Abstract
To support wireless communication traffic of Internet-of-Things (IoT) systems in terms of massive connectivity, dynamic spectrum access (DSA) is important issue. This paper proposes spectrum sensor-aided DSA system based on a reinforcement learning (RL) algorithm that aims at efficient spectrum usage for IoT network over the incumbent network. Due to small-form-factor of IoT devices, they do not have spectrum sensing capability. To support DSA of IoT devices, we introduce sensor-aided DSA system that enhances spatial spectrum reusability by means of RL algorithm. With the RL algorithm, proposed DSA system provides self-organizing feature for massive number of IoT devices. We show that the performance of proposed RL based DSA system in various densities of IoT devices utilizing slotted ALOHA protocol that has spectrum access probability learned by proposed DSA system. We also present the performance of proposed RL based DSA system surpass that of distributed Carrier Sensing Multiple Access with Collision Avoidance (CSMA/CA) protocol for channel access coordination. We also present the consistent performance of incumbent user when the IoT devices access to the spectrum band with learned spectrum access probability.
| Original language | English |
|---|---|
| Title of host publication | 2019 IEEE International Conference on Communications, ICC 2019 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781538680889 |
| DOIs | |
| Publication status | Published - 2019 May |
| Event | 2019 IEEE International Conference on Communications, ICC 2019 - Shanghai, China Duration: 2019 May 20 → 2019 May 24 |
Publication series
| Name | IEEE International Conference on Communications |
|---|---|
| Volume | 2019-May |
| ISSN (Print) | 1550-3607 |
Conference
| Conference | 2019 IEEE International Conference on Communications, ICC 2019 |
|---|---|
| Country | China |
| City | Shanghai |
| Period | 19/5/20 → 19/5/24 |
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All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Electrical and Electronic Engineering
Cite this
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A Reinforcement Learning Approach to Dynamic Spectrum Access in Internet-of-Things Networks. / Cha, Han; Kim, Seong Lyun.
2019 IEEE International Conference on Communications, ICC 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. 8762091 (IEEE International Conference on Communications; Vol. 2019-May).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - A Reinforcement Learning Approach to Dynamic Spectrum Access in Internet-of-Things Networks
AU - Cha, Han
AU - Kim, Seong Lyun
PY - 2019/5
Y1 - 2019/5
N2 - To support wireless communication traffic of Internet-of-Things (IoT) systems in terms of massive connectivity, dynamic spectrum access (DSA) is important issue. This paper proposes spectrum sensor-aided DSA system based on a reinforcement learning (RL) algorithm that aims at efficient spectrum usage for IoT network over the incumbent network. Due to small-form-factor of IoT devices, they do not have spectrum sensing capability. To support DSA of IoT devices, we introduce sensor-aided DSA system that enhances spatial spectrum reusability by means of RL algorithm. With the RL algorithm, proposed DSA system provides self-organizing feature for massive number of IoT devices. We show that the performance of proposed RL based DSA system in various densities of IoT devices utilizing slotted ALOHA protocol that has spectrum access probability learned by proposed DSA system. We also present the performance of proposed RL based DSA system surpass that of distributed Carrier Sensing Multiple Access with Collision Avoidance (CSMA/CA) protocol for channel access coordination. We also present the consistent performance of incumbent user when the IoT devices access to the spectrum band with learned spectrum access probability.
AB - To support wireless communication traffic of Internet-of-Things (IoT) systems in terms of massive connectivity, dynamic spectrum access (DSA) is important issue. This paper proposes spectrum sensor-aided DSA system based on a reinforcement learning (RL) algorithm that aims at efficient spectrum usage for IoT network over the incumbent network. Due to small-form-factor of IoT devices, they do not have spectrum sensing capability. To support DSA of IoT devices, we introduce sensor-aided DSA system that enhances spatial spectrum reusability by means of RL algorithm. With the RL algorithm, proposed DSA system provides self-organizing feature for massive number of IoT devices. We show that the performance of proposed RL based DSA system in various densities of IoT devices utilizing slotted ALOHA protocol that has spectrum access probability learned by proposed DSA system. We also present the performance of proposed RL based DSA system surpass that of distributed Carrier Sensing Multiple Access with Collision Avoidance (CSMA/CA) protocol for channel access coordination. We also present the consistent performance of incumbent user when the IoT devices access to the spectrum band with learned spectrum access probability.
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U2 - 10.1109/ICC.2019.8762091
DO - 10.1109/ICC.2019.8762091
M3 - Conference contribution
AN - SCOPUS:85070187267
T3 - IEEE International Conference on Communications
BT - 2019 IEEE International Conference on Communications, ICC 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
ER -