Deep Reinforcement Learning Based Intelligent Reflecting Surface Optimization for MISO Communication Systems
This letter investigates the intelligent reflecting surface (IRS)-aided multiple-input single-output wireless transmission system. Particularly, the optimization of the passive phase shift of each element at IRS to maximize the downlink received signal-to-noise ratio is considered. Inspired by the h...
Saved in:
| Published in | IEEE wireless communications letters Vol. 9; no. 5; pp. 745 - 749 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Piscataway
IEEE
01.05.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | This letter investigates the intelligent reflecting surface (IRS)-aided multiple-input single-output wireless transmission system. Particularly, the optimization of the passive phase shift of each element at IRS to maximize the downlink received signal-to-noise ratio is considered. Inspired by the huge success of deep reinforcement learning (DRL) on resolving complicated control problems, we develop a DRL based framework to solve this non-convex optimization problem. Numerical results reveal that the proposed DRL based framework can achieve almost the upper bound of the received SNR with relatively low time consumption. |
|---|---|
| AbstractList | This letter investigates the intelligent reflecting surface (IRS)-aided multiple-input single-output wireless transmission system. Particularly, the optimization of the passive phase shift of each element at IRS to maximize the downlink received signal-to-noise ratio is considered. Inspired by the huge success of deep reinforcement learning (DRL) on resolving complicated control problems, we develop a DRL based framework to solve this non-convex optimization problem. Numerical results reveal that the proposed DRL based framework can achieve almost the upper bound of the received SNR with relatively low time consumption. |
| Author | Wang, Qisheng Li, Xiao Wen, Chao-Kai Feng, Keming |
| Author_xml | – sequence: 1 givenname: Keming orcidid: 0000-0002-5721-4566 surname: Feng fullname: Feng, Keming email: keming_feng@seu.edu.cn organization: National Mobile Communications Research Laboratory, Southeast University, Nanjing, China – sequence: 2 givenname: Qisheng surname: Wang fullname: Wang, Qisheng email: qishengw@seu.edu.cn organization: National Mobile Communications Research Laboratory, Southeast University, Nanjing, China – sequence: 3 givenname: Xiao orcidid: 0000-0001-9660-9053 surname: Li fullname: Li, Xiao email: li_xiao@seu.edu.cn organization: National Mobile Communications Research Laboratory, Southeast University, Nanjing, China – sequence: 4 givenname: Chao-Kai orcidid: 0000-0001-5952-232X surname: Wen fullname: Wen, Chao-Kai email: chaokai.wen@mail.nsysu.edu.tw organization: Institute of Communications Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan |
