Mengqi Han
Illinois Institute of Technology
16 Papers
46 Citations
Mengqi Han is an academic researcher from Illinois Institute of Technology. The author has contributed to research in topics: Random access & Throughput. The author has an hindex of 6, co-authored 13 publications.
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Papers
Enabling Sustainable Underwater IoT Networks With Energy Harvesting: A Decentralized Reinforcement Learning Approach
TL;DR: A multiagent reinforcement learning approach for each node to autonomously adapt the random access parameter based on the interactions with the dynamic network environment is proposed, and the proposed learning algorithm greatly improves the throughput performance compared with the existing solutions, and approaches the derived theoretical bound.
72
Performance Analysis of Opportunistic Channel Bonding in Multi-Channel WLANs
Mengqi Han,Sami Khairy,Lin Cai,Yu Cheng +3 more
- 01 Dec 2016
TL;DR: An analytical framework is developed to study the performance of opportunistic channel bonding in IEEE 802.11 WLANs and shows that with multi-channel bonding, ac users achieves a higher throughput at the cost of reduced throughput of legacy users in the secondary channels.
22
Enabling efficient multi-channel bonding for IEEE 802.11ac WLANs
Sami Khairy,Mengqi Han,Lin Cai,Yu Cheng,Zhu Han +4 more
- 21 May 2017
TL;DR: An analytical model is developed to study the performance of distributed and opportunistic multichannel bonding in IEEE 802.11ac WLANs, and proposes a channel selection scheme for ac users to select the best primary channel in order to mitigate the contentions in the network and attain the maximal network throughput.
18
Capacity Analysis of Opportunistic Channel Bonding Over Multi-Channel WLANs Under Unsaturated Traffic
TL;DR: A heuristic bonding policy is proposed which can provide important guidelines to control the number of flows to satisfy the QoS requirement and achieve the maximum network capacity.
14
A Deep Reinforcement learning based Approach for Channel Aggregation in IEEE 802.11 ax
Mengqi Han,Ziru Chen,Lin Cai,Tom H. Luan,Fen Hou +4 more
- 01 Dec 2020
TL;DR: In this paper, an efficient probabilistic channel aggregation scheme is proposed to maximize the network throughput under the quality of service constraints. But, it is shown that a simple CA that aggregates all available channels does not always promote but may degrade the network performance due to the increased inter-channel contentions in a random access wireless local area network (WLAN).
10