Energy-Efficient Power Allocation in Cell-Free Massive MIMO via Graph Neural Networks
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910324083392512 |
|---|---|
| author | Raghunath, Ramprasad Peng, Bile Jorswieck, Eduard A. |
| author_facet | Raghunath, Ramprasad Peng, Bile Jorswieck, Eduard A. |
| contents | CF-mMIMO systems are a promising solution to enhance the performance in 6G wireless networks. Its distributed nature of the architecture makes it highly reliable, provides sufficient coverage and allows higher performance than cellular networks. EE is an important metric that reduces the operating costs and also better for the environment. In this work, we optimize the downlink EE performance with MRT precoding and power allocation. Our aim is to achieve a less complex, distributed and scalable solution. To achieve this, we apply unsupervised ML with permutation equivariant architecture and use a non-convex objective function with multiple local optima. We compare the performance with the centralized and computationally expensive SCA. The results indicate that the proposed approach can outperform the baseline with significantly less computation time. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_14281 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Energy-Efficient Power Allocation in Cell-Free Massive MIMO via Graph Neural Networks Raghunath, Ramprasad Peng, Bile Jorswieck, Eduard A. Signal Processing CF-mMIMO systems are a promising solution to enhance the performance in 6G wireless networks. Its distributed nature of the architecture makes it highly reliable, provides sufficient coverage and allows higher performance than cellular networks. EE is an important metric that reduces the operating costs and also better for the environment. In this work, we optimize the downlink EE performance with MRT precoding and power allocation. Our aim is to achieve a less complex, distributed and scalable solution. To achieve this, we apply unsupervised ML with permutation equivariant architecture and use a non-convex objective function with multiple local optima. We compare the performance with the centralized and computationally expensive SCA. The results indicate that the proposed approach can outperform the baseline with significantly less computation time. |
| title | Energy-Efficient Power Allocation in Cell-Free Massive MIMO via Graph Neural Networks |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2401.14281 |