Background. As smart appliances become more attractive, the demand from public consumers grows, and producers are in search of innovative technologies that may aid in the creation of smart homes. Current products may use screens and buttons, voice commands and motion detection to create an interactive experience for consumers. A rather new technology that has gathered attention in recent years is millimetre wave radar sensors (mmWave). This technology uses electromagnetic waves to detect objects in the vicinity of the physical sensor; it may detect both the range, velocity and orientation of an object in relation to itself. The current research has had a main focus in automotive and industrial industries, and the technology has thus far been applied to areas such as vital signs monitoring, people counting, motion control, object detection and collision aversion among others. An attractive feature for use in smart homes that the sensor provides, or rather lacks, is its inability to identify different people. As the information gathered is a point cloud -- in low resolution -- any monitored people retain their privacy under normal circumstances. Objectives. The aim of this thesis is to verify the usability of mmWave sensors in smart homes, as well as reaching an initial understanding of people's opinions regarding the mmWave technology. Method. Experiments are performed to test how well the mmWave sensors can determine if a person is standing, sitting, lying or if they have fallen. The approach for the developed program to make these predictions are done through simple algorithms. Experiments were performed in an environment that was meant to mimic the conditions of a home. Participants were also asked about their opinion of potentially using the technology in their home, both regarding imagined usage and whether the sensor would cause them any discomfort. Results. The results show that while the implemented software in this thesis helps validate the proof of concept for the intended purpose, the technology shows a lot of promise for the future. Further algorithmic efforts will however be required to reach the desired maturity. The opinions of the participant show a generally positive response in using the sensor, however, they also note that if the sensor is to be used in their home, any data gathered should be both available and in control of the consumer to ease suspicions of misuse. Conclusions. The authors conclude that while not yet quite ready, the sensor is indeed a probable candidate to be integrated into smart homes of the future. / Bakgrund. Då smarta apparater blir mer attraktiva växer efterfrågan för dessa produkter. Tillverkare söker därmed efter innovativa teknologier som kan bistå i skapandet av smarta hem. De produkter som finns idag använder sig av skärmar och knappar, röstkommandon och rörelsedetektering för att skapa en interaktiv upplevelse för användarna. En relativt ny tekonologi som har fått uppmärksamhet de senaste åren är radarsensorer med millimetervågor (mmWave). Denna teknologi använder elektromagnetiska vågor för att upptäcka föremål i sin närhet; sensorn kan känna av både avstånd, hastighet och orientering av ett objekt i relation till sig själv. Existerande forskning har framförallt fokuserat på bil- och tillverkningsindustrierna, och teknologin har hittills applicerats på områden som bland annat övervakning av vitala tecken, räkning av människor, rörelsestyrning, detektion av föremål och kollisionsundvikande system. En attraktiv funktionalitet för användande i smarta hem som den här sortens sensor tillhandahåller, eller snarare saknar, är dess oförmåga att identifiera olika människor. Eftersom datan sensorn samlar in består av ett punktmoln -- i låg upplösning -- kommer den under normala förhållanden inte inkräkta på privatliv och integritet hos användarna. Syfte. Målet med detta projekt är att undersöka användbarheten av mmWave-sensorer i smarta hem, samt att komma till en initial insikt om folks åsikter angående mmWave-teknologin. Metod. Experiment har utförts för att verifiera hur väl sensorerna kan avgöra om en person står upp, sitter ner, ligger ner eller har fallit. Mjukvaran som utvecklades för att avgöra vilken handling en person utför tar sig an detta med hjälp av enkla algoritmer. Experimenten utfördes i en miljö som var tänkt att efterlikna förhållandena i ett hem. Deltagarna fick också frågor angående sina åsikter om att potentiellt använda teknologin i sina hem, både vad gäller möjliga användningsområden samt huruvida varandet av sensorn i hemmet skulle orsaka dem något obehag. Resultat. Resultaten visar att även om den skapade mjukvaran är otillförlitlig för det tänkta användandet så visar teknologin lovande tecken för framtiden. Deltagarnas åsikter visar också på ett generellt sett positivt gensvar gentemot användandet av sensorn, men de påpekar också att om sensorn ska användas i deras hem bör all data vara tillgänglig för och kontrollerad av användaren, allt för att lindra möjliga misstankar om missbruk av datan. Slutsatser. Författarna kommer fram till att även om de inte än är riktigt redo, så är mmWave-sensorerna en sannolik kandidat till att användas i framtidens smarta hem.

