Massive MIMO Channel Estimation With an Untrained Deep Neural Network

Date

2020-03-01

Author

Balevi, Eren
Doshi, Akash
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

191
views

0
downloads

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 approach minimum mean square error (MMSE) estimator performance for high-dimensional signals, while avoiding complex channel inversions and knowledge of the channel covariance matrix. This analytical result, while asymptotic, is observed in simulations to be operational for just 64 antennas and 64 subcarriers per OFDM symbol. The proposed method also does not require any training and utilizes several orders of magnitude fewer parameters than conventional DNNs. The proposed deep channel estimator is also robust to pilot contamination and can even completely eliminate it under certain conditions.

Subject Keywords

Deep learning, channel estimation, massive MIMO, OFDM, deep image prior, SYSTEMS, UPLINK

URI

https://hdl.handle.net/11511/100135

Journal

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS

DOI

https://doi.org/10.1109/twc.2019.2962474

Collections

Department of Electrical and Electronics Engineering, Article

Suggestions

OpenMETU
Core

Wideband Channel Estimation With a Generative Adversarial Network Balevi, Eren; Andrews, Jeffrey G. (2021-05-01) 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 v...
DEEP LEARNING AIDED PARAMETRIC CHANNEL COVARIANCE MATRIX ESTIMATION FOR MILLIMETER WAVE HYBRID MASSIVE MIMO Esen, Özbay; Güvensen, Gökhan Muzaffer; Department of Electrical and Electronics Engineering (2021-9-09) Millimeter-wave (mmWave) channels, which occupy frequency ranges much higher than those being used in previous wireless communications systems, are utilized to meet the increased throughput requirements that come with 5G communications. The high levels of attenuation experienced by electromagnetic waves in these frequencies causes MIMO channels to have high spatial correlation. To attain desirable error performances, systems require knowledge about the channel correlations. In this thesis, a deep neural net...
Optimal Array size for Multiuser MIMO Hameed, Khalid W.; Noras, James M.; Radwan, Ayman; Al-Turjman, Fadi; Rodriguez, Jonathan; Abd-Alhameed, Raed A. (2018-06-29) This paper investigates the optimal number of antennas at a base station, in contrast to what has been accepted in the past: that increasing the number of antennas at base station always enhances performance. In this study, we show that increasing the number of antennas does not always improve the desired performance. Additionally, such increase in antennas consumes more power in transmission and adds to the computation complexity, which in turn needs more time and is more difficult to implement. The optimu...
Energy-efficient and fault-tolerant drone-BS placement in heterogeneous wireless sensor networks Deniz, Fatih; Bagci, Hakki; KÖRPEOĞLU, İBRAHİM; Yazıcı, Adnan (2020-11-01) This paper introduces a distributed and energy-aware algorithm, called Minimum Drone Placement (MDP) algorithm, to determine the minimum number of base stations mounted on resource-rich Unmanned Aerial Vehicles (UAV-BS), commonly referred to as drone-BS, and their possible locations to provide fault tolerance with high network connectivity in heterogeneous wireless sensor networks. This heterogeneous model consists of resource-rich UAV-BSs, acting as gateways of data, as well as ordinary sensor nodes that a...
Deep convolutional neural networks for airport detection in remote sensing images Budak, Umit; Sengur, Abdulkadir; Halıcı, Uğur (2018-05-05) This study investigated the use of deep convolutional neural networks (CNNs) in providing a solution for the problem of airport detection in remote sensing images (RSIs). In recent years, Deep CNNs have gained much attention with numerous applications having been undertaken in the area of computer vision. Researchers generally approach airport detection as a pattern recognition problem, in which first various distinctive features are extracted, and then a classifier is adopted to detect airports. CNNs not o...

Citation Formats

E. Balevi, A. Doshi, and J. G. Andrews, “Massive MIMO Channel Estimation With an Untrained Deep Neural Network,” IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, vol. 19, no. 3, pp. 2079–2090, 2020, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/100135.