10,409 research outputs found
Deep Unfolded Simulated Bifurcation for Massive MIMO Signal Detection
Multiple-input multiple-output (MIMO) is a key ingredient of next-generation
wireless communications. Recently, various MIMO signal detectors based on deep
learning techniques and quantum(-inspired) algorithms have been proposed to
improve the detection performance compared with conventional detectors. This
paper focuses on the simulated bifurcation (SB) algorithm, a quantum-inspired
algorithm. This paper proposes two techniques to improve its detection
performance. The first is modifying the algorithm inspired by the
Levenberg-Marquardt algorithm to eliminate local minima of maximum likelihood
detection. The second is the use of deep unfolding, a deep learning technique
to train the internal parameters of an iterative algorithm. We propose a
deep-unfolded SB by making the update rule of SB differentiable. The numerical
results show that these proposed detectors significantly improve the signal
detection performance in massive MIMO systems.Comment: 5pages, 4 figure
Deep HyperNetwork-Based MIMO Detection
Optimal symbol detection for multiple-input multiple-output (MIMO) systems is
known to be an NP-hard problem. Conventional heuristic algorithms are either
too complex to be practical or suffer from poor performance. Recently, several
approaches tried to address those challenges by implementing the detector as a
deep neural network. However, they either still achieve unsatisfying
performance on practical spatially correlated channels, or are computationally
demanding since they require retraining for each channel realization. In this
work, we address both issues by training an additional neural network (NN),
referred to as the hypernetwork, which takes as input the channel matrix and
generates the weights of the neural NN-based detector. Results show that the
proposed approach achieves near state-of-the-art performance without the need
for re-training
DNN-based Detectors for Massive MIMO Systems with Low-Resolution ADCs
Low-resolution analog-to-digital converters (ADCs) have been considered as a
practical and promising solution for reducing cost and power consumption in
massive Multiple-Input-Multiple-Output (MIMO) systems. Unfortunately,
low-resolution ADCs significantly distort the received signals, and thus make
data detection much more challenging. In this paper, we develop a new deep
neural network (DNN) framework for efficient and low-complexity data detection
in low-resolution massive MIMO systems. Based on reformulated maximum
likelihood detection problems, we propose two model-driven DNN-based detectors,
namely OBMNet and FBMNet, for one-bit and few-bit massive MIMO systems,
respectively. The proposed OBMNet and FBMNet detectors have unique and simple
structures designed for low-resolution MIMO receivers and thus can be
efficiently trained and implemented. Numerical results also show that OBMNet
and FBMNet significantly outperform existing detection methods.Comment: 6 pages, 8 figures, submitted for publication. arXiv admin note: text
overlap with arXiv:2008.0375
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