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DESCRIPTION:Special Presentation by Dr. Harish Kumar Sahoo (VSSUT\, India)\
 n\nHosted by the Future Networks Massive MIMO Working Group\n\nDate/Time: 
 Friday\, June 6th\, 2025 @ 11:00 am EDT\n\nTopic:\n\nSingle Layer Neural N
 etwork Based Precoding and Equalization for 5G Communication System\n\nAbs
 tract:\n\nImplementation of hybrid precoding in massive MIMO enhances the 
 spectral efficiency of the receiver in 5G communication system. As hybrid 
 precoding reduces the number of RF chains\, the hardware complexity is com
 paratively less in presence of large number of antenna elements. Moreover\
 , the optimal precoding matrix is helpful to develop efficient equalizer w
 hich improves the overall performance of the receiver. Functional link art
 ificial Neural network (FLANN) and recurrent neural network (RNN) can be u
 sed as efficient machine learning (ML) models with appropriate training al
 gorithms to generate optimal baseband precoding matrix at the transmitter.
  FLANN architecture is simpler which consists of a single layer and functi
 onal expansion block. Hybrid precoding matrix can be formulated by combini
 ng with RF precoding matrix. However\, FLANN or RNN based optimization req
 uires reference matrix to carry out the training process which updates the
  elements of the precoding matrix. Singular value decomposition (SVD) can 
 be one of the approaches to generate the reference matrix through orthogon
 al decomposition of channel matrix. The research work is focussed on the p
 erformance of ML model based precoder for known channel state information 
 (CSI) through simulation by analysing the spectral efficiency with a varia
 tion in number of base station (BS) antennas and users within a range of S
 NR values.\n\nSpeaker:\n\nDr. Harish Kumar Sahoo:\n\nHarish Kumar Sahoo is
  a senior member of IEEE. He is also a member of IEEE signal processing so
 ciety\, IEEE ComSoc and IEEE Future Networks. He is the current Chair of T
 echnical and Educational Activity Committee IEEE Bhubaneswar Section. He c
 ompleted his M.Tech from National Institute of Technology\, Rourkela\, Ind
 ia and Ph.D. from Sambalpur University\, India. He is now working as Profe
 ssor in the Department of Electronics and Telecommunication Engineering\, 
 Veer Surendra Sai University of Technology\, Burla\, Sambalpur\, India. He
  has also additional responsibility of Dean Faculty and Planning of the Un
 iversity. He has more than twenty-four years of teaching and research expe
 rience. Six PhD. Scholars have already been awarded under his guidance. He
  has several research publications in the journals of IEEE\, Elsevier\, Sp
 ringer\, Wiley\, Taylor and Francis. He is an active reviewer of journal a
 rticles for more than thirty-five international publishers including IEEE.
