LOS) scenario by estimating and modeling thedominant component separately.As for the wideband case, two NLOS MIMO channel modelsare proposed. The rst model uses the power delay prole and theKronecker structure of the second order moments of each channeltap to model the wideband MIMO channel while the second modelcombines a simple single-input single-output (SISO) model withthe same Kronecker structure of the second order moments.Monte-Carlo simulations are used to generate indoor MIMOchannel realizations according to the above models. The resultsare compared with the measured data and good agreement has beenobserved. / <p>NR 20140805</p>

<div>Millimeter-wave (mmWave) bands have become the most promising candidate for enlarging the usable radio spectrum in future wireless networks such as 5G. Since frequent and location-specific blockages are expected for mmWaves, the challenge is understanding the propagation characteristics of mmWave signals and accordingly predicting the channel state information. This research direction has garnered great attention worldwide from industry, academia, and government. However, the majority of current research on mmWave communications has focused on urban areas with high population densities, with very few measurement campaigns in suburban and rural environments. These environments are extremely important for future wireless applications in areas including residential welfare, digital agriculture, and transportation. To fill in this research gap, we developed broadband mmWave channel sounding systems and carried out intensive measurement campaigns at 28 GHz, covering clear line-of-sight as well as non-line-of-sight scenarios over buildings and foliage clutters, to fully characterize the mmWave propagation in suburban and rural environments.</div><div><br></div><div>Moreover, the accuracy provided by traditional statistical models is insufficient for next-generation wireless networks with higher-frequency carriers, because they are unable to predict abrupt channel changes caused by site-specific blockages. To overcome this issue, we explored the possibility of utilizing site-specific geographic features such as buildings and trees in improving mmWave propagation models. A new channel modeling methodology highlighting site-specific parameter evaluation based on easily obtainable data sources (e.g., LiDAR) was proposed for accurate, fast, and automated channel state predictions. Accordingly, an overall root mean square error (RMSE) improvement of 11.79 dB was achieved in a one-building blockage scenario and a regional RMSE improvement of over 20 dB was observed in a coniferous forest. This approach also enables channel simulations for large-scale system performance evaluation, demonstrating a powerful and promising approach for planning and tuning future wide-area wireless networks. The simulation results showed that network densification alone is not enough for closing the digital gap, especially with mmWaves because of the impractical number of required towers. They also backed up supplementary solutions including private data relays, e.g., via drones and portable towers.</div>

With the emerging Internet of Things (IoT) paradigm, more than a billion sensing devices will be collecting an unprecedented amount of data. Simultaneously, the field of data analytics is being revolutionized by modern machine learning (ML) techniques that enable sophisticated processing of massive datasets. Many researchers are envisioning a combination of these two technologies to support exciting applications such as environmental monitoring, Industry 4.0, and vehicular communications. However, traditional wireless communication protocols are inefficient in supporting distributed ML services, where data and computations are distributed over wireless networks. This motivates the need for new wireless communication methods. One such method, over-the-air computation (AirComp), promises to communicate with massive gains in terms of energy, latency, and spectrum efficiency compared to traditional methods. The expected efficiency of AirComp is due to the complete spectrum sharing for all participating devices. Unlike in traditional physical-layer communications, where interference is avoided by allocating orthogonal communication channels, AirComp promotes interference to compute a function of the individually transmitted messages. However, AirComp can not reconstruct functions perfectly but introduces errors in the process, which harms the convergence rate and region of optimality of ML algorithms. The main objective of this thesis is to develop methods that reduce these errors and analyze their effects on ML performance. In the first part of this thesis, we consider the general problem of designing wireless methods for ML applications. In particular, we present an extensive survey which divides the field into two broad categories, digital communications and analog over-the-air-computation. Digital communications refers to orthogonal communication schemes that are optimized for ML metrics, such as classification accuracy, privacy, and data-importance, rather than traditional communication metrics such as fairness, data rate, and reliability. Analog