PHY3: Channel Estimation
Thursday, 10 June 2021, 9:30-11:00, Zoom Room
Session Chair: Jaap van de Beek (Luleå Univ. of Tech., Sweden)
Channel Estimation and Hybrid Architectures for RIS-Assisted Communications
Jiguang He, Nhan Thanh Nguyen, Rafaela Schroeder, Visa Tapio, Joonas Kokkoniemi and Markku Juntti (University of Oulu, Finland)
Reconfigurable intelligent surfaces (RISs) are considered as potential technologies for the upcoming sixth-generation (6G) wireless communication system. Various benefits brought by deploying one or multiple RISs include increased spectrum and energy efficiency, enhanced connectivity, extended communication coverage, reduced complexity at transceivers, and even improved localization accuracy. However, to unleash their full potential, fundamentals related to RISs, ranging from physical-layer (PHY) modelling to RIS phase control, need to be addressed thoroughly. In this paper, we provide an overview of some timely research problems related to the RIS technology, i.e., PHY modelling (including also physics), channel estimation, potential RIS architectures, and RIS phase control (via both model-based and data-driven approaches), along with recent numerical results. We envision that more efforts will be devoted towards intelligent wireless environments, enabled by RISs.
On the Position of Intelligent Reflecting Surfaces
Emad Ibrahim, Rickard Nilsson and Jaap van de Beek (Luleå University of Technology, Sweden)
We study the positional impact of an intelligent reflecting surface (IRS) on the achievable rate for single and multiple antenna systems. We show that in IRS-aided single antenna systems, it is always best to place the IRS as close as possible to the transmitter or receiver since the large-scale fading for IRS-reflected links is the main factor that characterizes the performance gain. However, for IRS-aided multiple antenna systems, the propagation environment has an important role in characterizing the efficient regions of IRS placement. In the case of a line-of-sight environment, the channel matrix turns out to be rank-deficient. Thus, both far and near IRS placements result in significant achievable rate improvements where the former provides a substantial additional degree-of-freedom, while the latter results in a power gain. Furthermore, as the wireless channel becomes richer with multipath, the rank of the channel matrix increases. Thus, the efficient far placement regions gradually shrink until they disappear in the case of a Rayleigh fading channel where IRS near placements become more efficient than far placements as they result in higher power gains.
Channel Charting Based Beam SNR Prediction
Parham Kazemi, Tushara Ponnada 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 machine learning for Intra Cell Beam Handovers (ICBH) in mmWave 5GNR systems by leveraging Channel Charting (CC). We develop a Base Station (BS) centric approach for predicting the Signal-to-Noise-Ratio (SNR) of beams. Beam SNRs are predicted based on measured signal at the BS without the need to exchange information with UEs. In an offline training phase, we construct a beam-specific dimensionality reduction of Channel State Information (CSI) to a low-dimensional CC, annotate the CC with beam-wise SNRs and then train SNR predictors for different target beams. In the online phase, we predict target beam SNRs. K-nearest neighbors, Gaussian Process Regression and Neural Network based prediction are considered. Based on SNR difference between the serving and target beams a handover can be decided. To evaluate the efficiency of the proposed framework, we perform simulations for a street segment with synthetically generated CSI. SNR prediction accuracy of average root mean square error less than 0.3 dB is achieved.
A Deep Learning-Based Approach to 5G-New Radio Channel Estimation
Elisa Zimaglia (Tim S.p.A., Italy); Daniel G. Riviello and Roberto Garello (Politecnico di Torino, Italy); Roberto Fantini (Telecom Italia SpA, Italy)
In this paper, we present a deep learning-based technique for channel estimation. By treating the time-frequency grid of the channel response as a low-resolution 2D-image, we propose a 5G-New Radio Convolutional Neural Network, called NR-ChannelNet, which can be properly trained to predict the channel coefficients. Our study employs a 3GPP-compliant 5G-New Radio simulator that can reproduce a realistic scenario by including multiple transmitting/receiving antenna schemes and clustered delay line channel model. Simulation results show that our deep learning approach can achieve competitive performance with respect to traditional techniques such as 2D-MMSE: indeed, under certain conditions, our new NR-ChannelNet approach achieves remarkable gains in terms of throughput.
Enabling Energy-Efficient Tbit/s Communications by 1-Bit Quantization and Oversampling
Peter Neuhaus (Technische Universität Dresden, Germany); Martin Schlüter(Dresden University of Technology, Germany); Christoph Jans (Technische Universität Dresden, Germany); Meik Dörpinghaus (TU Dresden, Germany); Gerhard P. Fettweis (Technische Universität Dresden, Germany)
In this work, we investigate the energy efficiency (EE) of future wireless communications systems. We argue that operating at high bandwidth and a comparatively low signal-to-noise ratio (SNR) is beneficial in terms of EE. Furthermore, we discuss how employing single-carrier modulations in combination with 1-bit quantization and temporal oversampling at the receiver enables energy-efficient wideband wireless communications systems. Our main contribution is to compare the EE of such a system to an idealized conventional system under various assumptions on the ADC power consumption. Our numerical results suggest that the EE can be improved significantly by employing 1-bit quantization and oversampling at the receiver at the cost of increased bandwidth.