Table 3 Computation complexity analysis.
From: RNN-Bi-LSTM spectrum sensing algorithm for NOMA waveform with diverse channel conditions
S. No | Algorithms | Total operations | Operation per batch | Remarks |
|---|---|---|---|---|
1 | RNN-Bi-LSTM | 4.1×1012 | 10.5×106 | ED is the least computationally expensive with only \(9.8\times {10}^{6}\) total operations MF and CSD require significantly more operations than ED but remain much lower than neural network-based approaches LSTM is the most expensive, requiring \(8.2\times {10}^{12}\) operations due to its gating mechanisms RNN-Bi-LSTM, while computationally expensive, provides superior spectrum sensing performance but needs \(4.1\times {10}^{12}\) operations, about twice that of a standard RNN This analysis highlights the trade-off between computational complexity and spectrum sensing accuracy for NOMA waveforms |
2 | RNN | 2.05×1012 | 5.2×106 | |
3 | LSTM | 8.2×1012 | 20.9×106 | |
4 | CSD | 2.6×109 | 67.5×103 | |
5 | MF | 2.5×109 | 65.5×103 | |
6 | ED | 9.8×106 | 256 |