<p> The use of smart devices such as smartphones, tablets, and laptops skyrocketed in the last decade. These devices enable ubiquitous applications for entertainment, communication, productivity, and healthcare but also introduce big concern about user privacy and data security. In addition to various authentication techniques, automatic and immediate device locking based on user leaving detection is an indispensable way to secure the devices. Current user leaving detection techniques mainly rely on acoustic ranging and do not work well in environments with multiple moving objects. In this paper, we present mmLock, a system that enables faster and more accurate user leaving detection in dynamic environments. mmLock uses a mmWave FMCW radar to capture the user’s 3D mesh and detects the leaving gesture from the 3D human mesh data with a hybrid PointNet-LSTM model. Based on explainable user point clouds, mmLock is more robust than existing gesture recognition systems which can only identify the raw signal patterns. We implement and evaluate mmLock with a commercial off-the-shelf (COTS) TI mmWave radar in multiple environments and scenarios. We train the PointNet-LSTM model out of over 1 TB mmWave signal data and achieve 100% true-positive rate in most scenarios. </p>

Yes / A shared ground shared radiator with wide angular coverage for mmWave 5G smartphones is proposed in this paper. A four-element corporate-fed array with conventional impedance matched power divider is designed. Stepped impedance transformers are integrated with the corner most elements to achieve pattern diversity with wide angular coverage without signifi-cant compromise in gain. The proposed three-port shared radiator conformal commercial an-tenna could be easily integrated with commercial mmWave 5G smartphones. All the three ports’ excitations operate in the 28 GHz band. Radiation pattern bandwidth of the multi-port system is high. The gain variation is from 6 to11 dBi amongst the ports and across the operating spectrum. The highest mutual coupling is 10 dB, in spite of the electrically connected structure. The pro-posed shared radiator element has a wide angular coverage of 100°, maintaining high front-to-back ratio when the respective port is excited. Simulation and measurement results for the proposed structure are illustrated in detail. / This work is supported by the Moore4Medical project, funded within ECSEL JU in collaboration with the EU H2020 Framework Programme (H2020/2014-2020) under grant agreement H2020-ECSEL-2019-IA-876190, and Fundação para a Ciência e Tecnologia (ECSEL/0006/2019).

Millimeter wave systems leverage beamforming to generate narrow, high-powered beams for overcoming the increased path loss in the millimeter wave spectrum. These beams are spa- tially confined, making millimeter wave links more resilient to eavesdropping and jamming attacks. However, the millimeter wave radios locate each other and establish communica- tion by exhaustively probing all possible angular directions, increasing their susceptibility to attacks. In this thesis, we showcase a secure beam management solution where we apply an adaptive beam management procedure that avoids probing the directions of potential attackers. We employ a reinforcement learning agent to control the probing and dynami- cally restrict sweeps to a subset of beams in the millimeter wave transmitter codebook to avoid the locations of potential attackers based on a proposed metric that quantifies the beam sweeping secrecy over a pre-defined area. We evaluate our proposed system through numerical simulations and an experimental real-life implementation on the CCI xG Testbed. / Master of Science / Millimeter wave systems leverage beamforming, a technique that's used to direct both trans- mission and reception of a signal to create narrow, high-powered beams that can overcome the signal deterioration that comes with millimeter wave spectrum. The spatially confined nature of these beams makes millimeter wave links resilient to eavesdropping and jamming attacks. However, the millimeter wave radios find each other and establish communication by searching every possible angular direction, which increases the potential for the millimeter wave radios to be attacked. In this thesis, we showcase a secure method of establishing this communication link that avoids looking in the direction of a potential attacker. We then employ an artificial intelligence capable of controlling this search by sweeping a subset of all possible directions in the millimeter wave transmitter codebook based on a proposed metric that quantifies the secrecy of communication. We evaluate our proposed system through numerical simulations and an experimental real-life implementation on the CCI xG Testbed.

