Various performance bounds on the estimation of low-rank probability mass function tensors from partial observations. - In: IEEE ICASSP 2023 conference proceedings, (2023), insges. 5 S.
Probability mass function (PMF) estimation using a low-rank model for the PMF tensor has gained increased popularity in recent years. However, its performance evaluation relied mostly on empirical testing. In this work, we derive theoretical bounds on the attainable performance under this model assumption. We begin by deriving the constrained Cramér-Rao Bound (CCRB) on the low-rank decomposition parameters, and then extend the CCRB to bounds on the mean square error in the resulting estimates of the PMF tensor’s elements, as well as on the mean Kullback-Leibler divergence (KLD) between the estimated and true PMFs. The asymptotic tightness of these bounds is demonstrated by comparing them to the performance of the Maximum Likelihood estimate in a small-scale simulation example.
https://doi.org/10.1109/ICASSP49357.2023.10095206
Rate splitting and precoding strategies for multi-user MIMO broadcast channels with common and private streams. - In: IEEE ICASSP 2023 conference proceedings, (2023), insges. 5 S.
In this paper, we present a precoder design for multi-user multiple-input multiple-output (MU-MIMO) broadcast systems with rate splitting at the transmitter. The proposed scheme applies to both underloaded and overloaded communication systems and supports the transmission of multiple common and private streams. We show how the generalized singular value (GSVD) and multilinear generalized singular value (ML-GSVD) decompositions can be used to define the number of common and private streams and adjust the message split. Additionally, we present transmit precoding and receive combining designs that allow the simultaneous transmission of common and private streams but do not require successive interference cancellation (SIC) at the receivers and can be used in cases where the total number of streams does not exceed the number of transmit antennas.
https://doi.org/10.1109/ICASSP49357.2023.10095138
Equivalence of aperture reduction in element space and constrained combination of DFT beams in beamspace. - In: IEEE ICASSP 2023 conference proceedings, (2023), insges. 5 S.
In this paper, we present an analytical proof of equivalence of the signal processing in the reduced aperture element space and in beamspace produced by the combination of multiple adjacent DFT beams with a subsequent constraining of the resulting magnitudes. This link finds applications in millimeter wave (mmWave) communications and radars that are typically equipped with a small number of RF chains and employ hybrid beamforming with analog phase shifters. This result unifies the transceiver designs, reduces complexity, and proves the applicability of state-of-the-art beamspace-based methods. It has a special implication for channel estimation at the initial stage when terminals acquire coarse estimates of the Sectors-of-Interest (SoIs). We show that the constrained groups of beams are equivalent to DFT beamformers of a smaller size aperture and present a closed-form expression of the corresponding effective aperture length as a function of the number of beams. We also derive an approximation of this expression to find the indices of the active array elements in a closed form. Finally, we verify this theory and analyze the accuracy of the proposed approximation using numerical simulations.
https://doi.org/10.1109/ICASSP49357.2023.10096523
Multi-environment based meta-learning with CSI fingerprints for radio based positioning. - In: 2023 IEEE Wireless Communications and Networking Conference (WCNC), (2023), insges. 6 S.
Radio based positioning of a user equipment (UE) based on deep learning (DL) methods using channel state information (CSI) fingerprints have shown promising results. DL models are able to capture complex properties embedded in the CSI about a particular environment and map UE’s CSI to the UE’s position. However, the CSI fingerprints and the DL models trained on such fingerprints are highly dependent on a particular propagation environment, which generally limits the transfer of knowledge of the DL models from one environment to another. In this paper, we propose a DL model consisting of two parts: the first part aims to learn environment independent features while the second part combines those features depending on the particular environment. To improve transfer learning, we propose a meta learning scheme for training the first part over multiple environments. We show that for positioning in a new environment, initializing a DL model with the meta learned environment independent function achieves higher UE positioning accuracy compared to regular transfer learning from one environment to the new environment, or compared to training the DL model from scratch with only fingerprints from the new environment. Our proposed scheme is able to create an environment independent function which can embed knowledge from multiple environments and more effectively learn from a new environment.
https://doi.org/10.1109/WCNC55385.2023.10118753
Framework for simulation models and algorithms in ISAC networks. - In: 2023 IEEE 3rd International Symposium on Joint Communications & Sensing (JC & S), (2023), insges. 6 S.
In Integrated Sensing and Communications (ISAC), radar and communications functionalities share the same radio channel and radio resources. This sharing allows the deployment of both functionalities at a low cost without additional frequency bands and infrastructure. Because radar and communications have different radio resource requirements, new radio access and radio resource management (RRM) methods are required in a shared system. To develop and evaluate new ISAC methods, this paper presents a modular simulation framework for the system level and link level of ISAC networks. The main components of the framework are the data storage, the simulators, and the algorithms. This paper presents the framework's architecture, the integrated simulation models for channel and signal simulation, and the integrated algorithms for multiple radar target localization and data transmission.
https://doi.org/10.1109/JCS57290.2023.10107507
VANET-based traffic light management for an emergency vehicle. - In: Ubiquitous networking, (2023), S. 129-137
Wireless communications have affected our lifestyle in the last decades. It is helpful to improve quality of life for communities. Communications among vehicles usually take place in vehicular ad-hoc networks (VANETs). Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications are aspects of communications in the transportation which are growing rapidly. They can play a pivotal role in the transportation field. Management of traffic lights (TLs) is crucial to control traffic flow especially at an intersection. The goal of this paper is to manage the TLs at an intersection when an emergency vehicle (EV) is approaching. First, we simulate an intersection which includes TLs via simulation of urban mobility (SUMO). Later, we simulate VANETs communication to manage the TLs at the intersection when the EV is coming with the help of objective modular network testbed in C++ (OMNeT++) and vehicles in network simulation (Veins). Finally, the impact of V2I communication on delivery efficiency of the emergency service is investigated. Simulation results show an improvement in delivery efficiency of the emergency service.
