Unsupervised Deep Learning for Ultra-Reliable and Low-Latency Communications

Sun, Chengjian; Yang, Chenyang

doi:10.1109/globecom38437.2019.9013851

Cited by 17 publications

(30 citation statements)

References 22 publications

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“…To find the solution of constrained functional optimization problems, unsupervised learning techniques are proposed in [6,8,9]. A constrained optimization problem can be transformed into an unconstrained problem by using the Lagrangian approach.…”

Section: B Functional Optimization Using Unsupervised Learning and Rmentioning

confidence: 99%

“…In practice, there may also exist constraints imposed on a function averaged over the environment's status, e.g., average power constraints or average data rate constraints, as shown in P2. For more general wireless optimization problems, the "variables" to be optimized consist of both functions and variables, as exemplified by jointly optimizing a multi-timescale policy [6].…”

Section: A Learning Variable Optimization Without Labelsmentioning

confidence: 99%

“…wireless networks are often solved by heuristic techniques [3], which inevitably leads to performance loss [6].…”

Section: Introductionmentioning

confidence: 99%

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Optimizing Wireless Systems Using Unsupervised and Reinforced-Unsupervised Deep Learning

et al. 2020

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Resource allocation and transceivers in wireless networks are usually designed by solving optimization problems subject to specific constraints, which can be formulated as variable or functional optimization. If the objective and constraint functions of a variable optimization problem can be derived, standard numerical algorithms can be applied for finding the optimal solution, which however incur high computational cost when the dimension of the variable is high. To reduce the online computational complexity, learning the optimal solution as a function of the environment's status by deep neural networks (DNNs) is an effective approach. DNNs can be trained under the supervision of optimal solutions, which however, is not applicable to the scenarios without models or for functional optimization where the optimal solutions are hard to obtain. If the objective and constraint functions are unavailable, reinforcement learning can be applied to find the solution of a functional optimization problem, which is however not tailored to optimization problems in wireless networks. In this article, we introduce unsupervised and reinforced-unsupervised learning frameworks for solving both variable and functional optimization problems without the supervision of the optimal solutions. When the mathematical model of the environment is completely known and the distribution of environment's status is known or unknown, we can invoke unsupervised learning algorithm. When the mathematical model of the environment is incomplete, we introduce reinforcedunsupervised learning algorithms that learn the model by interacting with the environment. Our simulation results confirm the applicability of these learning frameworks by taking a user association problem as an example.

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Section: B Functional Optimization Using Unsupervised Learning and Rmentioning

confidence: 99%

Section: A Learning Variable Optimization Without Labelsmentioning

confidence: 99%

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Optimizing Wireless Systems Using Unsupervised and Reinforced-Unsupervised Deep Learning

et al. 2020

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“…Most DNN training techniques are not immediately usable here as the latency requirements of the system pose constraints in the optimization problem [16]. Recent advancements apply techniques from both reinforcement learning and deep learning for control-aware scheduling in simple systems [12]- [14] and traditional wireless systems with latency constraints [17], [18]. Learning-based scheduling policies are well suited for URLLC and control as the computational complexity at each scheduling round is very low and can furthermore be implemented model-free when system dynamics and communication models are unknown.…”

Section: Introductionmentioning

confidence: 99%

Control-Aware Scheduling for Low Latency Wireless Systems with Deep Learning

Eisen

Rashid

Ribeiro

2020

2020 IEEE International Conference on Communications Workshops (ICC Workshops)

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We consider the problem of scheduling transmissions over low-latency wireless communication links to control various control systems. Low-latency requirements are critical in developing wireless technology for industrial control, but are inherently challenging to meet while also maintaining reliable performance. An alternative to ultra reliable low latency communications is a framework in which reliability is adapted to control system demands. We formulate the control-aware scheduling problem as a constrained statistical optimization problem in which the optimal scheduler is a function of current control and channel states. The scheduler is parameterized with a deep neural network, and the constrained problem is solved using techniques from primal-dual learning, which have a necessary model-free property in that they do not require explicit knowledge of channels models, performance metrics, or system dynamics to execute. The resulting control-aware deep scheduler is evaluated in empirical simulations and strong performance is shown relative to other model-free heuristic scheduling methods.

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“…Update θJ , θg, θc by (7) and update θ f , θ λ , ξ by (8). 10: end for So far, we have implicitly assumed that the policy to be learned is continuous (i.e., f (h) is a continuous function of h), and learn its parameterized formf (h; θ f ) as a deterministic policy in both model-based and model free unsupervised learning frameworks.…”

mentioning

confidence: 99%

Model-Free Unsupervised Learning for Optimization Problems with Constraints

Sun

Yang

2019

2019 25th Asia-Pacific Conference on Communications (APCC)

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In many optimization problems in wireless communications, the expressions of objective function or constraints are hard or even impossible to derive, which makes the solutions difficult to find. In this paper, we propose a model-free learning framework to solve constrained optimization problems without the supervision of the optimal solution. Neural networks are used respectively for parameterizing the function to be optimized, parameterizing the Lagrange multiplier associated with instantaneous constraints, and approximating the unknown objective function or constraints. We provide learning algorithms to train all the neural networks simultaneously, and reveal the connections of the proposed framework with reinforcement learning. Numerical and simulation results validate the proposed framework and demonstrate the efficiency of model-free learning by taking power control problem as an example.

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Unsupervised Deep Learning for Ultra-Reliable and Low-Latency Communications

Cited by 17 publications

References 22 publications

Optimizing Wireless Systems Using Unsupervised and Reinforced-Unsupervised Deep Learning

Optimizing Wireless Systems Using Unsupervised and Reinforced-Unsupervised Deep Learning

Control-Aware Scheduling for Low Latency Wireless Systems with Deep Learning

Model-Free Unsupervised Learning for Optimization Problems with Constraints

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