8,317 research outputs found
Near-Field Channel Estimation for Extremely Large-Scale Array Communications: A model-based deep learning approach
Extremely large-scale massive MIMO (XL-MIMO) has been reviewed as a promising
technology for future wireless communications. The deployment of XL-MIMO,
especially at high-frequency bands, leads to users being located in the
near-field region instead of the conventional far-field. This letter proposes
efficient model-based deep learning algorithms for estimating the near-field
wireless channel of XL-MIMO communications. In particular, we first formulate
the XL-MIMO near-field channel estimation task as a compressed sensing problem
using the spatial gridding-based sparsifying dictionary, and then solve the
resulting problem by applying the Learning Iterative Shrinkage and Thresholding
Algorithm (LISTA). Due to the near-field characteristic, the spatial
gridding-based sparsifying dictionary may result in low channel estimation
accuracy and a heavy computational burden. To address this issue, we further
propose a new sparsifying dictionary learning-LISTA (SDL-LISTA) algorithm that
formulates the sparsifying dictionary as a neural network layer and embeds it
into LISTA neural network. The numerical results show that our proposed
algorithms outperform non-learning benchmark schemes, and SDL-LISTA achieves
better performance than LISTA with ten times atoms reduction.Comment: 4 pages, 5 figure
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 Learning for Physical-Layer 5G Wireless Techniques: Opportunities, Challenges and Solutions
The new demands for high-reliability and ultra-high capacity wireless
communication have led to extensive research into 5G communications. However,
the current communication systems, which were designed on the basis of
conventional communication theories, signficantly restrict further performance
improvements and lead to severe limitations. Recently, the emerging deep
learning techniques have been recognized as a promising tool for handling the
complicated communication systems, and their potential for optimizing wireless
communications has been demonstrated. In this article, we first review the
development of deep learning solutions for 5G communication, and then propose
efficient schemes for deep learning-based 5G scenarios. Specifically, the key
ideas for several important deep learningbased communication methods are
presented along with the research opportunities and challenges. In particular,
novel communication frameworks of non-orthogonal multiple access (NOMA),
massive multiple-input multiple-output (MIMO), and millimeter wave (mmWave) are
investigated, and their superior performances are demonstrated. We vision that
the appealing deep learning-based wireless physical layer frameworks will bring
a new direction in communication theories and that this work will move us
forward along this road.Comment: Submitted a possible publication to IEEE Wireless Communications
Magazin
Multi-Agent Double Deep Q-Learning for Beamforming in mmWave MIMO Networks
Beamforming is one of the key techniques in millimeter wave (mmWave)
multi-input multi-output (MIMO) communications. Designing appropriate
beamforming not only improves the quality and strength of the received signal,
but also can help reduce the interference, consequently enhancing the data
rate. In this paper, we propose a distributed multi-agent double deep
Q-learning algorithm for beamforming in mmWave MIMO networks, where multiple
base stations (BSs) can automatically and dynamically adjust their beams to
serve multiple highly-mobile user equipments (UEs). In the analysis, largest
received power association criterion is considered for UEs, and a realistic
channel model is taken into account. Simulation results demonstrate that the
proposed learning-based algorithm can achieve comparable performance with
respect to exhaustive search while operating at much lower complexity.Comment: To be published in IEEE International Symposium on Personal, Indoor
and Mobile Radio Communications (PIMRC) 202
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