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  • Presentation: 2024-05-06 09:00 B3, Stockholm
    Gürgünoglu, Doga
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Channel Estimation Aspects of Reconfigurable Intelligent Surfaces2024Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    In the sixth generation of wireless communication systems (6G), there exist multiple candidate enabling technologies that help the wireless network satisfy the ever-increasing demand for speed, coverage, reliability, and mobility. Among these technologies, reconfigurable intelligent surfaces (RISs) extend the coverage of a wireless network into dead zones, increase capacity, and facilitate integrated sensing and communications tasks by consuming very low power, thus contributing to energy efficiency as well.

    RISs are meta-material-based devices whose electromagnetic reflection characteristics can be controlled externally to cater to the needs of the communication links. Most ubiquitously, this comes in the form of adding a desired phase shift to an incident wave before reflecting it, which can be used to phase-align multiple incident waves to increase the strength of the signal at the receiver and provide coverage to an area that otherwise would be a dead zone.

    While this portrays an image of a dream technology that would boost the existing wireless networks significantly, RISs do not come without engineering problems. First of all, the individual elements do not exhibit ideal reflection characteristics, that is, they attenuate the incident signal in a fashion depending on the configured phase shift. This creates the phenomenon called "phase-dependent amplitude". Another problem caused by RISs is the channel estimation overhead. In a multiple-antenna communication system, the channel between two terminals is as complex as the product of the number of antennas at each end. However, when an RIS comes into the equation, the cascade of the transmitter-RIS and RIS-receiver channels has a complexity further multiplied by the number of RIS elements. Consequently, the channel estimation process to utilize the RIS effectively becomes more demanding, that is, more pilot signals are required to estimate the channel for coherent reception. This adversely affects the effective data rate within a communication system since more resources need to be spent for pilot transmission and fewer resources can be allocated for data transmission. While there exists some work on reducing the channel dimensions by exploiting the channel structure, this problem persists for unstructured channels. In addition, for the wireless networks using multiple RISs, a new kind of pilot contamination arises, which is the main topic of this thesis.

    In the first part of this thesis, we study this new kind of pilot contamination in a multi-operator context, where two operators provide services to their respective served users and share a single site. Each operator has a single dedicated RIS and they use disjoint frequency bands, but each RIS inadvertently reflects the transmitted uplink signals of the user equipment devices in multiple bands. Consequently, the concurrent reflection of pilot signals during the channel estimation phase introduces a new inter-operator pilot contamination effect. We investigate the implications of this effect in systems with either deterministic or correlated Rayleigh fading channels, specifically focusing on its impact on channel estimation quality, signal equalization, and channel capacity. The numerical results demonstrate the substantial degradation in system performance caused by this phenomenon and highlight the pressing need to address inter-operator pilot contamination in multi-operator RIS deployments. To combat the negative effect of this new type of pilot contamination, we propose to use orthogonal RIS configurations during uplink pilot transmission, which can mitigate or eliminate the negative effect of inter-operator pilot contamination at the expense of some inter-operator information exchange and orchestration.

    In the second part of this thesis, we consider a single-operator-two-RIS integrated sensing and communication (ISAC) system where the single user is both a communication terminal and a positioning target. Based on the uplink positioning pilots, the base station aims to estimate both the communication channel and the user's position within the indoor environment by estimating the angle of arrival (AoA) of the impinging signals on both RISs and then exploiting the system and array geometries to estimate the user position and user channels respectively. Although there is a single operator, due to the presence of multiple RISs, pilot contamination occurs through the same physical means as multi-operator pilot contamination unless the channel estimation process is parameterized. Since the communication links are considered to be pure line-of-sight (LOS), their structure allows the reduction of the number of unknown parameters. Consequently, the reduction of information caused by pilot contamination does not affect the channel estimation procedure, hence the pilot contamination is overcome. On the other hand, the position of the user is determined by intersecting the lines drawn along the AoA estimates. We adopt the Cramér-Rao Lower Bound (CRLB), the lower bound on the mean squared error (MSE) of any unbiased estimator, for both channel estimation and positioning. Our numerical results show that it is possible to utilize positioning pilots for parametric channel estimation when the wireless links are LOS.

    The full text will be freely available from 2024-05-06 16:25
  • Presentation: 2024-05-06 13:00 B1, Brinellvägen 23, KTH Campus, Stockholm
    Afridi, Muhammad Amjad
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Building Materials. Skellefteå Municipality, Strömsörgatan 15, 93134 Skellefteå, Sweden.
    Municipal street pavement maintenance and management practices in Sweden2024Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    A well-functioning street network is pivotal in the socio-economic development of a region. Street networks not only facilitate the movement of people and goods but also allocate space for utility services. Maintaining the street network in good condition and meeting the sustainability targets necessitate implementing optimal street maintenance strategies, leading to an efficient utilization of taxpayers' money. 

    The objectives of this Licentiate thesis are to analyse pavement management practices and challenges faced by Swedish municipalities, specifically focusing on asphalt concrete (AC) pavements within street networks. Additionally, it seeks to integrate a sustainability tool into pavement maintenance to select maintenance measures that contribute to sustainability goals at the municipal street network management level. Furthermore, the study aims to enhance municipal-level pavement maintenance approaches through the implementation of machine learning (ML) models within a pavement management system (PMS). 

