Troubleshooting radio devices that power modern mobile networks is currently a manual and labour-intensive process, where speed is crucial to minimize network downtime. Therefore, there is a strong interest in building a retrieval system capable of intelligent search and retrieval in radio logs. To facilitate effective retrieval, several retrievers were evaluated using different configurations. First, a RoBERTa language model was further pretrained on a dataset of unannotated radio logs. Then, a proprietary annotated retrieval dataset named the Event LogRetrieval (ELR) dataset was collected and utilized along with the MS MARCOretrieval dataset for training and evaluating the retrieval models. The evaluation compared different retrieval paradigms for log retrieval; evaluated the impact of further pretraining the language model on log data; and investigated which con-figuration yielded the best performance. The results of the investigation show that the late interaction retrieval paradigm used by the ColBERT model performs best for log retrieval. The results also showed that while further pretraining the language model on logs did improve the representations of log data, it did not improve the performance of the implemented retriever. The investigation into the retrieval datasets showed that fine-tuning on the small ELR dataset is insufficient and that fine-tuning on the larger MS MARCO dataset yielded better performance. The best performance was seen though when first fine-tuning on MSMARCO and then on ELR.

Lately, models utilizing channel impulse response (CIR) data for training deep neural networks used for positioning in radio networks have shown promise, particularly in simulated indoor environments. Research has extended to real outdoor setups as well. In this study, deep neural networks originally designed for image classification were employed to estimate positions using both real and simulated outdoor CIR data. A ray tracing simulator was utilized to generate a simulated dataset which corresponded to a real-world dataset. Models were trained and tested on both datasets. To facilitate training on simulated data and testing on real data, a generative adversarial network (GAN) was employed.

The thesis concludes that deep neural networks can effectively be used in real outdoor scenarios, but dense data sampling is likely necessary to achieve satisfactory performance across an area. Additionally, it was found that the simulated data used in this study differed significantly from reality, and the employed GAN could not effectively bridge this gap. Consequently, models trained on simulated data performed poorly when tested on real data. However, it was found that deep neural networks significantly outperformed the baseline K-nearest neighbor algorithm when trained and tested on simulated data. However, this was the only case where such a significant advantage for the deep models was observed.

Average consensus is a fundamental concept in distributed computing, where distributed agents exchange messages in order to obtain the average of their ini- tial values without relying on a centralized computing unit. However, achiev- ing average consensus in the presence of communication imperfections, such as quantization and random link or node failures, becomes more challenging. This thesis evaluates various average consensus algorithms regarding their ability to mitigate quantization effects and explores node dropout for reducing communi- cation cost per iteration. It also identifies the conditions required for achieving average consensus in both scenarios. 

The first part of this thesis deals with average consensus with quantized up- dates, comparing algorithms such as quantized gossip, average preserving quan- tized gossip, and CHOCO-GOSSIP. CHOCO-GOSSIP stands out as the most ef- fective algorithm, which shows the importance of pre-compensating the quanti- zation error before transmitting the node values. Among other algorithms, aver- age preserving quantized gossip shows slightly better performance. Additionally, graphs with higher connectivity tend to perform better. 

The second part of this thesis focuses on energy-efficient average consensus with random node dropout. It compares the optimized node dropout proba- bilities with heuristic designs such as the degree-based method and Metropolis- Hastings method. The degree-based method is shown to give good convergence performance despite its simplicity. Furthermore, in irregular graphs, the perfor- mance difference between optimized probabilities and heuristic designs tends to be more pronounced. 

With an ever worsening geopolitical situation, not least due to Russia’s offensive war in Ukraine, the need to detect enemy movement and disrupt their detection capabilities has increased in defensive value. Using real equipment to test different strategies and placements of radar systems is both costly and time consuming, and this thesis therefore investigates methods of simulating large-scale scenarios of electronic warfare in real-time. The proposed methods include using approximations, multithreading, simplified signal representations and fast convolutions. The results show that if high efficiency is required, the developed simulation structure with a simplified signal representation is able to process one million signals every 50 ms which indicates that realistic and large-scale simulations of this kind is possible. If a more realistic approach is required, a representation using sampled signals is proposed which also utilizes the GPU, though results show that the efficiency drops to around 185.1162 s per one million signals.

Integrated sensing and communication (isac) paradigm has increasingly attracted attention due to the benefits on having both functionalities, communication and sensing, operating over the same resources. However, sensing-based services and operations raise critical concerns regarding privacy. Indeed, guaranteeing the privacy of sensing data has emerged as a critical concern. Any potential vulnerability in this regard could open issues with unauthorized access and leakage of sensitive information. Consequently, ensuring robust privacy protections is highly important for the design of isac systems.

This thesis analyses privacy vulnerabilities in a multi-static isac system im- plemented in a cell-free massive multiple-input multiple-output (mimo) infras- tructure. Particularly, isac systems need balancing the often competing demands of sensing, communication, and privacy considerations. Specifically, the thesis examines the capability of an internal adversary of infering target location infor- mation from received signals, a scenario with critical security implications. To mitigate such risks, this study analyzes the conditions and scenarios where this risk has the greatest impact. Thus, this study aims at providing insights for future privacy-preserving design of isac.

Rapid advancements in modern AI applications have placed unprecedented demands on large-scale connectivity and data aggregation. The vision of Internet-of-Things (IoT) is supported by a massive amount of distributed sensors and wireless devices that generate useful data for these applications. This trend of ever-increasing data traffic creates high pressure on current wireless network capacity. As a response, over-the-air (OtA) computation has emerged as a method of wireless data aggregation with more efficient usage of available wireless resources. OtA computation takes advantage of the superposition property of multiple-access channels to perform both communication and computation simultaneously. Particularly, it has attracted a lot of attention in distributed learning for aggregating local model updates at a central parameter server. In this thesis, the main topic is one such distributed learning scheme, Federated Learning (FL). The integration of OtA computation with FL has been studied extensively, but not from a fault tolerance perspective.

Byzantine fault tolerance relates to the ability of a distributed system to operate despite some of its components exhibiting unexpected behaviours. There have been many suggested methods for robust aggregation in FL that deal with such byzantine users. However, they generally assume an orthogonal multiple-access scheme based on digital communication design. In this work, we propose and evaluate a hierarchical communication architecture with clustered aggregation and mixed digital and OtA transmission schemes.The motivation behind the design is to leverage the benefits of both OtA computation in terms of resource efficiency, and digital transmission enabling the use of robust aggregation methods that increase system resilience.