  His research interests include Massive MIMO\, AI/ML Optimization in 5G an
 d 6G communication networks\, Channel Estimation and Equalization using ad
 aptive estimation.\n\nCo-sponsored by: Future Networks Massive MIMO Workin
 g Group\n\nVirtual: https://events.vtools.ieee.org/m/487085
LOCATION:Virtual: https://events.vtools.ieee.org/m/487085
ORGANIZER:sneihil.gopal@ieee.org
SEQUENCE:95
SUMMARY:Single Layer Neural Network Based Precoding and Equalization for 5G
  Communication System
URL;VALUE=URI:https://events.vtools.ieee.org/m/487085
X-ALT-DESC:Description: &lt;br /&gt;&lt;p class=&quot;MsoNormal&quot; style=&quot;margin-top: .25in
 \;&quot;&gt;&lt;img src=&quot;https://events.vtools.ieee.org/vtools_ui/media/display/0dbf9
 8b3-5c54-4eed-bded-f5e3ca7192ca&quot; width=&quot;1030&quot; height=&quot;269&quot;&gt;&lt;/p&gt;\n&lt;p class=
 &quot;MsoNormal&quot; style=&quot;margin-top: 12.0pt\;&quot;&gt;&lt;span style=&quot;font-size: 14pt\; li
 ne-height: 107%\; font-family: Calibri\, sans-serif\;&quot;&gt;Special Presentatio
 n by&lt;strong&gt; Dr. Harish Kumar Sahoo (VSSUT\, India)&lt;/strong&gt;&lt;/span&gt;&lt;/p&gt;\n&lt;
 p class=&quot;MsoNormal&quot; style=&quot;margin-top: 6.0pt\;&quot;&gt;&lt;span style=&quot;font-size: 14
 pt\; line-height: 107%\; font-family: Calibri\, sans-serif\;&quot;&gt;Hosted by th
 e Future Networks&lt;strong&gt; Massive MIMO Working Group&lt;/strong&gt;&lt;/span&gt;&lt;/p&gt;\n
 &lt;p class=&quot;MsoNormal&quot; style=&quot;margin-top: 6.0pt\;&quot;&gt;&lt;span style=&quot;font-size: 1
 4pt\;&quot;&gt;&lt;strong&gt;&lt;span style=&quot;line-height: 107%\; font-family: Calibri\, san
 s-serif\;&quot;&gt;Date/Time&lt;/span&gt;&lt;/strong&gt;&lt;span style=&quot;line-height: 107%\; font-
 family: Calibri\, sans-serif\;&quot;&gt;: &lt;strong&gt;Friday\, June 6&lt;sup&gt;th&lt;/sup&gt;\, 2
 025 @ 11:00 am EDT&lt;/strong&gt;&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=
 &quot;margin-top: .25in\;&quot;&gt;&lt;strong&gt;&lt;u&gt;&lt;span style=&quot;font-size: 16.0pt\; font-fam
 ily: Copperplate\;&quot;&gt;Topic&lt;/span&gt;&lt;/u&gt;&lt;/strong&gt;&lt;strong&gt;&lt;span style=&quot;font-siz
 e: 16.0pt\; font-family: Copperplate\;&quot;&gt;:&lt;/span&gt;&lt;/strong&gt;&lt;/p&gt;\n&lt;p class=&quot;M
 soNormal&quot;&gt;&lt;span style=&quot;font-size: 24pt\;&quot;&gt;&lt;strong&gt;&lt;span lang=&quot;EN-IN&quot; style
 =&quot;line-height: 107%\; font-family: Calibri\, sans-serif\;&quot;&gt;Single Layer Ne
 ural Network Based Precoding and Equalization for 5G Communication System&lt;
 /span&gt;&lt;/strong&gt;&lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;margin-top: .25in\
 ;&quot;&gt;&lt;strong&gt;&lt;u&gt;&lt;span style=&quot;font-size: 16.0pt\; font-family: Copperplate\;&quot;
 &gt;Abstract&lt;/span&gt;&lt;/u&gt;&lt;/strong&gt;&lt;strong&gt;&lt;span style=&quot;font-size: 16.0pt\; font
 -family: Copperplate\;&quot;&gt;:&lt;/span&gt;&lt;/strong&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=
 &quot;margin-top: 12.0pt\; text-align: justify\;&quot;&gt;&lt;span lang=&quot;EN-IN&quot; style=&quot;fon
 t-family: &#39;Calibri&#39;\,sans-serif\; mso-ansi-language: EN-IN\;&quot;&gt;Implementati
 on of hybrid precoding in massive MIMO enhances the spectral efficiency of
  the receiver in 5G communication system. As hybrid precoding reduces the 
 number of RF chains\, the hardware complexity is comparatively less in pre
 sence of large number of antenna elements. Moreover\, the optimal precodin
 g matrix is helpful to develop efficient equalizer which improves the over
 all performance of the receiver. Functional link artificial Neural network
  (FLANN) and recurrent neural network (RNN) can be used as efficient machi
 ne learning (ML) models with appropriate training algorithms to generate o
 ptimal baseband precoding matrix at the transmitter. FLANN architecture is
  simpler which consists of a single layer and functional expansion block. 
