This study analyses Spectral Efficiency (SE) and throughput under varying user densities (50 to 200 users), mobility velocities (0 to 250 km/h), latency, packet loss, and fairness index at diverse Signal-to-Noise Ratio (SNR) levels for different scenarios. These scenarios encompass comprehensive massive Multiple-Input Multiple-Output (mMIMO) cooperative Non- Orthogonal Multiple Access (NOMA), mMIMO cooperative NOMA integrated with Cognitive Radio (CR), and CR-enabled mMIMO cooperative NOMA facilitated by Reconfigurable Intelligent Surfaces (RIS) using millimetre-Wave (mmWave) in 6G networks. The study investigates the enhancement of latency, packet loss, and fairness indexes in the proposed systems through a unique approach that dynamically optimizes power distribution via a Q-learning algorithm. The mathematical clarification of each equation offers a comprehensive understanding of signal reception by users, the dynamics and implications of CR, and the influence of intelligent RIS optimization on system performance. The findings demonstrate that the incorporation of RIS enhances resource allocation, improves user performance in high-density settings, increases average throughput, reduces latency and packet loss, and raises the fairness index by mitigating interference and optimizing channel access, particularly when employing the proposed optimization algorithm. These results support the advancement of scalable and efficient communication networks in the realm of 6G technology.

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