一种基于随机森林的OFDM系统自适应算法

Random Forest-Based Adaptive Algorithm for OFDM System

针对动态变化的信道环境,自适应正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)系统可以对子载波间隔和循环前缀长度进行调整,以最大化系统的吞吐量。为了能够快速准确地找到OFDM系统在不同信道环境中的最优子载波间隔和循环前缀长度取值,本文提出了基于随机森林的OFDM系统自适应算法。随机森林算法基于集成的思想,能够有效处理高维度数据,并且具有高效率、高准确率和强泛化能力等优势,可以在复杂的数据场景下进行有效的分类。通过提取通信过程中信噪比、用户移动速度、最大多普勒频率和均方根时延扩展等信道特征与OFDM系统的子载波间隔和循环前缀长度组成训练样本,利用随机森林算法创建了OFDM系统参数多分类模型。所提模型可以根据输入的信道特征,实现OFDM系统子载波间隔和循环前缀长度的自适应分配。同时,针对训练样本主要集中在少数几个系统参数类别的情况,利用合成少数类过采样技术对较少样本数的类别进行扩充,满足了随机森林算法对训练样本类别平衡化的需求,进一步提高了算法的分类准确率。相比传统的自适应算法,所提算法具有更高的分类准确率和模型泛化能力。分析和仿真结果表明,与子载波间隔和循环前缀长度固定的OFDM系统相比,本文所提出的自适应算法能够准确选择出最优的系统参数,可以有效地减轻信道中符号间干扰和子载波间干扰的影响,从而在整个信噪比范围上提供最大的平均频谱效率。基于随机森林的OFDM系统自适应算法能够动态地分配子载波间隔和循环前缀长度,增强OFDM系统的通信质量和抗干扰能力,实现在不同信道环境下的可靠传输。

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 combin‐ing them with the subcarrier spacing and cyclic prefix length of the OFDM system.The proposed model could adaptively allo‐cate 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 capa‐bility.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.