Master of Science / The 6 GHz spectrum band (5.925 GHz – 7.125 GHz) is an important frequency range for many industries due to its high bandwidth capabilities, low latency, and ability to support high data transfer rates. Different types of services, both fixed and mobile, are utilizing the 6 GHz frequency band at present. The incumbents of this band comprise governmental and commercial entities that depend on the 6 GHz spectrum for services like transportation and public safety. The 6 GHz spectrum has also been identified for use by various wireless communication systems, including Wi-Fi, Bluetooth, and 5G. Incumbent licensed operators need to be able to access the spectrum without significant interference to operate effectively. As more wireless communication systems are developed and deployed, the demand for spectrum continues to grow. There is a need for spectrum sharing due to the scarcity of coverage-friendly low band spectrum. Indeed, 6G is expected to use spectrum sharing to a much larger extent compared to previous generations of wireless systems. This thesis provides extensive experimentation results using a commercial FML system that can be used to understand resiliency of FML receivers to interference at 6 GHz.

Classification problems with multiple classes and imbalanced sample sizes present a new challenge than the binary classification problems. Methods have been proposed to handle imbalanced learning, however most of them are specifically designed for binary classification problems. Multi-class imbalance imposes additional challenges when applied to time series classification problems, such as weather classification. In this thesis, we introduce, apply and evaluate a new algorithm for handling multi-class imbalanced problems involving time series data. Our proposed algorithm is designed to handle both multi-class imbalance and time series classification problems and is inspired by the Imbalanced Fuzzy-Rough Ordered Weighted Average Nearest Neighbor Classification algorithm. The feasibility of our proposed algorithm is studied through an empirical evaluation performed on a telecom use-case at Ericsson, Sweden where data from commercial microwave links is used for weather classification. Our proposed algorithm is compared to the currently used model at Ericsson which is a one-dimensional convolutional neural network, as well as three other deep learning models. The empirical evaluation indicates that the performance of our proposed algorithm for weather classification is comparable to that of the current solution. Our proposed algorithm and the current solution are the two best performing models of the study.

International Telemetering Conference Proceedings / October 20-23, 2003 / Riviera Hotel and Convention Center, Las Vegas, Nevada / Unmanned Telemetry Ground System (UTGS) was implemented in Defense System Test Center (DSTC), Agency for Defense Development (ADD). The components of UTGS are Antenna, NPS (Network Power Switch), RCB (Receiver/Combiner/Bit synchronizer) and microwave link. We have installed RCB which is composed of receiver, combiner and bit synchronizer. RCB can be controlled and monitored by RS232 serial communication and microwave network. NPS controls its power supplies. UTGS sends PCM stream to local site using E1-class HDSL and microwave link. This system is possible the signal acquisition and reduction of man power at remote site. The usability and performance of UTGS was proved in flight tests. This paper describes the hardware, software design and an implementation of UTGS.

This work is dealing with the design of intermediate part of transmitter and reciever that use digital BPSK and QPSK modulations for microwave link that works in 24 – 26 GHz bands. Besides choosing the suitable modulator and demodulator there was need to provide proper connection of this part with transceiver Nortel CTR26-01M. Input and output of this intermediate part was take out to baseband, where was need to ensure the transfer of diferential inputs of modulator and demodulator to symetric leading for reason of data communication with PC, for which was created the algorithms to provide transmitting and receiving data. Complete structure controlled by the microcontroller has been enliven and furthemore there was created the measurement of chosen parameteres.

Prevailing propagation phenomena in certain areas play a vital role in deciding terrestrial wireless systems performance. Vertical refractivity profile below 1 km is a critical parameter for designing reliable systems; noting that there is a shortage of upper-air data worldwide. Anomalous phenomena may cause severe signal fading and interference beyond the horizon. The objectives of this thesis are to investigate dominant refractive conditions in the subtropical Arabian Gulf region, develop new approaches and empirical models for evaluating vertical refractivity profiles and relevant propagation parameters in the low troposphere, and to examine the impact of frequently experienced anomalous phenomena on terrestrial microwave links. Twenty-three years of meteorological measurements, from 1990 to 2013, are utilized using spatially separated surface stations and a single radiosonde in the United Arab Emirates (UAE). Profiles of sea level, surface and upper refractivity components are statistically analysed. Three major atmospheric layers; namely 65 m, 100 m and 1 km above the ground are studied to analyse relevant propagation parameters such as sub-refraction, super-refraction, anomalous propagation probability parameter β0 and point refractivity gradient not exceeded for 1% of time. The effective earth radius factor k is investigated using a new weighted averaged approach. In addition, the seasonal structure of atmospheric ducting is dimensioned within 350 m layer above ground. Finally, microwave measurement campaign is conducted using multiple radio links operating in UAE using various frequency bands. The link budget simulations are compared with the signal strength measurements. Fading scenarios are studied against the observed anomalous conditions and several recommendations are concluded.

