A Low-Complexity Algorithm for a Reinforcement Learning-Based Channel Estimator for MIMO Systems
This paper proposes a low-complexity algorithm for a reinforcement learning-based channel estimator for multiple-input multiple-output systems. The proposed channel estimator utilizes detected symbols to reduce the channel estimation error. However, the detected data symbols may include errors at th...
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| Vydané v: | Sensors (Basel, Switzerland) Ročník 22; číslo 12; s. 4379 |
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| Abstract | This paper proposes a low-complexity algorithm for a reinforcement learning-based channel estimator for multiple-input multiple-output systems. The proposed channel estimator utilizes detected symbols to reduce the channel estimation error. However, the detected data symbols may include errors at the receiver owing to the characteristics of the wireless channels. Thus, the detected data symbols are selectively used as additional pilot symbols. To this end, a Markov decision process (MDP) problem is defined to optimize the selection of the detected data symbols. Subsequently, a reinforcement learning algorithm is developed to solve the MDP problem with computational efficiency. The developed algorithm derives the optimal policy in a closed form by introducing backup samples and data subblocks, to reduce latency and complexity. Simulations are conducted, and the results show that the proposed channel estimator significantly reduces the minimum-mean square error of the channel estimates, thus improving the block error rate compared to the conventional channel estimation. |
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| AbstractList | This paper proposes a low-complexity algorithm for a reinforcement learning-based channel estimator for multiple-input multiple-output systems. The proposed channel estimator utilizes detected symbols to reduce the channel estimation error. However, the detected data symbols may include errors at the receiver owing to the characteristics of the wireless channels. Thus, the detected data symbols are selectively used as additional pilot symbols. To this end, a Markov decision process (MDP) problem is defined to optimize the selection of the detected data symbols. Subsequently, a reinforcement learning algorithm is developed to solve the MDP problem with computational efficiency. The developed algorithm derives the optimal policy in a closed form by introducing backup samples and data subblocks, to reduce latency and complexity. Simulations are conducted, and the results show that the proposed channel estimator significantly reduces the minimum-mean square error of the channel estimates, thus improving the block error rate compared to the conventional channel estimation. This paper proposes a low-complexity algorithm for a reinforcement learning-based channel estimator for multiple-input multiple-output systems. The proposed channel estimator utilizes detected symbols to reduce the channel estimation error. However, the detected data symbols may include errors at the receiver owing to the characteristics of the wireless channels. Thus, the detected data symbols are selectively used as additional pilot symbols. To this end, a Markov decision process (MDP) problem is defined to optimize the selection of the detected data symbols. Subsequently, a reinforcement learning algorithm is developed to solve the MDP problem with computational efficiency. The developed algorithm derives the optimal policy in a closed form by introducing backup samples and data subblocks, to reduce latency and complexity. Simulations are conducted, and the results show that the proposed channel estimator significantly reduces the minimum-mean square error of the channel estimates, thus improving the block error rate compared to the conventional channel estimation.This paper proposes a low-complexity algorithm for a reinforcement learning-based channel estimator for multiple-input multiple-output systems. The proposed channel estimator utilizes detected symbols to reduce the channel estimation error. However, the detected data symbols may include errors at the receiver owing to the characteristics of the wireless channels. Thus, the detected data symbols are