| 1. |
- Khorsandmanesh, Yasaman
(författare)
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Hardware Distortion-Aware Beamforming for MIMO Systems
- 2024
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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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| 2. |
- Lundén, Daniel, 1993-
(författare)
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Correct and Efficient Monte Carlo Inference for Universal Probabilistic Programming Languages
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Probabilistic programming languages (PPLs) allow users to express statistical inference problems that the PPL implementation then, ideally, solves automatically. In particular, PPL users can focus on encoding their inference problems, and need not concern themselves with the intricacies of inference. Universal PPLs are PPLs with great expressive power, meaning that users can express essentially any inference problem. Consequently, universal PPL implementations often use general-purpose inference algorithms that are compatible with all such inference problems. A problem, however, is that general-purpose inference algorithms can often not efficiently solve complex inference problems. Furthermore, for certain inference algorithms, there are no formal correctness proofs in the context of universal PPLs.This dissertation considers research problems related to Monte Carlo inference algorithms—sampling-based general-purpose inference algorithms that universal PPL implementations often apply. The first research problem concerns the correctness of sequential Monte Carlo (SMC) inference algorithms. A contribution in the dissertation is a proof of correctness for SMC algorithms in the context of universal PPLs. The second research problem concerns execution time improvements when suspending executions—a requirement in many Monte Carlo inference algorithms. The dissertation addresses the problem through two separate approaches. The first approach is a compilation technique targeting high-performance platforms. The second approach is a static suspension analysis guiding a selective continuation-passing style (CPS) transformation, reducing overhead compared to a full CPS transformation. The third research problem concerns inference improvements through alignment—a useful and often overlooked property in PPLs. The dissertation contributions are a formal definition of alignment, a static analysis technique that automatically aligns programs, and aligned versions of SMC and Markov chain Monte Carlo (MCMC) inference algorithms. The final research problem is more practical, and concerns the effective implementation of PPLs. Specifically, the contribution is the Miking CorePPL universal PPL and its compiler. Overall, the contributions in the dissertation significantly improve the efficiency of Monte Carlo algorithms as applied in universal PPLs.
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| 3. |
- Carlsson, Håkan
(författare)
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Inertial Sensor Arrays : Sensor Fusion and Calibration
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Motion estimation using inertial sensors is today used in a wide range of applications, from aircraft navigation to inflatable bicycle helmets. The accuracy with which the motion can be estimated using inertial sensors depends on how large the measurement errors are. One approach to reducing the inertial sensors' measurement errors is to use more sensors than what is necessary for motion estimation. By averaging the measurements from a redundant amount of sensors, the impact of independent errors can be reduced. But by placing multiple inertial sensors on a rigid body, more information about the motion is available than what can be obtained from simple averaging. For instance, point-wise accelerations of a rigid body contain information on the rotation of the rigid body. This thesis examines and proposes methods for how to fuse the measurements from an inertial sensor array and how systematic measurement errors present in the sensors can be estimated and calibrated.The inertial sensor array contains multiple accelerometers and multiple gyroscopes. In motion estimation applications, it is common to estimate the angular velocity from the gyroscopes measurements and then integrate the angular velocity into an orientation. The angular velocity can also be estimated from multiple accelerometers. This thesis proposes different models for fusing the accelerometer and gyroscope measurements for more accurate orientation estimation. By increasing the accuracy with which the orientation can be estimated, the integrated error in the position and velocity estimates can be decreased.The performance of the fusion algorithms for multiple inertial sensors depends on how large the systematic measurement errors are. The amount of rotational information from multiple accelerometers depends on how well the locations of the accelerometers are known. Other calibration parameters in the inertial sensor array are sensor biases. These calibration parameters are estimated in conventional calibration by exposing the inertial sensors to a known reference motion. However, creating such reference motion requires external equipment that may not be available to the user. Therefore, this thesis proposes methods to jointly estimate the motion and the sensor parameters, thereby omitting the need for external calibration equipment.
