Mobile/wireless networks aim to support diverse services with numerous and sophisticated requirements, such as energy efficiency, spectral efficiency, negligible latency, robustness against time and frequency selective channels, low hardware complexity, among others. From the central station to the base stations, radio-over-fiber orthogonal frequency division multiplexing (RoF-OFDM) schemes with direct-detection are then implemented. Unfortunately, laser phase noise, chromatic fiber dispersion, and carrier frequency offset impair the orthogonality of the subcarriers; hence, deteriorating the performance of the RoF-OFDM system. In order to take all the processing tasks to the cognitive level (the last goal in the telecommunication industry), various extreme learning machines (ELMs), composed by only a single hidden layer, have been recently adopted as equalizers. The reason behind this trend comes from the lower computational complexity, higher detection accuracy, and minimum human intervention of the ELM algorithms. In this article, we introduce a multilayer ELM-based receiver for RoF schemes transmitting phase-correlated OFDM signals affected by phase and frequency errors. Results report that by appropriately setting the hyper-parameters of the multilayer ELMs, the ELM with 3 hidden layers outperforms most of the ELMs reported in the literature (the ELM with 2 hidden layers, original ELM, regularized ELM, and 2 fully-independent ELMs defined in the real domain), as well as the benchmark pilot-assisted equalizer in terms of bit error rate. Nevertheless, this benefit comes with excessive computational cost. Finally, we show that the fully-complex ELM is still the best equalizer taking into account several key metrics.

中文翻译：

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多层极限学习机作为基于 OFDM 的光纤无线电系统中的均衡器


移动/无线网络旨在支持具有众多复杂要求的多样化服务，例如能源效率、频谱效率、可忽略的延迟、针对时间和频率选择性信道的鲁棒性、低硬件复杂性等。从中心站到基站，然后实施具有直接检测的光纤无线电正交频分复用（RoF-OFDM）方案。不幸的是，激光相位噪声、色度光纤色散和载波频率偏移会损害子载波的正交性。因此，RoF-OFDM系统的性能恶化。为了将所有处理任务提升到认知水平（电信行业的最后目标），仅由单个隐藏层组成的各种极限学习机（ELM）最近已被采用作为均衡器。这一趋势背后的原因来自于 ELM 算法较低的计算复杂度、较高的检测精度以及最少的人为干预。在本文中，我们介绍了一种基于 ELM 的多层接收器，用于 RoF 方案，传输受相位和频率误差影响的相位相关 OFDM 信号。结果表明，通过适当设置多层 ELM 的超参数，具有 3 个隐藏层的 ELM 优于文献中报道的大多数 ELM（具有 2 个隐藏层的 ELM、原始 ELM、正则化 ELM 和 2 个完全独立的 ELM）在实数域中定义），以及误码率方面的基准导频辅助均衡器。然而，这种好处伴随着过多的计算成本。最后，我们证明，考虑到几个关键指标，完全复杂的 ELM 仍然是最好的均衡器。