| BookMark | eNp9UD1PwzAQtVCRKKU7Eksk5hR_5MsjhK9KQZVaEGPkOufKVeIU2x3KrychVQcGbrnT3Xvv7t4lGpnWAELXBM8Iwfyu-MxnFFM8ozzhJEnP0JiShIaURfHoVLP0Ak2d2-IuEkwoycaofgTYBUvQRrVWQgPGBwUIa7TZBA_CQRXMjYe61pt-tARVg_T9cLW3SkgIFjuvG_0tvG5N0IkEb_PVIsjbptkbLYf26uA8NO4KnStRO5ge8wR9PD-9569hsXiZ5_dFKCknPqx4Bt0zlYpktc6UiJmSfC1IpQgRa5xRyhVhMUm4AhnLDsV5HKciYjTLBKNsgm4H3Z1tv_bgfLlt99Z0K0saYUIoS6O4QyUDStrWOQuqlNr_3uut0HVJcNmbW3bmlr255dHcjoj_EHdWN8Ie_qPcDBQNACd4xpOMxZj9ADo7h4w |
| CODEN | IWCLAF |
| CitedBy_id | crossref_primary_10_1109_TCOMM_2024_3384951 crossref_primary_10_1109_TWC_2022_3219140 crossref_primary_10_1109_TIFS_2021_3103062 crossref_primary_10_1109_TNNLS_2021_3117878 crossref_primary_10_1109_OJVT_2020_3044569 crossref_primary_10_1109_TWC_2023_3336742 crossref_primary_10_1109_ACCESS_2023_3312546 crossref_primary_10_1109_COMST_2020_3004197 crossref_primary_10_1109_TWC_2023_3324641 crossref_primary_10_1109_JSTSP_2022_3172587 crossref_primary_10_1109_TGCN_2023_3237132 crossref_primary_10_1109_JIOT_2022_3195543 crossref_primary_10_1631_FITEE_2300136 crossref_primary_10_1109_TCOMM_2024_3409538 crossref_primary_10_3390_s22145405 crossref_primary_10_1109_TVT_2024_3357821 crossref_primary_10_1109_TVT_2023_3332107 crossref_primary_10_1109_TSP_2023_3322830 crossref_primary_10_1109_LWC_2022_3194192 crossref_primary_10_1109_LWC_2023_3278171 crossref_primary_10_1109_ACCESS_2024_3522783 crossref_primary_10_1109_LCOMM_2024_3376713 crossref_primary_10_1109_TVT_2023_3278459 crossref_primary_10_1016_j_procs_2022_04_010 crossref_primary_10_1109_JSTSP_2021_3134162 crossref_primary_10_1109_JSAC_2021_3087259 crossref_primary_10_1109_TWC_2023_3255406 crossref_primary_10_3390_electronics10222786 crossref_primary_10_1109_LWC_2024_3504592 crossref_primary_10_1109_TVT_2023_3275581 crossref_primary_10_1109_JIOT_2022_3177705 crossref_primary_10_1109_TII_2024_3459025 crossref_primary_10_1109_JIOT_2022_3218740 crossref_primary_10_1109_TWC_2022_3159564 crossref_primary_10_1109_LWC_2021_3091164 crossref_primary_10_1109_TGCN_2024_3393554 crossref_primary_10_1002_ett_70007 crossref_primary_10_1109_JSAC_2023_3310050 crossref_primary_10_1109_JIOT_2023_3279196 crossref_primary_10_1109_TWC_2021_3072694 crossref_primary_10_1109_JIOT_2023_3278384 crossref_primary_10_1109_OJCOMS_2022_3210648 crossref_primary_10_1109_TWC_2024_3418011 crossref_primary_10_1109_TVT_2023_3249353 crossref_primary_10_1515_freq_2022_0189 crossref_primary_10_1109_TMC_2021_3114167 crossref_primary_10_1016_j_vehcom_2024_100859 crossref_primary_10_1007_s11276_022_03037_2 crossref_primary_10_1109_JSAC_2023_3240707 crossref_primary_10_1109_OJVT_2024_3447109 crossref_primary_10_1109_JSAC_2020_3041401 crossref_primary_10_1109_LWC_2021_3056702 crossref_primary_10_1109_LCOMM_2020_3042344 crossref_primary_10_1109_LWC_2024_3482720 crossref_primary_10_1109_LWC_2021_3100901 crossref_primary_10_1109_JPROC_2022_3174030 crossref_primary_10_1109_JIOT_2024_3449969 crossref_primary_10_1109_COMST_2021_3077737 crossref_primary_10_1109_LWC_2022_3229441 crossref_primary_10_1109_TII_2023_3280314 crossref_primary_10_1109_JSAC_2021_3078502 crossref_primary_10_1109_TCOMM_2023_3286453 crossref_primary_10_1109_JSEN_2024_3370993 crossref_primary_10_1016_j_procs_2023_01_090 crossref_primary_10_1109_JPROC_2021_3121790 crossref_primary_10_1109_TCOMM_2023_3249227 