Showing papers by "Mats Bengtsson published in 2022"

EVM Mitigation With PAPR and ACLR Constraints in Large-Scale MIMO-OFDM Using TOP-ADMM

Abstract: Although signal distortion-based peak-to-average power ratio (PAPR) reduction is a feasible candidate for orthogonal frequency division multiplexing (OFDM) to meet standard/regulatory requirements, the error vector magnitude (EVM) stemming from the PAPR reduction has a deleterious impact on the performance of high data-rate achieving multiple-input multiple-output (MIMO) systems. Moreover, these systems must constrain the adjacent channel leakage ratio (ACLR) to comply with regulatory requirements. Several recent works have investigated the mitigation of the EVM seen at the receivers by capitalizing on the excess spatial dimensions inherent in the large-scale MIMO that assume the availability of perfect channel state information (CSI) with spatially uncorrelated wireless channels. Unfortunately, practical systems operate with erroneous CSI and spatially correlated channels. Additionally, most standards support user-specific/CSI-aware beamformed and cell-specific/non-CSI-aware broadcasting channels. Hence, we formulate a robust EVM mitigation problem under channel uncertainty with nonconvex PAPR and ACLR constraints catering to beamforming/broadcasting. To solve this formidable problem, we develop an efficient scheme using our recently proposed three-operator alternating direction method of multipliers (TOP-ADMM) algorithm and benchmark it against two three-operator algorithms previously presented for machine learning purposes. Numerical results show the efficacy of the proposed algorithm under imperfect CSI and spatially correlated channels.

Using Probabilistic Geometrical Map Information For Train Localization

Abstract: Determination of train positions within a railway network must be fail-safe and of high accuracy. This is an essential task to solve to achieve a secure and efficient railway operation. In this paper, we present a method to estimate position and velocity of a train in the track net using given position estimates from an arbitrary information source, and improving the estimate by using geometrical track information. We focus on modelling and exploiting of the geometrical track information including possible uncertainties and examine the impact of uncertainties on the state estimate. We store the track information as a set of supporting points with Gaussian uncertainties and interpolate linearly. The track information is fed into a Kalman filter in form of soft constraints that is modified to account for state-dependent observation noise. A simulated test run shows that the average position and velocity error along track decreases significantly when modelling the uncertainty of the constraints, compared to using a Kalman filter with hard constraints. We evaluate the presented filter for different supporting point and measurement uncertainties and show that the performance within a typical parameter setting for train positioning is improved compared to the unconstrained Kalman filter and the Kalman filter with hard constraints.

Power Delay Profile investigation in Industrial Indoor Environments at the 24 GHz ISM band

Abstract: Millimeter wave (mmWave) wireless technology is primarily considered for low latency communication in fifth-generation mobile technology (5G) and has the potential to revolutionize industrial automation and manufacturing processes. This article investigates multipath radio propagation in indoor industrial environments at the 24 GHz industrial, scientific and medical (ISM) mmWave frequency band. The wideband radio channel measurements were carried out in four different industrial environments in Sweden. The measurements were conducted using an affordable but highly competent in-house assembled mmWave testbed, reusing radio instruments available in our lab. The measurement environments were chosen based on their radio wave reflection characteristics. The multipath propagation characteristics are analyzed with respect to the power delay profile (PDP), coherence bandwidth, and root mean square (RMS) delay spread. Additionally, the Saleh-Valenzuela model parameters are estimated for these industrial environments.

Donor-derived urologic cancers after renal transplantation: A retrospective non-randomized scientific analysis

Abstract: Background Malignancies in the urinary tract and the kidney graft are quite common after kidney transplantation. In some selected cases tumours develop from donor-derived tissue. Objectives We hypothesised that there is a clinical value to investigate donor/recipient origin in urologic malignancies in renal transplant recipients. Methods In this retrospective study, including patients transplanted between the years 1969 and 2014 at Uppsala University Hospital, Sweden, 11 patients with malignancies in urinary tract and 4 patients with malignancies in kidney transplants were investigated. Donor/recipient origin of tumour tissue was analysed by polymerase chain reaction (PCR) of human leucocyte antigen (HLA) genotypes or by fluorescence in situ hybridization (FISH analysis) of sex chromosomes. HLA genotype and sex chromosomes of the tumour were compared to the known HLA genotype and sex chromosomes of recipient and donor. Results Three of ten cancers in the urinary tract and three of four cancers in the kidney transplants were donor-derived. Conclusions We suggest that urologic malignancies in renal transplant recipients can be investigated for transplant origin. In addition to conventional therapy the allograft immune response against these tumours can be valuable to treat donor-derived cancers.

Federated Learning Using Three-Operator ADMM

Abstract: Federated learning (FL) has emerged as an instance of distributed machine learning paradigm that avoids the transmission of data generated on the users' side. Although data are not transmitted, edge devices have to deal with limited communication bandwidths, data heterogeneity, and straggler effects due to the limited computational resources of users' devices. A prominent approach to overcome such difficulties is FedADMM, which is based on the classical two-operator consensus alternating direction method of multipliers (ADMM). The common assumption of FL algorithms, including FedADMM, is that they learn a global model using data only on the users' side and not on the edge server. However, in edge learning, the server is expected to be near the base station and has often direct access to rich datasets. In this paper, we argue that it is much more beneficial to leverage the rich data on the edge server then utilizing only user datasets. Specifically, we show that the mere application of FL with an additional virtual user node representing the data on the edge server is inefficient. We propose FedTOP-ADMM, which generalizes FedADMM and is based on a three-operator ADMM-type technique that exploits a smooth cost function on the edge server to learn a global model in parallel to the edge devices. Our numerical experiments indicate that FedTOP-ADMM has substantial gain up to 33% in communication efficiency to reach a desired test accuracy with respect to FedADMM, including a virtual user on the edge server.

Evaluating the Impact of Map Inaccuracies on Path Discrimination Behind Railway Turnouts

Abstract: Determination of train positions within a railway network must be fail-safe and of high accuracy. In train-bourne positioning, exploitation of geometrical map features is an important factor and uncertainties in the map information may affect the position estimate. In this paper, we present a method to estimate the position of a train in the track net and to identify the correct path behind a turnout, using absolute position estimates and geometrical map information of various accuracies. We evaluate the impact of uncertainties in the map representation on the correct identification of a path behind a turnout. We derive a formulation of a probabilistic track map and include the map information into a constrained multi-hypothesis Kalman filter. We show in numerical simulations on a crossover and a turnout that modelling existing map uncertainties significantly improves the track discrimination.

Achievable Rates of Orthogonal Time Frequency Space (OTFS) Modulation in High Speed Railway Environments

Abstract: The development of future railway systems is contingent on the evolution of wireless communications technologies and their ability to serve more sophisticated use cases. Recent proposals to extend the 3rd Generation Partnership Project (3GPP) 4G or 5G standard for use in next-generation railway wireless communications presents a problem in that they are still based on Orthogonal Frequency Division Multiplexing (OFDM), which is vulnerable to Doppler-related effects when traveling at high speed. Orthogonal Time Frequency Space (OTFS) is a promising new modulation technique that can handle commu-nication even in very high vehicle speed cases. In this paper, we investigate the performance of OTFS in terms of achievable rate under different High Speed Rail (HSR) environments, while taking into account the impact of practical but non-biorthogonal pulse shapes. Simulation results show that OTFS provides consistently high achievable rates regardless of the environment, and that the rates are relatively insensitive to the speed of travel.