In the intelligent communication field, deep learning (DL) has attracted much attention due to its strong fitting ability and data-driven learning capability. Compared with the typical DL feedforward network structures, an enhancement structure with direct data feedback have been studied and proved to have better performance than the feedfoward networks. However, due to the above simple feedback methods lack sufficient analysis and learning ability on the feedback data, it is inadequate to deal with more complicated nonlinear systems and therefore the performance is limited for further improvement. In this paper, a novel multi-agent feedback enabled neural network (MAFENN) framework is proposed, consisting of three fully cooperative intelligent agents, which make the framework have stronger feedback learning capabilities and more intelligence on feature abstraction, denoising or generation, etc. Furthermore, the MAFENN frame work is theoretically formulated into a three-player Feedback Stackelberg game, and the game is proved to converge to the Feedback Stackelberg equilibrium. The design of MAFENN framework and algorithm are dedicated to enhance the learning capability of the feedfoward DL networks or their variations with the simple data feedback. To verify the MAFENN framework’s feasibility in wireless communications, a multi-agent MAFENN based equalizer (MAFENN-E) is developed for wireless fading channels with inter-symbol interference (ISI). Experimental results show that when the quadrature phase-shift keying (QPSK) modulation scheme is adopted, the SER performance of our proposed method outperforms that of the traditional equalizers by about 2 dB in linear channels. When in nonlinear channels, the SER performance of our proposed method outperforms that of either traditional or DL based equalizers more significantly, which shows the effectiveness and robustness of our proposal in the complex channel environment.

中文翻译：

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用于智能通信的多代理反馈神经网络


在智能通信领域，深度学习（DL）因其强大的拟合能力和数据驱动的学习能力而备受关注。与典型的DL前馈网络结构相比，研究了直接数据反馈的增强结构，并证明比前馈网络具有更好的性能。然而，由于上述简单的反馈方法对反馈数据缺乏足够的分析和学习能力，不足以处理更复杂的非线性系统，因此性能的进一步提高受到限制。本文提出了一种新颖的多智能体反馈使能神经网络（MAFENN）框架，由三个完全协作的智能智能体组成，使得该框架具有更强的反馈学习能力，并且在特征抽象、去噪或生成等方面更加智能。此外，MAFENN框架在理论上被表述为三人反馈Stackelberg博弈，并且证明该博弈收敛于反馈Stackelberg均衡。 MAFENN 框架和算法的设计致力于通过简单的数据反馈来增强前馈深度学习网络或其变体的学习能力。为了验证 MAFENN 框架在无线通信中的可行性，针对具有符号间干扰 (ISI) 的无线衰落信道开发了基于多代理 MAFENN 的均衡器 (MAFENN-E)。实验结果表明，当采用正交相移键控（QPSK）调制方案时，我们提出的方法的SER性能在线性信道中优于传统均衡器约2 dB。 在非线性信道中，我们提出的方法的 SER 性能明显优于传统均衡器或基于 DL 的均衡器，这表明了我们提出的方法在复杂信道环境中的有效性和鲁棒性。