The number of subscribers and use cases of mobile communication networks are expanding expeditiously with the evolution of technology. The available spectrum in lower frequency ranges does not meet the unprecedented increase in demand for user data throughput in mobile networks. Facing the problem of limited spectrum in traditional cellular bands that are below 6 GHz, Millimeter Wave (mmWave) frequency bands are being standardized for the 5th Generation (5G) mobile networks as a promising means for handling the unprecedented data traffic surge. Enabling higher carrier frequencies introduces new channel conditions. Propagating signals are exposed to higher diffraction loss and are highly susceptible to blockage caused by surrounding objects, which leads to rapid signal degradation and challenges user mobility. On the other hand, higher carrier frequencies enable the deployment of many small-sized antennas that are used for directional signal transmission, resulting in beamforming gain. In recent studies, a conditional handover procedure has been adopted for 5G networks to enhance user mobility robustness. Besides, contention-free random access procedure has been defined for beamformed systems aiming at minimizing the signaling and service interruption time caused by the random access procedure. An improper configuration of the mobility parameters, e.g., handover preparation and execution offsets, access beam selection threshold of random access procedure, leads User Equipments (UEs) to experience Handover Failures (HOFs) and Radio Link Failures (RLFs), and causes unnecessary signaling and inefficient resource utilization in the network. Each cell border has unique propagation characteristics and user mobility pattern, and, therefore, mobility parameters should be configured for each cell border individually. Moreover, mobility parameters should be updated for dynamic propagation environment (e.g., construction of buildings, seasonal changes in the vegetation) and for temporal mobility patterns. Considering the individual cell border configuration, temporal adaptation of the mobility parameters, and ultra-dense deployment, optimization of the conditional handover and random access parameters is a complex task that cannot be carried by human interaction. Therefore, an automatic optimization of the parameters is needed where the network collects statistics of the mobility events and adjusts the parameters autonomously. To investigate user mobility under these new propagation conditions, a proper model is needed that captures spatial and temporal characteristics of the channel in beamformed networks. Current channel models that have been developed for 5G networks are too detailed for the purpose of mobility simulations and lead to infeasible simulation time for most user mobility simulations. In this work, a simplified channel model is presented that captures the spatial and temporal characteristics of the 5G propagation channel and runs in feasible simulation time. To this end, the coherence time and path diversity originating from a fully fledged Geometry-based Stochastic Channel Model (GSCM) are analyzed and adopted in Jake’s channel model with reduced computational complexity. Furthermore, the deviation of multipath beamforming gain from single ray beamforming gain is analyzed and a regression curve is obtained to be used in the system-level simulations. In a typical system-level mobility simulator, the average downlink signal-to-interference and noise ratio (SINR) is used for RLF detection and throughput calculation. In addition to the channel model, models of desired and interfering signals are formulated first, by considering the impact of antenna beamforming, and a closed-form expression of average downlink SINR is derived by taking into account the user and beam scheduling probabilities. Then, an accurate approximation of the average downlink SINR with low computational complexity is presented, for 5G networks where the base station forms multiple beams. In addition, an SINR model is derived for both strict and opportunistic resource-fair scheduler, where the latter targets a higher utilization of radio resources when multiple beams are scheduled simultaneously. The mobility performance of conditional handover and contention-free random access are investigated by using the proposed channel and SINR models. Besides, a resource efficient random access procedure is proposed that aims at maximizing the utilization of contention-free random access resources. Moreover, simple, yet, effective decision tree-based supervised learning method is proposed to minimize the HOFs that are caused by the beam preparation phase of the random access procedure. Similarly, a decision-tree-based supervised learning method is proposed for automatic optimization of the conditional handover parameters. In addition, enhanced logging and emergency reporting methods are introduced first time in this study to mitigate the cell detection problems that are caused by rapid signal degradation. Results show that the optimum operation point of random access (in terms of minimizing the HOFs and maximizing the random access resource utilization) is achievable with the proposed learning algorithm for random access procedure in conditional handover. Results also show that the mobility performance of conditional handover is improved by automatic optimization of the handover parameters. In addition, the proposed enhanced logging and emergency reporting methods mitigate the mobility problems related with cell detection and further improve the mobility performance in combination with the decision-tree-based supervised learning methods.

