# Performance of Fuzzy Inference System for Adaptive Resource Allocation in C-V2X Networks
## 1 Introduction  
Advances in automotive network technology have encouraged the standardization of millimeter-wave wireless network technology [1]. The development of wireless technology, especially for vehicles, is divided into two standardizations, namely 802.11 or Wi-Fi technology and cellular network technology. One of the agencies regulating cellular network technology standardization is 3 GPP, where 3 GPP has formulated a Vehicle-to-Everything (V2X) standard that covers various kinds of vehicle communications, such as Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), Vehicle-to-Pedestrian (V2P), and Vehicle-to-Network (V2N) [2]. In terms of an autonomous vehicle (AV), a report [3] explained that integrating current LTE and 5G would unlock various advantages for electric cars and smart cars. The current computational resources could enable AV capabilities, and more technological challenges need to be assessed, from accident prevention to control systems. In the 3 GPP release 14 standards, the Mode 4 communication type is defined as vehicle communication autonomously selecting and managing radio resources. To perform the task of autonomously managing resources, Mode 4 applies a new regulatory mechanism called Sensing-Based Semi-Persistent Scheduling (SB-SPS). The research in [4, 5] describes the analytical model of SB-SPS Mode 4 in Long-Term Evolution (LTE) networks. A report [6] also discusses several parameters at the PHY and MAC layers that can affect the performance of Mode 4. The authors of [7] described the general range limits and blind spots that can occur in V2X communications. Several previous studies have been conducted to further explore the potential and challenges in V2X technology, such as in [8], who tried to apply multiaccess technology to improve vehicle network performance. Research [9] applied Deep-Learning technology to perform resource management in Cellular V2X (C-V2X). Several other studies [10, 11, 12] also discussed resource management methods in V2X technology. A report [13] specifically proposed a solution to reduce the possibility of collisions in C-V2X broadcast communications. Meanwhile, [PERSON]. [14] discussed security countermeasures in C-V2X communication. Still related to the scheme of managing network resources in a vehicular network, [PERSON]. [15; 16] proposed a network slicing technique and path similarity selection to optimize the use of radio resources for future Fifth-Generation (5G) communications. In order to explore the potential of Mode 4 in particular, the authors in [17; 18; 19] proposed solutions of adaptive power management mechanisms and discussed the effects of using message delivery time intervals. A report [20] describes the performance of Mode 4 in congested highway conditions. The application of a Fuzzy Inference System (FIS) is also reported in [21], where dynamic resource management was carried out by applying Fuzzy Matching Learning. An integrated resource management mechanism was also reported in [22], prioritizing a Quality-of-Forwarding (QoF) mechanism in a C-V2X environment. A model of the LTE and 5G radio resource management mechanism using fuzzy detection is also reported in [23], which focuses on the optimal base station allocation mechanism. Because radio resource availability is limited, SB-SPS applies a probability approach (\(P_{rk}\)) to enable the vehicle to autonomously reselect the radio resource slots. A report [24] explains that the \(P_{rk}\) probability approach can affect the performance of V2V and C-V2X Mode 4 communication. Authors [25] proposed an adaptive approach in using \(P_{rk}\) values with the help of CSI information as a consideration in making decisions. In [25], the decision-making method still uses an IF-statement logic approach. Authors [25] observed notable performance improvement by applying the matching table mechanism. However, the authors of [25] still employed a single variable for determining the final \(P_{rk}\) value, which is CSI.