# Carrier-Sense Multiple Access for Heterogeneous Wireless Networks Using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1810.06830 · HTML: https://mlsanity.com/paper/1810.06830/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2018.

## Built on by (most cited first, 1 of 1)

- [Deep Reinforcement Learning for Power Control in Next-Generation WiFi Network Systems](https://mlsanity.com/paper/2211.01107/) (2022)

## Abstract (arXiv)

This 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.