Channel Estimation for Sparse mm-Wave MIMO System
The fifth-generation (5G) cellular networks will provide gigabit-per-second data rates from massive antenna array combined with the emerging use of large and unexploited millimeter wave (mm-Wave) bands (30–300 GHz) in small cells. Channel estimation for sparse mm-Wave MIMO systems is a difficult tas...
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| Veröffentlicht in: | Wireless personal communications Jg. 129; H. 3; S. 2123 - 2140 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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01.04.2023
Springer Nature B.V |
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| ISSN: | 0929-6212, 1572-834X |
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| Abstract | The fifth-generation (5G) cellular networks will provide gigabit-per-second data rates from massive antenna array combined with the emerging use of large and unexploited millimeter wave (mm-Wave) bands (30–300 GHz) in small cells. Channel estimation for sparse mm-Wave MIMO systems is a difficult task. This is because of a large number of coefficients to be estimated, lower scattering nature, and blockage of mm-Wave by many materials in the environment. This paper will be the opportunity to implement the sparse channel estimation in the 5G cellular networks. In this work, we propose compressed-sensing (CS) based solutions and implements hybrid MIMO architecture for the proposed algorithm, OMP algorithm, and oracle estimator with different mm-Wave MIMO setups. Simulation results show that as compared to the OMP algorithm, proposed algorithm requires 16.9 times less computation time, and significant improvement is seen in normalized mean squared error (NMSE). Also, in the analysis, we found that the performance of the hybrid MIMO approaches near-optimal to conventional fully digital precoder. |
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| AbstractList | The fifth-generation (5G) cellular networks will provide gigabit-per-second data rates from massive antenna array combined with the emerging use of large and unexploited millimeter wave (mm-Wave) bands (30–300 GHz) in small cells. Channel estimation for sparse mm-Wave MIMO systems is a difficult task. This is because of a large number of coefficients to be estimated, lower scattering nature, and blockage of mm-Wave by many materials in the environment. This paper will be the opportunity to implement the sparse channel estimation in the 5G cellular networks. In this work, we propose compressed-sensing (CS) based solutions and implements hybrid MIMO architecture for the proposed algorithm, OMP algorithm, and oracle estimator with different mm-Wave MIMO setups. Simulation results show that as compared to the OMP algorithm, proposed algorithm requires 16.9 times less computation time, and significant improvement is seen in normalized mean squared error (NMSE). Also, in the analysis, we found that the performance of the hybrid MIMO approaches near-optimal to conventional fully digital precoder. The fifth-generation (5G) cellular networks will provide gigabit-per-second data rates from massive antenna array combined with the emerging use of large and unexploited millimeter wave (mm-Wave) bands (30–300 GHz) in small cells. Channel estimation for sparse mm-Wave MIMO systems is a difficult task. This is because of a large number of coefficients to be estimated, lower scattering nature, and blockage of mm-Wave by many materials in the environment. This paper will be the opportunity to implement the sparse channel estimation in the 5G cellular networks. In this work, we propose compressed-sensing (CS) based solutions and implements hybrid MIMO architecture for the proposed algorithm, OMP algorithm, and oracle estimator with different mm-Wave MIMO setups. Simulation results show that as compared to the OMP algorithm, proposed algorithm requires 16.9 times less computation time, and significant improvement is seen in normalized mean squared error (NMSE). Also, in the analysis, we found that the performance of the hybrid MIMO approaches near-optimal to conventional fully digital precoder. |
| Author | Gupta, Namit Purohit, Naresh |
| Author_xml | – sequence: 1 givenname: Naresh orcidid: 0000-0002-6810-8128 surname: Purohit fullname: Purohit, Naresh email: nareshpurohit0711@gmail.com organization: Department of Electrical and Electronics Engineering, Shri Vaishnav Vidyapeeth Vishwavidyalaya – sequence: 2 givenname: Namit surname: Gupta fullname: Gupta, Namit organization: Department of Electrical and Electronics Engineering, Shri Vaishnav Vidyapeeth Vishwavidyalaya |
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| Cites_doi | 10.1109/ACCESS.2020.2984204 10.1109/TWC.2016.2614495 10.1109/JSTSP.2014.2334278 10.1109/MCOM.2014.6894453 10.1109/COMST.2017.2783541 10.1109/ACCESS.2013.2260813 10.1109/JSTSP.2016.2520912 10.1109/COMST.2017.2787460 10.1109/TAP.2017.2734243 10.3390/electronics8090981 10.1109/COMST.2018.2843719 10.1109/MCOM.2016.7402270 10.1109/JSAC.2014.2328111 10.1016/j.aeue.2018.03.038 10.1109/MCE.2018.2868111 10.1109/TCOMM.2016.2557791 10.1109/ACCESS.2019.2903166 10.1109/TSP.2018.2811742 10.1109/TWC.2014.011714.130846 10.1109/COMST.2018.2844322 10.1109/ACCESS.2018.2886471 10.1109/TCOMM.2016.2616390 10.1007/s11276-015-0942-z 10.1007/s11277-020-07815-z 10.1109/JSAC.2017.2719924 10.1109/ACCESS.2017.2754979 10.1109/TENCON.2018.8650065 10.1109/ACSSC.2014.7094443 10.1109/JPROC.2014.2299397 |
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