We analyze how the proposed architecture compares to the cases with pure digital or OtA transmission, and focus on the tradeoff between their advantages and disadvantages when varying the number of clusters K. We implement Krum for robust aggregation, which places a lower bound of 3 + f on K based on the number of byzantine users f in the system. Increasing K improves the signal-to-noise ratio due to a reduction in communication distance, but this gain diminishes greatly when K increases. These observations depend on the applied clustering algorithm, which is K-means in this thesis. The diminishing improvement in signal-to-noise ratio as K increases places a soft upper bound on K that should be enforced unless the lower bound exceeds it. We conclude that to achieve efficient and resilient data aggregation in OtA FL, the number of clusters needs to be carefully designed. The optimal number of clusters will depend on the channel quality, the number of byzantine users, and the amount of available wireless resources.

The rapid progress with machine learning (ML) technology has solved previously unsolved problems, but training these ML models requires ever larger datasets and increasing amounts of computational resources. One potential solution is to enable parallelization of the computations and allow local processing of training data in distributed nodes, such as Federated Learning (FL). 

For the implementation of FL over wireless networks, the limitations of communication resources pose constraints on how many information bits can be reliably transmitted, which further affects the accuracy of the aggregated model. Over-the-Air (OtA) computation has recently appeared as an alternative solution for aggregating data over distributed nodes, by exploiting the superposition property of wireless channels. This thesis explores how the linearity of OtA computation and compressed sensing (CS) can be used in combination for update aggregation in an FL system. Particularly, the thesis uses CS with iterative hard thresholding (IHT) as the reconstruction algorithm, and the transmission of a compressed update vector from each node is subject to some total power constraints. 

Simulation results show that for an OtA FL system with total power constraints, when all nodes have the same (and known) sparsity pattern, applying CS does not bring any obvious benefits as compared to the case of transmitting directly uncompressed sparsified update vectors. However, this conclusion does not rule out the possibility that CS can be an efficient method for OtA FL under different scenarios, e.g., when the sparsity patterns are unknown.  

Sound source separation is a popular and active research area, especially with modern machine learning techniques. In this thesis, the focus is on single-channel separation of two speakers into individual streams, and specifically considering the case where two speakers are also accompanied by background noise. There are different methods to separate speakers and in this thesis three different methods are evaluated: the Conv-TasNet, the DPTNet, and the FaSNetTAC. 

The methods were used to train models to perform the sound source separation. These models were evaluated and validated through three experiments. Firstly, previous results for the chosen separation methods were reproduced. Secondly, appropriate models applicable for NFC's datasets and applications were created, to fulfill the aim of this thesis. Lastly, all models were evaluated on an independent dataset, similar to datasets from NFC. The results were evaluated using the metrics SI-SNRi and SDRi. This thesis provides recommended models and methods suitable for NFC applications, especially concluding that the Conv-TasNet and the DPTNet are reasonable choices.

According to the United Nations, improvised explosive devices (ieds) kill thousandsof people every year and disrupt military work. Therefore, it is of interest to in-vestigate how an ied can be neutralized safely. With an autonomous uav, it ispossible to jam a radio controlled ied (rcied) if the uav can land close enough,making the rcied unable to be triggered remotely. If the radio connected to thedevice uses mixers and is poorly shielded, it is possible to detect and localize thercied with unintentional stimulated emission (sue). It is possible to use an uav,with a radio transmitter and radio receiver, to do so as long as the uav can trans-mit a stimulating signal with enough power. Drawing this power from the uav’sonboard battery decreases the uavs maximal flight time. Simulations suggestthat it is possible to localize a radio using a uav with an error of 2.5 meters.

The ever-expanding volume of data generated by network devices such as smartphones, personal computers, and sensors has significantly contributed to the remarkable advancements in artificial intelligence (AI) and machine learning (ML) algorithms. However, effectively processing and learning from this extensive data usually requires substantial computational capabilities centralized in a server. Moreover, concerns regarding data privacy arise when collecting training data from distributed network devices. To address these challenges, collaborative ML with decentralized data has emerged as a promising solution for large-scale machine learning across distributed devices, driven by the parallel computing and learning trends. Collaborative and distributed ML can be broadly classified into two types: server-based and fully decentralized, based on whether the model aggregation is coordinated by a parameter server or performed in a decentralized manner through peer-to-peer communication. In cases where communication between devices occurs over wireless links, which are inherently imperfect, unreliable, and resource-constrained, how can we design communication protocols to achieve the best learning performance?

This thesis investigates decentralized learning using decentralized stochastic gradient descent, an established algorithm for decentralized ML, in a novel setting with imperfect and constrained communication. "Imperfect" implies that communication can fail and "constrained" implies that communication resources are limited. The communication across a link between two devices is modeled as a binary event with either success or failure, depending on if multiple neighbouring devices are transmitting information. To compensate for communication failures, every communication round can have multiple communication slots, which are limited and must be carefully allocated over the learning process. The quality of communication is quantified by introducing normalized throughput, describing the ratio of successful links in a communication round. To decide when devices should broadcast, both random and deterministic medium access policies have been developed with the goal of maximizing throughput, which has shown very efficient learning performance. Finally, two schemes for allocating communication slots over communication rounds have been defined and simulated: Delayed-Allocation and the Periodic-Allocation schemes, showing that it is better to allocate slots late rather than early, and neither too frequently nor infrequently which can depend on several factors and requires further study

In the field of wireless communication systems, the interest in machine learning has increased in recent years. Adversarial machine learning includes attack and defense methods on machine learning components. It is a topic that has been thoroughly studied in computer vision and natural language processing but not to the same extent in wireless communication. In this thesis, a Fast Gradient Sign Method (FGSM) attack on a neural receiver is studied. Furthermore, the thesis investigates whether it is possible to make a neural receiver robust against these attacks. The study is made using the python library Sionna, a library used for research on for example 5G, 6G and machine learning in wireless communication. The effect of a FGSM attack is evaluated and mitigated with different models within adversarial training. The training data of the models is either augmented with adversarial samples, or original samples are replaced with adversarial ones. Furthermore, the power distribution and range of the adversarial samples included in the training are varied. The thesis concludes that a FGSM attack decreases the performance of a neural receiver and needs less power than a barrage jamming attack to achieve the same performance loss. A neural receiver can be made more robust against a FGSM attack when the training data of the model is augmented with adversarial samples. The samples are concentrated on a specific attack power range and the power of the adversarial samples is normally distributed. A neural receiver is also proven to be more robust against a barrage jamming attack than conventional methods without defenses. 

