We propose spectrum-sliced reservoir computer-based (RC) multi-symbol equalization for 32-GBd PAM4 transmission. RC with 17 symbols at the output achieves an order of magnitude reduction in multiplications/symbol versus single output case while maintaining simple training.
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在本文中,我们介绍了一种基于结构的神经网络体系结构,即RC结构,用于MIMO-OFDM符号检测。 RC结构通过储层计算(RC)利用MIMO-OFDM信号的时间结构。二进制分类器利用系统中的重复星座结构来执行多级检测。 RC的合并允许以纯粹的在线方式学习RC结构,并在每个OFDM子帧中具有极为有限的飞行员符号。二进制分类器可以有效利用宝贵的在线培训符号,并可以轻松地扩展到高级调制,而无需大幅度提高复杂性。实验表明,在BIT错误率(BER)方面,引入的RC结构优于常规模型的符号检测方法和基于最新学习的策略。当采用等级和链接适应时,RC结构比现有方法的优势变得更加重要。引入的RC结构阐明了将通信领域知识和基于学习的接收处理结合在一起,可用于5G/5G高级及以后。
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由于自动驾驶,物联网和流媒体服务的快速发展,现代通信系统必须应对各种渠道条件以及用户和设备的稳步增加。这以及仍在上升的带宽需求只能通过智能网络自动化来满足,这需要高度灵活和盲目的收发器算法。为了应对这些挑战,我们提出了一种新颖的自适应均衡计划,该计划通过训练用对抗性网络训练均衡器来利用深度学习的繁荣进步。该学习仅基于发射信号的统计数据,因此它对通道模型的实际发送符号和不可知论是盲目的。所提出的方法独立于均衡器拓扑,并实现了强大的基于神经网络的均衡器的应用。在这项工作中,我们证明了这一概念在对线性和非线性传输通道的模拟中,并证明了拟议的盲目学习方案的能力,可以接近非盲均衡器的性能。此外,我们提供了理论观点,并强调了方法的挑战。
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MIMO-OFDM接收处理的主要开放挑战之一是如何有效地利用极有限的空中飞行员符号来检测传输数据符号。最近的进步致力于调查利用有限飞行员的有效方法。但是,我们注意到,除了利用飞行员外,还可以利用数据符号来提高检测性能。因此,本文介绍了一种基于在线子框架的方法,即RC-screstnet,该方法可以从宝贵的飞行员符号中有效学习,并使用检测到的有效载荷数据使用决策反馈(DF)方法进行动态更新。该网络由时域中的储层计算(RC)模块组成,频域中的神经网络结构网络组成。网络的唯一设计使其可以通过从检测到的数据符号中学习来通过通道的更改进行动态更新。实验证明了RC结构网络在动态传输模式下检测以及在采用DF方法时的训练开销需求方面的有效性。
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The current optical communication systems minimize bit or symbol errors without considering the semantic meaning behind digital bits, thus transmitting a lot of unnecessary information. We propose and experimentally demonstrate a semantic optical fiber communication (SOFC) system. Instead of encoding information into bits for transmission, semantic information is extracted from the source using deep learning. The generated semantic symbols are then directly transmitted through an optical fiber. Compared with the bit-based structure, the SOFC system achieved higher information compression and a more stable performance, especially in the low received optical power regime, and enhanced the robustness against optical link impairments. This work introduces an intelligent optical communication system at the human analytical thinking level, which is a significant step toward a breakthrough in the current optical communication architecture.
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在本文中,提出了一种新的方法,该方法允许基于神经网络(NN)均衡器的低复杂性发展,以缓解高速相干光学传输系统中的损伤。在这项工作中,我们提供了已应用于馈电和经常性NN设计的各种深层模型压缩方法的全面描述和比较。此外,我们评估了这些策略对每个NN均衡器的性能的影响。考虑量化,重量聚类,修剪和其他用于模型压缩的尖端策略。在这项工作中,我们提出并评估贝叶斯优化辅助压缩,其中选择了压缩的超参数以同时降低复杂性并提高性能。总之,通过使用模拟和实验数据来评估每种压缩方法的复杂性及其性能之间的权衡,以完成分析。通过利用最佳压缩方法,我们表明可以设计基于NN的均衡器,该均衡器比传统的数字背部传播(DBP)均衡器具有更好的性能,并且只有一个步骤。这是通过减少使用加权聚类和修剪算法后在NN均衡器中使用的乘数数量来完成的。此外,我们证明了基于NN的均衡器也可以实现卓越的性能,同时仍然保持与完整的电子色色散补偿块相同的复杂性。我们通过强调开放问题和现有挑战以及未来的研究方向来结束分析。
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FPGA中首次实施了针对非线性补偿的经常性和前馈神经网络均衡器,其复杂度与分散均衡器的复杂度相当。我们证明,基于NN的均衡器可以胜过1个速度的DBP。
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多节点WDM网络的数字双胞胎模型是从单个访问点获得的。该模型用于预测和优化网络中每个链接的发射功率配置文件,并获得最多2.2 〜db的边距改进。不优化的传输。
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To circumvent the non-parallelizability of recurrent neural network-based equalizers, we propose knowledge distillation to recast the RNN into a parallelizable feedforward structure. The latter shows 38\% latency decrease, while impacting the Q-factor by only 0.5dB.
