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A Tutorial on Ultra-Reliable and Low-Latency Communications in 6G: Integrating Domain Knowledge into Deep Learning
As one of the key communication scenarios in the 5th and also the 6th generation (6G) of mobile communication networks, ultra-reliable and low-latency communications (URLLC) will be central for the development of various emerging mission-critical applications.
Unsupervised Deep Learning for Optimizing Wireless Systems with Instantaneous and Statistic Constraints
Deep neural networks (DNNs) have been introduced for designing wireless policies by approximating the mappings from environmental parameters to solutions of optimization problems.
Improving Learning Efficiency for Wireless Resource Allocation with Symmetric Prior
The samples required to train a DNN after ranking can be reduced by $15 \sim 2, 400$ folds to achieve the same system performance as the counterpart without using prior.
Optimizing Wireless Systems Using Unsupervised and Reinforced-Unsupervised Deep Learning
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.
Proactive Optimization with Machine Learning: Femto-caching with Future Content Popularity
In this paper, we introduce a proactive optimization framework for anticipatory resource allocation, where the future information is implicitly predicted under the same objective with the policy optimization in a single step.
Model-Free Unsupervised Learning for Optimization Problems with Constraints
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.
Learning to Optimize with Unsupervised Learning: Training Deep Neural Networks for URLLC
Learning the optimized solution as a function of environmental parameters is effective in solving numerical optimization in real time for time-sensitive applications.
Unsupervised Deep Learning for Ultra-reliable and Low-latency Communications
In this paper, we study how to solve resource allocation problems in ultra-reliable and low-latency communications by unsupervised deep learning, which often yield functional optimization problems with quality-of-service (QoS) constraints.
