Revisiting smart antenna array design with multiple interferers using basic adaptive beamforming algorithms: Comparative performance study with testbed results
Abstract Smart antennas are becoming popular in the area of cellular wireless communication for capacity enhancement and reduction of the multipath effect and interference. The demand for smart antennas is widely increasing as 5G cellular communication evolves to support the higher data rate and ban...
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doaj-885bc9fccecf458ba2f8ab61f0790ce12021-03-23T16:20:06ZengWileyEngineering Reports2577-81962021-01-0131n/an/a10.1002/eng2.12295Revisiting smart antenna array design with multiple interferers using basic adaptive beamforming algorithms: Comparative performance study with testbed resultsSK Imtiaj0Iti Saha Misra1Sandipan Bhattacharya2ETCE Department Jadavpur University Kolkata IndiaETCE Department Jadavpur University Kolkata IndiaETCE Department Jadavpur University Kolkata IndiaAbstract Smart antennas are becoming popular in the area of cellular wireless communication for capacity enhancement and reduction of the multipath effect and interference. The demand for smart antennas is widely increasing as 5G cellular communication evolves to support the higher data rate and bandwidth. The basic principle of smart antenna design is adaptive beamforming using the best suited digital signal processing algorithms, such as least mean square (LMS), normalized least mean square (NLMS), sample matrix inversion (SMI), and recursive least square (RLS), each having its pros and cons. Among these, the LMS and NLMS are iterative approaches while SMI is a block adaptive method and RLS is a recursive method. The contribution of this article includes easy implementation of four adaptive beamforming algorithms, namely LMS, NLMS, SMI, and RLS. Furthermore, an exhaustive comparative performance analysis is carried out under five interferers and evaluated in terms of beamwidth, null depth, maximum sidelobe level, rate of convergence, error variation for the number of antenna elements, and spacing. Finally, a contrast table is presented to demonstrate the pros and cons of the listed algorithms. Time and dynamic space complexity are studied for RLS and SMI beamforming algorithms. Performance results from a designed reconfigurable testbed model for weight adaptation and smart beamforming are also presented for analysis and a better understanding of the real implementation of the smart algorithms through testbed design. The simulation and testbed results create the ground work for future exploration in the design of smart antenna beamforming.https://doi.org/10.1002/eng2.12295adaptive beamformingcomparative studycomplexity analysisLMSmultiple interferersNLMS |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
SK Imtiaj Iti Saha Misra Sandipan Bhattacharya |
spellingShingle |
SK Imtiaj Iti Saha Misra Sandipan Bhattacharya Revisiting smart antenna array design with multiple interferers using basic adaptive beamforming algorithms: Comparative performance study with testbed results Engineering Reports adaptive beamforming comparative study complexity analysis LMS multiple interferers NLMS |
author_facet |
SK Imtiaj Iti Saha Misra Sandipan Bhattacharya |
author_sort |
SK Imtiaj |
title |
Revisiting smart antenna array design with multiple interferers using basic adaptive beamforming algorithms: Comparative performance study with testbed results |
title_short |
Revisiting smart antenna array design with multiple interferers using basic adaptive beamforming algorithms: Comparative performance study with testbed results |
title_full |
Revisiting smart antenna array design with multiple interferers using basic adaptive beamforming algorithms: Comparative performance study with testbed results |
title_fullStr |
Revisiting smart antenna array design with multiple interferers using basic adaptive beamforming algorithms: Comparative performance study with testbed results |
title_full_unstemmed |
Revisiting smart antenna array design with multiple interferers using basic adaptive beamforming algorithms: Comparative performance study with testbed results |
title_sort |
revisiting smart antenna array design with multiple interferers using basic adaptive beamforming algorithms: comparative performance study with testbed results |
publisher |
Wiley |
series |
Engineering Reports |
issn |
2577-8196 |
publishDate |
2021-01-01 |
description |
Abstract Smart antennas are becoming popular in the area of cellular wireless communication for capacity enhancement and reduction of the multipath effect and interference. The demand for smart antennas is widely increasing as 5G cellular communication evolves to support the higher data rate and bandwidth. The basic principle of smart antenna design is adaptive beamforming using the best suited digital signal processing algorithms, such as least mean square (LMS), normalized least mean square (NLMS), sample matrix inversion (SMI), and recursive least square (RLS), each having its pros and cons. Among these, the LMS and NLMS are iterative approaches while SMI is a block adaptive method and RLS is a recursive method. The contribution of this article includes easy implementation of four adaptive beamforming algorithms, namely LMS, NLMS, SMI, and RLS. Furthermore, an exhaustive comparative performance analysis is carried out under five interferers and evaluated in terms of beamwidth, null depth, maximum sidelobe level, rate of convergence, error variation for the number of antenna elements, and spacing. Finally, a contrast table is presented to demonstrate the pros and cons of the listed algorithms. Time and dynamic space complexity are studied for RLS and SMI beamforming algorithms. Performance results from a designed reconfigurable testbed model for weight adaptation and smart beamforming are also presented for analysis and a better understanding of the real implementation of the smart algorithms through testbed design. The simulation and testbed results create the ground work for future exploration in the design of smart antenna beamforming. |
topic |
adaptive beamforming comparative study complexity analysis LMS multiple interferers NLMS |
url |
https://doi.org/10.1002/eng2.12295 |
work_keys_str_mv |
AT skimtiaj revisitingsmartantennaarraydesignwithmultipleinterferersusingbasicadaptivebeamformingalgorithmscomparativeperformancestudywithtestbedresults AT itisahamisra revisitingsmartantennaarraydesignwithmultipleinterferersusingbasicadaptivebeamformingalgorithmscomparativeperformancestudywithtestbedresults AT sandipanbhattacharya revisitingsmartantennaarraydesignwithmultipleinterferersusingbasicadaptivebeamformingalgorithmscomparativeperformancestudywithtestbedresults |
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