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| 4. |
- del Aguila Pla, Pol, 1990-
(författare)
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Inverse problems in signal processing : Functional optimization, parameter estimation and machine learning
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Inverse problems arise in any scientific endeavor. Indeed, it is seldom the case that our senses or basic instruments, i.e., the data, provide the answer we seek. It is only by using our understanding of how the world has generated the data, i.e., a model, that we can hope to infer what the data imply. Solving an inverse problem is, simply put, using a model to retrieve the information we seek from the data.In signal processing, systems are engineered to generate, process, or transmit signals, i.e., indexed data, in order to achieve some goal. The goal of a specific system could be to use an observed signal and its model to solve an inverse problem. However, the goal could also be to generate a signal so that it reveals a parameter to investigation by inverse problems. Inverse problems and signal processing overlap substantially, and rely on the same set of concepts and tools. This thesis lies at the intersection between them, and presents results in modeling, optimization, statistics, machine learning, biomedical imaging and automatic control.The novel scientific content of this thesis is contained in its seven composing publications, which are reproduced in Part II. In five of these, which are mostly motivated by a biomedical imaging application, a set of related optimization and machine learning approaches to source localization under diffusion and convolutional coding models are presented. These are included in Publications A, B, E, F and G, which also include contributions to the modeling and simulation of a specific family of image-based immunoassays. Publication C presents the analysis of a system for clock synchronization between two nodes connected by a channel, which is a problem of utmost relevance in automatic control. The system exploits a specific node design to generate a signal that enables the estimation of the synchronization parameters. In the analysis, substantial contributions to the identifiability of sawtooth signal models under different conditions are made. Finally, Publication D brings to light and proves results that have been largely overlooked by the signal processing community and characterize the information that quantized linear models contain about their location and scale parameters.
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| 5. |
- Saxena, Vidit
(författare)
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Machine Learning for Wireless Link Adaptation : Supervised and Reinforcement Learning Theory and Algorithms
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Wireless data communication is a complex phenomenon. Wireless links encounter random, time-varying, channel effects that are challenging to predict and compensate. Hence, to optimally utilize the channel, wireless links adapt the data transmission parameters in real time. This process, known as wireless link adaptation, can lead to large gains in link performance. Link adaptation is hence an integral part of state-of-the-art wireless deployments.Existing link adaptation schemes use simple heuristics that match the data transmission rate to the estimated channel. These schemes have proven to be useful for the ubiquitous wireless services of voice telephony and mobile broadband. However, as wireless networks increase in complexity and also evolve to support new service types, these link adaptation schemes are rapidly becoming inadequate. The reason for this change is threefold: first, in several operating scenarios, simple heuristics-based link adaptation does not fully exploit the available channel. Second, the heuristics are typically tuned empirically for good performance, which incurs additional expense and can be error-prone. Finally, traditional link adaptation does not naturally extend to applications beyond the traditional wireless services, for example to industrial control or vehicular communications.In this thesis, we address wireless link adaptation through machine learning. Our proposed solutions efficiently navigate the link parameter space by learning from the available information. These solutions thus improve the link performance compared to the state-of-the-art, for example by doubling the link throughput. Further, we advance link adaptation support for new wireless services by optimizing the link for complex performance objectives. Finally, we also introduce mechanisms that autonomously tune the link adaptation parameters with respect to the operating environment. Our schemes hence mitigate the dependence on empirical configurations adopted in current wireless networks.This thesis is composed of six technical papers. Based on these papers, there are three key contributions of this thesis: a neural link adaptation model (Paper I, Paper II, and Paper III), link adaptation under packet error rate constraints (Paper IV and Paper V), and efficient model-based link adaptation (Paper VI).In this thesis, we emphasise the theoretical underpinnings of our proposed machine learning schemes for link adaptation. We approach this goal in three ways: First, we make theoretically reasoned choices for machine learning models and learning algorithms for link adaptation. Second, we extend these models for the specific problem formulations encountered in link adaptation. For this, we develop rigorous problem formulations that are analyzed using classical techniques. Third, we develop theoretical results for the real-time behaviour of the proposed schemes. These bounds extend the machine learning state-of-the-art in terms of performance bounds for stochastic online optimization. The contributions of this thesis hence go beyond the realm of wireless optimization, and extend to new developments applicable to broader machine learning problems.
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| 6. |
- Carlsson, Håkan, et al.
(författare)
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Quantifying the Uncertainty of the Relative Geometry in Inertial Sensors Arrays
- 2021
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Ingår i: IEEE Sensors Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 1530-437X .- 1558-1748. ; 21:17, s. 19362-19373
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Tidskriftsartikel (refereegranskat)abstract
- We present an algorithm to estimate and quantify the uncertainty of the accelerometers' relative geometry in an inertial sensor array. We formulate the calibration problem as a Bayesian estimation problem and propose an algorithm that samples the accelerometer positions' posterior distribution using Markov chain Monte Carlo. By identifying linear substructures of the measurement model, the unknown linear motion parameters are analytically marginalized, and the remaining non-linear motion parameters are numerically marginalized. The numerical marginalization occurs in a low dimensional space where the gyroscopes give information about the motion. This combination of information from gyroscopes and analytical marginalization allows the user to make no assumptions of the motion before the calibration. It thus enables the user to estimate the accelerometer positions' relative geometry by simply exposing the array to arbitrary twisting motion. We show that the calibration algorithm gives good results on both simulated and experimental data, despite sampling a high dimensional space.
