Lightweight LSTM-Based Adaptive CQI Feedback Scheme for IoT Devices
As the number of Internet of things (IoT) devices increases exponentially, scheduling and managing the radio resources for IoT devices has become more important. To efficiently allocate radio resources, the base station (BS) needs the channel state information (CSI) of devices every time. Hence, eac...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/10/4929 |
| _version_ | 1797598341573378048 |
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| author | Noel Han Il-Min Kim Jaewoo So |
| author_facet | Noel Han Il-Min Kim Jaewoo So |
| author_sort | Noel Han |
| collection | DOAJ |
| description | As the number of Internet of things (IoT) devices increases exponentially, scheduling and managing the radio resources for IoT devices has become more important. To efficiently allocate radio resources, the base station (BS) needs the channel state information (CSI) of devices every time. Hence, each device needs to periodically (or aperiodically) report its channel quality indicator (CQI) to the BS. The BS determines the modulation and coding scheme (MCS) based on the CQI reported by the IoT device. However, the more a device reports its CQI, the more the feedback overhead increases. In this paper, we propose a long short-term memory (LSTM)-based CQI feedback scheme, where the IoT device aperiodically reports its CQI relying on an LSTM-based channel prediction. Additionally, because the memory capacity of IoT devices is generally small, the complexity of the machine learning model must be reduced. Hence, we propose a lightweight LSTM model to reduce the complexity. The simulation results show that the proposed lightweight LSTM-based CSI scheme dramatically reduces the feedback overhead compared with that of the existing periodic feedback scheme. Moreover, the proposed lightweight LSTM model significantly reduces the complexity without sacrificing performance. |
| first_indexed | 2024-03-11T03:20:53Z |
| format | Article |
| id | doaj.art-e1b8f88d09b440f390f86f8c87c89130 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T03:20:53Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e1b8f88d09b440f390f86f8c87c891302023-11-18T03:14:43ZengMDPI AGSensors1424-82202023-05-012310492910.3390/s23104929Lightweight LSTM-Based Adaptive CQI Feedback Scheme for IoT DevicesNoel Han0Il-Min Kim1Jaewoo So2Department of Electronic Engineering, Sogang University, Seoul 04107, Republic of KoreaDepartment of Electrical and Computer Engineering, Queen’s University, Kingston, ON K7L 3N6, CanadaDepartment of Electronic Engineering, Sogang University, Seoul 04107, Republic of KoreaAs the number of Internet of things (IoT) devices increases exponentially, scheduling and managing the radio resources for IoT devices has become more important. To efficiently allocate radio resources, the base station (BS) needs the channel state information (CSI) of devices every time. Hence, each device needs to periodically (or aperiodically) report its channel quality indicator (CQI) to the BS. The BS determines the modulation and coding scheme (MCS) based on the CQI reported by the IoT device. However, the more a device reports its CQI, the more the feedback overhead increases. In this paper, we propose a long short-term memory (LSTM)-based CQI feedback scheme, where the IoT device aperiodically reports its CQI relying on an LSTM-based channel prediction. Additionally, because the memory capacity of IoT devices is generally small, the complexity of the machine learning model must be reduced. Hence, we propose a lightweight LSTM model to reduce the complexity. The simulation results show that the proposed lightweight LSTM-based CSI scheme dramatically reduces the feedback overhead compared with that of the existing periodic feedback scheme. Moreover, the proposed lightweight LSTM model significantly reduces the complexity without sacrificing performance.https://www.mdpi.com/1424-8220/23/10/4929channel quality indicator feedbacklong short-term memorylightweight modelmodulation and coding schemefeedback overhead |
| spellingShingle | Noel Han Il-Min Kim Jaewoo So Lightweight LSTM-Based Adaptive CQI Feedback Scheme for IoT Devices Sensors channel quality indicator feedback long short-term memory lightweight model modulation and coding scheme feedback overhead |
| title | Lightweight LSTM-Based Adaptive CQI Feedback Scheme for IoT Devices |
| title_full | Lightweight LSTM-Based Adaptive CQI Feedback Scheme for IoT Devices |
| title_fullStr | Lightweight LSTM-Based Adaptive CQI Feedback Scheme for IoT Devices |
| title_full_unstemmed | Lightweight LSTM-Based Adaptive CQI Feedback Scheme for IoT Devices |
| title_short | Lightweight LSTM-Based Adaptive CQI Feedback Scheme for IoT Devices |
| title_sort | lightweight lstm based adaptive cqi feedback scheme for iot devices |
| topic | channel quality indicator feedback long short-term memory lightweight model modulation and coding scheme feedback overhead |
| url | https://www.mdpi.com/1424-8220/23/10/4929 |
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