On the IRS Deployment in Smart Factories Considering Blockage Effects: Collocated or Distributed?

In this article, we study the collocated and distributed deployment of intelligent reflecting surfaces (IRS) for a fixed total number of IRS elements to support enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) services inside a factory. We build a channel model that incorporates the line-of-sight (LOS) probability and power loss of each transmission path, and propose three metrics, namely, the expected received signal-to-noise ratio (SNR), expected finite-blocklength (FB) capacity, and expected outage probability, where the expectation is taken over the probability distributions of interior blockages and channel fading. The expected received SNR and expected FB capacity for extremely high blockage densities are derived in closed-form as functions of the amount and height of IRSs and the density, size, and penetration loss of blockages, which are verified by Monte Carlo simulations. Results show that deploying IRSs vertically higher leads to higher expected received SNR and expected FB capacity. By analysing the average/minimum/maximum of the three metrics versus the number of IRSs, we find that for high blockage densities, both eMBB and URLLC services benefit from distributed deployment; and for low blockage densities, URLLC services benefit from distributed deployment while eMBB services see limited difference between collocated and distributed deployment

HybridDeepRx: Deep Learning Receiver for High-EVM Signals

In this paper, we propose a machine learning (ML) based physical layer receiver solution for demodulating OFDM signals that are subject to a high level of nonlinear distortion. Specifically, a novel deep learning based convolutional neural network receiver is devised, containing layers in both time- and frequency domains, allowing to demodulate and decode the transmitted bits reliably despite the high error vector magnitude (EVM) in the transmit signal. Extensive set of numerical results is provided, in the context of 5G NR uplink incorporating also measured terminal power amplifier characteristics. The obtained results show that the proposed receiver system is able to clearly outperform classical linear receivers as well as existing ML receiver approaches, especially when the EVM is high in comparison with modulation order. The proposed ML receiver can thus facilitate pushing the terminal power amplifier (PA) systems deeper into saturation, and thereon improve the terminal power-efficiency, radiated power and network coverage.Comment: To be presented in the 2021 IEEE International Symposium on Personal, Indoor and Mobile Radio Communication

5G Spectrum: enabling the future mobile landscape

© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The arrival of the fifth generation (5G) is expected to come together with three important enablers. First, the densification of access nodes will continue. Second, 5G networks must be highly flexible and adapt to the dynamism of the traffic location and patterns. For this, some of the radio access network (RAN) functionalities will run in large computer centers, able to dynamically assign more or fewer units of computation to the virtual cells distributed in the network. Finally, a complex landscape of spectrum availability and access will emerge where multiple frequency bands, subject to different regulations including various forms of shared spectrum, are expected to be available to wireless communication systems.Schotten, HD.; Uusitalo, MA.; Monserrat Del Río, JF.; Queseth, O. (2015). 5G Spectrum: enabling the future mobile landscape. IEEE Communications Magazine. 53(7):16-17. doi:10.1109/MCOM.2015.7158260S161753

28 GHz NLOS Channel Measurements Revealing Low Path Loss and High Angular Spread in Container Ports

This paper presents results from a comprehensive measurement campaign conducted at 28 GHz inside a container canyon within a commercial port environment. The measurements are performed at various points inside the container canyon, considering two types of container stacking and two different Transmitter (TX) locations, using a narrowband channel sounder equipped with a rotating horn antenna. The measurements are used to evaluate the azimuthal spectrum and spatial correlation, as well as the impact of a vehicle inside a canyon on these parameters. Further, the measurement data is utilized to validate a simulation setup from which the path loss and the elevation spectrum inside the canyon is obtained. Lastly, a propagation model inside the canyon is hypothesized and shown to be consistent with the measurements. The analysis show a low path loss compared to free space, as well as a high angular spread and short spatial correlation.Comment: 10 pages, 19 figures. Submitted to Transactions on Antennas and Propagatio

Detection of Impaired OFDM Waveforms Using Deep Learning Receiver

With wireless networks evolving towards mmWave and sub-THz frequency bands, hardware impairments such as IQ imbalance, phase noise (PN) and power amplifier (PA) nonlinear distortion are increasingly critical implementation challenges. In this paper, we describe deep learning based physical-layer receiver solution, with neural network layers in both time- and frequency-domain, to efficiently demodulate OFDM signals under coexisting IQ, PN and PA impairments. 5G NR standard-compliant numerical results are provided at 28 GHz band to assess the receiver performance, demonstrating excellent robustness against varying impairment levels when properly trained.acceptedVersionPeer reviewe