over-the-air-computation refers to the AirComp method and its application to distributed ML, where communication-efficiency, function estimation, and privacy are key concerns. In the second part of this thesis, we focus on the analog over-the-air computation problem. We consider a network setup with multiple devices and a server that can be reached via a single hop, where the wireless channel is modeled as a multiple-access channel with fading and additive noise. Over such a channel, the AirComp function estimate is associated with two types of error: 1) misalignment errors caused by channel fading and 2) noise-induced errors caused by the additive noise. To mitigate these errors, we propose AirComp with retransmissions and develop the optimal power control scheme for such a system. Furthermore, we use optimization theory to derive bounds on the convergence of an AirComp-supported ML system that reveal a relationship between the number of retransmissions and loss of the ML model. Finally, with numerical results we show that retransmissions can significantly improve ML performance, especially for low-SNR scenarios. / Med Internet of Things (IoT)-paradigmen, kommer över en miljard sensorenheter att samla en mängd data som saknar motstycke. Samtidigt har dataanalys revolutionerats av moderna maskininlärningstekniker (ML) som möjliggör avancerad behandling av massiva dataset. Många forskare föreställer sig en kombination av dessa två two teknologier för att möjliggöra spännande applikationer som miljöövervakning, Industri 4.0, och fordonskommunikation. Tyvärr är traditionella kommunikationsprotokoll ineffektiva när det kommer till att stödja distribuerad maskininlärning, där data och beräkningar är utspridda över trådlösa nätverk. Detta motiverar behovet av nya trådlösa kommunikationsprotokoll. Ett protokoll, over-the-air computation (AirComp), lovar att kommunicera med enorma fördelar när det kommer till energieffektivitet, latens, and spektrumeffektivitet jämfört med traditionella protkoll. AirComps effektivitet beror på den fullständiga spektrumdelningen mellan alla medverkande enheter. Till skillnad från traditionell ortogonal kommunikation, där interferens undviks genom att allokera ortogonala radioresurser, så uppmuntrar AirComp interferens och nyttjar den för att räkna ut en funktion av de kommunicerade meddelanderna. Dock kan inte AirComp rekonstruera funktioner perfekt, utan introducerar fel i processen vilket försämrar konvergensen av ML-algoritmer. Det huvudsakliga målet med den här avhandlingen är att utveckla metoder som minskar dessa fel och att analysera de effekter felen har på prestandan av distribuerade ML-algoritmer. I den första delen av avhandlingen behandlar vi det allmänna problemet med att designa trådlösa nätverksprotokoll för att stödja ML. Specifikt så presenterar vi en utförlig kartläggning som delar upp fältet i två kategorier, digital kommunikation och analog AirComp. Digital kommunikation syftar på ortogonala kommunikationsprotokoll som är optimerade för ML-måttstockar, t.ex. klassifikationskapabilitet, integritet, och data-vikt (data-importance), snarare än traditionella kommunikationsmål såsom jämlikhet, datahastighet, och tillförlitlighet. Analog AirComp syftar till AirComps applicering till distribuerad ML, där kommunikationseffektivitet, funktionsestimering, och integritet är viktiga måttstockar. I den andra delen av avhandlingen fokuserar vi på det analoga AirComp-problemet. Vi beaktar ett nätverk med flera enheter och en server som kan nås via en länk, där den trådlösa kanalen modelleras som en multiple-access kanal (MAC) med fädning och additivt brus. Över en sådan kanal så associeras AirComps funktionsestimat med två sorters fel: 1) felinställningsfel orsakade av fädning och 2) brusinducerade fel orsakade av det additiva bruset. För att mildra felen föreslår vi AirComp med återsändning och utvecklar den optimala "power control"-algoritmen för ett sådant system. Dessutom använder vi optimeringsteori för att härleda begränsningar på konvergensen av ett AirCompsystem för distribuerad ML som tydliggör ett förhållande mellan antalet återsändningar och förlustfunktionen för ML-modellen. Slutligen visar vi att återsändningar kan signifikant förbättra ML-prestanda genom numeriska resultat, särskilt när signal-till-brus ration är låg. / <p>QC 20220909</p>

One of the major driving elements behind the explosive boom in wireless revolution is the advances in the field of modulation which plays a fundamental role in any communication system, and especially in cellular radio systems. Hence, the elaborate choice of an efficient modulation scheme is of paramount importance in the design and employment of any communications system. Work presented in this thesis is an investigation (study) of the feasibility of whether multilevel FSK modulation scheme would provide a viable alternative modem that can be employed in TDMA cordless communications systems. In the thesis the design and performance analysis of a non-coherent multi-level modem that offers a great deal of bandwidth efficiency and hardware simplicity is studied in detail. Simulation results demonstrate that 2RC pre-modulation filter pulse shaping with a modulation index of 0.3, and