Efficient Last-Mile Link Adaptation in Next-Gen Wireless Heterogeneous Networks
The forthcoming generations of wireless networks have a high demand for reliable and enhanced data rate transmissions which are crucial for emerging smart cities. Thus, there is a need to develop efficient Link Adaptation (LA) for the last hop between end-users and the base stations in order to miti...
Gespeichert in:
| Veröffentlicht in: | International Conference on Communication Systems and Networks (Online) S. 31 - 36 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Tagungsbericht |
| Sprache: | Englisch |
| Veröffentlicht: |
IEEE
04.01.2022
|
| Schlagworte: | |
| ISSN: | 2155-2509 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | The forthcoming generations of wireless networks have a high demand for reliable and enhanced data rate transmissions which are crucial for emerging smart cities. Thus, there is a need to develop efficient Link Adaptation (LA) for the last hop between end-users and the base stations in order to mitigate the severe interference resulting from the dense heterogeneous networks. We develop a Machine Learning (ML) based last-mile link adaptation method for a 5G wireless communication network. Dynamic selection of MCS for Resource Block (RB) allocation is efficient in terms of better network throughput and reduced BER which is verified through simulations for 5G New Radio (NR). We assume perfect channel estimation in our analysis. We have used a Deep Neural Network (DNN) model that dynamically selects the appropriate Modulation and Coding Scheme (MCS) ensuring 10 percent Bit Error Rate (BER) and maximizes the system spectral efficiency. Further, we evaluate the Signal to Interference and Noise Ratio (SINR) corresponding to varied channel states for different frequencies of operation and the DNN model selects the Channel Quality Indicator (CQI) corresponding to the optimal MCS available at the corresponding base stations for the end -users. This results in seamless connectivity for mobile users adapting to the last-mile link efficiently and achieving a higher downlink network throughput. |
|---|---|
| AbstractList | The forthcoming generations of wireless networks have a high demand for reliable and enhanced data rate transmissions which are crucial for emerging smart cities. Thus, there is a need to develop efficient Link Adaptation (LA) for the last hop between end-users and the base stations in order to mitigate the severe interference resulting from the dense heterogeneous networks. We develop a Machine Learning (ML) based last-mile link adaptation method for a 5G wireless communication network. Dynamic selection of MCS for Resource Block (RB) allocation is efficient in terms of better network throughput and reduced BER which is verified through simulations for 5G New Radio (NR). We assume perfect channel estimation in our analysis. We have used a Deep Neural Network (DNN) model that dynamically selects the appropriate Modulation and Coding Scheme (MCS) ensuring 10 percent Bit Error Rate (BER) and maximizes the system spectral efficiency. Further, we evaluate the Signal to Interference and Noise Ratio (SINR) corresponding to varied channel states for different frequencies of operation and the DNN model selects the Channel Quality Indicator (CQI) corresponding to the optimal MCS available at the corresponding base stations for the end -users. This results in seamless connectivity for mobile users adapting to the last-mile link efficiently and achieving a higher downlink network throughput. |
| Author | Singhal, Chetna Sahoo, Shubham Somnath Pati, Preeti Samhita Datta, Raja |
| Author_xml | – sequence: 1 givenname: Preeti Samhita surname: Pati fullname: Pati, Preeti Samhita email: preetispati@iitkgp.ac.in organization: Indian Institute of Technology,Dept. of Electronics and Electrical Communication Engineering,Kharagpur,India – sequence: 2 givenname: Shubham Somnath surname: Sahoo fullname: Sahoo, Shubham Somnath email: shubhamsomnath@iitkgp.ac.in organization: Indian Institute of Technology,Dept. of Electronics and Electrical Communication Engineering,Kharagpur,India – sequence: 3 givenname: Chetna surname: Singhal fullname: Singhal, Chetna email: chetna@ece.iitkgp.ac.in organization: Indian Institute of Technology,Dept. of Electronics and Electrical Communication Engineering,Kharagpur,India – sequence: 4 givenname: Raja surname: Datta fullname: Datta, Raja email: rajadatta@e.iitkgp.ac.in organization: Indian Institute of Technology,Dept. of Electronics and Electrical Communication Engineering,Kharagpur,India |
