# Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review
## 4 Throughput Maximization 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
[42] & replicator dynamics to solve evolutionary game model & stable convergence in presence of time delay & dependent on information exchange among BSs and PON & guaranteed QoS \\ \hline
[51] & volume adjustable water filling-dynamic programming & able to adapt different coverage, blockage, and power conditions & UE high mobility scenario is not considered & throughput \\ \hline
[52] & deficit DBA and QoS parameter tuning for voice, video and best effort traffic models & guarantees high throughput for prioritized traffic and achieves fairness for low priority traffic & DBA algorithm has exponential time complexity, therefore not suitable for populated scenarios & QoS, average throughput, fairness \\ \hline
[53] & two heuristics for joint beam selection and user association & the algorithm converges with low number of iterations & user association conditions are simplified as extensive search leads to large delays & spectrum efficiency \\ \hline
[54] & interleaved polling with adaptive cycle time DBA scheme & improves bandwidth utilization, less average delay for high traffic load & solution depends on the reporting time between radio and optical nodes & bandwidth utilization \\ \hline
[43] & game based stochastic DBA & keeps fairness with more throughput and less delay for populated ONUs & not suitable for fully distributed scenarios as loaded balancing strategies of players are selected centrally & goodput, fairness \\ \hline
[55] & three iterative algorithms for fronthaul and access link optimization & improves QoS achievable data rate & the solution is convergent but has polynomial complexity & aggregate arrival rate \\ \hline
[56] & five matching theory based scheduling algorithms & achieves the optimal max min throughput performance for network & an approximation algorithm cannot be used under mutual interference & max–min throughput \\ \hline
[57] & Lagrange duality-based optimal algorithm and a greedy search-based heuristic & maximizes the weighted sum rate of all users and outperforms centralized scheduling & complexity increases exponentially with increasing number of RRHs, including the greedy heuristic & sum rate \\ \hline
[58] & differential evolution algorithm & higher achievable sum-rates for varying number of antennas and increasing number of RRHs & increasing user number affects the sum-rate negatively due to the increasing interference & spectrum efficiency (\(\text{pps}/\text{Hz}\)) \\ \hline
[59] & user selection strategy based on fuzzy clustering & reduces interference to achieve total data rate & complexity increases exponentially with the number of users & total data rate \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Analysis of resource allocation papers with throughput maximization objective. link throughput in a FiWi mmWave network. In [55], the downlink data arrival rate is maximized for a mmWave small cell cloud radio access network (C-RAN) with free space optical fronthaul between RRHs and baseband units (BBUs). A resource optimization solution with Lagrangian dual decomposition is proposed in which the sub-problems are iteratively solved with a combination of separate optical fronthaul beam selection, fronthaul link selection, access link power allocation, and UE-RRH association algorithms. Interference reduction is a common objective for throughput maximization in mmWave networks, and ML-based beamforming is used in [56; 59] to solve the traditional interference reduction problems. A hybrid beamforming design based on joint spatial division and multiplexing and fuzzy c-means (FCM) clustering is proposed in [59]. FCM gives membership grades to UEs so that they can belong to several clusters.