Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Experienced Deep Reinforcement Learning with Generative Adversarial
  Networks (GANs) for Model-Free Ultra Reliable Low Latency Communication   In this paper, a novel experienced deep reinforcement learning (deep-RL)
framework is proposed to provide model-free resource allocation for ultra
reliable low latency communication (URLLC). The proposed, experienced deep-RL
framework can guarantee high end-to-end reliability and low end-to-end latency,
under explicit data rate constraints, for each wireless without any models of
or assumptions on the users' traffic. In particular, in order to enable the
deep-RL framework to account for extreme network conditions and operate in
highly reliable systems, a new approach based on generative adversarial
networks (GANs) is proposed. This GAN approach is used to pre-train the deep-RL
framework using a mix of real and synthetic data, thus creating an experienced
deep-RL framework that has been exposed to a broad range of network conditions.
Formally, the URLLC resource allocation problem is posed as a power
minimization problem under reliability, latency, and rate constraints. To solve
this problem using experienced deep-RL, first, the rate of each user is
determined. Then, these rates are mapped to the resource block and power
allocation vectors of the studied wireless system. Finally, the end-to-end
reliability and latency of each user are used as feedback to the deep-RL
framework. It is then shown that at the fixed-point of the deep-RL algorithm,
the reliability and latency of the users are near-optimal. Moreover, for the
proposed GAN approach, a theoretical limit for the generator output is
analytically derived. Simulation results show how the proposed approach can
achieve near-optimal performance within the rate-reliability-latency region,
depending on the network and service requirements. The results also show that
the proposed experienced deep-RL framework is able to remove the transient
training time that makes conventional deep-RL methods unsuitable for URLLC.

36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
24
The categories of this paper are: dg-ga
15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics