Abstract
Over the last few years, the Deep Reinforcement Learning (DRL) paradigm has been widely adopted for 5G and beyond network optimization because of its extreme adaptability to many different scenarios. However, collecting and processing learning data entail a significant cost in terms of communication and computational resources, which is often disregarded in the networking literature. In this work, we analyze the cost of learning in a resource-constrained system, defining an optimization problem in which training a DRL agent makes it possible to improve the resource allocation strategy but also reduces the number of available resources. Our simulation results show that the cost of learning can be critical when evaluating DRL schemes on the network edge and that assuming a cost-free learning model can lead to significantly overestimating performance.
Original language | English |
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Title of host publication | ICC 2022 - IEEE International Conference on Communications |
Number of pages | 6 |
Publisher | IEEE |
Publication date | 2022 |
Pages | 631-636 |
ISBN (Electronic) | 9781538683477 |
DOIs | |
Publication status | Published - 2022 |
Event | 2022 IEEE International Conference on Communications, ICC 2022 - Seoul, Korea, Republic of Duration: 16 May 2022 → 20 May 2022 |
Conference
Conference | 2022 IEEE International Conference on Communications, ICC 2022 |
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Country/Territory | Korea, Republic of |
City | Seoul |
Period | 16/05/2022 → 20/05/2022 |
Sponsor | et al., Huawei Technologies Co., Ltd., LG, Qualcomm, Samsung, Technology Innovation Institute (TII) |
Series | I E E E International Conference on Communications |
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Volume | 2022-May |
ISSN | 1550-3607 |
Bibliographical note
Publisher Copyright:© 2022 IEEE.
Keywords
- Continual Learning
- Mobile Edge Computing
- Network Slicing
- Reinforcement Learning