Knowledge about forest measurements is essential for efficient and sustainableforestry. One important measurement is wood volume, both from an economicand an environmental perspective. Therefore it is crucial to measure wood vol-ume accurately and reliably. With airborne laser scanners, wood volume can beestimated at a large scale, more time efficiently than traditional, manual measure-ments. By utilising deep neural networks, we present methods to predict woodvolume on point clouds efficiently and accurately. Different network structuresfor point cloud regression are devised using field measurements from wood har-vesters and manual field measurements. To achieve more data, techniques tosplit up areas into smaller subareas and data augmentation methods were imple-mented. Our version of GDANet adapted for regression provided the best resultswith a RMSE of 62.68, MAPE of 24.7%, and relative RMSE of 28.0%. Furthermore,the final model produces more accurate wood volume predictions than more shal-low machine learning methods and predictions from Skoglig Grunddata.

Global Navigation Satellite Systems (GNSS) are vital tools for accurate navigation and timing for both civil and military use. Due to the low power of the GNSS signals, these systems are sensitive to interference attacks. For wideband GNSS jamming, adaptive antenna arrays are commonly used to suppress interference. 

This thesis focuses on how distortions induced by adaptive antenna arrays can affect the performance of a GNSS receiver and how prone different beamforming algorithms are to suffer from such distortions. To investigate this, simulations in software have been performed for static scenarios with two different beamforming algorithms and four different antenna arrays.

The results show that the method for interference suppression that uses constraints in direction and frequency achieves a higher signal-to-interference-plus-noise ratio, more stable acquisition over the hemisphere, and less fluctuating code delay error than the method that only minimizes the power of the output signal.

This thesis examines resource allocation for Federated Learning in wireless networks. In Federated learning a server and a number of users exchange neural network parameters during training. This thesis aims to create a realistic simulation of a Federated Learning process by creating a channel model and using compression when channel capacity is insufficient.

In the thesis we learn that Federated learning can handle high ratios of sparsification compression. We will also investigate how the choice of users and scheduling schemes affect the convergence speed and accuracy of the training process. This thesis will conclude that the choice of scheduling schemes will depend on the distributed data distribution.

Real-time multimedia streaming is an extensively researched topic. The possibility of streaming video over the internet in real time requires smart solutions on many levels at the player and streamer side, as well as along the intermediate network. There are many different methods used to achieve this, but not all of them are suitable for the low latency real-time streaming needed for remote operations of vehicles.

This thesis focuses on the bit-rate control at the streamer side to achieve low latency, meaning how the video quality is changed to adapt to the changes in the network. A literature study was conducted, in order to find what algorithms are currently being used for real-time streaming. It investigated both what control methods are used, as well as what feedback metrics are feed to these controllers. These approaches where then evaluated from a theoretical standpoint for real-time low latency streaming on 4G networks together with the rest of the assumed system.

Using these discovered methods, two new algorithms were created. They were tested against an already existing benchmark controller, both in simulation and on a real network. As the benchmark algorithm proved to already be using all suitable feedback metrics, only small control alterations where done to the existing benchmark algorithm. The goal for the new algorithms was to increase the total throughput of the video stream, without decreasing the robustness and causing a higher latency. 

Simulation and real network tests proved that the new algorithms are unable to provide a higher throughput without increasing the latency. The conclusion is that the benchmark controller is well designed and explicitly configured to work for the goal of low latency video streaming. This being the case with many controllers in the industry, as they are well designed and extensively trimmed for their specific task. 

This master thesis studied methods for tracking and localising a moving target from an autonomous seeker drone. Feasible methods for automatic control of the seeker drone and different antenna configurations were explored as well. Two different tracking filters and two different controllers were tested for this purpose. The algorithm was developed in Python and MATLAB. The evaluation of the filters and controllers was done both theoretically with simulations but also practically with flight tests. Performance and robustness were measured by examining the estimated target position and the smoothness of the seeker path. Both filters performed satisfactorily, the same conclusion could be made for the automatic controllers as well. Regardless of the sufficient results, for future work there are several aspects which can be improved. The communication with the drone’s motors, the noise models and one of the automatic controllers are all examples of areas which canbe improved further. 

Estimating an object’s position can be of great interest in several applications,and there exists many different methods to do so. One approach is with Directionof Arrival (DOA) measurements from receivers to use the triangulation techniqueto estimate one or more transmitter’s position. One algorithm which can find theDOA measurements from several transmitters is the MUltiple SIgnal Classification(MUSIC) algorithm. However, this still leaves a ambiguity problem which givesfalse solutions, so called ghost points, if the number of receivers is not sufficient.In this report solving this problem with the help of deep learning is studied. Thethesis’s main objective is to investigate and study whether it is possible to performpositioning with measurements from the MUSIC-algorithm using deep learningand image processing methods.

A deep neural network is built in TensorFlow and trained and tested using datagenerated from MATLAB. This thesis’s setup consists of two receivers, which areused to locate two transmitters. The network uses two MUSIC spectra from thetwo receivers, and returns a probability distribution of where the transmittersare located. The results are compared with a traditional method and are analysed.The results presented in this thesis show that it is possible to perform positioningusing deep learning methods. However, there is a lot of room for improvementwith accuracy, which can be an important future research direction to explore.

This master thesis aims for Inter-carrier interference (ICI) mitigation in orthogonal frequency division multiplexing (OFDM) system by considering designs of frequency domain cyclic extension(FDCE) and optimal windowing pulse shape. Although OFDM system has been put forward in the 1970s, it has just emerged in 4G. In the early stage, it has been restricted by its high computational complexity. With the discovery that modulation and demodulation process of OFDM can be realized by discrete Fourier transform (DFT) and inverse discrete Fourier transform (IDFT), it is widely used in 4G and 5G-New Ratio (NR). Based on OFDM system, a variety of derivative systems are further proposed and applied. With the development of 5G technology in the mobile communication, the requirement of signal propagation between high-speed mobile user and base station (BS) is higher and higher. With the increase of the moving speed of objects, the frequency shift caused by Doppler eff ect can not be underestimated. ICI caused by Doppler shift is becoming more and more serious. Therefore, how to eliminate the ICI caused by Doppler shift has become an inevitable potential problem.

