A Novel ANN Equalizer to Mitigate Nonlinear Interference in Analog-RoF Mobile Fronthaul

Liu, Siming; Alfadhli, Yahya; Shen, Shuyi; Xu, Mu; Tian, Huiping; Chang, Gee‐Kung

doi:10.1109/lpt.2018.2865529

Cited by 29 publications

(9 citation statements)

References 9 publications

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“…To measure the quality of the received signals, we performed a quantitative evaluation of the received signal constellation via EVM estimation with the aid of Simulink/MATLAB software. According to the 3GPP specifications, 58 the minimum requirements for EVM in user equipment (UE) are: 17.5% for BPSK and QPSK, 12.5% for 16-QAM and 8% for 64-QAM. Due to the lack of specific reference to the fronthaul performance, we compared the EVM of the data delivered to the wireless transmitter with these 3GPP specifications.…”

Section: Resultsmentioning

confidence: 99%

Double‐RSOA colorless WDM‐PON for 5G fronthaul applications

Souza

Plazas

Celino

et al. 2022

Trans Emerging Tel Tech

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The introduction of 5G mobile systems poses significant challenges to the design of the fronthaul structure. The dense coverage, comprising several small cells operating in the millimeter‐wave range, with an enormous number of antennas, makes conventional digital‐radio‐over‐fiber unfeasible. On the other hand, the centralized architecture of the optical fronthaul is well‐suited for the passive optical network (PON) topology, with WDM‐PON as a strong contender due to its capability to establish logical point‐to‐point optical links to each cell site. However, the large‐scale deployment of WDM‐PON requires the availability of colorless transmitters, where the same hardware is able to generate any wavelength needed by the network. In this context, this paper investigates an analog fronthaul configuration over a colorless WDM‐PON for 5G and beyond, employing the techniques of self‐seeding in the downlink and carrier reuse in the uplink. We numerically evaluated the EVM when transmitting 1.25 Gbps at 3.5 GHz and 10 Gbps at 26 GHz in a 20 km optical link. The results are within the 3GPP requirements, demonstrating the feasibility of the proposed fronthaul.

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Section: Resultsmentioning

confidence: 99%

Double‐RSOA colorless WDM‐PON for 5G fronthaul applications

Souza

Plazas

Celino

et al. 2022

Trans Emerging Tel Tech

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“…Leveraging these advantages, models based on neural networks have become a promising candidate for physical layer signal processing in RoF systems [80][81][82][83][84][85][86][87][88][89][90][91][92][93][94]. Compared with other types of optical communication systems, because of the RF carrier, the wireless communication link and the analog optical link, the RoF system suffers more from complicated impairments, especially the nonlinear impairments, which are challenging for conventional signal processing schemes.…”

Section: Summary Of Neural Network-based Signal Processing In Rof Systemsmentioning

confidence: 99%

“…In addition to the intra-band XM, the mm-wave RoF system also suffers from the inter-band XM under the multi-user scenario in the uplink, which leads to inter-user interference. The FCNN NLE has also been investigated to mitigate the inter-band and intra-band XM simultaneously [83,84]. Both joint FCNN equalization of all users (the input layer, hidden layer and output layer of FCNN has 20, 24 and 4 neurons, respectively) and individual user FCNN equalizations (the input layer, hidden layer and output layer of each FCNN has 5, 12 and 1 neurons, respectively) have been studied, as shown in Figure 9.…”

Section: Neural Network Equalizers In Rof Systemsmentioning

confidence: 99%

Machine Learning Techniques in Radio-over-Fiber Systems and Networks

Lee

Kandeepan

et al. 2020

Photonics

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The radio-over-fiber (RoF) technology has been widely studied during the past decades to extend the wireless communication coverage by leveraging the low-loss and broad bandwidth advantages of the optical fiber. With the increasing need for wireless communications, using millimeter-waves (mm-wave) in wireless communications has become the recent trend and many attempts have been made to build high-throughput and robust mm-wave RoF systems during the past a few years. Whilst the RoF technology provides many benefits, it suffers from several fundamental limitations due to the analog optical link, including the fiber chromatic dispersion and nonlinear impairments. Various approaches have been proposed to address these limitations. In particular, machine learning (ML) algorithms have attracted intensive research attention as a promising candidate for handling the complicated physical layer impairments in RoF systems, especially the nonlinearity during signal modulation, transmission and detection. In this paper, we review recent advancements in ML techniques for RoF systems, especially those which utilize ML models as physical layer signal processors to mitigate various types of impairments and to improve the system performance. In addition, ML algorithms have also been widely adopted for highly efficient RoF network management and resource allocation, such as the dynamic bandwidth allocation and network fault detection. In this paper, we also review the recent works in these research domains. Finally, several key open questions that need to be addressed in the future and possible solutions of these questions are also discussed.

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“…In [27], authors have demonstrated an RoF system equalization using a multi-level ANN equalizer for compensating the nonlinear signal compression due to the in-band distortions. Liu et al have proposed an ANN equalizer to mitigate the interference between multiple users in uplink transmissions [28].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Linearization Schemes for Radio Over Fiber Systems

et al. 2022

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This manuscript proposes a novel machine learning (ML)-based linearization scheme for radio-over-fiber (RoF) systems with external modulation. The proposed approach has the advantage of not requiring new training campaigns in case the Mach-Zehnder modulator (MZM) parameters are changed over time. Our innovative digital pre-distortion (DPD) was designed to favor enhanced remote areas (eRAC) scenarios, in which the non-linearities introduced by the MZM become more severe. It employs a multi-layer perceptron (MLP) artificial neural network (ANN) to model the RoF system and estimate its post-inverse response, which is then applied to the DPD block. We investigate the ML-based DPD performance in terms of adjacent channel leakage ratio (ACLR), normalized mean square error (NMSE), resultant signal root mean square error vector magnitude error (EVM RMS ), and complexity. Numerical results demonstrate that the intended DPD method is less complex and outperforms the orthogonal scalar feedback linearization (OSFL) scheme, which has been considered a state-of-the-art DPD technique. The proposal has the potential to effectively and efficiently compensate for the RoF nonlinear distortions, especially in a time-variant system, without needing new training campaigns.

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A Novel ANN Equalizer to Mitigate Nonlinear Interference in Analog-RoF Mobile Fronthaul

Cited by 29 publications

References 9 publications

Double‐RSOA colorless WDM‐PON for 5G fronthaul applications

Double‐RSOA colorless WDM‐PON for 5G fronthaul applications

Machine Learning Techniques in Radio-over-Fiber Systems and Networks

Machine Learning-Based Linearization Schemes for Radio Over Fiber Systems

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