Artificial intelligence is receiving a great deal of attention in various fields of science and engineering due to its promising applications. In today’s society, weather classification models with high accuracy are of utmost importance. An alternative to using conventional weather radars is to use measured attenuation data in microwave links as the input to deep learning-based weather classification models. Detecting anomalies in the measured attenuation data is of great importance as the output of a classification model cannot be trusted if the input to the classification model contains anomalies. Designing an accurate classification model poses some challenges due to the absence of predefined features to discriminate among the various weather conditions, and due to specific domain requirements in terms of execution time and detection sensitivity. In this thesis we investigate the relationship between anomalies in signal attenuation data, which is the input to a weather classification model, and the model’s misclassifications. To this end, we propose and evaluate two deep learning models based on long short-term memory networks (LSTM) and convolutional neural networks (CNN) for anomaly detection in a weather classification problem. We evaluate the feasibility and possible generalizations of the proposed methodology in an industrial case study at Ericsson AB, Sweden. The results show that both proposed methods can detect anomalies that correlate with misclassifications made by the weather classifier. Although the LSTM performed better than the CNN with regards to top performance on one link and average performance across all 5 tested links, the CNN performance is shown to be more consistent. / Artificiell intelligens har fått mycket uppmärksamhet inom olika teknik- och vetenskapsområden på grund av dess många lovande tillämpningar. I dagens samhälle är väderklassificeringsmodeller med hög noggrannhet av yttersta vikt. Ett alternativ till att använda konventionell väderradar är att använda uppmätta dämpningsdata i mikrovågslänkar som indata till djupinlärningsbaserade väderklassificeringsmodeller. Detektering av avvikelser i uppmätta dämpningsdata är av stor betydelse eftersom en klassificeringsmodells pålitlighet minskar om träningsdatat innehåller avvikelser. Att utforma en noggrann klassificeringsmodell är svårt på grund av bristen på fördefinierade kännetecken för olika typer av väderförhållanden, och på grund av de specifika domänkrav som ofta ställs när det gäller exekveringstid och detekteringskänslighet. I det här examensarbetet undersöker vi förhållandet mellan avvikelser i uppmätta dämpningsdata från mikrovågslänkar, och felklassificeringar gjorda av en väderklassificeringsmodell. För detta ändamål utvärderar vi avvikelsedetektering inom ramen för väderklassificering med hjälp av två djupinlärningsmodeller, baserade på long short-term memory-nätverk (LSTM) och faltningsnätverk (CNN). Vi utvärderar genomförbarhet och generaliserbarhet av den föreslagna metodiken i en industriell fallstudie hos Ericsson AB. Resultaten visar att båda föreslagna metoder kan upptäcka avvikelser som korrelerar med felklassificeringar gjorda av väderklassificeringsmodellen. LSTM-modellen presterade bättre än CNN-modellen både med hänsyn till toppprestanda på en länk och med hänsyn till genomsnittlig prestanda över alla 5 testade länkar, men CNNmodellens prestanda var mer konsistent.

Prevailing propagation phenomena in certain areas play a vital role in deciding terrestrial wireless systems performance. Vertical refractivity profile below 1 km is a critical parameter for designing reliable systems; noting that there is a shortage of upper-air data worldwide. Anomalous phenomena may cause severe signal fading and interference beyond the horizon. The objectives of this thesis are to investigate dominant refractive conditions in the subtropical Arabian Gulf region, develop new approaches and empirical models for evaluating vertical refractivity profiles and relevant propagation parameters in the low troposphere, and to examine the impact of frequently experienced anomalous phenomena on terrestrial microwave links. Twenty-three years of meteorological measurements, from 1990 to 2013, are utilized using spatially separated surface stations and a single radiosonde in the United Arab Emirates (UAE). Profiles of sea level, surface and upper refractivity components are statistically analysed. Three major atmospheric layers; namely 65 m, 100 m and 1 km above the ground are studied to analyse relevant propagation parameters such as sub-refraction, super-refraction, anomalous propagation probability parameter β0 and point refractivity gradient not exceeded for 1% of time. The effective earth radius factor k is investigated using a new weighted averaged approach. In addition, the seasonal structure of atmospheric ducting is dimensioned within 350 m layer above ground. Finally, microwave measurement campaign is conducted using multiple radio links operating in UAE using various frequency bands. The link budget simulations are compared with the signal strength measurements. Fading scenarios are studied against the observed anomalous conditions and several recommendations are concluded.