Wideband Channel Estimation With a Generative Adversarial Network

Date

2021-05-01

Author

Balevi, Eren
Andrews, Jeffrey G.

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

211
views

0
downloads

Communication at high carrier frequencies such as millimeter wave (mmWave) and terahertz (THz) requires channel estimation for very large bandwidths at low SNR. Hence, allocating an orthogonal pilot tone for each coherence bandwidth leads to excessive number of pilots. We leverage generative adversarial networks (GANs) to accurately estimate frequency selective channels with few pilots at low SNR. The proposed estimator first learns to produce channel samples from the true but unknown channel distribution via training the generative network, and then uses this trained network as a prior to estimate the current channel by optimizing the network's input vector in light of the current received signal. Our results show that at an SNR of -5 dB, even if a transceiver with one-bit phase shifters is employed, our design achieves the same channel estimation error as an LS estimator with SNR = 20 dB or the LMMSE estimator at 2.5 dB, both with fully digital architectures. Additionally, the GAN-based estimator reduces the required number of pilots by about 70% without significantly increasing the estimation error and required SNR. We also show that the generative network does not appear to require retraining even if the number of clusters and rays change considerably.

Subject Keywords

Channel estimation, Radio frequency, OFDM, Signal to noise ratio, Frequency estimation, Gallium nitride, Transceivers, Frequency selective channel estimation, GAN, MIMO, terahertz and millimeter wave communication, MASSIVE MIMO, SYSTEMS

URI

https://hdl.handle.net/11511/100483

Journal

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS

DOI

https://doi.org/10.1109/twc.2020.3047100

Collections

Department of Electrical and Electronics Engineering, Article

Suggestions

OpenMETU
Core

Massive MIMO Channel Estimation With an Untrained Deep Neural Network Balevi, Eren; Doshi, Akash; Andrews, Jeffrey G. (2020-03-01) This paper proposes a deep learning-based channel estimation method for multi-cell interference-limited massive MIMO systems, in which base stations equipped with a large number of antennas serve multiple single-antenna users. The proposed estimator employs a specially designed deep neural network (DNN) based on the deep image prior (DIP) network to first denoise the received signal, followed by conventional least-squares (LS) estimation. We analytically prove that our LS-type deep channel estimator can app...
Blind Channel Estimation Based on the Lloyd-Max Algorithm in Narrowband Fading Channels and Partial-Band Jamming Dizdar, Onur; Yılmaz, Ali Özgür (2012-07-01) In wireless communications, knowledge of the channel coefficients is required for coherent demodulation. In this work, a blind channel estimation method based on the Lloyd-Max algorithm is proposed for single-tap fading channels. The algorithm estimates the constellation points for the received signal using the Lloyd-Max algorithm. The algorithm is investigated for frequency hopping systems with small hop durations and operating under partial-band jamming for both detecting the jammer and estimating the cha...
Blind Communications Sytem Structures For SIMO Channels Karakuetuek, Serkan; Tuncer, Temel Engin (2008-04-22) Frame based single input multi output (SIMO) communications systems are considered. In these systems frame length, offset, channel order and channel coefficients are all unknowns and there is no training session. Different system structures are considered for the estimation these unknowns blindly and best system structure is found. In addition a new method for channel order estimation is proposed.
Parallelization of noise subspace-based doa estimation algorithms on cpu and gpu Eray, Hamza; Temizel, Alptekin; Department of Modeling and Simulation (2021-2-11) Direction-of-Arrival (DOA) estimation is known as an active research area, and it is studied under array signal processing. The algorithms in this area are widely used in various applications such as sonar, search-and-rescue, navigation, and geolocation. However, achieving a real-time system performance is sometimes a challenging task for these algorithms. In this thesis, four noise subspace-based DOA estimation algorithms (PHD, MUSIC, EV, and MN) were considered and implemented in MATLAB, C/C++, and CUDA. ...
Temporal and spatial forecasting of the foF2 values up to twenty four hours in advance Tulunay, E; Ozkaptan, C; Tulunay, Yurdanur (2000-01-01) Radio waves of a wide range of frequencies from very low frequency (VLF) to high frequency (HF), (broadly 3 to 30 MHz) can be propagated to great distances via the ionosphere.

Citation Formats

E. Balevi and J. G. Andrews, “Wideband Channel Estimation With a Generative Adversarial Network,” IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, vol. 20, no. 5, pp. 3049–3060, 2021, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/100483.