Joint Machine Learning Based Resource Allocation and Hybrid Beamforming Design for Massive MIMO Systems

Ahmed, Irfan; Khammari, Hédi

doi:10.1109/glocomw.2018.8644454

Cited by 23 publications

(20 citation statements)

References 15 publications

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“…Aspired by this fact, several contributions studied the use ML approaches in order to design suitable resource allocation policies. Indicative examples are (Ahmed and Khammari, 2018;Peng et al, 2019;Tauqir and Habib, 2019;Huang H. et al, 2020;Cao et al, 2020;Jang and Yang, 2020). In particular, in (Cao et al, 2020), a centralized NN was employed to return the channel allocation strategy that minimizes the co-channel interference in an ultra-dense wireless network.…”

Section: Mac and Rrm Layermentioning

confidence: 99%

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Machine Learning: A Catalyst for THz Wireless Networks

Boulogeorgos

Yaqub²,

Renzo

et al. 2021

Front. Comms. Net.

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With the vision to transform the current wireless network into a cyber-physical intelligent platform capable of supporting bandwidth-hungry and latency-constrained applications, both academia and industry turned their attention to the development of artificial intelligence (AI) enabled terahertz (THz) wireless networks. In this article, we list the applications of THz wireless systems in the beyond fifth generation era and discuss their enabling technologies and fundamental challenges that can be formulated as AI problems. These problems are related to physical, medium/multiple access control, radio resource management, network and transport layer. For each of them, we report the AI approaches, which have been recognized as possible solutions in the technical literature, emphasizing their principles and limitations. Finally, we provide an insightful discussion concerning research gaps and possible future directions.

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Section: Mac and Rrm Layermentioning

confidence: 99%

“…The approach uses as inputs the channel vectors, precoding matrix and the power allocation factors. Finally, in (Ahmed and Khammari, 2018), a feedforward NN that takes as inputs the uplink channel state information and returns a channel allocation strategy in a rank and power constrained massive MIMO wireless system, was employed.…”

Section: Mac and Rrm Layermentioning

confidence: 99%

Machine Learning: A Catalyst for THz Wireless Networks

Boulogeorgos

Yaqub²,

Renzo

et al. 2021

Front. Comms. Net.

View full text Add to dashboard Cite

show abstract

“…Calculate the gradient ∇ w (l) nm e 2 according to (16), (17), (18), (19) and (20); 7: Perform the back propagation via SGD and update the weights according to (12), (13) and (14); 8: Calculate the error of the test set. If the error is smaller than the threshold, skip to step 10; 9: end while 10: return Optimized hybrid precoding neural network F. falls to an acceptable range.…”

Section: Algorithm Summarymentioning

confidence: 99%

“…With the rapid development of artificial intelligence (AI) technology, the neural network principle provides the possibility of surpassing traditional methods for the design and optimization of 5G systems benefiting from its powerful information extraction capabilities. There have been some works combining artificial intelligence with mobile communication systems, like RF resource allocation, non-orthogonal multiple access (NOMA), optimal reception and channel estimation [16]- [20]. However, few researches have been done to solve the hybrid precoding problem via AI technology.…”

Section: Introductionmentioning

confidence: 99%

Complex-BP-Neural-Network-based Hybrid Precoding for Millimeter Wave Multiuser Massive MIMO Systems

Chen

Yang

et al. 2019

2019 Computing, Communications and IoT Applications (ComComAp)

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The high energy consumption of massive multi-input multi-out (MIMO) system has become a prominent problem in the millimeter wave(mm-Wave) communication scenario. The hybrid precoding technology greatly reduces the number of radio frequency(RF) chains by handing over part of the coding work to the phase shifting network, which can effectively improve energy efficiency. However, conventional hybrid precoding algorithms based on mathematical means often suffer from performance loss and high computational complexity. In this paper, a novel BPneural-network-enabled hybrid precoding algorithm is proposed, in which the full-digital zero-forcing(ZF) precoding is set as the training target. Considering that signals at the base station are complex, we choose the complex neural network that has a richer representational capacity. Besides, we present the activation function of the complex neural network and the gradient derivation of the back propagation process. Simulation results demonstrate that the performance of the proposed hybrid precoding algorithm can optimally approximate the ZF precoding.Index Terms-hybrid precoding, complex neural network, mm-Wave, massive MIMO

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“…In [21], a reinforcement learning based framework for radio resource management in radio access networks has been proposed. In our previous work [22], we used neural networks to reduce the execution time of the computationally intensive resource allocation part of the joint resource allocation and hybrid beamforming design in [15]. However, in this work, we use K-mean based unsupervised machine learning scheme to group the users based on their spatial locations.…”

Section: Introductionmentioning

confidence: 99%

Spatio-Radio Resource Management and Hybrid Beamforming for Limited Feedback Massive MIMO Systems

et al. 2019

Self Cite

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In this paper, a joint spatio–radio frequency resource allocation and hybrid beamforming scheme for the massive multiple-input multiple-output (MIMO) systems is proposed. We consider limited feedback two-stage hybrid beamformimg for decomposing the precoding matrix at the base-station. To reduce the channel state information (CSI) feedback of massive MIMO, we utilize the channel covariance-based RF precoding and beam selection. This beam selection process minimizes the inter-group interference. The regularized block diagonalization can mitigate the inter-group interference, but requires substantial overhead feedback. We use channel covariance-based eigenmodes and discrete Fourier transforms (DFT) to reduce the feedback overhead and design a simplified analog precoder. The columns of the analog beamforming matrix are selected based on the users’ grouping performed by the K-mean unsupervised machine learning algorithm. The digital precoder is designed with joint optimization of intra-group user utility function. It has been shown that more than 50 % feedback overhead is reduced by the eigenmodes-based analog precoder design. The joint beams, users scheduling and limited feedbacK-based hybrid precoding increases the sum-rate by 27 . 6 % compared to the sum-rate of one-group case, and reduce the feedback overhead by 62 . 5 % compared to the full CSI feedback.

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Joint Machine Learning Based Resource Allocation and Hybrid Beamforming Design for Massive MIMO Systems

Cited by 23 publications

References 15 publications

Machine Learning: A Catalyst for THz Wireless Networks

Machine Learning: A Catalyst for THz Wireless Networks

Complex-BP-Neural-Network-based Hybrid Precoding for Millimeter Wave Multiuser Massive MIMO Systems

Spatio-Radio Resource Management and Hybrid Beamforming for Limited Feedback Massive MIMO Systems

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