HybridDeepRx : Deep Learning Receiver for High-EVM Signals

In this paper, we propose a machine learning (ML) based physical layer receiver solution for demodulating OFDM signals that are subject to a high level of nonlinear distortion. Specifically, a novel deep learning based convolutional neural network receiver is devised, containing layers in both time- and frequency domains, allowing to demodulate and decode the transmitted bits reliably despite the high error vector magnitude (EVM) in the transmit signal. Extensive set of numerical results is provided, in the context of 5G NR uplink incorporating also measured terminal power amplifier characteristics. The obtained results show that the proposed receiver system is able to clearly outperform classical linear receivers as well as existing ML receiver approaches, especially when the EVM is high in comparison with modulation order. The proposed ML receiver can thus facilitate pushing the terminal power amplifier (PA) systems deeper into saturation, and thereon improve the terminal power-efficiency, radiated power and network coverage.acceptedVersionPeer reviewe

The METIS 5G System Concept: Meeting the 5G Requirements

(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.[EN] The development of every new generation of wireless communication systems starts with bold, high-level requirements and predictions of its capabilities. The 5G system will not only have to surpass previous generations with respect to rate and capacity, but also address new usage scenarios with very diverse requirements, including various kinds of machine-type communication. Following this, the METIS project has developed a 5G system concept consisting of three generic 5G services: extreme mobile broadband, massive machine-type communication, and ultra-reliable MTC, supported by four main enablers: a lean system control plane, a dynamic radio access network, localized contents and traffic flows, and a spectrum toolbox. This article describes the most important system-level 5G features, enabled by the concept, necessary to meet the very diverse 5G requirements. System-level evaluation results of the METIS 5G system concept are presented, and we conclude that the 5G requirements can be met with the proposed system concept.This work was supported in part by the European Commission under FP7, grant number ICT-317669 METIS.Tullberg, H.; Popovski, P.; Li, Z.; Uusitalo, MA.; Hoglund, A.; Bulakci, O.; Fallgren, M.... (2016). The METIS 5G System Concept: Meeting the 5G Requirements. IEEE Communications Magazine. 54(12):132-139. https://doi.org/10.1109/MCOM.2016.1500799CMS132139541

Increased risk for dementia in neurofibromatosis type 1

Purpose: To determine the risk for dementia in neurofibromatosis type 1 (NF1) using a Finnish nationwide cohort of individuals with NF1, and data from national registries.Methods: A Finnish cohort of 1,349 individuals with confirmed NF1 according to the US National Institutes of Health (NIH) diagnostic criteria was compared with a control cohort of 13,870 individuals matched for age, sex, and area of residence. Dementia-related hospital visits were retrieved from the Finnish Care Register for Health Care using International Classification of Diseases, 10th revision (ICD-10) diagnosis codes G30 and F00-F03. Purchases of antidementia drugs were queried with Anatomical Therapeutic Chemical (ATC) classification code N06D from the drug reimbursement register maintained by the Social Insurance Institution of Finland. The follow-up spanned 1998-2014.Results: Totals of 16 and 165 individuals with at least two dementia-related diagnoses or drug purchases were identified in the NF1 and control cohorts, respectively. The hazard ratio for dementia in NF1 was 1.67 (95% confidence interval [CI] 1.00-2.80, P = 0.050). In an analysis stratified by the type of dementia, the risk for Alzheimer disease was increased in NF1 compared to controls with a hazard ratio of 2.88 (95% CI 1.47-5.66, P = 0.002).Conclusion: Dementia and especially Alzheimer disease are previously unrecognized neurological complications of NF1.© 2021. The Author(s).</p

6G Vision, Value, Use Cases and Technologies from European 6G Flagship Project Hexa-X

While 5G is being deployed and the economy and society begin to reap the associated benefits, the research and development community starts to focus on the next, 6th Generation (6G) of wireless communications. Although there are papers available in the literature on visions, requirements and technical enablers for 6G from various academic perspectives, there is a lack of joint industry and academic work towards 6G. In this paper a consolidated view on vision, values, use cases and key enabling technologies from leading industry stakeholders and academia is presented. The authors represent the mobile communications ecosystem with competences spanning hardware, link layer and networking aspects, as well as standardization and regulation. The second contribution of the paper is revisiting and analyzing the key concurrent initiatives on 6G. A third contribution of the paper is the identification and justification of six key 6G research challenges: (i) “connecting”, in the sense of empowering, exploiting and governing, intelligence; (ii) realizing a network of networks, i.e., leveraging on existing networks and investments, while reinventing roles and protocols where needed; (iii) delivering extreme experiences, when/where needed; (iv) (environmental, economic, social) sustainability to address the major challenges of current societies; (v) trustworthiness as an ingrained fundamental design principle; (vi) supporting cost-effective global service coverage. A fourth contribution is a comprehensive specification of a concrete first-set of industry and academia jointly defined use cases for 6G, e.g., massive twinning, cooperative robots, immersive telepresence, and others. Finally, the anticipated evolutions in the radio, network and management/orchestration domains are discussed