Random Forest-Based Adaptive Algorithm for OFDM System

‍ ‍In order to adapt to a dynamic channel environment， adaptive orthogonal frequency division multiplexing（OFDM） systems can adjust the subcarrier spacing and cyclic prefix length to maximize the system throughput. In order to quickly and accurately determine the optimal values for the subcarrier spacing and cyclic prefix length for an OFDM system operating in different channel environments， this study investigated a random forest-based adaptive algorithm for OFDM systems. The random forest algorithm， which is based on the principle of ensemble learning， is capable of effectively handling high-dimensional data and possesses advantages such as high efficiency， high accuracy， and strong generalization ability. It can effectively be used for classification in complex data scenarios. A dataset was created to train a multi-classification model based on a random forest for OFDM system parameters by extracting channel characteristics， including the signal-to-noise ratio （SNR）， user movement speed， maximum Doppler frequency， and root mean square delay spread during the communication process， and then combining them with the subcarrier spacing and cyclic prefix length of the OFDM system. The proposed model could adaptively allocate the subcarrier spacing and cyclic prefix length in an OFDM system based on the input channel characteristics. Meanwhile， because the training samples were probably concentrated in a few categories， a synthetic minority oversampling technique was utilized to augment the samples in other categories with fewer samples. The classification accuracy of the algorithm was further improved by meeting the requirement of category balancing in the training samples for the random forest algorithm. Compared to traditional adaptive algorithms， the proposed algorithm exhibited a higher classification accuracy and model generalization capability. The analysis and simulation results indicated that compared to OFDM systems with a fixed subcarrier spacing and cyclic prefix length， the proposed adaptive algorithm could accurately select the optimal system parameters. It effectively mitigated the impact of inter-symbol interference and inter-subcarrier interference in the channel， thereby providing the maximum average spectral efficiency across the entire SNR range. In conclusion， the random forest-based adaptive algorithm for OFDM systems could dynamically allocate the subcarrier spacing and cyclic prefix length， enhance the communication quality and interference resilience of the OFDM system， and enable reliable transmission in different channel environments.