Carrier-Sense Multiple Access for Heterogeneous Wireless Networks using Deep Reinforcement Learning
Refereed conference paper presented and published in conference proceedings

Full Text

Other information
AbstractThis paper investigates a new class of carrier-sense multiple access (CSMA) protocols that employ deep reinforcement learning (DRL) techniques for heterogeneous wireless networking, referred to as carrier-sense deep-reinforcement learning multiple access (CS-DLMA). Existing CSMA protocols, such as the medium access control (MAC) of WiFi, are designed for a homogeneous network environment in which all nodes adopt the same protocol. Such protocols suffer from severe performance degradation in a heterogeneous environment where there are nodes adopting other MAC protocols. This paper shows that DRL techniques can be used to design efficient MAC protocols for heterogeneous networking. In particular, in a heterogeneous environment with nodes adopting different MAC protocols (e.g., CS-DLMA, TDMA, and ALOHA), a CS-DLMA node can learn to maximize the sum throughput of all nodes. Furthermore, compared with WiFi’s CSMA, CS-DLMA can achieve both higher sum throughput and individual throughputs when coexisting with other MAC protocols. Last but not least, a salient feature of CS-DLMA is that it does not need to know the operating mechanisms of the co-existing MACs. Neither does it need to know the number of nodes using these other MACs.
All Author(s) ListYiding Yu, Soung Chang Liew, Taotao Wang
Name of ConferenceIEEE WCNC International Workshop on Smart Spectrum
Start Date of Conference15/04/2019
End Date of Conference19/04/2019
Place of ConferenceMarrakech, Morocco
Country/Region of ConferenceMorocco
LanguagesEnglish-United States

Last updated on 2019-17-10 at 15:50