Low-Complexity separable beamformers for massive antenna array systems
Future cellular systems will likely employ massive bi-dimensional arrays to improve performance by large array gain and more accurate spatial filtering, motivating the design of low-complexity signal-processing methods. The authors propose optimising a Kronecker-separable beamforming filter that tak...
| Autores: | , , , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2019 |
| País: | Brasil |
| Recursos: | Universidade Federal do Ceará (UFC) |
| Repositorio: | Repositório Institucional da Universidade Federal do Ceará (UFC) |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.ufc.br:riufc/70568 |
| Acesso em linha: | http://www.repositorio.ufc.br/handle/riufc/70568 |
| Access Level: | acceso abierto |
| Palavra-chave: | Kronecker-separable beamforming Massive bi-dimensional arrays Minimum mean square error (MMSE) Beamformers |
| Resumo: | Future cellular systems will likely employ massive bi-dimensional arrays to improve performance by large array gain and more accurate spatial filtering, motivating the design of low-complexity signal-processing methods. The authors propose optimising a Kronecker-separable beamforming filter that takes advantage of the bi-dimensional array geometry to reduce computational costs. The Kronecker factors are obtained using two strategies: alternating optimisation and sub-array minimum mean square error (MMSE) beamforming with Tikhonov regularisation. According to the simulation results, the proposed methods are computationally efficient but come with source recovery degradation, which becomes negligible when the sources are sufficiently separated in space. |
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