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我们展示了一种简单,高效的“直接学习”方法来利用神经网络培训基于Volterra系列的数字预失真滤波器。我们使用64-QAM 64-GBaud模拟发射器显示出对传统训练方法的卓越性能,具有不同的发射器非线性和嘈杂的条件。
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In this work, we demonstrate the offline FPGA realization of both recurrent and feedforward neural network (NN)-based equalizers for nonlinearity compensation in coherent optical transmission systems. First, we present a realization pipeline showing the conversion of the models from Python libraries to the FPGA chip synthesis and implementation. Then, we review the main alternatives for the hardware implementation of nonlinear activation functions. The main results are divided into three parts: a performance comparison, an analysis of how activation functions are implemented, and a report on the complexity of the hardware. The performance in Q-factor is presented for the cases of bidirectional long-short-term memory coupled with convolutional NN (biLSTM + CNN) equalizer, CNN equalizer, and standard 1-StpS digital back-propagation (DBP) for the simulation and experiment propagation of a single channel dual-polarization (SC-DP) 16QAM at 34 GBd along 17x70km of LEAF. The biLSTM+CNN equalizer provides a similar result to DBP and a 1.7 dB Q-factor gain compared with the chromatic dispersion compensation baseline in the experimental dataset. After that, we assess the Q-factor and the impact of hardware utilization when approximating the activation functions of NN using Taylor series, piecewise linear, and look-up table (LUT) approximations. We also show how to mitigate the approximation errors with extra training and provide some insights into possible gradient problems in the LUT approximation. Finally, to evaluate the complexity of hardware implementation to achieve 400G throughput, fixed-point NN-based equalizers with approximated activation functions are developed and implemented in an FPGA.
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我们根据光学通信中的载体回收率的变异推断研究了自适应盲人均衡器的潜力。这些均衡器基于最大似然通道估计的低复杂性近似。我们将变异自动编码器(VAE)均衡器的概念概括为包括概率星座塑形(PCS)的高阶调制格式,无处不在,在光学通信中,对接收器进行过度采样和双极化传输。除了基于卷积神经网络的黑盒均衡器外,我们还提出了基于线性蝴蝶滤波器的基于模型的均衡器,并使用变异推理范式训练过滤器系数。作为副产品,VAE还提供了可靠的通道估计。我们在具有符号间干扰(ISI)的经典添加剂白色高斯噪声(AWGN)通道和色散线性光学双极化通道上分析了VAE的性能和灵活性。我们表明,对于固定的固定通道但也随时间变化的通道,它可以超越最先进的恒定算法(CMA)来扩展盲人自适应均衡器的应用范围。评估伴随着超参数分析。
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Ultra-reliable short-packet communication is a major challenge in future wireless networks with critical applications. To achieve ultra-reliable communications beyond 99.999%, this paper envisions a new interaction-based communication paradigm that exploits feedback from the receiver. We present AttentionCode, a new class of feedback codes leveraging deep learning (DL) technologies. The underpinnings of AttentionCode are three architectural innovations: AttentionNet, input restructuring, and adaptation to fading channels, accompanied by several training methods, including large-batch training, distributed learning, look-ahead optimizer, training-test signal-to-noise ratio (SNR) mismatch, and curriculum learning. The training methods can potentially be generalized to other wireless communication applications with machine learning. Numerical experiments verify that AttentionCode establishes a new state of the art among all DL-based feedback codes in both additive white Gaussian noise (AWGN) channels and fading channels. In AWGN channels with noiseless feedback, for example, AttentionCode achieves a block error rate (BLER) of $10^{-7}$ when the forward channel SNR is 0 dB for a block size of 50 bits, demonstrating the potential of AttentionCode to provide ultra-reliable short-packet communications.
