CNN-based blind SIR classification framework for STPA-BAA spectrum superposing
Spectrum sharing in the spatial domain among the plurality of wireless communication systems should be addressed due to its exhaustion. We previously conceptualized a spectrum superposing enabled by subcarrier transmission power assignment (STPA) and blind adaptive array (BAA), where co-channel inte...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-02-01
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| Series: | ICT Express |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405959521001740 |
| Summary: | Spectrum sharing in the spatial domain among the plurality of wireless communication systems should be addressed due to its exhaustion. We previously conceptualized a spectrum superposing enabled by subcarrier transmission power assignment (STPA) and blind adaptive array (BAA), where co-channel interference can be suppressed even without a priori information of interferer. However, it requires knowledge of input signal-to-interference power ratio (SIR) to realize completely blind operation. This paper proposes a blind SIR estimation framework by convolutional neural network (CNN) using power spectrum images. Simulation verifies the proposed scheme can maximize throughput performance based on SIR classification results with 97% accuracy. |
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| ISSN: | 2405-9595 |