Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.09910 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912894568890368 |
|---|---|
| author | Bohnacker, Levi Müller, Ralf R. |
| author_facet | Bohnacker, Levi Müller, Ralf R. |
| contents | Applying Nearest Convex Hull Classification (NCHC) to blind user identification in a massive Multiple Input Multiple Output (MIMO) communications system is proposed. The method is blind in the way that the Base Station (BS) only requires a training sequence containing unknown data symbols obtained from the user without further knowledge on the channel, modulation, coding or even noise power. We evaluate the algorithm under the assumption of gaussian transmit signals using the non-rigorous replica method. To facilitate the computations the existence of an Operator Valued Free Fourier Transform is postulated, which is verified by Monte Carlo simulation. The replica computations are conducted in the large but finite system by applying saddle-point integration with inverse temperature $β$ as the large parameter. The classifier accuracy is estimated by gaussian approximation through moment-matching. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_09910 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Geometric Analysis of Blind User Identification for Massive MIMO Networks Bohnacker, Levi Müller, Ralf R. Signal Processing Information Theory Applying Nearest Convex Hull Classification (NCHC) to blind user identification in a massive Multiple Input Multiple Output (MIMO) communications system is proposed. The method is blind in the way that the Base Station (BS) only requires a training sequence containing unknown data symbols obtained from the user without further knowledge on the channel, modulation, coding or even noise power. We evaluate the algorithm under the assumption of gaussian transmit signals using the non-rigorous replica method. To facilitate the computations the existence of an Operator Valued Free Fourier Transform is postulated, which is verified by Monte Carlo simulation. The replica computations are conducted in the large but finite system by applying saddle-point integration with inverse temperature $β$ as the large parameter. The classifier accuracy is estimated by gaussian approximation through moment-matching. |
| title | Geometric Analysis of Blind User Identification for Massive MIMO Networks |
| topic | Signal Processing Information Theory |
| url | https://arxiv.org/abs/2602.09910 |