In this thesis, two eff ective approaches for ICI mitigation have been explored and studied. By adding FDCE and optimal windowing pulse shape, the system performance is analyzed and the system simulation is constructed in MATLAB.

Synchronization of distributed time difference of arrival (TDOA) sensor networks can be performed using reference signals from GPS satellites. This method provides high accuracy, but is vulnerable to jamming, and is not reliable enough to be used in military applications. A solution that does not depend on any signals transmitted from external actors is preferred. One way to achieve this is to use reference signals transmitted from a UAV. A UAV is suitable since only local synchronization for a geographically restricted area is necessary. The local synchronization is achieved by estimating the time-delay between the transmission and reception of a reference signal. The estimated time-delay can be used to detect drifts in the clocks of the TDOA sensors. This thesis analyzes com- mon reference signals, to evaluate which provide high accuracy for time-delay estimation, and what properties of the signals influence the estimation accuracy the most. The simulations show that the time-delay estimation performance can reach the same accuracy as synchronization against GPS for different types of signals. An increased bandwidth is more important than an increased signal length or signal-to-noise ratio to improve the estimation accuracy.

This thesis studies the physical layer (PHY layer) of the IEEE 802.11n wireless local area network (WLAN) standard. The possibility of integrating a receiver designed according to the standard with software-defined radios is investigated. The proposed design was implemented in MATLAB and tested using two softwaredefined radios. One of the radios transmitted IEEE 802.11n signals whilst the other one captured them and sent them to a computer for decoding. In this way, evaluation of the proposed receiver design was done. The tests resulted in successfully decoded WLAN packets, although errors occured regularly due to distortions in the air. The proposed MATLAB design can be developed further, with more features, for future tests and research.

Wireless underwater acoustic (UWA) communications is a developing field with various applications. The underwater acoustic communication channel is very special and its behavior is environment-dependent. The UWA channel is characterized by low available bandwidth, and severe motion-introduced Doppler effect compared to wireless radio communication. Recent literature suggests that machine learning (ML)-based channel estimation and equalization offer benefits over traditional techniques (a decision feedback equalizer), in UWA communications. ML can be advantageous due to the difficultly in designing algorithms for UWA communication, as finding general channel models have proven to be difficult. This study aims to explore if ML-based channel estimation and equalization as a part of a sophisticated physical layer structure can offer improved performance. In the study, supervised ML using a deep neural network and a recurrent neural network will be utilized to improve the bit error rate. A channel simulator with environment-specific input is used to study a wide range of channels. The simulations are utilized to study in which environments ML should be tested. It is shown that in highly time-varying channels, ML outperforms traditional techniques if trained with prior information of the channel. However, utilizing ML without prior information of the channel yielded no improvement of the performance.

Drone usage is increasing, both in recreational use and in the industry. With it comes a number of problems to tackle. Primarily, there are certain areas in which flying drones pose a security threat, e.g., around airports or other no-fly zones. Other problems can appear when there are drones in mobile networks which can cause interference. Such interference comes from the fact that radio transmissions emitted from drones can travel more freely than those from regular UEs (User Equipment) on the ground since there are few obstructions in the air. Additionally, the data traffic sent from drones is often high volume in the form of video streams. The goal of this thesis is to identify so-called "rogue drones" connected to an LTE network. Rogue drones are flying drones that appear to be regular UEs in the network. Drone identification is a binary classification problem where UEs in a network are classified as either a drone or a regular UE and this thesis proposes machine learning methods that can be used to solve it. Classifications are based on radio measurements and statistics reported by UEs in the network. The data for the work in this thesis is gathered through simulations of a heterogenous LTE network in an urban scenario. The primary idea of this thesis is to use a type of cascading classifier, meaning that classifications are made in a series of stages with increasingly complex models where only a subset of examples are passed forward to subsequent stages. The motivation for such a structure is to minimize the computational requirements at the entity making the classifications while still being complex enough to achieve high accuracy. The models explored in this thesis are two-stage cascading classifiers using decision trees and ensemble learning techniques. It is found that close to 60% of the UEs in the dataset can be classified without errors in the first of the two stages. The rest is forwarded to a more complex model which requires more data from the UEs and can achieve up to 98% accuracy. 

I samband med att trådlösa kommunikationssystem blir en allt större del av våraliv och mängden data som skickas fortsätter att stiga, skapas en efterfrågan förökade datatakter. En av teknologierna som används för att skapa högre datatakterär bäraraggregering (carrier aggregation), som möjliggör för trådlösa enheteratt kombinera flertalet uppkopplingar mot det mobila nätverket. Det finns dockbara ett begränsat antal kombinationer definierade, vilket skapar ett behov av attsöka upp den bästa kombinationen i varje givet tillfälle. Detta arbete introducerarmjukvara som organiserar dessa kombinationer i trädstrukturer, vilket förenklarsökning efter optimala kombinationer tillsammans med möjligheten att visualiserade potentiella uppkopplingarna. I arbetet presenteras en föreslagen metodför att skapa dessa träd, tillsammans med uppslag på hur viktiga egenskaperhos kombinationerna kan visualiseras. Olika trädsökningsalgoritmer har ocksåundersökts, och det visas att det inte är nödvändigt att söka igenom hela träd.Istället visar sig giriga algoritmer ha hög prestanda, samtidigt som sökstorlekenkan hållas kraftigt begränsad.

A common trend, in general as well as in the field of combat vehicles, is the rapidly increasing demand for data network capacity and even more in transferred data. To handle this increased demand, different countries with their armed forces and equipment manufacturers evaluate methods to increase the data transmission capacity in combat vehicles. The different types of transmitted data are of different criticality and have different security demands. An easy solution to this is to have separated networks for each type of traffic, but that is quite expensive and uses a lot of hardware. This thesis focuses on a different solution, with a shared network for all types of data transmissions.

