LLRSymNet: A Low-Complex Neural Network for LLR Estimation Through Symmetry Exploitation

In wireless communication receivers, soft demodulation translates the noisy received symbols into soft decision information, typically in the form of Log Likelihood Ratios (LLR). Of late, Neural Network (NN)-based demodulators show promise, offering improved performance. However, there is a growing concern about the rising complexity in NN based LLR estimation, particularly with denser modulation schemes. This increased complexity leads to higher area and power consumption, posing challenges for efficiency in edge device applications. To address this issue, this paper proposes a novel NN architecture named LLRSymNet, which works by exploiting the symmetries found in the LLR functions of the bits in the received symbols, which stems from the symmetries present in the modulation constellations used for transmission. By doing so, it reduces the computational complexity compared to existing NN-based soft demodulators. Experimental evaluation of LLRSymNet within the DVB-S.2 receiver system demonstrates performance enhancements and achieves complexity reductions of up to 75% for M-PSK and M-APSK modulation schemes, and up to 81% for denser M-QAM modulations, compared to conventional NN architectures.