Masters thessis deals with the problematics of wireless signal propagation modeling inside buildings. The theoretical part of this thessis describes principles and methods of electromagnetic waves spreading in open areas and in indoor deployment. There are also described methods used for calculating the path of signal propagation ray-launching and ray-tracing. This part also includes description of an algorithm and equations used for simulating 5GHz WiFi signal propagation inside the Department of telecommunications corridors. Second part of this thessis includes a description of a NS-3 module mmWave, which was used for simulations of IEEE 802.11ad (WiGig) standard. There are also results of these simulations and their detailed description. At the end of this thessis comparison of these results with values gained by real environment measurements can be found.

Autonomous drone navigation in classical approaches typically involves constructing a map representation and employing path planning and collision checking algorithms within that map. Recently, novel deep learning techniques combined with depth camera observations have emerged as alternative approaches capable of achieving comparable collision-free performance. While these methods have demonstrated effective collision-free performance in dense environments, they rely on low-noise range or visual data, which may not be feasible in extreme degraded environments characterized by factors such as dust, smoke, weak geometries, or low-texture areas. A possible alternative is to leverage recent progress in mmWave radar imaging, which previously has produced data of insufficient resolution for such purposes. Through the use of a Variational Autoencoder and existing collision prediction algorithms, the goal of this study is to prove the use of mmWave radar for navigating difficult environments. The results of the study exhibit successful navigation in simulated scenarios featuring sparse obstacles. Additionally, results of utilizing real-world mmWave radar data in example scenarios is provided to demonstrate the potential for further application of this technology. / Autonom navigation för drönare i klassiska tillvägagångssätt innebär vanligtvis att man konstruerar en kartrepresentation och använder vägplanerings- och kollisionskontrollalgoritmer inom den kartan. Nyligen har nya djupinlärningstekniker kombinerat med djupkameraobservationer framträtt som alternativa tillvägagångssätt som kan uppnå jämförbar prestanda utan kollisioner. Även om dessa metoder har visat effektiv prestanda utan kollisioner i täta miljöer, är de beroende av störningsfria avstånds- eller visuella data, vilket kanske inte är genomförbart i extrema försämrade miljöer som karakteriseras av faktorer som damm, rök, svaga geometrier eller områden med låg textur. Ett möjligt alternativ är att dra nytta av de senaste framstegen inom mmWave-radaravbildning, vilket tidigare har producerat data med otillräcklig upplösning för sådana ändamål. Genom användning av en varieabel autoencoder och befintliga kollisionsprognosalgoritmer syftar denna studie till att bevisa användningen av mmWave-radar för att navigera i svåra miljöer. Resultaten från studien visar framgångsrik navigering i simulerade scenarier med glesa hinder. Dessutom presenteras resultat från användning av verkliga mmWave-radardata i exempelscenarier för att visa potentialen för ytterligare tillämpningar av denna teknik.