This is done by evaluating different types of data networks and add-on protocols and then testing the networks practically with varying transmission rates. In the thesis, all the practical testing is done with data networks according to the Ethernet standard, which is the standard evaluated with a throughput that is high enough for the use case. Ethernet as a standard is not suitable for critical data traffic and therefore add-on protocols for Ethernet to optimize the system for critical data traffic are tested. With these optimizations made, Ethernet can be considered more suitable for critical traffic, but this depends entirely on the system requirements.

In order to achieve Full Duplex (FD) communication, currently studied solutionsfor the commercial sector rely on advanced Self-Interferece Cancellation (SIC)techniques to remove the transmitted signal from the received one. This thesis expandsthe research of these techniques by evaluating their potential usage in militarycommunication scenarios where requirements and conditions are distinct,firstly, by identifying, categorizing and describing a set of previously proposedsic techniques and secondly, by performing a comprehensive simulation andanalysis of two suggested sic techniques as part of a FD transceiver. Though themajority of the considered SIC techniques was determined to be potentially implementablein military FD transceivers, some frequency-dependent techniquesand techniques prohibiting omnidirectional communication could not be used.The simulation and analysis of the two suggested SIC techniques show that undercertain conditions, such as limited transmission power and/or reflective environment,close to complete suppression could be realized even with high nonlineardistortion in the transmit chain.

Radio networks are expanding, becoming more advanced, and pushing the limits of what is possible. Services utilizing the radio networks are also being developed in order to provide new functionality to end-users worldwide. When discussing 5G radio networks, concepts such as driverless vehicles, drones and near zero communication delay are recurrent. However, measures of delay are needed in order to verify that such services can be provided -- and measuring this is an extensive task. Ericsson has developed a platform for simulating a radio environment surrounding a radio base station. Using this simulator, this project involved measuring one-way packet delay in a radio network, and performing a Quality of Service evaluation of a radio network with a number of network applications in concern. Application data corresponding to video streams, or Voice over IP conversations, were simulated and packet delay measurements were used to calculate and evaluate the Quality of Service provided by a radio network. One of the main conclusions of this project was that packet delay variations are asymmetric in uplink, which suggests usage of non-conventional jitter measurement techniques.

In video transmission there is a need to compose a wide-band signal from a numberof narrow-band sub-signals. A flexible solution offers the possibility to place any narrow-band sub-signal anywhere in the wide-band signal, making better use of the frequency space of the wide-band signal. A multi-standard supportive solution will also consider the three standard bandwidths of digital and analog video transmissions, both terrestrial and cable (6; 7 and 8 MHz), in use today.

This thesis work will study the efficiency of a flexible aggregation solution, in terms of computational complexity and error vector magnitude (EVM). The solution uses oversampled complex modulated filter banks and inner channelizers, to reduce the total workload on the system.

Each sub-signal is channelized through an analysis filter bank and together all channelized sub-signals are aggregated through one synthesis filter bank to form the wide-band composite signal. The EVM between transmitted and received sub-signals are investigated for an increasing number of sub-signals.

The solution in this thesis work is performing good for the tested number of up to 100 narrow-band sub-signals. The result indicates that the multi-standard flexible aggregation solution is efficient for an increasing number of transmitted sub-signals.

From the first generation, 1G, to the fourth generation, 4G, the development and technological advancements in telecommunications network systems have been remarkable. Faster and better connections have opened up for new markets, ideas and possibilities, to that extent that there now is a demand that surpasses the supply. Despite all these advancements made in the mobile communications field most of the concept of how the technology works and its infrastructure has remained the same. This however, is about to change with the introduction of the fifth generation (5G) mobile communication.

With the introduction of 5G much of the technology introduced will be different from that of previous generations. This change extends to include the entire infrastructure of the mobile communications system. With these major changes, many of the tools available today for telecommunications network evaluation do not really suffice to include the 5G network standard. For this reason, there is a need to develop a new kind of tool that will be able to include the changes brought by this new network standard.

In this thesis a simulation framework adapted for the next generation telecommunication standard 5G is set to be developed. This framework should include many of the characteristics that set 5G aside from previous generations.

When transmitting data from an aircraft being prepared at the apron (the area in front of the hangar) telemetry data is transmitted to ground personnel. The transmitted data is subject to severe distortion due to multipath propagation created by the surroundings, resulting in erroneous detection. By equalizing the signal using the Constant Modulus Algorithm a significant increase in detection performance has been observed, both in simulations and tests on collected data. The most sufficient parameters were chosen after testing a set of different parameter combinations on simulations with single delays. These parameters were then used to equalize simulated multipath as well as collected data. The results show that short delays with low power can be resolved without any equalizer. Longer delays with relatively low power can be resolved using the proposed equalizer but long delays with high power cannot be resolved by the equalizer at all. The thesis shows that it is worth investigating implementation of the equalizer.

With wireless signals, it is possible to send all kind of data through a communication system. Many companies today having problems implementing wireless communication system because of big areas and many obstacles that blocks the wireless signals. Recent releases of mesh network standards that has the main objective to provide a network over a large area. Toyota Material Handling Europe (TMHE) that develops and produce trucks is one company that wants to use this new network topology to get to the next level of communication.

TMHE is developing a new communication system between their trucks and the goal is to be independent of Wi-Fi and fixed structures in an industry. The aim is to establish communication through Bluetooth and create a mesh network between the trucks at a site. This kind of communication can create many new possibilities for new features, which can make the daily work easier.

The problem with obstacles and interferences is still a problem and TMHE created a thesis to research Bluetooth mesh networks to see if this kind of implementation is suitable for the environments that exists in an industry. This thesis research the architecture of a Bluetooth mesh network and different setups of a network and tests how it stands in an industry with many different surrounding.