pre-detection filter normalized equivalent noise bandwidth of 1.5 are optimum system parameter values. Results reported in chapter 5 signify that an adjacent channel rejection factor of around 40 dB has been achieved at channel spacing of 1.5 times the symbol rate while the DECT system standards stipulated a much lower rejection limit criterion (25-30dB), implying that CP-QFSK modulation out-performs the conventional GMSK as it causes significantly less ACI, thus it is more spectrally efficient in a multi-channel system. However, measured system performance in terms of BER indicates that this system does not coexist well with other interferers as at delay spreads between 100ns to 200ns, which are commonly encountered in such indoor environment, a severe degradation in system performance apparently caused by multi-path fading has been noticed, and there exists a noise floor of about 40 dB, i.e. high irreducible error rate of less than 5.10-3. Implementing MRC diversity combiner and BCH codec has brought in a good gain. / Higher Education Ministry

Machine learning (ML) has shown its success in many areas such as computer vision, natural language processing, robot control, and gaming. ML also draws significant attention in the wireless communication society. However, applying ML schemes to wireless communication networks is not straightforward, there are several challenges need to addressed: 1). Training data in communication networks, especially in physical and MAC layer, are extremely limited; 2). The high-dynamic wireless environment and fast changing transmission schemes in communication networks make offline training impractical; 3). ML tools are treated as black boxes, which lack of explainability. This dissertation tries to address those challenges by selecting training-efficient neural networks, devising online training frameworks for wireless communication scenarios, and incorporating communication domain knowledge into the algorithm design. Training-efficient ML algorithms are customized for three communication applications: 1). Symbol detection, where real-time online learning-based symbol detection algorithms are designed for MIMO-OFDM and massive MIMO-OFDM systems by utilizing reservoir computing, extreme learning machine, multi-mode reservoir computing, and StructNet; 2) Channel estimation, where residual learning-based offline method is introduced for WiFi-OFDM systems, and a StructNet-based online method is devised for MIMO-OFDM systems; 3) Radio resource management, where reinforcement learning-based schemes are designed for dynamic spectrum access, as well as ORAN intelligent network slicing management. All algorithms introduced in this dissertation have demonstrated outstanding performance in their application scenarios, which paves the path for adopting ML-based solutions in practical wireless networks. / Doctor of Philosophy / Machine learning (ML), which is a branch of computer science that trains machine how to learn a solution from data, has shown its success in many areas such as computer vision, natural language processing, robot control, and gaming. ML also draws significant attention in the wireless communication society. However, applying ML schemes to wireless communication networks is not straightforward, there are several challenges need to addressed: 1). Training issue: unlike areas such as computer vision where large amount of training data are available, the training data in communication systems are limited; 2). Uncertainty in generalization: ML usually requires offline training, where the ML models are trained by artificially generated offline data, with the assumption that offline training data have the same statistical property as the online testing one. However, when they are statistically different, the testing performance can not be guaranteed; 3). Lack of explainability, usually ML tools are treated as black boxes, whose behaviors can hardly be explained in an analytical way. When designed for wireless networks, it is desirable for ML to have similar levels of explainability as conventional methods. This dissertation tries to address those challenges by selecting training-efficient neural networks, devising online training frameworks for wireless communication scenarios, and incorporating communication domain knowledge into the algorithm design. Training-efficient ML algorithms are customized for three communication applications: 1). Symbol detection, which is a critical step of wireless communication receiver processing, it aims to recover the transmitted signals from the corruption of undesired wireless channel effects and hardware impairments; 2) Channel estimation, where transmitter transmits a special type of symbol called pilot whose value and position are known for the receiver, receiver estimates the underlying wireless channel by comparing the received symbols with the known pilots information; 3) Radio resource management, which allocates wireless resources such bandwidth and time slots to different users. All algorithms introduced in this dissertation have demonstrated outstanding performance in their application scenarios, which paves the path for adopting ML-based solutions in practical wireless networks.