One-Bit Feedback Exponential Learning for Beam Alignment in Mobile mmWave
Efficient beam alignment in wireless networks capable of supporting device mobility is currently one of the major challenges in mmWave communications. In this context, we formulate the beam-alignment problem via the adversarial multi-armed bandit (MAB) framework, which copes with arbitrary network d...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9237929/ |
| _version_ | 1818727298083323904 |
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| author | Irched Chafaa E. Veronica Belmega Merouane Debbah |
| author_facet | Irched Chafaa E. Veronica Belmega Merouane Debbah |
| author_sort | Irched Chafaa |
| collection | DOAJ |
| description | Efficient beam alignment in wireless networks capable of supporting device mobility is currently one of the major challenges in mmWave communications. In this context, we formulate the beam-alignment problem via the adversarial multi-armed bandit (MAB) framework, which copes with arbitrary network dynamics including non-stationary or adversarial components. Building on the well known exponential weights algorithm (EXP3) and by exploiting the structure and sparsity of the mmWave channel, we propose a modified (MEXP3) policy that requires solely one-bit of feedback information (reducing the amount of exchanged data during the beam-alignment process). Our MEXP3 comes with optimal theoretical guarantees in terms of asymptotic regret. Moreover, for finite horizons, our regret upper-bound is tighter than that of the original EXP3 suggesting better performance in practice. We then introduce an additional modification that accounts for the temporal correlation between successive beams and propose another beam-alignment policy. Our numerical results demonstrate that our beam-alignment policies outperform existing ones with respect to the regret but also to the outage, throughput and delay in typical mobile mmWave settings. |
| first_indexed | 2024-12-17T22:11:52Z |
| format | Article |
| id | doaj.art-a89b820c62a14dde9ccefb803f1da870 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T22:11:52Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a89b820c62a14dde9ccefb803f1da8702022-12-21T21:30:44ZengIEEEIEEE Access2169-35362020-01-01819457519458910.1109/ACCESS.2020.30334199237929One-Bit Feedback Exponential Learning for Beam Alignment in Mobile mmWaveIrched Chafaa0https://orcid.org/0000-0003-1467-5933E. Veronica Belmega1https://orcid.org/0000-0003-4336-4704Merouane Debbah2ETIS, CY Cergy Paris Université, ENSEA, CNRS, Cergy, FranceETIS, CY Cergy Paris Université, ENSEA, CNRS, Cergy, FranceETIS, CY Cergy Paris Université, ENSEA, CNRS, Cergy, FranceEfficient beam alignment in wireless networks capable of supporting device mobility is currently one of the major challenges in mmWave communications. In this context, we formulate the beam-alignment problem via the adversarial multi-armed bandit (MAB) framework, which copes with arbitrary network dynamics including non-stationary or adversarial components. Building on the well known exponential weights algorithm (EXP3) and by exploiting the structure and sparsity of the mmWave channel, we propose a modified (MEXP3) policy that requires solely one-bit of feedback information (reducing the amount of exchanged data during the beam-alignment process). Our MEXP3 comes with optimal theoretical guarantees in terms of asymptotic regret. Moreover, for finite horizons, our regret upper-bound is tighter than that of the original EXP3 suggesting better performance in practice. We then introduce an additional modification that accounts for the temporal correlation between successive beams and propose another beam-alignment policy. Our numerical results demonstrate that our beam-alignment policies outperform existing ones with respect to the regret but also to the outage, throughput and delay in typical mobile mmWave settings.https://ieeexplore.ieee.org/document/9237929/Beam alignmentexponential weightsmobile mmWavemulti-armed bandits |
| spellingShingle | Irched Chafaa E. Veronica Belmega Merouane Debbah One-Bit Feedback Exponential Learning for Beam Alignment in Mobile mmWave IEEE Access Beam alignment exponential weights mobile mmWave multi-armed bandits |
| title | One-Bit Feedback Exponential Learning for Beam Alignment in Mobile mmWave |
| title_full | One-Bit Feedback Exponential Learning for Beam Alignment in Mobile mmWave |
| title_fullStr | One-Bit Feedback Exponential Learning for Beam Alignment in Mobile mmWave |
| title_full_unstemmed | One-Bit Feedback Exponential Learning for Beam Alignment in Mobile mmWave |
| title_short | One-Bit Feedback Exponential Learning for Beam Alignment in Mobile mmWave |
| title_sort | one bit feedback exponential learning for beam alignment in mobile mmwave |
| topic | Beam alignment exponential weights mobile mmWave multi-armed bandits |
| url | https://ieeexplore.ieee.org/document/9237929/ |
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