crossref_primary_10_1109_TWC_2024_3354052 crossref_primary_10_1016_j_dcan_2021_11_002 crossref_primary_10_1109_TVT_2022_3233469 crossref_primary_10_3390_fi17020050 crossref_primary_10_1109_JIOT_2024_3398413 crossref_primary_10_1038_s41377_024_01729_2 crossref_primary_10_1109_TWC_2024_3358372 crossref_primary_10_1109_TCOMM_2023_3233988 crossref_primary_10_3390_s23146321 crossref_primary_10_1109_TWC_2021_3100148 crossref_primary_10_1109_TCOMM_2022_3158954 crossref_primary_10_1109_JIOT_2022_3232587 crossref_primary_10_1109_TWC_2022_3159187 crossref_primary_10_1109_TWC_2022_3217679 crossref_primary_10_1186_s13638_022_02184_6 crossref_primary_10_1109_TCOMM_2022_3162645 crossref_primary_10_1109_TWC_2022_3224009 crossref_primary_10_1109_TWC_2024_3449074 crossref_primary_10_1109_TWC_2021_3103256 crossref_primary_10_1109_TWC_2022_3222864 crossref_primary_10_1109_TIV_2023_3275632 crossref_primary_10_1109_TWC_2023_3245820 crossref_primary_10_1109_TVT_2024_3401750 crossref_primary_10_1016_j_jiixd_2023_06_003 crossref_primary_10_1007_s11277_024_11714_y crossref_primary_10_1109_LCOMM_2022_3170061 crossref_primary_10_1109_TCOMM_2024_3382332 crossref_primary_10_1109_TVT_2024_3391941 crossref_primary_10_1109_COMST_2023_3340099 crossref_primary_10_3390_app142412014 crossref_primary_10_1109_MNET_211_2100386 crossref_primary_10_1049_cmu2_12674 crossref_primary_10_1109_TWC_2023_3325772 crossref_primary_10_1109_ACCESS_2023_3342320 crossref_primary_10_1109_LWC_2024_3402235 crossref_primary_10_3390_electronics13183732 crossref_primary_10_3390_s21186100 crossref_primary_10_1109_TVT_2024_3486360 crossref_primary_10_1109_TWC_2024_3389026 crossref_primary_10_1109_TWC_2022_3150021 crossref_primary_10_1109_LCOMM_2024_3363450 crossref_primary_10_1049_ell2_13118 crossref_primary_10_3390_sym14051018 crossref_primary_10_1109_TITS_2021_3123072 crossref_primary_10_1109_LWC_2023_3242449 crossref_primary_10_1049_cmu2_12669 crossref_primary_10_1109_JSTSP_2022_3162109 crossref_primary_10_3390_s23167164 crossref_primary_10_1109_COMST_2022_3155305 crossref_primary_10_1109_JIOT_2022_3232962 crossref_primary_10_1109_LWC_2021_3135602 crossref_primary_10_1109_LWC_2024_3441605 crossref_primary_10_1109_TWC_2023_3330977 crossref_primary_10_1145_3696414 crossref_primary_10_1109_TWC_2021_3133296 crossref_primary_10_4108_eetinis_v10i1_2864 crossref_primary_10_1109_TWC_2023_3260304 crossref_primary_10_1109_TCOMM_2023_3239621 crossref_primary_10_3390_s22145278 crossref_primary_10_1109_TCOMM_2021_3126626 crossref_primary_10_1109_ACCESS_2021_3053486 crossref_primary_10_1109_LWC_2023_3336999 crossref_primary_10_1109_JSTSP_2022_3195671 crossref_primary_10_3389_fnins_2023_1168864 crossref_primary_10_1109_TWC_2021_3054841 crossref_primary_10_1016_j_dcan_2022_10_031 crossref_primary_10_1016_j_dcan_2022_10_030 crossref_primary_10_1109_TVT_2024_3373819 crossref_primary_10_1016_j_dcan_2022_10_032 crossref_primary_10_1109_LCOMM_2021_3140155 crossref_primary_10_1109_LWC_2021_3056620 crossref_primary_10_1109_TWC_2021_3107306 crossref_primary_10_1109_TCCN_2021_3128605 crossref_primary_10_1109_TWC_2021_3105405 crossref_primary_10_1109_TCOMM_2022_3178755 