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  • Presentation: 2024-03-04 13:00 Amiga, Kista
    Enqvist, Anders
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Communication Systems, CoS.
    Optimizing Energy Efficiency in Wireless Links Through Optimal Ratios and Reconfigurable Intelligent Surfaces2024Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    This thesis explores the optimization of energy efficiency (EE) in the radiolink of wireless communication systems, focusing on both the user equipment (UE) and the base station (BS). The first part of the study examines strategies to minimize the energy consumption of the UE when transmitting short data payloads, utilizing a reconfigurable intelligent surface (RIS) controlled by the BS, to improve the channel conditions. The challenge lies in balancing the increased energy consumption due to additional pilot signals needed toconfigure the RIS against the energy savings during data transmission. We propose an innovative approach where the RIS is divided into subarrays of controllable sizes to shorten the pilot length. The analytical results provide a unique energy-minimizing solution in terms of pilot length and power which depends on an interplay between the payload size and path loss conditions between the UE, BS, and RIS. In the second part, the focus shifts to the EE of a multi-antenna BS. A comprehensive power consumption model is employed, accounting for both active and passive components of the transceiver circuitry. By treating the transmit power, bandwidth, and number of antennas as optimization variables, we derive novel closed-form solutions to the optimal value of these variables and propose an algorithm for their joint optimization. This part of the study not only optimizes the variables for maximum EE but also uncovers a new relationship between radiated power and passive transceiver power consumption, offering insights into the trade-offs between using maximum power and bandwidth. Together, these studies provide an updated view of EE optimization in wireless communication systems, offering novel theoretical insights for both UE and BS configurations.

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  • Presentation: 2024-03-12 13:00 B3, Brinellvägen, Campus KTH, Stockholm
    Yaqoob, Saima
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering.
    Concrete pavements’ repair techniques and numerical assessment of dowel bar load transfer efficiency2024Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Concrete pavements are a suitable alternative for high-traffic volume roads, concentrated loads and roads exposed to severe weather conditions. In Sweden, among other reasons, the scarcity of concrete pavements is attributed to the need for more national knowledge and expertise in the field. The most recent concrete pavement was constructed seventeen years ago in Uppsala. Concrete pavements are renowned for their longevity and durability. Jointed plain concrete pavements (JPCP) are the most frequent type of concrete pavements. However, it is important to note that the joints in concrete pavements are critical components that can lead to various distresses, necessitating rehabilitation. Rehabilitating concrete pavements is particularly challenging in areas with heavy traffic and requires substitute routes because of the imperative to maintain traffic flow during construction. Developing effective detours might involve significant alterations to the existing routes or building temporary roads, which entails substantial cost investment and time consumption.

    A literature review has been conducted to study the available methods that can be used to repair concrete pavements. The strategy for selecting a repair technique is based on rehabilitating the concrete pavement within a short work window, deterring traffic congestion and ensuring the long service life of the pavements. The study showed that the precast concrete technology based on the precast slab is a promising technology that effectively shortens the lane closure for repairing damaged pavements and produces durable pavements, thereby extending the service life of pavements. However, the construction or rehabilitation cost of concrete pavement using precast slabs is 1.6 to 4 times higher than that of conventional cast-in-place concrete. Therefore, rehabilitation using precast slabs is inappropriate for low-traffic roads and temporary routes.

    Joints are crucial for the rehabilitation of concrete pavements with precast slabs. The structural performance of concrete pavement is, however, greatly affected by the joints, as the presence of joints creates a discontinuity between adjacent slabs and thus diminishes the load transfer to the abutting slab. To maintain the structural integrity of the pavement system, dowel bars are used at the transverse joints.

    A numerical study has been conducted to evaluate the influence of various dowel-related parameters on the interaction between adjacent concrete slabs. The study revealed that the dowel bar’s position, mislocation and diameter had an obvious effect on joint efficiency, while the bond between the concrete slab and the dowel bar slightly affected the load transfer efficiency. It was also investigated that the dowel bar’s intended performance, i.e., load transfer efficiency, was reduced as the joint gap between adjacent slabs increased.

