# Trade-Off Analysis of Hardware Architectures for Channel-Quality Classification Models
###### Abstract
SDR, with its various components, can help solve these problems, for example, the channel quality, although new architectures must be proposed and evaluated. An important parameter is the Network Channel Quality Indicator (CQI) that is used to cope with channel variations [7]. CQI is modeled mainly with three different types of phenomena: slow fading, multi-path fading, and noise [8; 9]. To achieve better communication, devices must evaluate the quality of the communication channel, enabling the possibility of having fault-tolerant schemes or offering information to change the channel. Furthermore, concerning the provisioning of service quality, the physical level of communications is exposed to cosmic rays, lightning, and temperature changes, among others, which might cause noise and interference in the quality of the channel. Figure 1: An operating environment of communication networks based on SDR. In this way, vehicular communication networks search for driver safety and traffic efficiency on the road by interchanging traffic-related information among vehicles and infrastructures, considering security requirements and efficiency in performance for improving traffic management in the future [10]. Hence, CQI is a critical factor for data exchange. For example, intelligent transportation systems need to exchange data with their neighbors about the position, speed, velocity, acceleration, and brake status, among others [11]. Machine-to-Machine and Human-to-Machine connectivity requires identifying, locating, tracking, monitoring, and controlling [12; 13]; the vehicle-to-home (V2H), vehicle-to-vehicle (V2V), and vehicle-to-grid (V2G) concepts have energy-efficient requirements of power grids [14]; additionally, cooperative systems for infrastructure-to-vehicle communication aim to ensure safe and efficient driving in the increasingly overloaded infrastructure [15]. In these environments related to ad hoc networks, every node inside the network that communicates with each other is responsible for dynamically detecting available nodes by making rapid changes in connectivity [16]. In general, the trend in communication networks is to generate, transmit, store, and process large amounts of data, which can come from users, vehicles, or infrastructure. Specifically, it is essential to integrate systems capable of provisioning service quality to react to this enormous amount of infrastructure data, making an intelligent use of the available resources and leading to the application of Machine Learning (ML), a branch of Artificial Intelligence (AI), to identify and solve communication problems reactively. Hence, SDR has architectures that can be modified through components for examining channel quality and exploring the most suitable model among a wide range of options applied to the current problem: The most accurate models usually are not very explainable (e.g., support vector machines, deep neural networks, boosted trees, and random forests), and the most interpretive models are typically less accurate (e.g., linear or logistic regression) [17]. In addition, for hardware development, ML algorithms are computationally expensive. Several works have studied how to perform trade-offs for a wide variety of applications, such as error-resilient, Sobel edge detector, Finite Inverse Response filter, Discrete Cosine Transform, and with artificial intelligence algorithms [18; 19; 20], among others, always searching to find the best balance between different parameters, i.e., accuracy, and energy-efficiency. Therefore, implementations in platforms such as Graphics Processing Units (GPUs), Field-Programmable Gate Arrays (FPGAs), and Application-specific Integrated Circuits (ASICs) are enforced to support the application of AI and ML algorithms [21], facilitated thanks to the increase in computing power, the number of sensors, available data, transmission bandwidth, latency, and falling costs [22].