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1. Soft-Output Joint Channel Estimation and Data Detection using Deep Unfolding

   https://doi.org/10.1109/ITW48936.2021.9611404  

   Song, Haochuan; You, Xiaohu; Zhang, Chuan; Studer, Christoph (October 2021, IEEE Information Theory Workshop (ITW))

   We propose a novel soft-output joint channel estimation and data detection (JED) algorithm for multiuser (MU) multiple-input multiple-output (MIMO) wireless communication systems. Our algorithm approximately solves a maximum a-posteriori JED optimization problem using deep unfolding and generates soft-output information for the transmitted bits in every iteration. The parameters of the unfolded algorithm are computed by a hyper-network that is trained with a binary cross entropy (BCE) loss. We evaluate the performance of our algorithm in a coded MU-MIMO system with 8 basestation antennas and 4 user equipments and compare it to state-of-the-art algorithms separate channel estimation from soft-output data detection. Our results demonstrate that our JED algorithm outperforms such data detectors with as few as 10 iterations. 

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2. Variational Bayes for Joint Channel Estimation and Data Detection in Few-Bit Massive MIMO Systems

   https://doi.org/10.1109/TSP.2024.3429009  

   Nguyen, Ly V; Swindlehurst, A Lee; Nguyen, Duy_H N (July 2024, IEEE Transactions on Signal Processing)

   Massive multiple-input multiple-output (MIMO) communications using low-resolution analog-to-digital converters (ADCs) is a promising technology for providing high spectral and energy efficiency with affordable hardware cost and power consumption. However, the use of low-resolution ADCs requires special signal processing methods for channel estimation and data detection since the resulting system is severely non-linear. This paper proposes joint channel estimation and data detection methods for massive MIMO systems with low-resolution ADCs based on the variational Bayes (VB) inference framework. We first derive matched-filter quantized VB (MF-QVB) and linear minimum mean-squared error quantized VB (LMMSE-QVB) detection methods assuming the channel state information (CSI) is available. Then we extend these methods to the joint channel estimation and data detection (JED) problem and propose two methods we refer to as MF-QVB-JED and LMMSE-QVB-JED. Unlike conventional VB-based detection methods that assume knowledge of the second-order statistics of the additive noise, we propose to float the elements of the noise covariance matrix as unknown random variables that are used to account for both the noise and the residual inter-user interference. We also present practical aspects of the QVB framework to improve its implementation stability. Finally, we show via numerical results that the proposed VB-based methods provide robust performance and also significantly outperform existing methods. 

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3. Neural Network-based Channel Estimation for 2x2 and 4x4 MIMO Communication in Noisy Channels

   Yang, Kanghyeok; Vuran, Mehmet C.; Scott, Stephen; Guo, Fujuan; Ahn, Changbum R. (May 2018, International Balkan Conference on Communications and Networking)

   With increasing needs of fast and reliable commu- nication between devices, wireless communication techniques are rapidly evolving to meet such needs. Multiple input and output (MIMO) systems are one of the key techniques that utilize multiple antennas for high-throughput and reliable communication. However, increasing the number of antennas in communication also adds to the complexity of channel esti- mation, which is essential to accurately decode the transmitted data. Therefore, development of accurate and efficient channel estimation methods is necessary. We report the performance of machine learning-based channel estimation approaches to enhance channel estimation performance in high-noise envi- ronments. More specifically, bit error rate (BER) performance of 2 × 2 and 4 × 4 MIMO communication systems with space- time block coding model (STBC) and two neural network-based channel estimation algorithms is analyzed. Most significantly, the results demonstrate that a generalized regression neural network (GRNN) model matches BER results of a known-channel communication for 4 × 4 MIMO with 8-bit pilots, when trained in a specific signal to noise ratio (SNR) regime. Moreover, up to 9dB improvement in signal-to-noise ratio (SNR) for a target BER is observed, compared to least square (LS) channel estimation, especially when the model is trained in the low SNR regime. A deep artificial neural network (Deep ANN) model shows worse BER performance compared to LS in all tested environments. These preliminary results present an opportunity for achieving better performance in channel estimation through GRNN and highlight further research topics for deployment in the wild. 

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4. Efficient modeling of low‐resolution millimeter‐wave transceivers for massive MIMO wireless communications systems

   https://doi.org/10.1002/mop.32727  

   Estes, Nicholas; Gao, Kang; Hochwald, Bertrand; Laneman, J. Nicholas; Chisum, Jonathan (November 2020, Microwave and Optical Technology Letters)

   Abstract We present a high‐fidelity measurement‐based nonlinear model of low‐complexity millimeter‐wave transmit and receive circuits for design and analysis of 1‐bit on‐off‐key (OOK) massive MIMO wireless communications systems. The receive model is based upon a fabricated 38 GHz energy detector, representative of state‐of‐the‐art OOK millimeter‐wave receivers. The model is validated with measurements and includes nonlinear noise modeling. Performance of a large‐scale massive MIMO system is predicted with the model, and predictions are compared against a 4‐transmit‐element, N‐receive‐element testbed. Finally, we compute the channel capacity of several OOK massive MIMO systems, exploring tradeoffs in power consumption and number of transmit and receive cells. Results indicate a 1‐bit OOK array with low power pre‐amplifiers can achieve similar capacity to a classical linear receiver with less than one tenth the power consumption. The 1‐bit array compensates for the per‐cell simplicity by increasing the total number of cells while maintaining low overall power consumption. 

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5. "A GLRT Approach for Detecting Correlated Signalsin White Noise in Two MIMO Channels"

   Santamaria, I.; Via, J.; Scharf, L.L. (September 2017, EUSIPCO ...)

   In this work, we consider a second-order detection problem where rank-p signals are structured by an unknown, but common, p-dimensional random vector and then received through unknown M by p matrices at each of two M-element arrays. The noises in each channel are independent with identical variances. We derive generalized likelihood ratio (GLR) tests for this problem when the noise variance is either known or unknown. The resulting detection problems may be phrased as two-channel factor analysis problems. 

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