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  • Presentation: 2024-03-12 13:15 Ka-301, Stockholm
    Khorsandmanesh, Yasaman
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Communication Systems, CoS.
    Hardware Distortion-Aware Beamforming for MIMO Systems2024Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    In the upcoming era of communication systems, there is an anticipated shift towards using lower-grade hardware components to optimize size, cost, and power consumption. This shift is particularly beneficial for multiple-input multiple-output (MIMO) systems and internet-of-things devices, which require numerous components and extended battery lifes. However, using lower-grade components introduces impairments, including various non-linear and time-varying distortions affecting communication signals. Traditionally, these distortions have been treated as additional noise due to the lack of a rigorous theory. This thesis explores new perspective on how distortion structure can be exploited to optimize communication performance. We investigate the problem of distortion-aware beamforming in various scenarios. 

    In the first part of this thesis, we focus on systems with limited fronthaul capacity. We propose an optimized linear precoding for advanced antenna systems (AAS) operating at a 5G base station (BS) within the constraints of a limited fronthaul capacity, modeled by a quantizer. The proposed novel precoding minimizes the mean-squared error (MSE) at the receiver side using a sphere decoding (SD) approach. 

    After analyzing MSE minimization, a new linear precoding design is proposed to maximize the sum rate of the same system in the second part of this thesis. The latter problem is solved by a novel iterative algorithm inspired by the classical weighted minimum mean square error (WMMSE) approach. Additionally, a heuristic quantization-aware precoding method with lower computational complexity is presented, showing that it outperforms the quantization-unaware baseline. This baseline is an optimized infinite-resolution precoding which is then quantized. This study reveals that it is possible to double the sum rate at high SNR by selecting weights and precoding matrices that are quantization-aware. 

    In the third part and final part of this thesis, we focus on the signaling problem in mobile millimeter-wave (mmWave) communication. The challenge of mmWave systems is the rapid fading variations and extensive pilot signaling. We explore the frequency of updating the combining matrix in a wideband mmWave point-to-point MIMO under user equipment (UE) mobility. The concept of beam coherence time is introduced to quantify the frequency at which the UE must update its downlink receive combining matrix. The study demonstrates that the beam coherence time can be even hundreds of times larger than the channel coherence time of small-scale fading. Simulations validate that the proposed lower bound on this defined concept guarantees no more than 50 \% loss of received signal gain (SG).

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  • Presentation: 2024-03-15 10:00 D3, Stockholm
    Aguiar, Miguel
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Learning flow functions: architectures, universal approximation and applications to spiking systems2024Licentiate thesis, monograph (Other academic)
    Abstract [en]

    Learning flow functions of continuous-time control systems is considered in this thesis. The flow function is the operator mapping initial states and control inputs to the state trajectories, and the problem is to find a suitable neural network architecture to learn this infinite-dimensional operator from measurements of state trajectories. The main motivation is the construction of continuous-time simulation models for such systems. The contribution is threefold.

    We first study the design of neural network architectures for this problem, when the control inputs have a certain discrete-time structure, inspired by the classes of control inputs commonly used in applications. We provide a mathematical formulation of the problem and show that, under the considered input class, the flow function can be represented exactly in discrete time. Based on this representation, we propose a discrete-time recurrent neural network architecture. We evaluate the architecture experimentally on data from models of two nonlinear oscillators, namely the Van der Pol oscillator and the FitzHugh-Nagumo oscillator. In both cases, we show that we can train models which closely reproduce the trajectories of the two systems.

    Secondly, we consider an application to spiking systems. Conductance-based models of biological neurons are the prototypical examples of this type of system. Because of their multi-timescale dynamics and high-frequency response, continuous-time representations which are efficient to simulate are desirable. We formulate a framework for surrogate modelling of spiking systems from trajectory data, based on learning the flow function of the system. The framework is demonstrated on data from models of a single biological neuron and of the interconnection of two neurons. The results show that we are able to accurately replicate the spiking behaviour.

    Finally, we prove an universal approximation theorem for the proposed recurrent neural network architecture. First, general conditions are given on the flow function and the control inputs which guarantee that the architecture is able to approximate the flow function of any control system with arbitrary accuracy. Then, we specialise to systems with dynamics given by a controlled ordinary differential equation, showing that the conditions are satisfied whenever the equation has a continuously differentiable right-hand side, for the control input classes of interest.

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