crossref_primary_10_1109_JIOT_2020_3025365 crossref_primary_10_1109_LCOMM_2021_3117929 crossref_primary_10_1007_s11432_022_3723_5 crossref_primary_10_1109_TCOMM_2023_3242352 crossref_primary_10_1109_TVT_2021_3062870 crossref_primary_10_1109_JIOT_2024_3392358 crossref_primary_10_1587_elex_20_20230315 crossref_primary_10_1109_TCOMM_2022_3171837 crossref_primary_10_1109_JSAC_2021_3118405 crossref_primary_10_1109_TCOMM_2021_3051897 crossref_primary_10_1109_LWC_2023_3275283 crossref_primary_10_3390_s23187793 crossref_primary_10_1109_TCOMM_2021_3100860 crossref_primary_10_1016_j_comnet_2024_110729 crossref_primary_10_3390_s23063034 crossref_primary_10_1109_JIOT_2023_3326174 crossref_primary_10_1109_TCOMM_2022_3194134 crossref_primary_10_1109_JIOT_2023_3265011 crossref_primary_10_1109_ACCESS_2021_3087136 crossref_primary_10_1109_JIOT_2023_3264286 crossref_primary_10_1109_JSAC_2023_3288266 crossref_primary_10_1109_TWC_2021_3081423 crossref_primary_10_1016_j_dcan_2022_10_029 crossref_primary_10_1109_ACCESS_2022_3157651 crossref_primary_10_1109_MCOMSTD_0002_2200007 crossref_primary_10_1109_TVT_2024_3411593 crossref_primary_10_3390_drones8040122 crossref_primary_10_1109_JIOT_2021_3131771 crossref_primary_10_1109_TWC_2021_3098521 crossref_primary_10_3390_drones6120408 crossref_primary_10_1109_OJCOMS_2022_3165590 crossref_primary_10_1002_advs_202203747 crossref_primary_10_1109_TWC_2020_3024860 crossref_primary_10_3390_electronics12214490 crossref_primary_10_1109_JSAC_2020_3007211 crossref_primary_10_1109_TVT_2024_3359117 crossref_primary_10_1109_TWC_2024_3468298 crossref_primary_10_1109_LWC_2021_3100427 crossref_primary_10_1109_TVT_2024_3427001 crossref_primary_10_1109_JSAC_2022_3158696 |
| Cites_doi | 10.1109/GLOCOM.2018.8647620 10.1109/ICASSP.2019.8683145 10.1049/el.2019.0400 10.1023/B:BTTJ.0000047603.37042.33 10.1109/TVT.2019.2923997 10.1038/nature14236 10.1109/SPAWC.2019.8815412 10.1109/TCOMM.2016.2530796 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020 |
| DBID | 97E RIA RIE AAYXX CITATION 7SP 8FD L7M |
| DOI | 10.1109/LWC.2020.2969167 |
| DatabaseName | IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Xplore CrossRef Electronics & Communications Abstracts Technology Research Database Advanced Technologies Database with Aerospace |
| DatabaseTitle | CrossRef Technology Research Database Advanced Technologies Database with Aerospace Electronics & Communications Abstracts |
| DatabaseTitleList | Technology Research Database |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://proxy.k.utb.cz/login?url=https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 2162-2345 |
| EndPage | 749 |
| ExternalDocumentID | 10_1109_LWC_2020_2969167 8968350 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: Ministry of Science and Technology of Taiwan grantid: MOST 108-2628-E-110-001-MY3 funderid: 10.13039/501100004663 – fundername: National Natural Science Foundation of China grantid: 61971126; 61831013 funderid: 10.13039/501100001809 |
