Signal transmission optimization of intelligent metasurface-assisted communication system based on improved DQNN

At present, signal transmission in mobile communication is susceptible to multipath effects and interference, causing a decrease in spectral efficiency and an increase in energy consumption. However, existing methods cannot effectively synergistically optimize beamforming and power allocation. To address this issue, this paper constructs an intelligent metasurface-assisted communication system signal transmission optimization model based on I-DQNN. This model uses dual intelligent metasurface-assisted base stations as the communication architecture core, and combines GCNN and QNN to achieve channel estimation and beamforming optimization. This study introduces Squeeze-and-Excitation Networks, gradient constraints, and parameter sharing mechanisms to optimize and improve the model. In practical applications, the error rate, communication energy efficiency, average latency, and system throughput of the research method were 0.11%, 4.45 bit/Hz/J, 3.05 ms, and 1005.95 Mbps. During its operation, the system failure rate remained below 0.17%, and the number of communication interruptions was only 17 times. The proposed model can effectively enhance the stability and energy efficiency of signal transmission in complex channel environments, significantly reduce bit error rates and delays, and achieve efficient utilization of spectrum resources in high-density user scenarios.