## 2 Related Work
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Year** **[Ref.]** & **Estimation** **Domain** & **Algorithm and Adaptive Criteria** & **Contribution** & **Challenges** \\ \hline
2019 [35] & Post-FFT & SNR estimation. & The selection of the corresponding MIMO mode and its modulation size is based on the received SNR and target bit error rate for unipolar MIMO-OFDM visible light communication (VLC) systems. & Developing AM techniques that minimize PAPR while maintaining efficient spectral utilization is difficult. \\ \hline
2019 [39] & Post-FFT & SNR estimation using ANN exploiting PSD values. & AMC scheme enabled by ANN-aided SNR estimation in the MIMO system. & Ensuring accurate SNR predictions across diverse channel conditions, such as multipath fading. Doppler shifts, and noise variations, is complex. \\ \hline
2019 [35] & Post-FFT & SNR estimation using ANN exploiting PSD values. & AMC scheme enabled by ANN-aided SNR estimation in the MIMO system. & Ensuring accurate SNR predictions across diverse channel conditions, such as multipath fading. Doppler shifts, and noise variations, is complex. \\ \hline
2021 [39] & Post-FFT & SNR estimation using ANN exploiting PSD values. & The PSD values are trained for SNR classification, and is mapped to respective MCS sets. Improved accuracy of SNR estimation and throughput performance of the system. & A mismatch between training data and real-world conditions can lead to poor estimation performance. \\ \hline
2021 [37] & Post-FFT & CNN-trained data for SNR and Doppler estimation. & It proposes a novel CNN-based joint classification method to characterize the SNR and AMC design using spectrogram images in the MIMO system. & Required optimized models and hardware accelerators to avoid processing delays due to windowing. Fourier transforms, and noise filtering. \\  & & adaptive switching. & Improved accuracy of SNR estimation and throughput performance of the system. & High complexity. Supervised learning requires a sufficient set of data. \\ \hline
2023 [40] & Post-FFT & CSI estimation. & Adaptive algorithm for use in telemedicine communication based on MIMO-OFDM WiMAX standard. & The NLOS propagation and high mobility in 3D environments make accurate SNR, CSI, and Doppler estimation more challenging. \\  & & Adaptive algorithms can improve the efficiency of the transmitted medical image in 3D MIMO-OFDM system. & High complexity to compute CSI table from received instantaneous SNR values. \\ \hline
2020 [36] & Pre-FFT & SINR estimation. & A third Link adaptation algorithm for an MIMO 5G system was formulated by varying path to the target BER while yielding higher data rates at high modulation index and node rate, to yield an optimal algorithm that achieved the target BER with the highest data rate at any SNR. & Higher code rates delayed the achievement of target BER while yielding higher data rates at high SNRs. \\  & & Improved system throughput. & High complexity due to simultaneous optimization over modulation schemes, coding rates, MIMO configurations, and scheduling strategies. \\ \hline
2018 [41] & Post-FFT & CSI estimation; singular value decomposition (SVD)-based SNR estimation. & A framework based on the supervised learning approach the k-nearest neighbor (k-NN) algorithm for AMC in MIMO-OFDM wireless systems is proposed, with the SVD of the channel matrix and SNR on each spatial stream extracted as a feature set. & Collecting high-quality, labeled datasets for training supervised models is a critical challenge. The need for extensive datasets under diverse channel conditions (e.g., SNR, fading environments, mobility scenarios) increases the complexity of data acquisition. \\  & & & A classification scheme is then proposed to match channel implementations to different MCSs. Improved system throughput. & Online DL models must process high-dimensional data in real time, which is computationally demanding. \\ \hline
2022 [43] & Post-FFT & SINR estimation with neural network-based MCS selection. & The paper describes an online deep learning (DL) algorithm for the adaptive modulation and coding in 5G Massive MIMO. The algorithm is based on a fully connected neural network, which is initially trained on the output of the traditional algorithm and then is incrementally retrained by the service feedback of its own output. & The presence of noise, errors, and missing values in real-time CSI and performance metrics can degrade model performance. Memory overhead. \\ \hline \hline \end{tabular}
\end{table}