rmulated as a stochastic optimization problem and solved by using Lyapunov framework with the drift-plus-penalty method. In [82], a two-step energy efficiency algorithm for H-CRAN was proposed based on the underlaid and derived by using Dinkelbach, Lagrange multiplier, and alternating direction method of multiplier methods. This algorithm was found to outperform earlier research works in terms of energy efficiency. In [83], an energy efficient maximization algorithm was proposed for F-RAN. The maximization problem has been solved by using augmented Lagrangian with a heuristic solution to reduce the computational complexity. This algorithm was found to achieve a comparable energy efficiency result to AI algorithms. In [84], a joint optimization energy efficiency algorithm was proposed for O-RAN and solved as mixed-integer linear problem (MILP). The proposed algorithm was found to have low energy consumption at large network size [85-87] Spectrum In [85], an algorithm to optimize the spectrum pooling in management C-RAN was addressed. In [86], a cooperated HetNet and C-RAN architecture was deployed, and a user-weighted probability-based algorithm was proposed in order to utilize the spectrum efficiently and manage the interference. In [87], spectrum pricing and allocation scheme was proposed for Fog-RAN by considering a Stackelberg game. The proposed scheme was found to improve the utilization of the network resources including spectrum (continued) M. Alfaqawi et al. Table 1.4 (continued) Reference Challenge Methodology and outcomes [6, 88] and Security In [6], the security threats and attacks of the physical, control, and service planes in C-RAN have been comprehensively reviewed including challenges, methods, solutions, and research works. In [88], a network coding scheme was developed and implemented within H-CRAN in order to increase the transmission security by reducing the probability of data theft over multipath. In [89], a MACsec standard security protocol was integrated with O-RAN to secure the fronthaul d