    Within this context, three individual studies were conducted—two case studies and a survey involving Swedish municipalities. One case study explores sustainability framework application, whereas the other investigates the utilization of ML models in municipal AC pavement maintenance. The survey investigates the practices and challenges faced by municipal street network administrations in AC pavement maintenance.

    The sustainability framework SUNRA (Sustainability National Road Administrations) was adopted by the Swedish Transport Administration (STA) with a primary emphasis on promoting sustainability in pavement management on state-level roads. In this study, the framework has been tested, applied and further streamlined to be applicable for setting sustainability targets and monitoring sustainability performances at the project level within both the STA and municipal contexts. The aim was to simplify the framework so it is appropriate for investment, re-investments, maintenance and operation projects and also to enhance its applicability for various users. The study additionally explored how the framework could contribute to sustainability, identified the drivers and barriers for its application, and examined its applicability and adaptability to projects of varying complexities. The results indicate that the framework can be readily utilized and adapted for investment, reinvestment, maintenance, and operational pavement projects during the planning stage. Additionally, it is also suitable for small municipal establishments, construction or reconstruction of residential areas, and regular maintenance.

    A web-based questionnaire survey was disseminated to municipalities across the country to gather first-hand insights into the current practices and challenges associated with street maintenance at the municipal level in Sweden. Survey responses were received from 147 of the 290 (51%) municipalities nationwide. The study reveals that predominant pavement distress encompasses potholes, surface unevenness, and alligator cracking, with the most prevalent causes being pavement ageing, heavy traffic, and patches. Likewise, cold climate and population density serve as influential factors contributing to pavement deterioration. The automated survey methods for collecting pavement condition data, such as road surface scanning vehicles and application of commercial PMS, are very limited. On the contrary, the windshield method, a subjective approach for pavement condition assessment, is widely adopted among municipalities utilizing PMS. The allocation of the budget for maintenance, rehabilitation and reconstruction is higher in the northern regions of the country, as well as in densely populated municipalities.

    Manually collected pavement condition data for the years 2014 and 2018 were acquired from Skellefteå municipality to assess the performance of ML models in comparison to the observed pavement condition index (PCI) of the street network. In this context, the supervised ML models Linear Regression (LR), Random Forest (RF), and Neural Network (NN) were employed in conjunction with several variable combinations. The RF model, utilizing paired variables of pavement age (A) and pavement distresses (D) data, consistently demonstrated higher accuracy compared to the other models for residential streets. However, RF models constructed with paired variables of A and traffic (T) consistently outperformed other models in the context of non-residential streets. The significance of input variables fluctuates based on the model's complexity and the pavement performance objective. Nonetheless,  variable A consistently emerges as the predominant factor for predicting PCI in both residential and non-residential street models. 

    Further evaluation of the models and simplification of the SUNRA framework to enhance pavement performance and sustainability are recommended. 

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  • Presentation: 2024-05-13 13:00 Amiga, https://kth-se.zoom.us/j/68751222626, Stockholm
    Behdad, Zinat
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Communication Systems, CoS.
    Integrated Sensing and Communication in Cell-Free Massive MIMO2024Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Future mobile networks are anticipated to not only enhance communication performance but also facilitate new sensing-based applications. This highlights the essential role of integrated sensing and communication (ISAC) in sixth-generation (6G) and beyond mobile networks. The seamless integration of sensing and communication poses challenges in deployment and resource allocation. Cell-free massive multiple-input multiple-output (MIMO) networks, characterized by multiple distributed access points, offer a promising infrastructure for ISAC implementation. However, the effective realization of ISAC necessitates joint design and resource allocation optimization. In this thesis, we study ISAC within cell-free massive MIMO systems, with a particular emphasis on developing power allocation algorithms under various scenarios.

    In this thesis, we explore two scenarios: utilizing existing communication signals and incorporating additional sensing signals. We propose power allocation algorithms aiming to maximize the sensing performance while meeting communication and power constraints. In addition, we develop two maximum a posteriori ratio test (MAPRT) target detectors under clutter-free and cluttered scenarios. Results indicate that employing additional sensing signals enhances sensing performance, particularly in scenarios where the target has low reflectivity. Moreover, although the clutter-aware detector requires more advanced processing, it leads to better sensing performance. Furthermore, we introduced sensing spectral efficiency (SE) to measure the effect of resource block utilization, highlighting the integration advantages of ISAC over orthogonal resource sharing approaches. 

    In the next part of the thesis, we study the energy efficiency aspects of ISAC in cell-free massive MIMO systems with ultra-reliable low-latency communications (URLLC) users. We propose a power allocation algorithm aiming to maximize energy efficiency of the system while meeting communication and sensing requirements. We conduct a comparative analysis between the proposed power allocation algorithms and a URLLC-only approach which takes into account only URLLC and power requirements. The results reveal that while the URLLC-only algorithm excels in energy efficiency, it is not able to support sensing requirements.   Moreover, we study the impact of ISAC on end-to-end (including radio and processing) energy consumption. Particularly, we present giga-operations per second (GOPS) analysis for both communication and sensing tasks. Two optimization problems are formulated and solved to minimize transmission and end-to-end energy through blocklength and power optimization. Results indicate that while end-to-end energy minimization offers substantial energy savings, its efficacy diminishes with sensing integration due to processing energy requirements.

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