… We then highlight the deep reinforcement learning approach to address resource allocation
problems that are difficult to handle in the traditional optimization framework. We also …

… resource allocation strategy for arbitrary channel conditions is approximated by deep neural
network (… that combines supervised and unsupervised learning methods and a local CSI …

… a deep neural network (DNN), which contains an autoencoder and a fully-connected neural
network, to learn the resource allocation … , we believe that the approach is promising for …

… rate with different approaches. In our … a deep learning approach that reduces this overhead
by making it a simple multiplication problem O(mn) using a pre-trained Deep Neural Network (…

… present a deep learning approach that … deep learning model takes the Cu,k vector along
with the Vu,k of all users in a network as input and predicts the power and sub-band allocations …

… Therefore, some methods of applying machine learning to solve the resource … the
reinforcement learning method and several strategies adopted in our proposed resource allocation …

… used ML and DL methods to address the resource allocation problem in MEC. Particularly,
we discuss and classify current ML and DL-based methods for resource allocation from three …

… However, a mechanism for edge resource allocation to maximize the revenue for … deep
learning for the edge resource allocation. Specifically, we construct a multi-layer neural network …

… 2) To solve the proposed problem, we develop a multistack RL method. Compared to the …
record historical resource allocation schemes and users’ states, which can avoid learning the …

… the state-of-the-art machine learning algorithm, asynchronous advantage actor–critic (A3C),
to allocate the edge computing resources, which exemplifies how artificial intelligence (AI) …