# Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review
## 6 Energy Efficiency and Resource Allocation Algorithms
### Selected Papers
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Optimization Algorithm** & **Advantages** & **Limitations** & **Performance Metrics** \\ \hline
[79] & heuristic algorithm to solve network formation game & optimizes energy under traffic load and the energy consumed in sleep mode & fairness aspect is not considered for protection of the less demanding nodes & EE gain (Joules/s) \\ \hline
[44] & one-to-many matching game between users and subcarriers & maximizes EE gain and sum rate for both microwave and mmWave bands & the solution has polynomial time complexity & EE gain, sum rate \\ \hline
[80] & iterative algorithm with Dinkelbach method & improves EE by simultaneously assigning a subcarrier to multiple users & the solution assumes perfect channel state information & downlink EE, sum rate \\ \hline
[81] & SDN application monitors and estimates energy consumption & achieves minimum power for different arrival rates & the power gain diminishes with a high flow arrival rate & power consumption \\ \hline
[45] & iterative algorithm solves a non-cooperative transmission power game & optimizes after a few iterations, has a memory factor to overcome estimation errors & delay of exchanging information among BSs is not considered & per spectrum EE, power consumption \\ \hline
[77] & iterative solution with alternating descent algorithm & jointly solves minimization of the total power and maximization of minimum rate for each user & EE decreases with the increased minimum rate for users & achievable EE (bits/Joule) \\ \hline
[46] & two layered game with frequency assignment and power allocation & joint EE and SE maximization under increasing number of users and BSs & complexity analysis and possible complexity reduction options are not provided & EE (revenue per cost) \\ \hline
[82] & a matching heuristic and a user reallocation heuristic & user association successful in distributing high and low data rate & multi-connectivity option is not considered for users & total power consumption \\ \hline
[78] & iterative algorithm with Lagrangian dual decomposition & proposed algorithm requires low fronthaul bandwidth for a given EE & does not consider any interference between the macro BS and the RRHs & EE gain (bits/Joule) \\ \hline
[49] & adaptive ant colony optimization & avoids frequent deactivation and reactivation of lightpaths for new traffic requests & The complexity of the algorithm increases exponentially with the number of nodes in the network & total power consumption \\ \hline
[83] & alternating direction method of multipliers algorithm & minimizes energy consumption consumed computing and traffic transmission & high resource demanding multimedia applications are not considered for optimization & energy consumption \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Analysis of resource allocation papers with energy-efficiency objective. throughput gain maximization under varying number of users [44; 46; 80]. The coexistence of microwave and mmWave leads to a better performance in terms of both sum rate and EE when compared to mmWave and microwave only networks with a one-to-many matching game for frequency band selection in [44]. Downlink resource allocation is investigated for a non-standalone network with macrocell and small cells in [46], and a two layered hierarchical game approach is used for modeling the problem. A non-cooperative frequency assignment game is designed for small cells in the first layer and a power and subcarrier allocation via joint maximization of the revenue per cost based EE and spectral efficiency (SE) in the second layer. In [80], a joint access and fronthaul radio resource allocation method is proposed for downlink dual connectivity mode of a power domain non orthogonal multiple access-based C-RAN system with mmW and microwave carriers. The network paradigm shift towards softwarized solutions provides novel options to implement EE algorithms in the optical and radio networks, as seen from the SDN-based solutions proposed in [49; 81; 83].