 Hybrid precoding matrix can be formulated by combining with RF precoding m
 atrix. However\, FLANN or RNN based optimization requires reference matrix
  to carry out the training process which updates the elements of the preco
 ding matrix. Singular value decomposition (SVD) can be one of the approach
 es to generate the reference matrix through orthogonal decomposition of ch
 annel matrix. The research work is focussed on the performance of ML model
  based precoder for known channel state information (CSI) through simulati
 on by analysing the spectral efficiency with a variation in number of base
  station (BS) antennas and users within a range of SNR values.&lt;/span&gt;&lt;/p&gt;\
 n&lt;p&gt;&lt;strong&gt;&lt;span style=&quot;font-size: 16.0pt\; font-family: Copperplate\;&quot;&gt;&lt;
 u&gt;Speaker&lt;/u&gt;:&lt;/span&gt;&lt;/strong&gt;&lt;/p&gt;\n&lt;table style=&quot;border-collapse: collaps
 e\; width: 100%\;&quot; border=&quot;1&quot;&gt;&lt;colgroup&gt;&lt;col style=&quot;width: 21.017274%\;&quot;&gt;&lt;
 col style=&quot;width: 78.886756%\;&quot;&gt;&lt;/colgroup&gt;\n&lt;tbody&gt;\n&lt;tr&gt;\n&lt;td&gt;&lt;img src=&quot;
 https://events.vtools.ieee.org/vtools_ui/media/display/444f0088-b082-4ef3-
 8ebc-bf5ae83a426f&quot;&gt;&lt;/td&gt;\n&lt;td&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;margin-top: 6.
 0pt\;&quot;&gt;&lt;strong&gt;&lt;span style=&quot;font-size: 12.0pt\; line-height: 107%\; font-f
 amily: &#39;Calibri&#39;\,sans-serif\; mso-fareast-font-family: Aptos\; mso-fareas
 t-theme-font: minor-latin\; mso-ansi-language: EN-US\; mso-fareast-languag
 e: EN-US\; mso-bidi-language: AR-SA\;&quot;&gt;Dr. Harish Kumar Sahoo&lt;/span&gt;&lt;/stro
 ng&gt;:&amp;nbsp\;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;text-align: justify\;&quot;&gt;&lt;span 
 style=&quot;font-family: &#39;Calibri&#39;\,sans-serif\;&quot;&gt;Harish Kumar Sahoo is a senio
 r member of IEEE. He is also a member of IEEE signal processing society\, 
 IEEE ComSoc and IEEE Future Networks. He is the current&amp;nbsp\;Chair of Tec
 hnical and Educational Activity Committee IEEE Bhubaneswar Section. He com
 pleted his M.Tech from National Institute of Technology\, Rourkela\, India
  and Ph.D. from Sambalpur University\, India. He is now working as Profess
 or in the Department of Electronics and Telecommunication Engineering\, Ve
 er Surendra Sai University of Technology\, Burla\, Sambalpur\, India. He h
 as also additional responsibility of Dean Faculty and Planning of the Univ
 ersity.&amp;nbsp\; He has more than twenty-four years of teaching and research
  experience. Six PhD. Scholars have already been awarded under his guidanc
 e. He has several research publications in the journals of IEEE\, Elsevier
 \, Springer\, Wiley\, Taylor and Francis. He is an active reviewer of jour
 nal articles for more than thirty-five international publishers including 
 IEEE. His research interests include Massive MIMO\, AI/ML Optimization in 
 5G and 6G communication networks\, Channel Estimation and Equalization usi
 ng adaptive estimation.&lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;margin-top
 : 6.0pt\;&quot;&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;/tbody&gt;\n&lt;/table&gt;
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