selectively used as additional pilot symbols. To this end, a Markov decision process (MDP) problem is defined to optimize the selection of the detected data symbols. Subsequently, a reinforcement learning algorithm is developed to solve the MDP problem with computational efficiency. The developed algorithm derives the optimal policy in a closed form by introducing backup samples and data subblocks, to reduce latency and complexity. Simulations are conducted, and the results show that the proposed channel estimator significantly reduces the minimum-mean square error of the channel estimates, thus improving the block error rate compared to the conventional channel estimation. |
| Author | Kim, Tae-Kyoung Min, Moonsik |
| AuthorAffiliation | 3 School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Korea 1 Department of Electronic Engineering, Gachon University, Seongnam 13120, Korea; tk415kim@gmail.com 2 School of Electronics Engineering, Kyungpook National University, Daegu 41566, Korea |
| AuthorAffiliation_xml | – name: 2 School of Electronics Engineering, Kyungpook National University, Daegu 41566, Korea – name: 3 School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Korea – name: 1 Department of Electronic Engineering, Gachon University, Seongnam 13120, Korea; tk415kim@gmail.com |
| Author_xml | – sequence: 1 givenname: Tae-Kyoung orcidid: 0000-0002-9629-7413 surname: Kim fullname: Kim, Tae-Kyoung – sequence: 2 givenname: Moonsik orcidid: 0000-0002-1206-3805 surname: Min fullname: Min, Moonsik |
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| Cites_doi | 10.1109/TWC.2008.070228 10.1109/49.947034 10.1109/78.969514 10.3390/s21144861 10.1109/TCOMM.2021.3071537 10.1109/JCN.2014.000075 10.1109/JSTSP.2014.2317671 10.1109/TWC.2019.2956044 10.1109/TWC.2017.2768423 10.1109/MCOM.2014.6736761 10.1109/WCNC51071.2022.9771693 10.1109/TSP.2018.2879620 10.1109/TSP.2018.2799164 10.1109/TWC.2003.819022 10.3390/s22010309 10.1109/COMST.2007.382406 10.1109/ICC40277.2020.9149283 10.1109/TCOMM.2017.2688447 10.1109/TSP.2005.863008 10.1109/TVT.2021.3090087 10.1109/TIT.2003.810646 10.1109/TWC.2020.2969627 10.1002/ett.4460100604 10.1109/TSP.2015.2416684 10.1109/TSP.2004.826182 10.1109/TSP.2014.2321120 10.1002/bltj.2015 10.1109/TSP.2004.834270 |
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| References | Telatar (ref_2) 1999; 10 Foschini (ref_1) 1996; 1 Neumann (ref_12) 2018; 66 ref_14 ref_13 Huang (ref_22) 2018; 67 Yuan (ref_18) 2020; 19 Zheng (ref_3) 2003; 49 Kim (ref_30) 2021; 70 Larsson (ref_5) 2014; 52 Song (ref_25) 2004; 52 Simeone (ref_7) 2004; 3 ref_16 Lu (ref_6) 2014; 8 ref_15 Valenti (ref_24) 2001; 19 Biguesh (ref_10) 2006; 54 Park (ref_21) 2015; 63 (ref_17) 2021; 69 Kim (ref_9) 2014; 16 Zhao (ref_19) 2008; 7 Park (ref_23) 2017; 65 ref_28 Dong (ref_29) 2004; 52 ref_26 Hoydis (ref_4) 2018; 17 Ma (ref_20) 2014; 62 Jeon (ref_27) 2020; 19 Ozdemir (ref_11) 2007; 9 Morelli (ref_8) 2001; 49 |
| References_xml | – ident: ref_28 – volume: 7 start-page: 3174 year: 2008 ident: ref_19 article-title: Iterative Turbo Channel Estimation for OFDM System over Rapid Dispersive Fading Channel publication-title: IEEE Trans. Wirel. Commun. doi: 10.1109/TWC.2008.070228 – volume: 19 start-page: 1697 year: 2001 ident: ref_24 article-title: Iterative Channel Estimation and Decoding of Pilot Symbol Assisted Turbo Codes Over Flat-Fading Channels publication-title: IEEE J. Sel. Areas Commun. doi: 10.1109/49.947034 – volume: 49 start-page: 3065 year: 2001 ident: ref_8 article-title: A Comparison of Pilot-Aided Channel Estimation Methods for OFDM System publication-title: IEEE Trans. Signal Process. doi: 10.1109/78.969514 – ident: ref_14 doi: 10.3390/s21144861 – volume: 69 start-page: 4921 year: 2021 ident: ref_17 article-title: A Reduced Complexity Ungerboeck Receiver for Quantized Wideband Massive SC-MIMO publication-title: IEEE Trans. Commun. doi: 10.1109/TCOMM.2021.3071537 – volume: 16 start-page: 447 year: 2014 ident: ref_9 article-title: Frequency Domain Channel Estimation for MIMO SC-FDMA Systems with CDM Pilots publication-title: J. Commun. Netw. doi: 10.1109/JCN.2014.000075 – volume: 8 start-page: 742 year: 2014 ident: ref_6 article-title: An Overview of Massive MIMO: Benefits and Challenges publication-title: IEEE J. Sel. Top. Signal Process. doi: 10.1109/JSTSP.2014.2317671 – volume: 19 start-page: 1663 year: 2020 ident: ref_27 article-title: Robust Data Detection for MIMO Systems with One-Bit ADCs: A Reinforcement Learning Approach publication-title: IEEE Trans. Wirel. Commun. doi: 10.1109/TWC.2019.2956044 – volume: 17 start-page: 574 year: 2018 ident: ref_4 article-title: Massive MIMO Has Unlimited Capacity publication-title: IEEE Trans. Wirel. Commun. doi: 10.1109/TWC.2017.2768423 – volume: 52 start-page: 186 year: 2014 ident: ref_5 article-title: Massive MIMO for Next Generation Wireless Systems publication-title: IEEE Commun. Mag. doi: 10.1109/MCOM.2014.6736761 – ident: ref_16 doi: 10.1109/WCNC51071.2022.9771693 – volume: 67 start-page: 245 year: 2018 ident: ref_22 article-title: Iterative Channel Estimation Using LSE and Sparse Message Passing for mmWave MIMO Systems publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2018.2879620 – volume: 66 start-page: 2905 year: 2018 ident: ref_12 article-title: Learning the MMSE Channel Estimator publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2018.2799164 – volume: 3 start-page: 315 year: 2004 ident: ref_7 article-title: Pilot-based Channel Estimation for OFDM Systems by Tracking the Delay-Subspace publication-title: IEEE Trans. Wirel. Commun. doi: 10.1109/TWC.2003.819022 – ident: ref_15 doi: 10.3390/s22010309 – volume: 9 start-page: 18 year: 2007 ident: ref_11 article-title: Channel Estimation for Wireless OFDM Systems publication-title: IEEE Commun. Surv. Tutor. doi: 10.1109/COMST.2007.382406 – ident: ref_26 doi: 10.1109/ICC40277.2020.9149283 – volume: 65 start-page: 2397 year: 2017 ident: ref_23 article-title: Expectation-Maximization-based Channel Estimation for Multiuser MIMO Systems publication-title: IEEE Trans. Commun. doi: 10.1109/TCOMM.2017.2688447 – volume: 54 start-page: 884 year: 2006 ident: ref_10 article-title: Training-based MIMO Channel Estimation: A Study of Estimator Tradeoffs and Optimal Training Signals publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2005.863008 – volume: 70 start-page: 6999 year: 2021 ident: ref_30 article-title: Training Length Adaptation for Reinforcement Learning-Based Detection in Time-Varying Massive MIMO Systems With One-Bit ADCs publication-title: IEEE Trans. Veh. Technol. doi: 10.1109/TVT.2021.3090087 – volume: 49 start-page: 1073 year: 2003 ident: ref_3 article-title: Diversity and Multiplexing: A Fundamental Tradeoff in Multiple-Antenna Channels publication-title: IEEE Trans. Inf. Theory doi: 10.1109/TIT.2003.810646 – volume: 19 start-page: 2960 year: 2020 ident: ref_18 article-title: Machine Learning-Based Channel Prediction in Massive MIMO with Channel Aging publication-title: IEEE Trans. Wirel. Commun. doi: 10.1109/TWC.2020.2969627 – volume: 10 start-page: 585 year: 1999 ident: ref_2 article-title: Capacity of Multi-Antenna Gaussian Channels publication-title: Eur. Trans. Telecommun. doi: 10.1002/ett.4460100604 – volume: 63 start-page: 3032 year: 2015 ident: ref_21 article-title: Iterative Channel Estimation Using Virtual Pilot Signals for MIMO-OFDM Systems publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2015.2416684 – ident: ref_13 – volume: 52 start-page: 1403 year: 2004 ident: ref_29 article-title: Optimal Insertion of Pilot Symbols for Transmissions over Time-Varying Flat Fading Channels publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2004.826182 – volume: 62 start-page: 3111 year: 2014 ident: ref_20 article-title: Data-Aided Channel Estimation in Large Antenna Systems publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2014.2321120 – volume: 1 start-page: 41 year: 1996 ident: ref_1 article-title: Layered Space-Time Architecture for Wireless Communication in a Fading Environment When Using Multi-Element Antennas publication-title: Bell Labs Tech. J. doi: 10.1002/bltj.2015 – volume: 52 start-page: 2885 year: 2004 ident: ref_25 article-title: Soft Input Channel Estimation for Turbo Equalization publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2004.834270 |
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| SubjectTerms | Accuracy Algorithms channel estimation Markov decision process multiple-input multiple-output Optimization Probability Random variables reinforcement learning Sensors Transmitters |
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