# Trade-Off Analysis of Hardware Architectures for Channel-Quality Classification Models
## 4 Methods
### System Model  
SDR offers many advantages that networks such as SDVN, SDN, V2V, and V2I can exploit since SDR is a complete system, and each base station accesses the radio functions, where hardware devices with reconfiguration are important elements. These technologies must enable solving disadvantages that SDR cannot solve, such as scheduling problems, resource allocation, troubleshooting, load balancing, and security. In this way, our work is focused on the fact that if we have a communication network technology such as SDVN based on SDR, then we can evaluate and measure different characteristics to determine the quality of the channel so that this type of system can adjust security, resource management, improvements in the use of bandwidth, and power consumption, among others.  
This work used the database Eurecom/elasticmon5G2019 for SDR to evaluate the channel quality. Our proposal can be implemented as an OBS (On-Board system) integrated with the vehicles that operate in networks such as SDVN or VANET, see Figure 3. We remark that the devices that integrate an SDVN or VANET included in the system model are affected by noise and interferences that decrease the Quality of Service of the channel transmission. In addition, our proposal is evaluated in FPGA. Still, it is important to mention that there are Commercial Reconfigurable Processors [43] or Systems on Chip integrating FPGA [44; 45; 46; 47] and FPGAs that have been integrating Processing Cores for years. Therefore, it is possible to integrate our solution in vehicular systems as well as in industrial, business, or home systems, as we show in Figure 3.  
Figure 2: Proposed radio system based on SDR and the ML/DL Co-processor.  
We implemented four hardware architectures based on Decision Trees, MLP, Logistic Regression, and SVM for CQI classification. We selected these algorithms due to the following advantages: Decision trees do not require feature scaling (standardization and normalization) since they are based on rules instead of distance calculation. Neural networks are robust against noise since they are formed by individual units called neurons working in parallel, allowing correct functioning even if some unit fails. In addition, Neural networks capture optimal and effective complex characteristics, obtaining results with high precision. Logistic regression is easy to implement, interpret, very efficient to train, and is less inclined to over-fitting. SVM is effective in high dimensional spaces and uses a subset of training points, being memory efficient.  
Therefore, it is crucial to identify the common operations involved in the four machine learning algorithms and implement them in modules to reutilize each one. This section first introduced the standard components and how they are implemented. We describe the decision trees' architecture based entirely on comparators; analogously, the decision tree architecture is also based on an algorithm formed of if-else nested instructions. Multipliers, adders, comparators, and the modules of activation functions were used for the different layers form the MLP architecture, which is the most complex implementation. Finally, Logistic Regression and SVM are based on the re-utilization of the modules described. The data are represented using the standard IEEE 754 for Floating-Point Representation of 32 bits [47].