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...

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Vydáno v:	Sensors (Basel, Switzerland) Ročník 23; číslo 10; s. 4929
Hlavní autoři:	Han, Noel, Kim, Il-Min, So, Jaewoo
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland MDPI AG 20.05.2023 MDPI
Témata:	Accuracy Analysis Artificial intelligence channel quality indicator feedback Codes Communication feedback overhead Internet of Things lightweight model long short-term memory Machine learning modulation and coding scheme Neural networks Performance evaluation Wireless telecommunications equipment modulation and coding scheme feedback overhead channel quality indicator feedback lightweight model long short-term memory
ISSN:	1424-8220, 1424-8220
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Abstract	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.
AbstractList	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. 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.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.
Audience	Academic
Author	Kim, Il-Min So, Jaewoo Han, Noel
AuthorAffiliation	1 Department of Electronic Engineering, Sogang University, Seoul 04107, Republic of Korea 2 Department of Electrical and Computer Engineering, Queen’s University, Kingston, ON K7L 3N6, Canada
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BackLink	https://www.ncbi.nlm.nih.gov/pubmed/37430843$$D View this record in MEDLINE/PubMed
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Cites_doi	10.1109/LWC.2018.2818160 10.1109/TWC.2020.2968430 10.1109/ACCESS.2018.2825255 10.1017/9781108684163 10.1109/LWC.2022.3157263 10.1109/LCOMM.2018.2882829 10.1016/j.icte.2021.11.007 10.1109/ACCESS.2021.3117341 10.1109/TCOMM.2018.2821126 10.1109/LCOMM.2021.3076504 10.1109/WOCC53213.2021.9603019 10.1109/ICC40277.2020.9149229 10.1109/TCOMM.2022.3217777 10.1109/LWC.2021.3083331 10.1109/ACCESS.2020.2963896 10.1109/TCOMM.2020.3006575 10.1109/LWC.2021.3117032 10.1109/TWC.2021.3103120 10.1109/ACCESS.2019.2924673 10.1109/LCOMM.2019.2907622 10.1109/LWC.2019.2895039 10.1109/LCOMM.2021.3098419 10.3390/rs14163947 10.1049/iet-com.2019.1030 10.1109/ICFHR-2018.2018.00028 10.1109/JLT.2019.2963276 10.1109/WCNC.2019.8885616 10.1109/LWC.2021.3100493 10.1109/LWC.2020.2964550 10.1109/LWC.2018.2874264
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SubjectTerms	Accuracy Analysis Artificial intelligence channel quality indicator feedback Codes Communication feedback overhead Internet of Things lightweight model long short-term memory Machine learning modulation and coding scheme Neural networks Performance evaluation Wireless telecommunications equipment
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Title	Lightweight LSTM-Based Adaptive CQI Feedback Scheme for IoT Devices
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