[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123707-en":3,"doc-seo-123707-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123707,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","On Investigations of Machine Learning and Deep Learning Techniques for MIMO Detection - A Survey of Detectors and Challenges","This paper reviews multiple input multiple output (MIMO) detector algorithms and analyzes why current detectors struggle in massive MIMO (mMIMO) settings with many antennas. It explains that detection performance degrades as the antenna count increases, motivating research into machine learning and deep learning based detection. An extensive survey covers detector approaches, along with their advantages and challenges. The paper highlights remaining issues that must be solved before reliable deployment in real systems.","On Investigations of Machine Learning and Deep Learning Techniques for MIMO Detection  \nMehak Saini1, Surender K. Grewal2  \n1Ph.D. research scholar, Deptt. of ECE  \nDeenbandhu Chhotu Ram University of Science and Technology  \nMurthal, Haryana, India  \n[19001903006mehak@dcrustm.org](19001903006mehak@dcrustm.org)  \n2Deptt. of ECE  \nDeenbandhu Chhotu Ram University of Science and Technology  \nMurthal, Haryana, India  \n[skgrewal.ece@dcrustm.org](skgrewal.ece@dcrustm.org)  \nAbstract—This paper reviews in detail the various types of multiple input multiple output (MIMO) detector algorithms. The current MIMO detectors are not suitable for massive MIMO (mMIMO) scenarios where there are a large number of antennas. Their performance degrades with the increase in number of antennas in the MIMO system. For combatting the issues, machine learning (ML) and deep learning (DL) based detection algorithms are being researched and developed. An extensive survey of these detectors is provided in this paper, alongwith their advantages and challenges. The issues discussed have to be resolved before using them for final deployment.  \nKeywords-ML; DL; AI; MIMO detection; mMIMO.  \nI. INTRODUCTION  \nThe core problem of any type of communication is to accurately or nearly reproduce a message transmitted from one location to another [1] . In wireless communication networks, Multiple Input Multiple Output (MIMO) antenna technology has considerably improved data speeds, dependability, and overall performance. The advantages offered by MIMO systems, have revolutionized wireless communication and are now an essential part ofany modern wireless network. Multiple data streams may be sent at once over the same frequency range thanks to MIMO. The capacity of wireless networks may be efficiently increased using MIMO systems, which employ multiple antennas at the transmitter and receiver. More users may be served within a given bandwidth owing to increased data rates and improved spectral efficiency made possible due to this [2] .  \nMultiple data streams may be sent at once over the same frequency range thanks to MIMO. The capacity of wireless networks may be efficiently increased using MIMO systems, which employ multiple antennas at the transmitter and receiver. More users may be served within a given bandwidth owing to increased data rates and improved spectral efficiency made possible due to this [3] . Wireless networks can have better coverage and a longer range thanks to MIMO technologies.  \nMIMO may concentrate energy in certain directions by utilizing spatial multiplexing and beamforming methods,  \nenabling messages to travel further distances and scale barriers more successfully. This is especially advantageous in urban and interior situations, where signal attenuation and obstruction are frequent problems. In busy wireless scenarios, MIMO can reduce interference. MIMO systems may recognize and divide signals from multiple sources, even when they share the same frequency ranges, by taking advantage of spatial dimension. This enhances the overall quality of service and allows for amore harmonious union of various wireless networks.  \nWi-Fi, LTE, and 5G are just a few of the wireless communication technologies that MIMO technology is compatible with [4] . It is a flexible and scalable approach for enhancing wireless networks since current equipment may be upgraded using firmware or software. MIMO is still a crucial component of higher-capacity and more dependable communication networks as wireless technologies develop.  \nIn MIMO wireless communication, despite the several added advantages, a big obstacle is the subpar performance of detection techniques resulting from the trade-off between computational complexity and error rate performance [5] . The computational complexity is extraordinarily high while performance is at its best, and vice versa. In this research paper, a survey of various advances in AI for effective MIMO detection is done. The organizatio","cbCaijMFawhxiTkr","https://ap.wps.com/l/cbCaijMFawhxiTkr","pdf",282900,1,9,"English","en",105,"# Introduction\n## MIMO advantages and system impact\n## Detection complexity and error-rate trade-off\n# AI for Wireless Communication\n## ML and DL replacing classical optimization\n## Deep neural networks and reinforcement learning\n## Learning paradigms for MIMO tasks","[{\"question\":\"Why do conventional MIMO detectors perform poorly in massive MIMO (mMIMO) scenarios?\",\"answer\":\"Conventional detectors are not well suited to mMIMO, where a large number of antennas is used. Detection performance degrades as the antenna count increases, creating a practical limitation for scalable systems.\"},{\"question\":\"What role do machine learning (ML) and deep learning (DL) play in MIMO detection research?\",\"answer\":\"ML and DL based detection algorithms are being developed to address issues caused by the computational complexity versus error-rate trade-off. The paper surveys these approaches to show their potential and limitations.\"},{\"question\":\"What issues must be resolved before ML/DL based MIMO detectors can be deployed?\",\"answer\":\"The paper emphasizes that challenges and open issues related to detector effectiveness and practical constraints must be resolved prior to final deployment in real communication systems.\"}]","On Investigations of Machine Learning and Deep Learning Techniques for MIMO Detection - A Survey of Detectors and Challenges | PDF",1785818120,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"on-investigations-of-machine-learning-and-deep-learning-techniques-for-mimo-detection-a-survey-of-detectors-and-challenges","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/on-investigations-of-machine-learning-and-deep-learning-techniques-for-mimo-detection-a-survey-of-detectors-and-challenges/123707/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why do conventional MIMO detectors perform poorly in massive MIMO (mMIMO) scenarios?","Question",{"text":75,"@type":76},"Conventional detectors are not well suited to mMIMO, where a large number of antennas is used. Detection performance degrades as the antenna count increases, creating a practical limitation for scalable systems.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What role do machine learning (ML) and deep learning (DL) play in MIMO detection research?",{"text":80,"@type":76},"ML and DL based detection algorithms are being developed to address issues caused by the computational complexity versus error-rate trade-off. The paper surveys these approaches to show their potential and limitations.",{"name":82,"@type":73,"acceptedAnswer":83},"What issues must be resolved before ML/DL based MIMO detectors can be deployed?",{"text":84,"@type":76},"The paper emphasizes that challenges and open issues related to detector effectiveness and practical constraints must be resolved prior to final deployment in real communication systems.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]