# Trade-Off Analysis of Hardware Architectures for Channel-Quality Classification Models
###### Abstract  
The latest generation of communication networks, such as SDVN (Software-defined vehicular network) and VANETs (Vehicular ad-hoc networks), should evaluate their communication channels to adapt their behavior. The quality of the communication in data networks depends on the behavior of the transmission channel selected to send the information. Transmission channels can be affected by diverse problems ranging from physical phenomena (e.g., weather, cosmic rays) to interference or faults inherent to data spectra. In particular, if the channel has a good transmission quality, we might maximize the bandwidth use. Otherwise, although fault-tolerant schemes degrade the transmission speed by solving errors or failures should be included, these schemes spend more energy and are slower due to requesting lost packets (recovery). In this sense, one of the open problems in communications is how to design and implement an efficient and low-power-consumption mechanism capable of sensing the quality of the channel and automatically making the adjustments to select the channel over which transmit. In this work, we present a trade-off analysis based on hardware implementation to identify if a channel has a low or high quality, implementing four machine learning algorithms: Decision Trees, Multi-Layer Perceptron, Logistic Regression, and Support Vector Machines. We obtained the best trade-off with an accuracy of 95.01% and efficiency of 9.83 Mbps/LUT(LookUp Table) with a hardware implementation of a Decision Tree algorithm with a depth of five. machine learning; channel quality classification; hardware implementation; FPGA 202  
the QoS. The controller logic is implemented in the server as a software component, while the data plane logic is executed in the networking devices such as switches and routers [1], see Figure 1. The data plane forwards the packets to the appropriate destination, and one of these networks can be seen as an architecture that controls the network devices and controls the entire network. In this way, we can implement an SDR (Software Defined Radio) to carry out the programmability in the data plane, and an important application is to enable more efficient spectrum utilization through opportunistic use of the spectrum. Current SDR technology may act as an enabler, allowing researchers to design and develop new communication network technologies with special requirements focused on a software definition and allowing a cognitive radio to configure the transmission parameters of a device dynamically according to the environment in which it operates [1; 2]. In this sense, in Vehicular Ad hoc Networks, the open problem is how to evaluate channel quality to change the communication channel or modify its functions according to the conditions. If there is low quality, then they have to use an error-correction module or better modules in the radio hardware or other algorithms in the processor for fault tolerance. The architecture of this network must integrate different types of networks to provide a platform for communications within the infrastructure and vehicle-infrastructure communications. This architecture allows the creation of various kinds of networks such as V2V (vehicle-to-vehicle), V2I (vehicle-to-infrastructure), V2X (vehicle-to-all), V2P (vehicle-to-pedestrian), V2H (vehicle-to-home), V2B (vehicle-to-buildings), V2C (vehicle-to-the cloud), and 5G/6G [3; 4; 5]. The operating environment or the integrated applications environment of these communication networks carries many challenges, such as intermittent connectivity, the provisioning of quality of service (QoS), and the heterogeneity of applications when a vehicle is in motion or immobile [6]. Nevertheless, we expect those types of communication networks to continue working, offering different multimedia services such as web navigation, streaming, social networks, and routine operations of the vehicle.