| BookMark | eNotkEFLwzAYhqMoOGd_gZfgvTVfmqTNcZS6Cd162MTjyNqvElfT0UR0_96CO72HFx7e570nN25wSMgTsASA6eeiXm835W4rUwUy4YzzRCuVS5ZekUhnOSglBQcm4JrMOEgZc8n0HYm8_2SMpZBrmfIZqcuus41FF2hlfIjXtkdaWXeki9acggl2cNQ6usHfEC_R0Xc7Yo_e0xUGHIcPdDh8-6kPP8N49A_ktjO9x-iSc_L2Uu6KVVzVy9diUcUWIA-x4C0XTaawbRVroMuVlFojGj0N67I258APWpgGD4Y1UuiGKwbYKjEZSYHpnDz-cy0i7k-j_TLjeX-5IP0DEV5TLg |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/COMSNETS53615.2022.9668503 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Xplore POP ALL IEEE Xplore All Conference Proceedings IEEE Electronic Library (IEL) IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISBN | 9781665421041 1665421045 |
| EISSN | 2155-2509 |
| EndPage | 36 |
| ExternalDocumentID | 9668503 |
| Genre | orig-research |
| GroupedDBID | 6IE 6IF 6IK 6IL 6IN AAJGR AAWTH ABLEC ADZIZ ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ CBEJK CHZPO IEGSK IPLJI OCL RIE RIL |
| ID | FETCH-LOGICAL-i118t-42d24c76edd60c1f865599eea9189f7d8212b94aceba0c549c2601ed6404154e3 |
| IEDL.DBID | RIE |
| IngestDate | Wed Aug 27 03:03:04 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | true |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i118t-42d24c76edd60c1f865599eea9189f7d8212b94aceba0c549c2601ed6404154e3 |
| PageCount | 6 |
| ParticipantIDs | ieee_primary_9668503 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-Jan.-4 |
| PublicationDateYYYYMMDD | 2022-01-04 |
| PublicationDate_xml | – month: 01 year: 2022 text: 2022-Jan.-4 day: 04 |
| PublicationDecade | 2020 |
| PublicationTitle | International Conference on Communication Systems and Networks (Online) |
| PublicationTitleAbbrev | COMSNETS |
| PublicationYear | 2022 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| SSID | ssj0003189532 |
| Score | 2.1753814 |
| Snippet | The forthcoming generations of wireless networks have a high demand for reliable and enhanced data rate transmissions which are crucial for emerging smart... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 31 |
| SubjectTerms | Adaptation models Base stations Bit error rate Bit Error Rate(BER) Channel Quality Indicator (CQI) Deep Neural Network (DNN) Interference Link Adaptation (LA) Modulation and Coding Scheme(MCS) Network Throughput Neural networks Signal to Interference and Noise Ratio (SINR) Spectral efficiency Wireless networks |
| Title | Efficient Last-Mile Link Adaptation in Next-Gen Wireless Heterogeneous Networks |
| URI | https://ieeexplore.ieee.org/document/9668503 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3Pa8IwFA5Odtgu-6Fjv8lhx0VrmqbJcYjOw6yCDrxJmryCMFqx7f7-JbG4DXbZLTQklJekr9_L-76H0JMMlNICGOFBqAnLeEaEMoKEKszSTIOkXPtiE3GSiNVKzlvo-cCFAQCffAY91_R3-abQtQuV9e2vuYictOdRHPM9V-sQT7F7U0YhbXRFB4HsD2fTRTJaLqLQem2LBCntNRP8qqTiHcn47H-vcI6634w8PD_4mgvUgvwSnf4QE-yg2cirQdjh-E2VFZna844d1MQvRm33N-54k-PEYd1XyLFLfP2wHzo8cSkxhd1JUNSl7feJ4WUXvY9Hy-GENOUSyMaihIowaijTMQdjeKAHmaOcSgmgpDVNFhthvVQqmdKQqkBbXKidnBgYzhxNn0F4hdp5kcM1wgE3wkghjBOviUykUspApcFA2Wmlljeo40yz3u4VMdaNVW7_fnyHTpz1feCC3aN2tavhAR3rz2pT7h79Mn4BMIKewg |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3Pa8IwFH7INth22Q8d-70cdly0tmlNjkN0jmkVdOBN0uQVhNGKrfv7l8TiNthlt9DQEF7Svnwv7_sewKPwpFQcGY28QFGWRinlUnMayCBNUoXCj5QrNtGJYz6fi0kNnnZcGER0yWfYtE13l69ztbGhspY5mvPQSnvu28pZFVtrF1Exu1OEgV8pi7Y90eqOR9O4N5uGgfHbBgv6frMa4lctFedK-if_m8QpNL45eWSy8zZnUMPsHI5_yAnWYdxzehDmdTKURUlH5osnFmySZy1X2zt3ssxIbNHuC2bEpr5-mF8dGdikmNzsJcw3hel3qeFFA977vVl3QKuCCXRpcEJJma99pjoRah15qp1a0qkQiFIY06QdzY2fSgSTChPpKYMMlRUUQx0xS9RnGFzAXpZneAnEizTXgnNt5WtCHcrEZygTry3NsEKJK6hb0yxWW02MRWWV678fP8DhYDYaLoav8dsNHNmVcGEMdgt75XqDd3CgPstlsb53S_oFpv6iCw |
| 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%3Abook&rft.genre=proceeding&rft.title=International+Conference+on+Communication+Systems+and+Networks+%28Online%29&rft.atitle=Efficient+Last-Mile+Link+Adaptation+in+Next-Gen+Wireless+Heterogeneous+Networks&rft.au=Pati%2C+Preeti+Samhita&rft.au=Sahoo%2C+Shubham+Somnath&rft.au=Singhal%2C+Chetna&rft.au=Datta%2C+Raja&rft.date=2022-01-04&rft.pub=IEEE&rft.eissn=2155-2509&rft.spage=31&rft.epage=36&rft_id=info:doi/10.1109%2FCOMSNETS53615.2022.9668503&rft.externalDocID=9668503 |