This project consists of analyzing and finding solutions to the effect of non-linear distortionon the performance of a Massive Multiple Input Multiple Output (MIMO) system interms of Spectral Efficiency (SE) and Symbol Error Rate (SER). Massive MIMO is one ofthe technologies that are considered the backbone of the 5th generation of wireless communicationsand therefore this technology has gathered much interest from researchersand companies alike [19], as it is proven that this kind of system greatly improves thecapacity of the wireless connection [8]. Since Massive MIMO is still a relatively newtechnology and it is yet to be implemented for commercial use, there are several challengesthat arise when trying to implement such a system. One of these problems arisefrom the fact that the Power Amplifiers (PAs) in the transmitters of Massive MIMO systemsare non-linear and thus impose a distortion on the transmitted signals of the system[12]. The thesis aims to study this non-linear effect on the performance of massive MIMOsystems by first modelling the distortion effect on the transmitted signals using two differentnon-linear models. Moreover, closed-form expressions for one of the models areformed to facilitate the simulation of the non-linear model and facilitate the analysis ofthe distortion effect on the performance metrics. Then the established system model issimulated and based on the results, the effect of each of the power amplifier non-lineardistortion models on the performance metrics of the Massive MIMO system is studied.Furthermore, based on the analysis of the simulation results, a compensation mechanismis introduced to the Massive MIMO system in order to mitigate the distortion effect onthe system performance in terms of SER and SE.

The work is devoted in the development and the exploration of the capabilities of the state of art unlicensed 60GHz (V-Band) mm-wave band that has raised so much interest and attention from numerous companies and laboratories for implementing Multi-gigabit communications [17] and especially for the 5th generation of cellular network and wireless systems. Implementing a high wireless data transfer system requires a high bandwidth and the one around the 60GHz frequency turned out to be a very promising candidate [13]. In this thesis, different protocols were investigated and simulated on MATLAB and implemented on low-cost Field-Programmable Gate Arrays (FPGAs) in order to test its performance with different transmission protocols and systems and insure a robust communication system at the frequency band around 60GHz. Furthermore, the system was tested with a series of different binary sequences such as pseudo-random bi-nary sequences (PRBS-7, PRBS-15, PRBS-23 and PRBS-31) and a high data rate communication link also in the design. The link has been tested in the lab environment and two systems have successfully achieved a relatively low bit-error rate.

A radar works by radiating electromagnetic energy and detecting the reflected signal returned from the target. The nature of the reflected signal provides information about the target’s distance or speed. In this thesis, we will be using a UWB radar and a CW radar to help detect the presence and rough location of trapped survivors by detecting their motions. Range is estimated in the UWB radar using clutter removal with SVD and for the dual frequency CW Radar using STFT and median filtering. The effect of the algorithm parameters on their performance was analyzed. The performance of the implemented algorithms with regards to small motion detection, distance estimation and penetration capability was analyzed. Both systems are certainly capable of human detection and tracking.

The next generation of mobile networks (5G) is currently being standardized, and massive MIMO (Multiple-Input-Multiple-Output) is a strong candidate to be part of this standard. Other than providing higher data rates and lower latency, high accuracy positioning is also required. In this thesis, we evaluate the achievable performance of positioning using massive MIMO systems in open highway scenarios. Relevant theory from sensor array signal processing and Bayesian filtering is presented, and is used in a simulation environment on large antenna arrays representing massive MIMO base stations. Positioning is done by utilizing the uplink pilot reference signals, where the Direction of Arrival (DOA) of the pilot signal is estimated, and then used for position estimation. Estimation of the DOA is done by both a maximum-likelihood method and by using an Extended Kalman Filter (EKF). A positioning error of less than 8 m is achieved with absolute certainty when the vehicle is less than 300 m from the base station. It is also concluded that this result could be improved by using more sophisticated filtering algorithms.

In this thesis a known heuristic for decreasing a node's centrality scores while maintaining influence, called ROAM, is compared to a modified version specifically designed to decrease eigenvector centrality. The performances of these heuristics are also tested against the Shapley values of a cooperative game played over the considered network, where the game is such that influential nodes receive higher Shapley values. The modified heuristic performed at least as good as the original ROAM, and in some instances even better (especially when the terrorist network behind the World Trade Center attacks was considered). Both heuristics increased the influence score for a given targeted node when applied consecutively on the WTC network, and consequently the Shapley values increased as well. Therefore the Shapley value of the game considered in this thesis seems to be well suited for discovering individuals that are assumed to actively trying to evade social network analysis.

The positioning performance with the LVC (Last Visited Cells) report is evaluated and compared with extended reports with signal strength data. The LVC report contains cell identities and time spent in the last cells listened to. This is an off-line data source and the purpose of the positioning is to extract information about users’ whereabouts, which for example can be used to optimize the cellular network or vehicular traffic. The positioning evaluation is done in Matlab with a log-distance model, a fingerprinting algorithm, and a new LVC specific algorithm. A particle filter and a particle smoother is used to process simulated LVC reports and extended reports with different amount of information. The results are compared and evaluated with regard to the positioning accuracy and the information density of the reports.

För att möjliggöra trådlös kontroll av fabriker, till exempel trådlös sändning av data uppmätt av en sensor till ett ställdon som agerar på den emottagna signalen, så måste sannolikheten att ta emot datan korrekt vara väldigt hög och tiden det tar att leverera data från sensorn till ställdonet vara mycket kort. Tidigare har endast kablar klarat av dessa krav men i den femte generationens mobila nätverk är trådlös kontroll av fabriker ett av användningsområdena och arbete pågår för att skapa ett system som klarar av det. Ett av problemen i detta användningsområde är när all data i ett paket inte kan skickas i en sändning och klara av den väldigt höga sannolikheten för mottagning. Denna uppsats studerar detta problem i detalj och föreslår metoder för att hantera problemet samt utvärderar dessa metoder i en simulator. Uppsatsen visar att delning av ett paket i flera segment och sändning av varje segment med en ännu högre sannolikhet för mottagning är en bra kandidat, speciellt när det finns tid för en omsändning. När det endast finns tid för en sändning verkar det bättre att skicka samma paket två gånger. Även om det första paketet inte kan uppnå den höga sannolikheten för mottagning så kan kanske kombinationen av det första och andra paketet göra det.

Narrowband Internet-of-Things (NB-IoT) är en ny trådlös teknik som är designad för att hantera mobilnät med vidsträckt täckning för ett massivt antal mycket billiga och strömsnåla användarenheter. Studier har inletts för att operera NB-IoT i olicensierade frekvensband, varav några kräver att frekvenshoppande spridningsspektrum, med kort uppehållstid per kanal, används. För att en användarenhet ska kunna ansluta till en basstation måste den slutföra synkronisingsfasen inom uppehållstiden, så att basstationens hoppmönster kan avkodas. På grund utav den stora signalförsvagningen, den smala bandbredden och användarenhetens egenskaper är det en stor utmaning att förkorta synkroniseringstiden. Detta examensarbete studerar olika metoder för att förkorta synkroniseringstiden i NB-IoT utan att öka kraven på användarenheten. Arbetet visar att artificiell snabb-fädning kan kombineras med tätare referenssignalering för att uppnå förbättringar i synkroniseringsprocessen som är tillräckliga för att möjliggöra operation av NB-IoT i olicensierade frekvensband.

