Deep Reinforcement Learning Algorithms for D2D Communications: A Comparative Analysis
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-4284-8_6
Reference11 articles.
1. Ningombam DD, Shin S (2019) Optimal resource management and binary power control in network-assisted D2D communications for higher frequency reuse factor. Sensors 19:251. https://doi.org/10.3390/s19020251
2. Li Z, Sanchez Moya F, Gabor F, Da Silva Jr, J, Koufos K (2016) Device-to-device (D2D) communications. In: Dohler M, Nakamura T, Osseiran A, Monserrat J, Marsch P (eds), 5G mobile and wireless communications technology (pp 107–136). Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9781316417744.006
3. Ningombam DD, Lee CG, Shin S (2019) Interference mitigation for multicast D2D communications underlay cellular networks. International conference on artificial intelligence in information and communication (ICAIIC), 2019, pp 1–4, https://doi.org/10.1109/ICAIIC.2019.8668982
4. Telu VS, Padimi V, Ningombam DD (2022) Optimizing predictions of brain stroke using machine learning. J Neutrosophic Fuzzy Syst 2(2):31–43
5. Nyuyen KK, Masaracchia A, Yin C, Nyuyen LD, Dobre OA, Duong TQ (2021) Deep reinforcement learning for intelligent reflecting surface-assisted D2D communications
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