The Deep Learning Vision for Heterogeneous Network Traffic Control: Proposal, Challenges, and Future Perspective

Kato, Nei; Fadlullah, Zubair Md.; Mao, Bomin; Tang, Fengxiao; Akashi, Osamu; Inoue, Takeru; Mizutani, Kimihiro

doi:10.1109/mwc.2016.1600317wc

Cited by 374 publications

(143 citation statements)

References 5 publications

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“…Simulation results showed that each agent can effectively learn to satisfy the stringent latency constraints on V2V links while minimizing the interference to vehicle-to-infrastructure communications. In [19], applications of machine learning to improve heterogeneous network traffic control are researched. Based on traffic patterns at the edge routers, a supervised deep learning system is trained.…”

Section: A Related Workmentioning

confidence: 99%

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Mode Selection and Resource Allocation in Sliced Fog Radio Access Networks: A Reinforcement Learning Approach

Xiang

Peng

Sun

et al. 2020

IEEE Trans. Veh. Technol.

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The mode selection and resource allocation in fog radio access networks (F-RANs) have been advocated as key techniques to improve spectral and energy efficiency. In this paper, we investigate the joint optimization of mode selection and resource allocation in uplink F-RANs, where both of the traditional user equipments (UEs) and fog UEs are served by constructed network slice instances. The concerned optimization is formulated as a mixed-integer programming problem, and both the orthogonal and multiplexed subchannel allocation strategies are proposed to guarantee the slice isolation. Motivated by the development of machine learning, two reinforcement learning based algorithms are developed to solve the original high complexity problem under traditional and fog UEs' specific performance requirements. The basic idea of the proposals is to generate a good mode selection policy according to the immediate reward fed back by an environment. Simulation results validate the benefits of our proposed algorithms and show that a tradeoff between system power consumption and queue delay can be achieved.Index Terms-fog radio access network, network slicing, reinforcement learning.

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Section: A Related Workmentioning

confidence: 99%

“…Dividing (19) by ǫT and taking the limit as T → ∞, inequality (13) is obtained according to the fact that E {L(Q(T ))} < ∞. Theorem 1 suggests that by adjusting the value of parameter V , a near-to-optimal solution can be obtained which provides an average system power arbitrarily close to the optimum P * .…”

Section: B General Lyapunov Optimizationmentioning

confidence: 99%

Mode Selection and Resource Allocation in Sliced Fog Radio Access Networks: A Reinforcement Learning Approach

Xiang

Peng

Sun

et al. 2020

IEEE Trans. Veh. Technol.

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“…The proposed algorithms can be applied in many practical applications [36,37,38,39,40,41,42]. In this section, we conduct some typical applications about sparse image recovery and medical imaging to extend the applications of

L_{1 / 2}

and

L_{2 / 3}

regularizations and illustrate the excellent robustness and adaptation of the proposed SAITA-

L_{p}

(p \in {1 / 2, 2 / 3})

algorithm.…”

Section: Practical Experimentsmentioning

confidence: 99%

Sparse Adaptive Iteratively-Weighted Thresholding Algorithm (SAITA) for L p -Regularization Using the Multiple Sub-Dictionary Representation

Zhang

Fan

et al. 2017

Sensors

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“…But the conventional algorithms cannot satisfy the increasing coverage because UAV movements require much energy and slow navigation deteriorates the coverage performance. Recently, by exploiting the potentials of machine learning into wireless communication, a deep learning-based wireless communication method provides an alternative mean for optimizing the UAV navigation problem, whose performance has been corroborated in non-orthogonal multiple access (NOMA) [9], massive MIMO [10], [11], traffic control [12], [13], routing techniques [14], software defined network (SDN) [15], UAV [16], [17], and millimeter-wave (mmWave) communication [18], etc.. In particular, [17] proposed a deep learning-based method for UAV navigation without requiring sensing data that provides mapping information, however this method cannot converge quickly, which makes it difficult to be applied in real-time navigation scenarios.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning for UAV Navigation Through Massive MIMO Technique

Huang

Yang

Wang

et al. 2020

IEEE Trans. Veh. Technol.

121

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Unmanned aerial vehicles (UAVs) technique has been recognized as a promising solution in future wireless connectivity from the sky, and UAV navigation is one of the most significant open research problems, which has attracted wide interest in the research community. However, the current UAV navigation schemes are unable to capture the UAV motion and select the best UAV-ground links in real-time, and these weaknesses overwhelm the UAV navigation performance. To tackle these fundamental limitations, in this paper, we merge the state-of-theart deep reinforcement learning with the UAV navigation through massive multiple input multiple output (MIMO) technique. To be specific, we carefully design a deep Q-network (DQN) for optimizing the UAV navigation by selecting the optimal policy, and then we propose a learning mechanism for processing the DQN. The DQN is trained so that the agent is capable of making decisions based on the received signal strengths for navigating the UAVs with the aid of the powerful Q-learning. Simulation results are provided to corroborate the superiority of the proposed schemes in terms of the coverage and convergence compared with that of other schemes.Index Terms-Massive multiple input multiple output (MIMO), deep reinforcement learning, UAV navigation

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The Deep Learning Vision for Heterogeneous Network Traffic Control: Proposal, Challenges, and Future Perspective

Cited by 374 publications

References 5 publications

Mode Selection and Resource Allocation in Sliced Fog Radio Access Networks: A Reinforcement Learning Approach

Mode Selection and Resource Allocation in Sliced Fog Radio Access Networks: A Reinforcement Learning Approach

Sparse Adaptive Iteratively-Weighted Thresholding Algorithm (SAITA) for L p -Regularization Using the Multiple Sub-Dictionary Representation

Deep Reinforcement Learning for UAV Navigation Through Massive MIMO Technique

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