Fill level measurements using an M-sequence UWB radar. - In: International journal of microwave and wireless technologies, ISSN 1759-0795, Bd. 15 (2023), 1, S. 74-81
Due to increasingly complex and automated manufacturing processes, the demands on the control parameters of these processes are also increasing. In many applications, such a parameter is the fill quantity, whose precise determination is of ever growing importance. This paper shows with which accuracy and precision an M-sequence ultra-wideband radar can determine levels in small metallic and non-metallic containers with contact-based and contactless measurements. First, the principle of level measurement using guided wave radar is explained and the measurement setup is described. Afterward, the measurement results are shown and discussed. The measurements show that the level can be measured with an accuracy of better than 0.5 mm. In addition, level fluctuations can be detected with a precision of 3 μm. Based on the results of the guided wave radar, the possibilities of volumetric contactless measurement using an electrically small patch antenna are discussed. A particular challenge in contactless level measurement is the high number of multipath components, which strongly influence the accuracy. In addition, there are near-field effects when measuring close to the antenna. Exploiting these near-field effects, an additional method to accurately determine the full state of the container is investigated.
https://doi.org/10.1017/S1759078722000502
Efficient probability mass function estimation from partially observed data. - In: Conference record of the Fifty-Sixth Asilomar Conference on Signals, Systems & Computers, (2022), S. 256-262
Estimating the joint probability mass function (PMF) of a set of random variables from partially observed data is a crucial part of statistical learning and data analysis, with applications in areas such as recommender systems and data classification. Recently, it has been proposed to estimate the joint PMF based on the maximum likelihood (ML) of the data, fitted to a low-rank canonical polyadic decomposition (CPD) model of the joint PMF. To this end, a hybrid alternating-directions expectation-maximization (AD-EM) algorithm was proposed to solve the ML optimization problem, consisting of computationally expensive AD iterations followed by an EM refinement stage. It is well known that the convergence rate of EM decreases as the fraction of missing data increases. In this paper, we address the slow convergence of the EM algorithm. By adapting the squared iterative methods (SQUAREM) acceleration scheme to the context of PMF estimation, we propose the SQUAREM-PMF algorithm to speed up the convergence of the EM algorithm. Moreover, we demonstrate that running the computationally cheaper EM algorithm alone after an appropriate initialization is sufficient. Numerical results on both synthetic and real data in the context of movie recommendation show that our algorithm outperforms state-of-the-art PMF estimation algorithms.
https://doi.org/10.1109/IEEECONF56349.2022.10052047
Labeling custom indoor point clouds through 2D semantic image segmentation. - In: 2022 Sixth IEEE International Conference on Robotic Computing, (2022), S. 261-264
For effective Computer Vision (CV) applications, one of the difficult challenges service robots have to face concerns with complete scene understanding. Therefore, various strategies are employed for point-level segregation of the 3D scene, such as semantic segmentation. Currently Deep Learning (DL) based algorithms are popular in this domain. However, they require precisely labeled ground truth data. Generating this data is a lengthy and expensive procedure, resulting in a limited variety of available data. On the contrary, the 2D image domain offers labeled data in abundance. Therefore, this study explores how we can achieve accurate labels for the 3D domain by utilizing semantic segmentation on 2D images and projecting the estimated labels to the 3D space via the depth channel. The labeled data may then be used for vision related tasks such as robot navigation or localization.
https://doi.org/10.1109/IRC55401.2022.00050
Emulation of electromagnetic plane waves for 3D antenna pattern estimation. - In: 2022 IEEE 96th Vehicular Technology Conference:(VTC 2022-Fall), (2022), insges. 6 S.
With the fast development of wireless devices, over-the-air (OTA) testing is becoming the preferred method among developers and manufacturers of wireless equipment. The ability to recreate a scenario under controllable and repeatable conditions keeps the method under constant development, providing new features that increase the realism during the tests. A recent proof of that is the integration of 3D wave field synthesis (3DWFS) to OTA testing, which becomes a significant step to accurately emulate wireless scenarios within a controlled environment.In this context, this contribution improves the OTA system calibration for 3DWFS; efficiently increasing the emulation quality of electromagnetic plane waves impinging from any angular position within an anechoic chamber. In fact, this enhancement implicitly delivers a new method for accurate estimation of the antenna radiation pattern in 3D. This is not only a highly demanded application among antenna manufacturers but in this case also proves the validity of the results and consolidates the integration of 3DWFS to OTA testing.
https://doi.org/10.1109/VTC2022-Fall57202.2022.10013008