A Training-Based Mutual Information Lower Bound for Large-Scale Systems
We provide a mutual information lower bound that can be used to analyze the effect of training in models with unknown parameters. For large-scale systems, we show that this bound can be calculated using the difference between two derivatives of a conditional entropy function. We provide a step-by-st...
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| Veröffentlicht in: | IEEE transactions on communications 2022-08, Vol.70 (8), p.5151-5163 |
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| container_end_page | 5163 |
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| container_issue | 8 |
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| container_title | IEEE transactions on communications |
| container_volume | 70 |
| creator | Gao, Kang Meng, Xiangbo Laneman, J. Nicholas Chisum, Jonathan D. Bendlin, Ralf Chopra, Aditya Hochwald, Bertrand M. |
| description | We provide a mutual information lower bound that can be used to analyze the effect of training in models with unknown parameters. For large-scale systems, we show that this bound can be calculated using the difference between two derivatives of a conditional entropy function. We provide a step-by-step process for computing the bound, and apply the steps to a quantized large-scale multiple-antenna wireless communication system with an unknown channel. Numerical results demonstrate the interplay between quantization and training. |
| doi_str_mv | 10.1109/TCOMM.2022.3182747 |
| format | Article |
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| source | IEEE Electronic Library (IEL) |
| subjects | Analytical models Coherence time Entropy Gaussian noise Information rates Large-scale systems Lower bounds Mutual information Training Wireless communication systems |
| title | A Training-Based Mutual Information Lower Bound for Large-Scale Systems |
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