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Item type:Publication, Access status: with files , Channel modeling and performance analysis of ultra wideband in-body communication(2024); Wireless capsule endoscopes are a proven method for the diagnosis of gastrointestinal diseases. In this thesis, ultra wideband communication is investigated as an alternative to common narrowband transmission to increase the video quality. A reproducible and flexible channel model for ultra wideband in-body communication is derived. Based on this, fundamental limits for the communication performance are investigated by computing the channel capacity. The influence of different human body models, the receive antenna placement, and the dielectric properties of human tissue on the channel capacity are analyzed. Furthermore, an exemplary ultra wideband communication system is designed and its bit error probability is evaluated.Publicationtype: Doctoral Thesis77 249 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Spatiotemporal analysis of wireless internet of things networks(2021-10-27); This thesis models and analyzes large scale wireless internet of things networks from the communication and computation perspectives. For the communication aspect, this thesis develops rigorous frameworks for different traffic models from a spatiotemporal viewpoint. For the computation aspect, this thesis utilizes multi-access edge computing to enable efficient and faster offloaded task execution at the serving base stations. Latency reduction gains are demonstrated through different use-cases within the automotive and the industrial vertical segments. Owing to the reported results throughout this thesis, a spatiotemporal insight on the performance of future wireless networks is feasible.Publicationtype: Doctoral Thesis244 857 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Machine learning for reliable communication under coarse quantization(2021-09-24); This thesis addresses the use of machine learning methods for reliable transmission despite coarsely quantized soft information at the receiver. In particular, the design of powerful error correction algorithms under coarse quantization is presented. This requires an interdisciplinary approach based on the interplay of information theory, machine learning, and communications engineering. A particular focus is put on the information bottleneck method. Interestingly, the designed coarsely quantized signal processing units achieve almost the same performance in terms of reliability as conventional non-quantized methods.Publicationtype: Doctoral Thesis365 1458 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Cooperative multipath assisted positioning(2021); Location-awareness has become an important issue in modern life. In environments with good view to the sky, global navigation satellite systems can provide very accurate location estimates. Though, their positioning performance decreases significantly if the view to satellites is blocked. Indoors, no localization solution may be obtained at all. Terrestrial signals such as from telecommunication base stations or wireless local area network routers can be used for indoor localization. However, the localization performance may suffer from multipath propagation when traditional propagation delay-based algorithms are applied. The transmit signal is reflected, scattered and diffracted in the environment, distorting the signal and leading to a bias in the delay estimates. With multipath assisted positioning, a new approach has emerged, that exploits the information in multipath components (MPCs) by treating them as line-of-sight signals from virtual transmitters. While the locations of the physical and virtual transmitters are generally unknown, they can be estimated with simultaneous localization and mapping (SLAM). One SLAM based multipath assisted positioning algorithm is Channel-SLAM, where the user location is estimated simultaneously with creating a map of physical and virtual transmitters. The Channel-SLAM algorithm is limited to single users. There are many settings where multiple users move in the same indoor environment, such as in shopping malls or public buildings. Within this thesis, the Channel-SLAM algorithm is extended by cooperation among users in terms of exchanging maps of transmitter locations. This cooperation can drastically increase the positioning performance of all cooperating users. However, Channel-SLAM is only a relative localization system, as no relation of the user location to an absolute coordinate frame is known. The transformation parameters relating the coordinate systems of a user to the coordinate system of a map the user obtains from a different user are unknown. In addition, the correspondences among transmitters observed by the user and transmitters in the map are unknown. Estimating these transformation parameters and correspondences is referred to as map matching. A robust map matching scheme is crucial for such cooperation. Since the lack of diversity among virtual transmitters leads to ambiguities in map matching, the transmitter state space is augmented by information from where transmitters are visible. A transmitter is visible, if its signal can be received in a LoS condition. We derive two new tracking filters for Channel-SLAM that map not only the locations, but also the visibility information on transmitters. The visibility information increases the robustness of Channel-SLAM by facilitating the detection of loop closures. In addition, it is used to derive a robust data association scheme. Data association in multipath assisted positioning refers to the challenge of associating signal components with transmitters, leading to robust and accurate