## 7 Conclusions  
The DFE, MLD, and RDE algorithms are implemented in MATLAB to eliminate the ISI in a dispersive channel in an SSMF, which improves the system performance. A converged 5G mm-wave A-RoF system at 60 GHz is used as an example to evaluate the four equalization algorithms. When assessing these algorithms in the case of QPSK, 16-QAM, 64-QAM, and 128-QAM as a modulation format in OFDM subcarriers, the DFE algorithm performs slightly better than the MLD with an EVM improvement of 0.46%, 0.08%, 0.16%, and 0.12%, respectively, while RDE performance is the best among all for QPSK and 16-QAM modulation formats. When comparing MLD and DFE in terms of computational resource requirements, MLD searches all transmitted sequences for an \(M\)-ary modulation, and the complexity is proportional to \(M^{N}\) where \(N\) is the number of transmitted symbols, which grows exponentially with signal space, requiring greater computational resources. However, DFE is more computationally efficient because it has linear complexity in terms of the number of symbols, as it only depends on the number of feedback taps. Thus, it reduces the problem to a feedback mechanism that uses previously decoded symbols to decode the current symbol, avoiding exhaustive searches. Consequently, it performs better regarding the EVM than MLD and has less hardware complexity.  
The proposed algorithms, in comparison to the three most popular equalization algorithms, LMS, CMA, and AMF, achieved better EVM values and a fiber length that can extend up to 120 km. Moreover, algorithms such as LMS, CMA, RDE, and AMF fail miserably when applied to higher-order QAM constellations, while the DFE and the MLD allow us to reach EVMs of 7.19% and 7.35% for 64-QAM and 50 km SSMF distances, respectively, and 4.73% and 4.85% for 128-QAM and 40 km SSMF distances, respectively.  
Lastly, it is proven that if equalization algorithms are used, there is no need to increase the input power to achieve EVM values below the corresponding 3 GPP thresholds, especially in higher modulation formats.  
Moreover, in the last few years a separate class of machine learning (ML) algorithms [58; 59] has been developed that can be used to redefine the design of the coding and modulation either at the transmitter or the receiver of a communication system, showing promise in delivering lower bit error rates and better robustness to the wireless channel impairments. However, according to the literature, to handle larger constellation diagrams efficiently, the neural network may require more neurons in the input layer to represent them, and as the number of constellation points increases, the neural network needs more training data to correctly map the noisy received symbols to their correct positions in the constellation. Taking all the above into consideration, our next research endeavor will focus on the implementation of these algorithms and the comparison with the algorithms presented in this work.  
Conceptualization, U.F. and A.M.; methodology, U.F.; software, U.F.; validation, U.F. and A.M.; formal analysis, U.F.; investigation, U.F.; resources, A.M.; data curation, U.F.; writing--original draft preparation, U.F.; writing--review and editing, A.M.; visualization, U.F.;supervision, A.M.; project administration, A.M.; funding acquisition, A.M. All authors have read and agreed to the published version of the manuscript.  
The work presented in this article was carried out within the 5G STEP FWD Innovative Training Network, which has received funding from the European Union's Horizon 2020 Research and Innovation Programme 2018-2021 under the Marie Sklodowska-Curie Action grant agreement No. 722429.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.