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最近在无线通信领域的许多任务中考虑了机器学习算法。以前,我们已经提出了使用深度卷积神经网络(CNN)进行接收器处理的使用,并证明它可以提供可观的性能提高。在这项研究中,我们专注于发射器的机器学习算法。特别是,我们考虑进行波束形成并提出一个CNN,该CNN对于给定上行链路通道估计值作为输入,输出下链路通道信息用于波束成形。考虑到基于UE接收器性能的损失函数的上行链路传输和下行链路传输,CNN以有监督的方式进行培训。神经网络的主要任务是预测上行链路和下行链路插槽之间的通道演变,但它也可以学会处理整个链中的效率低下和错误,包括实际的光束成型阶段。提供的数值实验证明了波束形成性能的改善。
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Effective and adaptive interference management is required in next generation wireless communication systems. To address this challenge, Rate-Splitting Multiple Access (RSMA), relying on multi-antenna rate-splitting (RS) at the transmitter and successive interference cancellation (SIC) at the receivers, has been intensively studied in recent years, albeit mostly under the assumption of perfect Channel State Information at the Receiver (CSIR) and ideal capacity-achieving modulation and coding schemes. To assess its practical performance, benefits, and limits under more realistic conditions, this work proposes a novel design for a practical RSMA receiver based on model-based deep learning (MBDL) methods, which aims to unite the simple structure of the conventional SIC receiver and the robustness and model agnosticism of deep learning techniques. The MBDL receiver is evaluated in terms of uncoded Symbol Error Rate (SER), throughput performance through Link-Level Simulations (LLS), and average training overhead. Also, a comparison with the SIC receiver, with perfect and imperfect CSIR, is given. Results reveal that the MBDL receiver outperforms by a significant margin the SIC receiver with imperfect CSIR, due to its ability to generate on demand non-linear symbol detection boundaries in a pure data-driven manner.
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在多输入多输出(MIMO)系统中使用深度自动码器(DAE)进行端到端通信,是一种具有重要潜力的新概念。在误码率(BER)方面,已示出DAE-ADED MIMO以占地识别的奇异值分解(SVD)为基础的预编码MIMO。本文提出将信道矩阵的左右奇异矢量嵌入到DAE编码器和解码器中,以进一步提高MIMO空间复用的性能。 SVD嵌入式DAE主要优于BER的理论线性预编码。这是显着的,因为它表明所提出的DAES通过将通信系统视为单个端到端优化块来超出当前系统设计的极限。基于仿真结果,在SNR = 10dB,所提出的SVD嵌入式设计可以实现近10美元,并将BER减少至少10次,而没有SVD,相比增长了18倍的增长率最高18倍具有理论线性预编码。我们将这一点归因于所提出的DAE可以将输入和输出与具有有限字母输入的自适应调制结构匹配。我们还观察到添加到DAE的剩余连接进一步提高了性能。
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非正交多访问(NOMA)是一项有趣的技术,可以根据未来的5G和6G网络的要求实现大规模连通性。尽管纯线性处理已经在NOMA系统中达到了良好的性能,但在某些情况下,非线性处理是必须的,以确保可接受的性能。在本文中,我们提出了一个神经网络体系结构,该架构结合了线性和非线性处理的优势。在图形处理单元(GPU)上的高效实现证明了其实时检测性能。使用实验室环境中的实际测量值,我们显示了方法比常规方法的优越性。
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We design and implement an adaptive machine learning equalizer that alternates multiple linear and nonlinear computational layers on an FPGA. On-chip training via gradient backpropagation is shown to allow for real-time adaptation to time-varying channel impairments.
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基于深度学习的渠道代码设计最近引起了人们的兴趣,可以替代传统的编码算法,尤其是对于现有代码不提供有效解决方案的渠道。通过反馈渠道进行的沟通就是一个这样的问题,最近通过采用各种深度学习体系结构来获得有希望的结果。在本文中,我们为反馈渠道介绍了一种新颖的学习辅助代码设计,称为广义块注意反馈(GBAF)代码,i)使用模块化体系结构,可以使用不同的神经网络体系结构实现;ii)与现有设计相比,错误的可能性提高了误顺序;iii)可以以所需的代码速率传输。
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正交频分复用(OFDM)已广泛应用于当前通信系统。人工智能(AI)addm接收器目前被带到最前沿替换和改进传统的OFDM接收器。在这项研究中,我们首先比较两个AI辅助OFDM接收器,即数据驱动的完全连接的深神经网络和模型驱动的COMNet,通过广泛的仿真和实时视频传输,使用5G快速原型制作系统进行跨越式-Air(OTA)测试。我们在离线训练和真实环境之间的频道模型之间的差异差异导致的模拟和OTA测试之间找到了性能差距。我们开发一种新颖的在线培训系统,称为SwitchNet接收器,以解决此问题。该接收器具有灵活且可扩展的架构,可以通过在线训练几个参数来适应真实频道。从OTA测试中,AI辅助OFDM接收器,尤其是SwitchNet接收器,对真实环境具有鲁棒,并且对未来的通信系统有前途。我们讨论了本文初步研究的潜在挑战和未来的研究。
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