| GroupedDBID | 0R~ 4.4 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG AGQYO AGSQL AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ EBS EJD IES IFIPE IPLJI JAVBF M43 OCL RIA RIE RNS AAYXX CITATION RIG 7SP 8FD L7M |
| ID | FETCH-LOGICAL-c291t-d98e020df4cdb8fa53fc9ba1df11ab08229f135169fec5c4cd99557a43288a323 |
| IEDL.DBID | RIE |
| ISSN | 2162-2337 |
| IngestDate | Sun Jun 29 16:42:23 EDT 2025 Thu Apr 24 23:04:36 EDT 2025 Tue Jul 01 04:18:22 EDT 2025 Wed Aug 27 02:39:17 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 5 |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c291t-d98e020df4cdb8fa53fc9ba1df11ab08229f135169fec5c4cd99557a43288a323 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0001-9660-9053 0000-0002-5721-4566 0000-0001-5952-232X |
| PQID | 2401123745 |
| PQPubID | 2040496 |
| PageCount | 5 |
| ParticipantIDs | ieee_primary_8968350 crossref_primary_10_1109_LWC_2020_2969167 proquest_journals_2401123745 crossref_citationtrail_10_1109_LWC_2020_2969167 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2020-05-01 |
| PublicationDateYYYYMMDD | 2020-05-01 |
| PublicationDate_xml | – month: 05 year: 2020 text: 2020-05-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Piscataway |
| PublicationPlace_xml | – name: Piscataway |
| PublicationTitle | IEEE wireless communications letters |
| PublicationTitleAbbrev | LWC |
| PublicationYear | 2020 |
| Publisher | IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher_xml | – name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| References | lillicrap (ref14) 2015 ref11 ref16 tang (ref2) 2019 yu (ref6) 2019 ref8 ref7 ref9 ref3 sutton (ref13) 2000 ref5 cao (ref15) 2019 wu (ref1) 2019 taha (ref10) 2019 mnih (ref12) 2015; 518 abeywickrama (ref4) 2019 |
| References_xml | – ident: ref5 doi: 10.1109/GLOCOM.2018.8647620 – ident: ref8 doi: 10.1109/ICASSP.2019.8683145 – year: 2019 ident: ref15 article-title: Intelligent reflecting surface aided multi-user millimeter-wave communications for coverage enhancement – year: 2019 ident: ref6 article-title: MISO wireless communication systems via intelligent reflecting surfaces – ident: ref3 doi: 10.1049/el.2019.0400 – ident: ref11 doi: 10.1023/B:BTTJ.0000047603.37042.33 – year: 2019 ident: ref2 article-title: Wireless communications with programmable metasurface: New paradigms, opportunities, and challenges on transceiver design – ident: ref7 doi: 10.1109/TVT.2019.2923997 – year: 2019 ident: ref4 article-title: Intelligent reflecting surface: Practical phase shift model and beamforming optimization – volume: 518 start-page: 529 year: 2015 ident: ref12 article-title: Human-level control through deep reinforcement learning publication-title: Nature doi: 10.1038/nature14236 – year: 2019 ident: ref1 article-title: Towards smart and reconfigurable environment: Intelligent reflecting surface aided wireless network – ident: ref9 doi: 10.1109/SPAWC.2019.8815412 – start-page: 1057 year: 2000 ident: ref13 article-title: Policy gradient methods for reinforcement learning with function approximation publication-title: Proc Adv Neural Inf Process Syst (NIPS) – year: 2015 ident: ref14 article-title: Continuous control with deep reinforcement learning – ident: ref16 doi: 10.1109/TCOMM.2016.2530796 – year: 2019 ident: ref10 article-title: Enabling large intelligent surfaces with compressive sensing and deep learning |
