2019/03/09 by Toshiki Matsumine, Matsumine, Toshiki, Toshiaki Koike–Akino +4
Computer Science · Engineering · Mathematics · #Advanced Wireless Communication Technologies #Cooperative Communication and Network Coding #FOS: Computer and information sciences #FOS: Electrical engineering #Information Theory (cs.IT) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Signal Processing (eess.SP) #Wireless Communication Security Techniques #cs.IT #cs.LG #eess.SP #electronic engineering #information engineering #math.IT #stat.ML
paper · pdf · doi:10.48550/arxiv.1903.03713
arxiv created 2019/03/09 · openalex publication_date 2019/03/09 · arxiv updated 2019/03/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.