One option for enabling mobility between 5G nodes is to use a set of area-fixed reference beams in the downlink direction from each node. To save power these reference beams should be turned on only on demand, i.e. only if a mobile needs it. An User Equipment (UE) moving out of a beam's coverage will require a switch from one beam to another, preferably without having to turn on all possible beams to find out which one is the best.

This thesis investigates how to transform the beam selection problem into a format suitable for machine learning and how good such solutions are compared to baseline models. The baseline models considered were beam overlap and average Reference Signal Received Power (RSRP), both building beam-to-beam maps. Emphasis in the thesis was on handovers between nodes and finding the beam with the highest RSRP. Beam-hit-rate and RSRP-difference (selected minus best) were key performance indicators and were compared for different numbers of activated beams.

The problem was modeled as a Multiple Output Regression (MOR) problem and as a Multi-Class Classification (MCC) problem. Both problems are possible to solve with the random forest model, which was the learning model of choice during this work. An Ericsson simulator was used to simulate and collect data from a seven-site scenario with 40 UEs. Primary features available were the current serving beam index and its RSRP. Additional features, like position and distance, were suggested, though many ended up being limited either by the simulated scenario or by the cost of acquiring the feature in a real-world scenario.

Using primary features only, learned models' performance were equal to or worse than the baseline models' performance. Adding distance improved the performance considerably, beating the baseline models, but still leaving room for more improvements.

Kollektiva beslut blir en del av vardagen när grupper av människor står inför val. Vi anpassar ofta våra personliga övertygelser med hänsyn till våra vänner. Vi är naturligt beroende av lyckan hos dem som står oss nära.

I det här exjobbet undersöker vi en befintlig empatimodell som används för att välja en vinnare från en uppsättning alternativ genom att använda poängbaserade omröstningsprocedurer. Vi visar att en liten modifikation av modellen är tillräcklig för att kunna använda överlägsna omröstningsprocedurer som bygger på parvisa jämförelser av alternativen.

Sammanfattningsvis visar vi att det i grunden inte finns någon anledning att använda poängbaserade omröstningsprocedurer i de föreslagna modellerna, eftersom ett mer önskvärt resultat uppnås genom att använda de överlägsna omröstningsprocedurerna.

Screen content coding is used to improve coding efficiency of synthetic contents in videos, such as text and UI elements, as opposed to contents captured using photo-graphic equipment, which most video codecs are optimized for. One way of improving screen content coding efficiency is to utilize mixed block coding with matching pursuit. By separating the prediction and transformation steps for overlay and background elements, better contrast and signal-to-noise ratio can be achieved. The paper describes the implementation of such algorithm within the HEVC reference encoder, and discusses the experimental results on several test images.

By using existing radio network infrastructure, a user can be positioned even where GPS and other positioning technologies lack coverage. The LTE Positioning Protocol (LPP) supports user Reference Signal Time Difference (RSTD) reports based on the Time of Arrival (TOA) for a Positioning Reference Signal (PRS). In the current reporting format, only one RSTD for each base station is considered, but for indoor environments this is easily biased due to fading and multipath issues, resulting in a Non-Line of Sight (NLOS) bias. With a rich User Equipment (UE) feedback that can represent the multipath channel for each Base Station (BS), the positioning accuracy can be increased. This thesis develops and evaluates a UE reporting format representing multiple TDOA candidates, and a probabilistic positioning algorithm, in terms of positioning accuracy and amount of data reported. By modeling time measurements as Gaussian Mixture (GM), the time information can be compressed with arbitrary resolution and used in a Maximum-Likelihood (ML) estimation to find the position. Results were obtained through simulation in a radio network simulator and post-processing of simulation data in Matlab. The results suggest that several TOA candidates improve the positioning accuracy, but that the largest improvement comes from a noise based threshold by increasing LOS detectability reducing the NLOS bias, while suppressing noise. The results also suggest that the accuracy for the method can be further improved by combining multiple time measurement occasions.

Localization is one of the most interesting research areas in wireless networks. It is mostly used for tracking and monitoring applications such as traffic monitoring, search and rescue, navigation and so on. A good quality system can be defined from its accuracy when operating in severe interference environments that contaminate the signals and therefore reduce the system performance. The main issue for localization is channel propagation, e.g., line of sight or non-line of sight channel which should be studied in order to improve the system efficiency.

   In order to perform a localization, most algorithms use two steps: ranging and positioning. For ranging, the two popular techniques that are widely used for distance measurement are received signal strength (RSS) and time of arrival (TOA). RSS ranging technique uses the power of the received signals to identify the distance between a transmitter and a receiver. TOA ranging technique uses time of the signal traveling between a transmitter and a receiver to identify the distance, thus it requires synchronization. The measurements are processed by using a localization algorithm afterwards. However, these techniques suffer from multipath fading and other errors, so there always exists error in the estimated position.

   In this thesis, TOA ranging technique is used for different estimation methods. Simulation results are performed using MATLAB, while the real results are obtained from Pozyx indoor positioning platform. Several estimation algorithms comprising of maximum likelihood (ML), linearized least square (LLS), weighted centroid (WC), and fingerprinting (FP) are studied in detail. The testing area is indoor environment which is suitable for LOS, NLOS and combined situations. The measured data is then used for ranging and localization. We concentrate on comparing and discussing these results in this thesis.

This report presents an implementation of the software part in a software definedradio. The radio is not entirely implemented in software and therefore there arecertain limitations on the received signal. The parts implemented are oscillator,decimation filter, carrier synchronization, time synchronization, package detection,and demodulation. Different algorithms were tested for the different partsto measure the power consumption. To understand how the number of bits usedto represent the signal affects the power consumption, the number of bits wasreduced from 20 bits to 10 bits. This reduction reduced the power consumptionfrom 2.57mW to 1.89mW. A small change in the choice of algorithms was thenmade which reduced the power consumption to 1.86mW. Then the clock rate wasreduced for some parts of the system which reduced the power consumption to1.05mW.

