Deep Learning-Based Constellation Optimization for Physical Network Coding in Two-Way Relay Networks
This work addresses a domain-specific problem in wireless communication networks, offering an incremental improvement by applying existing deep learning methods to a new application area.
The paper tackles the problem of optimizing constellations for physical-layer network coding in two-way relay networks by applying deep learning to train neural networks for modulation and demodulation, resulting in a significant performance gain in achievable sum rate over conventional schemes.
This paper studies a new application of deep learning (DL) for optimizing constellations in two-way relaying with physical-layer network coding (PNC), where deep neural network (DNN)-based modulation and demodulation are employed at each terminal and relay node. We train DNNs such that the cross entropy loss is directly minimized, and thus it maximizes the likelihood, rather than considering the Euclidean distance of the constellations. The proposed scheme can be extended to higher level constellations with slight modification of the DNN structure. Simulation results demonstrate a significant performance gain in terms of the achievable sum rate over conventional relaying schemes. Furthermore, since our DNN demodulator directly outputs bit-wise probabilities, it is straightforward to concatenate with soft-decision channel decoding.