# Cross-Layer Methods for Ad Hoc Networks--Review and Classification
## 3 Cross-Layer Method Publications Review
### Multiple Layers
#### 3.2.1 External Entity Multiple Layers Control and Optimization  
External entities can control and optimize multiple layers. This approach is consistent with (inherited from) the \"CrossTalk\" architecture [27]. External entity control is divided into fuzzy logic-based methods and dual decomposition optimization-based methods. _External entity multiple layers control and optimization (fuzzy logic)_  
Fuzzy logic-based cross-layer methods use a set of rules on how to transform a set of input metrics from different layers into output metrics for tuning the layers. The input metrics are converted into classes depending on how the metrics value space is partitioned into ranges. The output of the fuzzy logic-based system is the classes of metrics, which are converted into parameter values. Most of the conversion rules in publications are chosen empirically by the authors (Table 11). [PERSON] et al. [97] propose the use of information from physical, channel, and application layers (vehicle traffic speed to account for signal fading due to the Doppler effect, average line transmission delay, probability of successful packet delivery in the line) as inputs to the fuzzy logic system. The system outputs correction factors to specify the type of modulation and error-correcting codes, transmission power, maximum number of retransmissions, and rate of packet stream creation at the application layer. [PERSON] et al. [98] propose a generalized method to evaluate the usefulness of nodes in selecting the next node for forwarding. The authors use fuzzy logic. Nodes may have many metrics, but the variance of some metrics among the set of nodes, among which the next node for forwarding is selected, may be different. If some metric does not differ much between nodes, less attention can be paid to it. Metrics normalized with respect to the mathematical expectation of the variance of metrics are fed into the fuzzy logic evaluator, and the evaluator outputs the weights of metrics. Then, the summation of metrics with weights yields the utility value of the node for forwarding. The node with the maximum value for forwarding is selected by the next node. The proposed method is suitable for the metrics of all layers. [PERSON] et al. [99] propose a cross-layer approach based on fuzzy logic. The fuzzy logic evaluator uses end-to-end transport layer delay, application layer packet delivery probability and physical layer transmission speed as inputs, and monitors parameters such as transmission power and signal-code constructions of the physical layer, number of channel layer retransmissions, number of network layer route hops and application layer transmission rate. The three input parameters are assigned three values (low, medium, and high) within the fuzzy logic framework. As a result, nine combinations of parameter values are obtained, and these nine values are matched with rules that set the output parameters in nine different ranges. _External entity multiple layers control and optimization (dual decomposition)_  
The dual decomposition can be used to solve the problem of finding the optimal parameters of network operation. Optimization constraints are the values of data flow volumes between pairs of nodes, available signal-code constructions of transceivers and their \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Publication** & **Year** & **Citations** & **Layers** & **Goal** \\ \hline
[97] & 2006 & 31 & 1, 2, 5 & line latency, line throughput, energy consumption \\
[98] & 2018 & 22 & 1, 2, 3, 4, 5 & route packet error rate \\
[99] & 2022 & 10 & 1, 2, 3, 4, 5 & route throughput, route packet error rate, route latency \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Cross-layer methods concentrated on multiple-layer optimization with fuzzy logic. transmission speeds, and parameters of MIMO antennas.