The ability for base stations to be able to beam-form their signals, directing the signal energy to specific users, is a topic of research that has been heavily studied during the last decades. The beam-forming technique aims to increase the signal-to-interference-and-noise-ratio of the user and, consequently, increase the capacity and coverage of the communication system. One such method is the Differential Beam-Forming technique, that has been developed at Ericsson Research. In this version of beam-forming, the beams can be dynamically sharpened and widened when tracking a specific terminal, to try to optimize the signal energy sent to that terminal.

Beam-forming, however, makes the link-adaptation algorithm process substantially harder to perform. The reason for this is that the link-adaptation algorithm now has to take into account not only the changing radio environment, but also the changing transmit signal that is being beam-formed. Fortunately, since the beam-formed signal is known at the point of transmission, there should be a potential to utilize this knowledge to make the link-adaptation more efficient.

This thesis, investigates how the link-adaptation algorithm could be changed to perform better in beam-forming setups, as well as what information from the beam-forming algorithm that could be included and utilized in the link-adaptation algorithm. This is done by designing and investigating three new link-adaptation algorithms, in the context of Differential Beam-Forming in an LTE FDD system. The algorithms that has been designed are both of a beam-forming-aware and beam-forming-unaware character, meaning if the beam-forming information is utilized within the algorithm, or not. These algorithms have been simulated for different base station antenna array-sizes. Unfortunately, due to simulator restrictions, the terminals have been simulated in a stationary environment, which has proven to be a limiting factor for the results. However, the results still show that smarter beam-forming-aware link-adaptation could possibly be used to increase the performance of the link-adaptation when using beam-forming.

 Internet of Things (iot) is a growing field enabled by many different technologies. One of these technologies is  Bluetooth Low Energy (ble). It is of interest to investigate the potential of ble and one interesting, currently unsupported, feature is mesh networking. This thesis work aims to investigate whether it is possible to design and implement a mesh network protocol using ble. To verify the implemented mesh network protocols functionality an indoor localization system has been implemented upon the BLE mesh network protocol. Furthermore this thesis work investigates if an indoor localization system can benefit from using a mesh network. The results of the investigation is a proof of concept of a functional ble mesh network protocol implemented on hardware and tested in a real environment. Tests show that the implemented localization system has similar accuracy as other rssi based indoor localization systems. The largest advantage found for a mesh based indoor localization is the ability to localize objects outside of the radio propagation range of the user. This feature is enabled by multi-hop messaging in the mesh network.

Massive multiple-input-multiple-output (MIMO) is a wireless communication technologythat usesa large number of antennas at the base station and servesmultiple terminals over the same time-frequency resource.This technique can achievehigher data rates than existing communication technology, which only serves one terminal per resource. That is why Massive MIMO is considered a promising candidate for 5G. Orthogonal frequency division multiplexing (OFDM) can be used for transmitting information at different sub-channels. The cyclic prefix (CP) is a repetition of the last samples in a symbol, which is appended at the beginning of the symbol.It serves as a guard interval between consecutive symbols to avoid inter-symbol interference (ISI) and to make sub-channels orthogonal. In this thesis it is proposed to shorten the CP length in Massive MIMO. The shortening of the CP length will increase the effective spectral efficiency but also create additional interference. This trade-off is investigated. A simulation based study is performed to analyse the effective achievable rate of anuplink Massive MIMO system in a single-cell scenario when 10000 Gaussian symbols are transmitted.In the simulation, the number of sub-channels is 128. They are transmitted through a channel with 10 taps. With the classical CP length9samples and a massive MIMO base stationwith 50 antennas serving 3 terminals,the effective achievable rate was 3.863 bits/s/Hz. It was found thatthe effective achievable rate is maximized when the CP length is shortened to 6 samples; the effective achievable rate then became 4.112 bits/s/Hz.In the same system when 100 antennas are used, the corresponding effective achievable rates are 4.791 bits/s/Hz and 4.895 bits/s/Hz with an optimum CP lengthof5 samples. It is shown that the optimum CP length in Massive MIMO is not equal to the number of taps minus one which is the conventional choice. Yes, the CP is needed in Massive MIMO, but it can be shorter than conventionally.

Loudness är ett subjektivt mått på hur högljudd en ljudsignal uppfattas. Till följd av kommersiellt tryck har loudness utnyttjats i sändningar för att locka och nå tittare. Genom signalbehandling är det möjligt att öka loudness-nivån på en ljudsignal och fortfarande uppfylla dagens lagstadgade signalnivåkrav. Med strävan att uppnå en lika medel-loudness-nivå mellan alla program har Europeiska radio- och TV-unionen publicerat en standard som föreslår metoder för att kvantifiera loudness. Denna rapport tillämpar dessa metoder och föreslår en algoritm som adaptivt normaliserar loudness-nivån i audiovisuella sändningar utan att påverka dynamiken inuti program. Huvudtillämpningen för algoritmen är att normalisera ljudsignalen i sändnings- och distributionsutrustning med realtidskrav. Resultaten erhölls från simuleringar i Matlab där kommersiella sändningar användes. Resultaten visade att för vissa typer av sändningar lyckades algoritmen minska variationen i medel-loudness-nivå med smärre påverkan på dynamik inuti program.

The 5th generation mobile telecommunication system (5G) is currently being specified and developed, with large expectations on throughput and efficiency. While 4G and more specifically LTE might constitute a basis of the design of the network, there are some parts that should be improved. One thing to improve is the static signalling that occurs very frequently in a 4G network, of which system information such as synchronization signals, detection of network frequencies, operators, configurations etc. is a part. It has been shown that the static signalling requires both much energy and time-frequency resources. Since the system information is not intended for a single user it is always broadcast so that any user, and any amount of users can read it when needed. 5G will use a technique called massive MIMO, where the base station is equipped with a large number of antennas which can be used to direct signals in space, called beamforming. This thesis presents a new method for distribution of system information that can utilize the beamforming capabilities of massive MIMO. A simple model together with simulated user channel statistics from urban 4G scenarios are used to show that the new method outperforms the classical method of only broadcasting the information, with respect to time-frequency resources. Especially if there are high requirements on the latency of the system information, the new method results in a large gain.