Edge AI enabled MIMO MC-CDMA for 6G optimizing spectrum and energy efficiency with SIC and deep reinforcement...

The rapid expansion of 6G wireless networks requires high SE techniques to improve spectral efficiency (SE), energy efficiency (EE), and interference. In this paper, an Edge AI MIMO MC-CDMA system with SIC and DRL is proposed for spectrum and energy efficiency. As for the difference from the traditional MIMO-OFDM and hybrid precoding, this paper is based on DRL for adaptive learning, which will show better performance for dense network. The proposed system is implemented using MATLAB to allow for real- time decision- making pertaining the streams' networking parameters at the network edge. DRL makes it possible to ensure interference cancellation, while Edge AI avoids increased computational load and guarantees fast responses. Simulation outcome shows that the proposed model has a spectral efficiency of about 32.7 bits/s/Hz and energy efficiency of 14.8 bits/Joule and thereby surpass than MC-CDMA, MIMO-OFDM and the hybrid precoding-based MIMO systems. Furthermore, the SINR is enhanced to be 34 dB while BER is decreased to 10^-5 to guarantee high transmission reliability. The deep learning-based mechanism effectively interferes the power allocation and enhances the stability of networking. The results fully support significantly optimizes power allocation, improving overall network stability and efficiency for the future 6G wireless network is highly scalable, capable of operating with minimum interference issues and very much energy efficient. Due to the fact that the proposed model can dynamically adjust to the conditions in the network, it conceptualises one approach to a smart future of wireless communication in the next generation applied to ultra-high densities of networks and users.