Deep Learning Based Transmit Power Control in Underlaid Device-to-Device Communication

Lee, Woongsup; Kim, Minhoe; Cho, Dong-Ho

doi:10.1109/jsyst.2018.2870483

Cited by 43 publications

(44 citation statements)

References 18 publications

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“…In consequence, a constrained training strategy is developed such that it performs iterative updates of the DNN and the dual variables via stateof-the-art SGD algorithms. Unlike unconstrained DL works in [7]- [12], the proposed constrained training algorithm ensures to produce an efficient feasible solution for arbitrarily given constraints.…”

Section: B Contributions and Organizationmentioning

confidence: 99%

“…As a result, the quantizer and optimizer units of all nodes can be jointly trained via the proposed constrained training algorithm. Then, the real-time computation of the trained DNN units can be implemented in a distributed manner, otherwise not applicable for the DL techniques in [7], [10]- [12], [19], since they all require the perfect knowledge of other cells' CSI. Finally, the proposed DL framework is verified from several numerical examples in cognitive multiple access channel (C-MAC) and IFC applications.…”

Section: B Contributions and Organizationmentioning

confidence: 99%

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Deep Learning for Distributed Optimization: Applications to Wireless Resource Management

Lee

Quek

2019

IEEE J. Select. Areas Commun.

112

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This paper studies a deep learning (DL) framework to solve distributed non-convex constrained optimizations in wireless networks where multiple computing nodes, interconnected via backhaul links, desire to determine an efficient assignment of their states based on local observations. Two different configurations are considered: First, an infinite-capacity backhaul enables nodes to communicate in a lossless way, thereby obtaining the solution by centralized computations. Second, a practical finitecapacity backhaul leads to the deployment of distributed solvers equipped along with quantizers for communication through capacity-limited backhaul. The distributed nature and the nonconvexity of the optimizations render the identification of the solution unwieldy. To handle them, deep neural networks (DNNs) are introduced to approximate an unknown computation for the solution accurately. In consequence, the original problems are transformed to training tasks of the DNNs subject to non-convex constraints where existing DL libraries fail to extend straightforwardly. A constrained training strategy is developed based on the primal-dual method. For distributed implementation, a novel binarization technique at the output layer is developed for quantization at each node. Our proposed distributed DL framework is examined in various network configurations of wireless resource management. Numerical results verify the effectiveness of our proposed approach over existing optimization techniques.Index Terms-Deep neural network, distributed deep learning, primal-dual method, wireless resource management.

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Section: B Contributions and Organizationmentioning

confidence: 99%

Section: B Contributions and Organizationmentioning

confidence: 99%

Deep Learning for Distributed Optimization: Applications to Wireless Resource Management

Lee

Quek

2019

IEEE J. Select. Areas Commun.

112

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“…The agent then estimates the modified version of Z new i,̃( D i ) given in Equation (17) and updates multiple weights using Z new i,̃( D i ) according to (16). In this process at each episode, the learner accesses the uncertainty transition set p(·|St i , Ac i ) for estimating the all possible next landed states that is denoted by setŜ( · | St i , Ac i ).…”

Section: Deep Q-learning With Ekfmentioning

confidence: 99%

“…Deep learning is a major breakthrough in machine learning especially in developing games, autonomous driving system, health care, robotic application, and many more. In the work of Lee et al, 16 authors discussed a transmit power control strategy, where the D2D user autonomously learns to control its transmit power by using DNN, thereby improving the average sum-rate of D2Ds by restricting the interference from CUs. In the work of Mao et al, 14 authors applied the Deep RL technique in machine intelligence, where the proposed system learns directly the resource management between various electronic devices by accumulating the experiences received within a cluster.…”

Section: Introductionmentioning

confidence: 99%

“…Le et al 15 proposed a Deep RL-based offloading mechanism, where a user learns through an online trial and error basis to offload his computational job to other mobile devices with the help of D2D communication in an underlay cellular network. In the work of Lee et al, 16 authors discussed a transmit power control strategy, where the D2D user autonomously learns to control its transmit power by using DNN, thereby improving the average sum-rate of D2Ds by restricting the interference from CUs. In the work of Naparstek and Cohen, 17 the authors proposed a spectrum assignment process, where the multiple users access the multiple numbers of channels.…”

Section: Introductionmentioning

confidence: 99%

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An efficient Deep reinforcement learning with extended Kalman filter for device‐to‐device communication underlaying cellular network

Khuntia

Hazra

2019

Trans Emerging Tel Tech

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In this paper, a novel Deep Q‐learning in addition with an extended Kalman filter (EKF) is proposed to solve the channel and power allocation issue for a device‐to‐device enabled cellular network, when the prior traffic information is not known to the base station. Furthermore, this paper work explores an optimal policy for resource and power allocation between users with the aim of maximizing the sum‐rate of the overall system. The proposed work comprises of four phases, ie, cell splitting, clustering, queuing model, channel allocation, and power allocation, simultaneously. The implementation of cell splitting with novel K‐means++ clustering technique increases the network coverage, reduces co‐channel cell interference, and minimizes the transmission power of nodes, whereas the M/M/C:N queuing model solves the issue of waiting time for users in a priority based data transmission. The difficulty with the Q‐learning and Deep Q‐learning environment is to achieve an optimal policy. This is because of the uncertainty of various parameters associated with the system, especially when the state space is extremely big. In order to improve the robustness of the learner, EKF together with the Deep Q‐network is presented in this paper, which incorporates weight uncertainty of the Q‐function as well as the state uncertainty during the transition. Furthermore, the use of EKF provides an improved version of the loss function that helps the learner in achieving an optimal policy. Through numerical simulation, the advantage of our resource sharing scheme over the other existing schemes is also verified.

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Application of Machine Learning Algorithms in Agriculture: An Analysis

Jain

Choudhary

2021

Studies in Autonomic, Data-Driven and Industrial Computing

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Deep Learning Based Transmit Power Control in Underlaid Device-to-Device Communication

Cited by 43 publications

References 18 publications

Deep Learning for Distributed Optimization: Applications to Wireless Resource Management

Deep Learning for Distributed Optimization: Applications to Wireless Resource Management

An efficient Deep reinforcement learning with extended Kalman filter for device‐to‐device communication underlaying cellular network

Application of Machine Learning Algorithms in Agriculture: An Analysis

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