Sparsification of Precoding Codebooks for PAPR Reduction via Grassmannian Representations
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866918459505377280 |
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| author | Asano, Joe Hama, Yuto Iimori, Hiroki Malomsoky, Szabolcs Ishikawa, Naoki |
| author_facet | Asano, Joe Hama, Yuto Iimori, Hiroki Malomsoky, Szabolcs Ishikawa, Naoki |
| contents | In this letter, we propose a sparsification method for precoding codebooks that reduces the peak-to-average power ratio (PAPR) while preserving the achievable rate. By exploiting the fact that precoder matrices lie on the Grassmann manifold, we formulate a codebook design problem that enables sparsification without modifying the existing feedback mechanism. We develop two sparsification approaches, namely exact sparsification via unitary transformation and approximate sparsification via sparse principal component analysis, and integrate them into a unified design algorithm. The proposed sparsified codebooks incur negligible performance loss while reducing PAPR by more than 1 dB in uplink scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_19273 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Sparsification of Precoding Codebooks for PAPR Reduction via Grassmannian Representations Asano, Joe Hama, Yuto Iimori, Hiroki Malomsoky, Szabolcs Ishikawa, Naoki Signal Processing In this letter, we propose a sparsification method for precoding codebooks that reduces the peak-to-average power ratio (PAPR) while preserving the achievable rate. By exploiting the fact that precoder matrices lie on the Grassmann manifold, we formulate a codebook design problem that enables sparsification without modifying the existing feedback mechanism. We develop two sparsification approaches, namely exact sparsification via unitary transformation and approximate sparsification via sparse principal component analysis, and integrate them into a unified design algorithm. The proposed sparsified codebooks incur negligible performance loss while reducing PAPR by more than 1 dB in uplink scenarios. |
| title | Sparsification of Precoding Codebooks for PAPR Reduction via Grassmannian Representations |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2604.19273 |