Millimeter Wave (mmWave) is a key technology to meet the challenge of data rates and the lack of bandwidth in sub-6GHz networks. Due to a high operation frequency, the mmWave network has unique channel characteristics and a relatively high pathloss. Therefore, a dense deployment of Base Station (BS) is necessary, leading to a more frequent handover, which may cause a degradation of User Equipment (UE) experience. Furthermore, a massive number of devices cause an interference issue and a high dropping probability. In this project, we propose a handover method based on Reinforcement Learning (RL). This handover method provides a seamless connection and considers the load balancing. To verify the proposed method, Q-learning is selected to solve this RL problem and a simulation environment of mmWave is set up, including the pathloss model, system model, and beamforming. The average data rate, number of handovers, and number of available resources are evaluated during the movement of UEs. The results are compared with rate-max method and random backup method in different interference scenarios. Our proposed method shows a notable performance in terms of data rate, for example, while doubling the interference, the data rate decreases 8.6% with our method while it decreases 20% with the random-backup method. Moreover, our method has the minimum number of handovers in the trajectory. The performance in multiple trajectories is also illustrated and it performs as expected. / Millimeter Wave (mmWave) är en nyckelteknologi för att möta utmaningen med datahastigheter och bristen på bandbredd i sub-6GHz-nätverk. På grund av den höga driftsfrekvensen har mmWave-nätverket unika kanalegenskaper och en relativt hög banförlust. Därför är en tät användning av basstationen (BS) nödvändig vilket leder till en mer frekvent överlämning, vilket kan orsaka en försämring av User Equipment (UE) upplevelse. Dessutom orsakar ett stort antal enheter störningsproblem och en hög dropping probability. I det här projektet föreslår vi en överlämningsmetod baserad på Reinforcement Learning (RL). Denna överlämningsmetod ger en sömlös anslutning och tar hänsyn till lastbalanseringen. För att verifiera den föreslagna metoden har en simuleringsmiljö på mmWave ställts in, inklusive banförlust-modellen, systemmodellen och strålformning. Genomsnitt datahastighet, antal överlämningar och antal tillgängliga resurser utvärderas under förflyttning av UE: er. Resultaten jämförs med rate-max metod och slumpmässig säkerhetskopieringsmetod i olika störningsscenarier. Vår föreslagna metod visar en anmärkningsvärd prestanda när det gäller datahastighet, till exempel, när interferensen fördubblas minskar datahastigheten 8,6% med vår metod medan den minskar 20% med slumpmässig säkerhetskopieringsmetod. Dessutom har vår metod det minsta antalet överlämningar i banan. Prestandan i flera banor illustreras också och den fungerar som förväntat.

This thesis describes the design of a rectenna that is capable of operating in 5G. 5G’s availability will create the opportunity to harvest energy everywhere in the network’s coverage. This thesis investigates a Rectenna device with a new proposed topology in order to eliminate coupling between input and output lines and increase the rectification efficiency. Moreover, it is designed to charge a rechargeable battery of 3V, 1mA, with a 4.8mm diameter. The current design describes using one antenna for energy harvesting; this could be expanded to use an antenna array, which would increase the input power. This would lead to higher output currents, leading to the ability to efficiently charge a wide variety of batteries. Because of its small size, the rectenna could be used for the remote charging of an implantable sensor battery or for other applications where miniaturization is a design consideration.

The use of multiple antennas to improve the reliability and capacity of wireless communication has been around for a while, leading to the concept of multiple-input multiple-output (MIMO) communications. To enable full MIMO potentials, the precoding design has been recognized as a crucial component. This thesis aims to design multiuser MIMO precoders of practical interest to achieve high reliability and capacity performance under various real-world constraints like inaccuracy of channel information acquired at the transmitter, hardware complexity, etc. Three prominent cases are considered which constitute the mainstream evolving directions of the current cellular communication standards and future 5G cellular communications. First, in a relay-assisted multiuser MIMO system, heavily quantized channel information obtained through limited feedback contributes to noticeable rate loss compared to when perfect channel information is available. This thesis derives an upper bound to characterize the system throughput loss caused by channel quantization error, and then develops a feedback quality control strategy to maintain the rate loss within a bounded range. Second, in a massive multiuser MIMO channel, due to the large array size, it is difficult to support each antenna with a dedicated radio frequency chain, thus making high-dimensional baseband precoding infeasible. To address this challenge, a low-complexity hybrid precoding scheme is designed to divide the precoding into two cascaded stages, namely, the low-dimensional baseband precoding and the high-dimensional phase-only processing at the radio frequency domain. Its performance is characterized in a closed form and demonstrated through computer simulations. Third, in a mmWave multiuser MIMO scenario, smaller wavelengths make it possible to incorporate excessive amounts of antenna elements into a compact form. However, we are faced with even worse hardware challenges as mixed signal processing at mmWave frequencies is more complex and power consuming. The channel sparsity is taken advantage of in this thesis to enable a simplified precoding scheme to steer the beam for each user towards its dominant propagation paths at the radio frequency domain only. The proposed scheme comes at significantly reduced complexity and is shown to be capable of achieving highly desirable performance based on asymptotic rate analysis. / Graduate