A Deep-Learning-Based Radio Resource Assignment Technique for 5G Ultra Dense Networks

Zhou, Yibo; Fadlullah, Zubair Md.; Mao, Bomin; Kato, Nei

doi:10.1109/mnet.2018.1800085

Cited by 153 publications

(71 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Recently, deep learning has also been introduced into resource allocation problems. [28] leverages the deep long short-term memory (LSTM) learning technique to make localized prediction of the traffic load at the ultra dense networks (UDN) base station. In [29], a damped three dimensional (D3D) message-passing algorithm (MPA) based on deep learning for resource allocation in cognitive radio networks has been proposed.…”

Section: Arxiv:191209302v1 [Csni] 18 Dec 2019mentioning

confidence: 99%

Multi-Agent Deep Reinforcement Learning Based Spectrum Allocation for D2D Underlay Communications

Guo

2020

IEEE Trans. Veh. Technol.

123

View full text Add to dashboard Cite

Device-to-device (D2D) communication underlay cellular networks is a promising technique to improve spectrum efficiency. In this situation, D2D transmission may cause severe interference to both the cellular and other D2D links, which imposes a great technical challenge to spectrum allocation. Existing centralized schemes require global information, which causes a large signaling overhead. While existing distributed schemes requires frequent information exchange among D2D users and cannot achieve global optimization. In this paper, a distributed spectrum allocation framework based on multiagent deep reinforcement learning is proposed, named multiagent actor critic (MAAC). MAAC shares global historical states, actions and policies during centralized training, requires no signal interaction during execution and utilizes cooperation among users to further optimize system performance. Moreover, in order to decrease the computing complexity of the training, we further propose the neighbor-agent actor critic (NAAC) based on the neighbor users' historical information for centralized training. The simulation results show that the proposed MAAC and NAAC can effectively reduce the outage probability of cellular links, greatly improve the sum rate of D2D links and converge quickly.Index Terms-Device-to-device (D2D) communications, multiagent deep reinforcement learning, spectrum allocation.

show abstract

Section: Arxiv:191209302v1 [Csni] 18 Dec 2019mentioning

confidence: 99%

Multi-Agent Deep Reinforcement Learning Based Spectrum Allocation for D2D Underlay Communications

Guo

2020

IEEE Trans. Veh. Technol.

123

View full text Add to dashboard Cite

show abstract

“…Then forward propagation is performed to obtain the corresponding network output and the cost function. Afterwards, the partial derivatives are calculated according to (17), (18), (19) and (20) to adjust the weight. The training process is terminated when the error Algorithm 1 Hybrid precoding algorithm based on BP neural network.…”

Section: Algorithm Summarymentioning

confidence: 99%

“…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%

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)

View full text Add to dashboard Cite

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

show abstract

“…However, most of the existing applications of using deep neural network (DNN) are seen as a black box trained by exploiting a large amount of data available [25][26][27]. Hence, this data-driven deep learning makes neural network topology being lack of theoretical understandings and explanations.…”

mentioning

confidence: 99%

“…values in the range [0, 1], p n(0) m,k as random value under the power limit P max m . The non-linear transforms of neurons in each layer are defined by(23),(24),(25).Random initialization: In current deep learning literature, the weights in deep neural networks are generally initialized by random values. In this way, h n m,k , σ 2 m,k , A are initialized as random values following a Gaussian distribution with zero mean and well-chosen variance level.…”

mentioning

confidence: 99%

A Model-Driven Deep Reinforcement Learning Heuristic Algorithm for Resource Allocation in Ultra-Dense Cellular Networks

Liao

Shi

et al. 2020

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

A model-driven deep reinforcement learning heuristic algorithm for resource allocation in ultra-dense cellular networks. IEEE Transactions on Vehicular Technology, 69(1), pp. 983-997.There may be differences between this version and the published version. You are advised to consult the publisher's version if you wish to cite from it.Abstract-Resource allocation in ultra dense network (UDN) is an multi-objective optimization problem since it has to consider the tradeoff among spectrum efficiency (SE), energy efficiency (EE) and fairness. The existing methods can not effectively solve this NP-hard nonconvex problem, especially in the presence of limited channel state information (CSI). In this paper, we investigate a novel model-driven deep reinforcement learning assisted resource allocation method. We first design a novel deep neural network (DNN)-based optimization framework consisting of a series of Alternating Direction Method of Multipliers (ADMM) iterative procedures, which makes the CSI as the learned weights. Then a novel channel information absent Q-learning resource allocation (CIAQ) algorithm is proposed to train the DNN-based optimization framework without massive labeling data, where the SE, the EE, and the fairness can be jointly optimized by adjusting discount factor. Our simulation results show that, the proposed CIAQ with rapid convergence speed not only well characterizes the extent of optimization objective with partial CSI, but also significantly outperforms the current random initialization method of neural network and the other existing resource allocation algorithms in term of the tradeoff among the SE, EE and fairness.

show abstract

A Deep-Learning-Based Radio Resource Assignment Technique for 5G Ultra Dense Networks

Cited by 153 publications

References 14 publications

Multi-Agent Deep Reinforcement Learning Based Spectrum Allocation for D2D Underlay Communications

Multi-Agent Deep Reinforcement Learning Based Spectrum Allocation for D2D Underlay Communications

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

A Model-Driven Deep Reinforcement Learning Heuristic Algorithm for Resource Allocation in Ultra-Dense Cellular Networks

Contact Info

Product

Resources

About