6ET12021-10-11T16:33:39+00:00

6ET1: 6G Enabling Technologies I

Wednesday, 9 June 2021, 11:30-13:00, Zoom Room

Session Chair: Yaning Zou (Tech. Univ. Dresden, Germany)

Analysis of Downlink Connectivity in NB-IoT Networks Employing NOMA with Imperfect SIC

Shashwat Mishra (Indian Institute of Technology, Madras, India & Nokia Bell Labs Paris-Saclay, France); Lou Salaun and Chung Shue Chen (Nokia Bell Labs, France); K Giridhar (Indian Institute of Technology, Madras, India)
We study the problem of maximizing the number of served devices in a non-orthogonal multiple access (NOMA) based Narrowband Internet of Things (NB-IoT) network for supporting massive connectivity in the downlink. We analyze this problem under practical system limitations of imperfect successive interference cancellation (SIC) at the receiver along with data rate, power and bandwidth constraints. We propose a strategy for joint device and power allocation through an iterative solution for a system of linear equations on each sub-carrier that maximizes the number of connected devices. We evaluate the performance of the proposed solution over a wide range of service scenarios through extensive computer simulations and demonstrate the sensitivity of connectivity in power domain NOMA based NB-IoT systems to the residual interference resulting from imperfect SIC.

 

Study of Reflection-Loss-Based Material Identification from Common Building Surfaces

Yi Geng (Ericsson, China); Vijaya Parampalli Yajnanarayana (Ericsson Research, India); Ali Behravan (Ericsson, Sweden); Erik Dahlman and Deep Shrestha (Ericsson Research, Sweden)
Perceiving and recognizing material properties of surfaces and objects are fundamental aspects of new and emerging use cases such as robotic perception, virtual reality (VR) applications, digital twins, and creating a 3D digital map of an environment. In this paper, we present results from our simulation-based study of reflection-loss-based material identification from eight common building materials. The study focuses on 2.6 GHz, 28 GHz, and 60 GHz radio carrier frequencies. Analysis of simulation results indicates that a combination of incident angle and reflection loss can be used to properly identify the common building materials. We, therefore, propose a novel joint communication and sensing method for material recognition using reflection loss of the radio signal by the scatterers around the propagation path in a wireless communication network. Compared to existing material identification methods, the proposed reflection-loss-based method is capable of identifying materials from a significant distance without requiring any contact with the object and without requiring dedicated sensors from the infrastructure point of view.

 

Best Beam Prediction in Non-Standalone mmWave Systems

Tushara Ponnada, Parham Kazemi and Hanan Al-Tous (Aalto University, Finland); Ying-Chang Liang (University of Electronic Science and Technology of China, China); Olav Tirkkonen (Aalto University, Finland)
We consider a machine learning approach to perform best beam prediction in Non-Standalone Millimeter Wave (mmWave) Systems utilizing Channel Charting (CC). The approach reduces communication overheads and delays associated with initial access and beam tracking in 5G New Radio (NR) systems. The network has a mmWave and a sub-6 GHz component. We devise a Base Station (BS) centric approach for best mmWave beam prediction, based on Channel State Information (CSI) measured at the sub-6 GHz BS, with no need to exchange information with UEs. In a training phase, we collect CSI at the sub-6 GHz BS from sample UEs, and construct a dimensional reduction of the sample CSI, called a CC. We annotate the CC with best beam information measured at a mmWave BS for the sample UEs, assuming autonomous beamformer at the UE side. A beam predictor is trained based on this information, connecting any sub-6 GHz CSI with a predicted best mmWave beam. To evaluate the efficiency of the proposed framework, we perform simulations for a street segment with synthetic spatially consistent CSI. With a neural network predictor, we obtain 91% accuracy for predicting best beam and 99% accuracy for predicting one of two best beams. The accuracy of CC based beam prediction is indistinguishable from true location based beam prediction.

 

Above-100 GHz Wave Propagation Studies in the European Project Hexa-X for 6G Channel Modelling

Pekka Kyösti (Keysight Technologies & University of Oulu, Finland); Katsuyuki Haneda (Aalto University, Finland); Jean-Marc Conrat (Orange Labs, France); Aarno Pärssinen (University of Oulu, Finland)
We describe capabilities and plans to characterize above 100 GHz radio channel and propagation effects as part of a 6G research project Hexa-X. The starting point is the existing knowledge of radio propagation gathered by prior measurement and theoretical studies. Then we define measurement equipment, planned or performed campaigns, and discuss some challenges related to measurements at upper mm-wave frequencies. For several reasons the channel measurements are more time consuming on higher frequencies and it is not easy to collect enough data for statistical analysis. Hence we briefly introduce a stored channel model that will be developed based on the gathered channel measurement data. This initial channel model can be used as it is for physical layer studies through simulations and also as a basis for future channel models.

 

Integrated Sensing and Communication in 6G: A Prototype of High Resolution THz Sensing on Portable Device

Oupeng Li (Huawei Technologies Co. Ltd, China); Jia He (Huawei Technologies Co., Ltd., China); Kun Zeng (Huawei Technologies Co. Ltd., China); Ziming Yu (Huawei Technologies CO., LTD, China); Xianfeng Du, Yuan Liang and Guangjian Wang (Huawei Technologies Co., Ltd., China); Yan Chen and Peiying Zhu (Huawei Technologies, Canada); Wen Tong (Huawei Technologies Canada Co., Ltd., Canada); David R Lister (Vodafone Group R&D, United Kingdom (Great Britain)); Luke Ibbetson (Vodafone, United Kingdom (Great Britain))
6G is believed to go beyond communication and provide integrated sensing and computing capabilities for a vision of Connected Intelligence with everything connected, everything sensed, and everything intelligent. Integrated sensing and communication will play a vital role for the fusion of physical and cyber worlds. The exploration of higher frequency bands, larger bandwidth, and more advanced large antenna technologies is paving the way towards the goal. In particular, the study of THz opens the possibility to have high resolution sensing and imaging capability on a communication mobile device. In this paper, we take a step along this direction and justify such possibility by building a THz sensing prototype with millimeter level imaging resolution while considering the physical aperture constraint of typical mobile device.

Go to Top