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Orthogonal Frequency Division Multiplexing (OFDM), as the core technology in mobile communications, is a multi-carrier modulation technology with high frequency spectrum utilization, which has strong anti-multipath interference and anti-fading ability. The significant advantage of OFDM signals lies in the anti-multipath effect, so its application environment is mostly multipath fading channels. Therefore, it is of great significance to study the identification of OFDM signals in multipath channels. Deep learning, with superior big data processing and classification capabilities, is a potential solution to these problems. Based on the problem of OFDM signal recognition in complex signals in multi-path channel, an OFDM signal recognition method based on hybrid grey wolf optimization algorithm to optimize deep neural network model is proposed. Because the basic grey wolf optimization algorithm (GWO) is easy to fall into a stasis state when attacking prey, differential evolution algorithm (differential evolution algorithm) is integrated into GWO to force GWO to jump out of the stasis state with its strong search ability. The convergence speed and recognition performance of the proposed algorithm are greatly improved. The experimental results show that under the condition of low SNR, the recognition accuracy of proposed algorithm is 9.95&#x0025; higher than the traditional DNN method, and nearly 4.5&#x0025; higher than the other two intelligent optimization methods, and the values of <italic>Precision</italic> and <italic>Recall</italic> increase obviously, which indicates that the hybrid algorithm not only improves the accuracy of recognition, but also makes the search more complete and accurate. Compared with classical particle swarm optimization (PSO) and whale algorithm optimization algorithm (WOA), the hybrid algorithm has strong competitiveness both in recognition performance and optimization stability, which provides a new, simpler and more effective method for modulation recognition of OFDM signals in wireless communications.

Improved Deep Neural Network for OFDM Signal Recognition Using Hybrid Grey Wolf Optimization