long-term SLAM. Finally, the increased transmitter diversity through visibility information improves the positioning performance of Channel SLAM by a more robust data association and by enabling user cooperation.Publicationtype: Doctoral Thesis328 241 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Position estimation in networks with sparse infrastructure(2020-04-20); This thesis considers radio-based positioning in the context of sparse infrastructure. The following two approaches have been considered: Cooperative positioning and single-anchor positioning. Theoretical studies have been conducted and positioning algorithms were developed. The results of this thesis show that the considered approaches allow for precise position estimation despite the sparsity of infrastructure.Publicationtype: Doctoral Thesis358 294 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , The information bottleneck method in communications(2020); The Information Bottleneck method is a generic information theoretical framework which aims for the compression of an observed random variable to a compressed random variable. The focal aim in designing this compression is to preserve relevant information. The method originates from machine learning and so far only has a few practical applications in communications. This thesis describes the application of the Information Bottleneck method to problems of receiver-sided signal processing in communications. The complexity of the receiver-sided baseband processing algorithms for demodulation and channel decoding causes a severe bottleneck in modern digital communication receivers. Their implementation complexity is mainly influenced by the bit width used to represent the signals processed in the hardware of the receiver and the arithmetical operations involved in the signal processing algorithms. Practical receiver implementations, therefore, have to be strongly quantized. Moreover, the involved signal processing algorithms have to be as simple as possible. The Information Bottleneck method provides algorithms which aim to maximize the preserved relevant information for a given bit width, hence motivating to apply the Information Bottleneck method to receiver design. The problems covered in this thesis are the design of scalar channel output quantizers, the decoding of low-density parity-check codes and channel estimation and detection algorithms. It is shown that the Information Bottleneck design principle allows to build signal processing blocks for these problems which allow for very small bit widths, typically around four to five bits per sample. Anyway, performance close to that of signal processing algorithms with double precision can be achieved. A key to achieve this small bit width is the signal representation using only quantization indices instead of real or complex representation values. Moreover, all operations required in the obtained signal processing blocks degenerate to simple lookup operations. As a result, the aforementioned design goals for communication receivers are inherently achieved by the receiver design with the Information Bottleneck method.Publicationtype: Doctoral Thesis540 1266 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Massive MIMO in cellular networks(2020); This thesis deals with the application of large antenna arrays, called massive multiple-input multiple-output (MIMO), at base stations in cellular systems to improve spectral efficiency in the downlink and direction-of-arrival (DoA) based localization in the uplink. This thesis shows that massive MIMO can also provide spectral efficiency gains in frequency-division-duplex systems using a combination of hybrid-precoding and explicit channel state information at the base station. This finding also holds true under realistic pilot/feedback overhead assumptions and in an interference limited multi-cell environment. Furthermore, this thesis provides a low-complexity search-based DoA estimation algorithm for 3D-positioning utilizing the large number of base stations with negligible performance loss compared to a brute-force-search.Publicationtype: Doctoral Thesis412 2402 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Rate-splitting methods for interference management in cellular wireless systemsPublicationtype: Doctoral Thesis31 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Information optimum design of discrete LDPC decoders for irregular codes(2018); Powerful forward error correction is inevitable for every modern communication system. However, leveraging all error correction capabilities offered by channel codes like low density parity check (LDPC) codes, introduces the burden of computationally demanding decoding at the receiver. Especially in power or latency restricted scenarios, efficient decoding algorithms are of crucial importance. In this thesis a novel decoding approach combining ideas from information theory and machine learning was developed, resulting in a so-called information-optimum LDPC decoder. In particular, the information bottleneck method was used to obtain very compact representations of the exchanged beliefs while performing belief propagation decoding. To generalize the decoding approach, to be applicable also for practically relevant irregular LDPC codes another technique called message alignment was devised. As a result, LDPC decoders can be constructed which pair very low-complexity and near-optimum performance (0.2dB performance degradation). Computer simulations for irregular LDPC codes from the IEEE 802.11 (WLAN) and DVB-S2 standard proof the applicability of the developed decoders also in standardized communication systems.Publicationtype: Master Thesis502 1921 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , 41