On the Performance of IRS-Assisted IoT-NTN With Joint Imperfect Phase Estimation and Quantization
Intelligent reflecting surface (IRS)-assisted communications technology is currently considered a key enabler for various wireless applications. The maximum gain of IRS is achieved when the phases of the reflected signals are optimally selected to maximize signal-to-noise ratio (SNR). However, pract...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of the Communications Society |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10368053/ |
| _version_ | 1827390698063659008 |
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| author | Ali A. Siddig Arafat Al-Dweik Ashraf Al-Rimawi Youssef Iraqi Anshul Pandey Jean-Pierre Giacalone |
| author_facet | Ali A. Siddig Arafat Al-Dweik Ashraf Al-Rimawi Youssef Iraqi Anshul Pandey Jean-Pierre Giacalone |
| author_sort | Ali A. Siddig |
| collection | DOAJ |
| description | Intelligent reflecting surface (IRS)-assisted communications technology is currently considered a key enabler for various wireless applications. The maximum gain of IRS is achieved when the phases of the reflected signals are optimally selected to maximize signal-to-noise ratio (SNR). However, practical hurdles such as imperfect phase estimation and hardware limitations such as phase quantization can reduce the potential gain of the IRS deployment. Internet of Things applications are more vulnerable to such limitations due to restrictions on device size, energy, cost, and computational power. Therefore, this work evaluates the joint impact of quantization and imperfect phase estimation where the probability density function (PDF) of the estimated and quantized phase is derived. Then, using the sinusoidal addition theorem, the PDF of the received signal envelope is derived and used to derive exact analytic expressions for the symbol error rate and outage probability. The analytical and simulation results obtained show that the impact of the joint estimation and quantization imperfections depends on the SNR and number of IRS elements. In particular, it is shown that increasing the number of IRS elements can effectively mitigate the impact of phase estimation and quantization problems. Furthermore, the results show that the impact of phase quantization increases as the accuracy of phase estimation decreases. |
| first_indexed | 2024-03-08T16:56:30Z |
| format | Article |
| id | doaj.art-0b3273333e084994b13059e220451447 |
| institution | Directory Open Access Journal |
| issn | 2644-125X |
| language | English |
| last_indexed | 2024-03-08T16:56:30Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Communications Society |
| spelling | doaj.art-0b3273333e084994b13059e2204514472024-01-05T00:04:58ZengIEEEIEEE Open Journal of the Communications Society2644-125X2024-01-01525627510.1109/OJCOMS.2023.334535710368053On the Performance of IRS-Assisted IoT-NTN With Joint Imperfect Phase Estimation and QuantizationAli A. Siddig0https://orcid.org/0000-0002-7568-6831Arafat Al-Dweik1https://orcid.org/0000-0002-3487-3438Ashraf Al-Rimawi2Youssef Iraqi3https://orcid.org/0000-0003-0112-2600Anshul Pandey4https://orcid.org/0000-0001-7911-3451Jean-Pierre Giacalone5Department of Electrical and Computer Science, 6G Research Center, Khalifa University, Abu Dhabi, UAEDepartment of Electrical and Computer Science, 6G Research Center, Khalifa University, Abu Dhabi, UAEDepartment of Electrical and Computer Engineering, Birzeit University, Birzeit, PalestineCollege of Computing, University Mohammed VI Polytechnic, Ben Guerir, MoroccoThe Secure Systems Research Center, Technology Innovation Institute, Abu Dhabi, UAEThe Secure Systems Research Center, Technology Innovation Institute, Abu Dhabi, UAEIntelligent reflecting surface (IRS)-assisted communications technology is currently considered a key enabler for various wireless applications. The maximum gain of IRS is achieved when the phases of the reflected signals are optimally selected to maximize signal-to-noise ratio (SNR). However, practical hurdles such as imperfect phase estimation and hardware limitations such as phase quantization can reduce the potential gain of the IRS deployment. Internet of Things applications are more vulnerable to such limitations due to restrictions on device size, energy, cost, and computational power. Therefore, this work evaluates the joint impact of quantization and imperfect phase estimation where the probability density function (PDF) of the estimated and quantized phase is derived. Then, using the sinusoidal addition theorem, the PDF of the received signal envelope is derived and used to derive exact analytic expressions for the symbol error rate and outage probability. The analytical and simulation results obtained show that the impact of the joint estimation and quantization imperfections depends on the SNR and number of IRS elements. In particular, it is shown that increasing the number of IRS elements can effectively mitigate the impact of phase estimation and quantization problems. Furthermore, the results show that the impact of phase quantization increases as the accuracy of phase estimation decreases.https://ieeexplore.ieee.org/document/10368053/Sixth generation (6G)Intelligent reflecting surface (IRS)imperfect phasediscrete phase noisequantizationsymbol error rate (SER) |
| spellingShingle | Ali A. Siddig Arafat Al-Dweik Ashraf Al-Rimawi Youssef Iraqi Anshul Pandey Jean-Pierre Giacalone On the Performance of IRS-Assisted IoT-NTN With Joint Imperfect Phase Estimation and Quantization IEEE Open Journal of the Communications Society Sixth generation (6G) Intelligent reflecting surface (IRS) imperfect phase discrete phase noise quantization symbol error rate (SER) |
| title | On the Performance of IRS-Assisted IoT-NTN With Joint Imperfect Phase Estimation and Quantization |
| title_full | On the Performance of IRS-Assisted IoT-NTN With Joint Imperfect Phase Estimation and Quantization |
| title_fullStr | On the Performance of IRS-Assisted IoT-NTN With Joint Imperfect Phase Estimation and Quantization |
| title_full_unstemmed | On the Performance of IRS-Assisted IoT-NTN With Joint Imperfect Phase Estimation and Quantization |
| title_short | On the Performance of IRS-Assisted IoT-NTN With Joint Imperfect Phase Estimation and Quantization |
| title_sort | on the performance of irs assisted iot ntn with joint imperfect phase estimation and quantization |
| topic | Sixth generation (6G) Intelligent reflecting surface (IRS) imperfect phase discrete phase noise quantization symbol error rate (SER) |
| url | https://ieeexplore.ieee.org/document/10368053/ |
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