| SSID | ssj0000601218 |
| Score | 2.6334708 |
| Snippet | This letter investigates the intelligent reflecting surface (IRS)-aided multiple-input single-output wireless transmission system. Particularly, the... |
| SourceID | proquest crossref ieee |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 745 |
| SubjectTerms | Communications systems Complexity theory Convexity Deep learning deep reinforcement learning Intelligent reflecting surface MISO (control systems) MISO communication non-convex optimization Optimization phase shift design Reconfigurable intelligent surfaces Reinforcement learning Signal to noise ratio Training Upper bounds Wireless communication |
| Title | Deep Reinforcement Learning Based Intelligent Reflecting Surface Optimization for MISO Communication Systems |
| URI | https://ieeexplore.ieee.org/document/8968350 https://www.proquest.com/docview/2401123745 |
| Volume | 9 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3JTsMwELWAExzYCqJs8oELEmkTO4njI6sAUZBYBLfIKwdKW0F64esZO25EASFukTK2rDzHfjP2vEFoDxit1ITA6ifjIkptwSNpJY0kUSJhguaKuWzk3nV-_pBePmVPM-igyYUxxvjLZ6bjHv1Zvh6qsQuVdQueA2EAB30WHLc6V6uJpzhdEeLDeSTJSUQoZZNTyZh3rx6PwRckcYfwHAgRm9qFfFmVH2ux32DOllBvMrT6XslLZ1zJjvr4ptr437Evo8XANPFhPTVW0IwZrKKFL_qDLdQ_MWaEb41XT1U-UIiD4OozPoL9TeOLRrKzAkPrgvzu5d34zQpl8A2sOK8hlRNDJ7h3cXeDp7JOcBBFX0MPZ6f3x-dRKL8QKcKTKtK8MPDJtE2VloUVGbWKS5FomyRCeqV46-r75dwalSmw4jzLmEgpKQpBCV1Hc4PhwGwgzKiVXPNE2tSmTMeS2ESA75QZYI_CsjbqTuAoVdAmdyUy-qX3UWJeAoClA7AMALbRftNiVOty_GHbcng0dgGKNtqeIF6GH_e9BIIDDJSyNNv8vdUWmnd913cet9Fc9TY2O8BLKrnrJ-QnNjHf2A |
| linkProvider | IEEE |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1LT9wwEB4hOLQ9QAutukBbH3pBanZjO4njI0_twi5IBVRukZ89lC6IZi_99Ywdb1TaquotUsaJlXE834xnvgH4iIhWW8Zw99N5nRW-lpn2mmeaGUWF4pURoRp5dl6Nr4vTm_JmBT71tTDOuZh85obhMp7l2zuzCKGyUS0rBAzooK-h3S9pV63VR1QCswiLAT1GK5YxzsXyXDKXo-mXQ_QGWT5kskJIJJ7YodhY5Y_dOJqYkw2YLSfXZZZ8Gy5aPTQ_f-Nt_N_Zv4T1hDXJfrc4XsGKm2_Ci18YCLfg9si5e_LZRf5UE0OFJFGufiUHaOEsmfSknS0K-hDmDzcvFw9eGUcucM_5noo5CT6EzCaXF-RJ3QlJtOiv4frk-OpwnKUGDJlhkraZlbXDT2Z9YayuvSq5N1Iraj2lSkeueB86_FXSO1MalJKyLIUqOKtrxRl_A6vzu7l7C0Rwr6WVVPvCF8Lmmnmq0HsqHeJH5cUARkt1NCaxk4cmGbdN9FJy2aACm6DAJilwAHv9iPuOmeMfsltBH71cUsUAdpcab9Kv-6NBiIMYlIui3P77qA_wbHw1mzbTyfnZDjwP7-kyIHdhtX1YuHeIUlr9Pi7ORwrI4yE |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Deep+Reinforcement+Learning+Based+Intelligent+Reflecting+Surface+Optimization+for+MISO+Communication+Systems&rft.jtitle=IEEE+wireless+communications+letters&rft.au=Feng%2C+Keming&rft.au=Wang%2C+Qisheng&rft.au=Li%2C+Xiao&rft.au=Wen%2C+Chao-Kai&rft.date=2020-05-01&rft.pub=IEEE&rft.issn=2162-2337&rft.volume=9&rft.issue=5&rft.spage=745&rft.epage=749&rft_id=info:doi/10.1109%2FLWC.2020.2969167&rft.externalDocID=8968350 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2162-2337&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2162-2337&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2162-2337&client=summon |