Multi-Band and Multi-Network Cooperative Transmission Algorithm for Wireless Resource Optimization

The emergence of various high bandwidth services has led to increasing demands for data transmission speed. Nowadays, a single network can no longer meet the diverse business needs of customers at any time. Therefore, utilizing wireless networks to achieve multi-path parallel transmission is an important approach. A multi-path parallel transmission control method is proposed to avoid the congestion and chaos of the receiver, so as to improve the transmission efficiency. Reinforcement learning is used to intelligently select the size and redundancy of code packets to overcome data packets chaos. Meanwhile, considering that network transmission services may become paralyzed after congestion occurs, this study also proposes a Q-learning-based congestion control algorithm. These results indicate that the network resource optimization algorithm proposed by this research can dynamically adjust the length of packets and redundant packets within packets based on network conditions, making it suitable for various network situations. The adaptive linear network coding method is usedtoget a larger buffer space faster, which takes 8 seconds. The congestion control algorithm proposed by the research can maintain a bandwidth utilization rate of nearly 99% while maintaining a high transmission rate. This shows that the proposed linear network coding transmission method can effectively improve the problem of packet disorder in multi-user information fusion and improve the efficiency of multi-user information fusion. A new network congestion control method proposed by the research can ensure the rapid recovery of the network after congestion while ensuring the transmission quality of the network.