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| 7. |
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| 8. |
- Jaldén, Joakim, 1976-
(författare)
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Detection for multiple input multiple output channels : analysis of sphere decoding and semidefinite relaxation
- 2006
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The problem of detecting a vector of symbols, drawn from a finite alphabet and transmitted over a multiple-input multiple-output (MIMO) channel with Gaussian noise, is of central importance in digital communications and is encountered in several different applications. Examples include, but are not limited to; detection of symbols spatially multiplexed over a multiple-antenna channel and the multiuser detection problem in a code division multiple access (CDMA) system. Two algorithms previously proposed in the literature are considered and analyzed. Both algorithms have their origin in other fields of science but have gained mainstream recognition as efficient algorithms for the detection problem considered herein. Specifically, we consider the sphere decoder and semidefinite relaxation detector. By incorporating assumptions applicable in the communications context the performance of the two algorithms is addressed. The first algorithm, the sphere decoder, offers optimal performance in terms of its error probability. Further, the algorithm has proved extremely efficient in terms of computational complexity for moderately sized problems at high signal to noise ratio (SNR). Although it is recognized that the algorithm has an exponential worst case complexity, there has been a widespread belief that the algorithm has a polynomial average complexity at high SNR. A contribution made herein is to show that this is incorrect and that the average complexity, as the worst case complexity, is exponential in the number of symbols detected. Instead, another explanation of the observed efficiency of the algorithm is offered by deriving the exponential rate of growth and showing that this rate, although strictly positive for finite SNR, is small in the high SNR regime. The second algorithm, the semidefinite relaxation (SDR) detector, offers polynomial complexity at the expense of suboptimal performance in terms of error probability. Nevertheless, previous numerical observations suggest that error probability of the SDR algorithm is close to that of the optimal detector. Herein, the near optimality is of the SDR algorithm is given a precise meaning by studying the diversity of the SDR algorithm when applied to the (real valued) i.i.d.~Rayleigh fading channel and it is shown that the SDR algorithm achieves the same diversity order as the optimal detector. Further, criteria under which the SDR estimates coincide with the optimal estimates are derived and discussed.
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| 9. |
- Khorsandmanesh, Yasaman, et al.
(författare)
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Beam Coherence Time Analysis for Mobile Wideband mmWave Point-to-Point MIMO Channels
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Multi-Gbps data rates are achievable in millimeter-wave (mmWave) bands, but a prominent issue is the tiny wavelength that results in rapid fading variations and significant pilot signaling for channel estimation. In this letter, we recognize that the angles of scattering clusters seen from the user equipment (UE) vary slowly compared to the small-scale fading. We characterize the \emph{beam coherence time}, which quantifies how frequently the UE must update its downlink receive combining matrix. The exact beam coherence time is derived in the single-cluster case, and an achievable lower bound is proposed for the multi-cluster case. These values are determined so that at least half of the received signal gain is maintained in between the combining updates. We demonstrate how the beam coherence time can be hundreds of times larger than the channel coherence time of the small-scale fading.
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| 10. |
- Khorsandmanesh, Yasaman, et al.
(författare)
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Beam Coherence Time Analysis for Mobile Wideband mmWave Point-to-Point MIMO Channels
- 2024
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Ingår i: IEEE Wireless Communications Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 2162-2337 .- 2162-2345. ; 13:6, s. 1546-1550
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Tidskriftsartikel (refereegranskat)abstract
- Multi-Gbps data rates are achievable in millimeter-wave (mmWave) bands, but a prominent issue is the tiny wavelength that results in rapid fading variations and significant pilot signaling for channel estimation. In this letter, we recognize that the angles of scattering clusters seen from the UE vary slowly compared to the small-scale fading. We characterize the beam coherence time, which quantifies how frequently the UE must update its downlink receive combining matrix. The exact beam coherence time is derived in the single-cluster case, and an achievable lower bound is proposed for the multi-cluster case. These values are determined so that at least half of the received signal gain is maintained in between the combining updates. We demonstrate how the beam coherence time can be hundreds of times larger than the channel coherence time of the small-scale fading.
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