IEICE TRANSACTIONS on Communications
Reinforcement Learning for QoS-Constrained Autonomous Resource Allocation with H2H/M2M Co-Existence in Cellular Networks
Summary :
Machine-to-Machine (M2M) communication plays a pivotal role in the evolution of Internet of Things (IoT). Cellular networks are considered to be a key enabler for M2M communications, which are originally designed mainly for Human-to-Human (H2H) communications. The introduction of M2M users will cause a series of problems to traditional H2H users, i.e., interference between various traffic. Resource allocation is an effective solution to these problems. In this paper, we consider a shared resource block (RB) and power allocation in an H2H/M2M coexistence scenario, where M2M users are subdivided into delay-tolerant and delay-sensitive types. We first model the RB-power allocation problem as maximization of capacity under Quality-of-Service (QoS) constraints of different types of traffic. Then, a learning framework is introduced, wherein a complex agent is built from simpler subagents, which provides the basis for distributed deployment scheme. Further, we proposed distributed Q-learning based autonomous RB-power allocation algorithm (DQ-ARPA), which enables the machine type network gateways (MTCG) as agents to learn the wireless environment and choose the RB-power autonomously to maximize M2M pairs' capacity while ensuring the QoS requirements of critical services. Simulation results indicates that with an appropriate reward design, our proposed scheme succeeds in reducing the impact of delay-tolerant machine type users on critical services in terms of SINR thresholds and outage ratios.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E105-B No.11 pp.1332-1341
- Publication Date
- 2022/11/01
- Publicized
- 2022/05/27
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2021TMP0011
- Type of Manuscript
- Special Section PAPER (Special Section on Towards Management for Future Communications and Services in Conjunction with Main Topics of APNOMS2021)
- Category
Authors
Xing WEI
Beijing Information Science and Technology University
Xuehua LI
Beijing Information Science and Technology University
Shuo CHEN
Beijing Information Science and Technology University
Na LI
the Baicells Technologies, Co., Ltd.
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Xing WEI, Xuehua LI, Shuo CHEN, Na LI, "Reinforcement Learning for QoS-Constrained Autonomous Resource Allocation with H2H/M2M Co-Existence in Cellular Networks" in IEICE TRANSACTIONS on Communications,
vol. E105-B, no. 11, pp. 1332-1341, November 2022, doi: 10.1587/transcom.2021TMP0011.
Abstract: Machine-to-Machine (M2M) communication plays a pivotal role in the evolution of Internet of Things (IoT). Cellular networks are considered to be a key enabler for M2M communications, which are originally designed mainly for Human-to-Human (H2H) communications. The introduction of M2M users will cause a series of problems to traditional H2H users, i.e., interference between various traffic. Resource allocation is an effective solution to these problems. In this paper, we consider a shared resource block (RB) and power allocation in an H2H/M2M coexistence scenario, where M2M users are subdivided into delay-tolerant and delay-sensitive types. We first model the RB-power allocation problem as maximization of capacity under Quality-of-Service (QoS) constraints of different types of traffic. Then, a learning framework is introduced, wherein a complex agent is built from simpler subagents, which provides the basis for distributed deployment scheme. Further, we proposed distributed Q-learning based autonomous RB-power allocation algorithm (DQ-ARPA), which enables the machine type network gateways (MTCG) as agents to learn the wireless environment and choose the RB-power autonomously to maximize M2M pairs' capacity while ensuring the QoS requirements of critical services. Simulation results indicates that with an appropriate reward design, our proposed scheme succeeds in reducing the impact of delay-tolerant machine type users on critical services in terms of SINR thresholds and outage ratios.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2021TMP0011/_p
Copy
@ARTICLE{e105-b_11_1332,
author={Xing WEI, Xuehua LI, Shuo CHEN, Na LI, },
journal={IEICE TRANSACTIONS on Communications},
title={Reinforcement Learning for QoS-Constrained Autonomous Resource Allocation with H2H/M2M Co-Existence in Cellular Networks},
year={2022},
volume={E105-B},
number={11},
pages={1332-1341},
abstract={Machine-to-Machine (M2M) communication plays a pivotal role in the evolution of Internet of Things (IoT). Cellular networks are considered to be a key enabler for M2M communications, which are originally designed mainly for Human-to-Human (H2H) communications. The introduction of M2M users will cause a series of problems to traditional H2H users, i.e., interference between various traffic. Resource allocation is an effective solution to these problems. In this paper, we consider a shared resource block (RB) and power allocation in an H2H/M2M coexistence scenario, where M2M users are subdivided into delay-tolerant and delay-sensitive types. We first model the RB-power allocation problem as maximization of capacity under Quality-of-Service (QoS) constraints of different types of traffic. Then, a learning framework is introduced, wherein a complex agent is built from simpler subagents, which provides the basis for distributed deployment scheme. Further, we proposed distributed Q-learning based autonomous RB-power allocation algorithm (DQ-ARPA), which enables the machine type network gateways (MTCG) as agents to learn the wireless environment and choose the RB-power autonomously to maximize M2M pairs' capacity while ensuring the QoS requirements of critical services. Simulation results indicates that with an appropriate reward design, our proposed scheme succeeds in reducing the impact of delay-tolerant machine type users on critical services in terms of SINR thresholds and outage ratios.},
keywords={},
doi={10.1587/transcom.2021TMP0011},
ISSN={1745-1345},
month={November},}
Copy
TY - JOUR
TI - Reinforcement Learning for QoS-Constrained Autonomous Resource Allocation with H2H/M2M Co-Existence in Cellular Networks
T2 - IEICE TRANSACTIONS on Communications
SP - 1332
EP - 1341
AU - Xing WEI
AU - Xuehua LI
AU - Shuo CHEN
AU - Na LI
PY - 2022
DO - 10.1587/transcom.2021TMP0011
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E105-B
IS - 11
JA - IEICE TRANSACTIONS on Communications
Y1 - November 2022
AB - Machine-to-Machine (M2M) communication plays a pivotal role in the evolution of Internet of Things (IoT). Cellular networks are considered to be a key enabler for M2M communications, which are originally designed mainly for Human-to-Human (H2H) communications. The introduction of M2M users will cause a series of problems to traditional H2H users, i.e., interference between various traffic. Resource allocation is an effective solution to these problems. In this paper, we consider a shared resource block (RB) and power allocation in an H2H/M2M coexistence scenario, where M2M users are subdivided into delay-tolerant and delay-sensitive types. We first model the RB-power allocation problem as maximization of capacity under Quality-of-Service (QoS) constraints of different types of traffic. Then, a learning framework is introduced, wherein a complex agent is built from simpler subagents, which provides the basis for distributed deployment scheme. Further, we proposed distributed Q-learning based autonomous RB-power allocation algorithm (DQ-ARPA), which enables the machine type network gateways (MTCG) as agents to learn the wireless environment and choose the RB-power autonomously to maximize M2M pairs' capacity while ensuring the QoS requirements of critical services. Simulation results indicates that with an appropriate reward design, our proposed scheme succeeds in reducing the impact of delay-tolerant machine type users on critical services in terms of SINR thresholds and outage ratios.
ER -
English
