18,923 research outputs found
Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks
This paper investigates the use of deep reinforcement learning (DRL) in a MAC
protocol for heterogeneous wireless networking referred to as
Deep-reinforcement Learning Multiple Access (DLMA). The thrust of this work is
partially inspired by the vision of DARPA SC2, a 3-year competition whereby
competitors are to come up with a clean-slate design that "best share spectrum
with any network(s), in any environment, without prior knowledge, leveraging on
machine-learning technique". Specifically, this paper considers the problem of
sharing time slots among a multiple of time-slotted networks that adopt
different MAC protocols. One of the MAC protocols is DLMA. The other two are
TDMA and ALOHA. The nodes operating DLMA do not know that the other two MAC
protocols are TDMA and ALOHA. Yet, by a series of observations of the
environment, its own actions, and the resulting rewards, a DLMA node can learn
an optimal MAC strategy to coexist harmoniously with the TDMA and ALOHA nodes
according to a specified objective (e.g., the objective could be the sum
throughput of all networks, or a general alpha-fairness objective)
Deep Learning Meets Cognitive Radio: Predicting Future Steps
Learning the channel occupancy patterns to reuse
the underutilised spectrum frequencies without interfering with
the incumbent is a promising approach to overcome the spectrum
limitations. In this work we proposed a Deep Learning (DL)
approach to learn the channel occupancy model and predict its
availability in the next time slots. Our results show that the
proposed DL approach outperforms existing works by 5%. We
also show that our proposed DL approach predicts the availability
of channels accurately for more than one time slot
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