1 INTRODUCTION

Automobiles are becoming smart mobile systems that offer convenience and safety to drivers by providing automated driving, infotainment, and road traffic information instead of simple transportation systems. An automated driving system is classified into four or five stages according to the functions and roles of the vehicle. Various events and situations occurring on a road are detected by mounted‐sensors or delivered by the vehicle‐to‐anything (V2X) communication devices. The traffic information is provided to the driver if necessary or used for autonomous driving 1, 2. Use cases comprising V2X communication technologies and the message formats supporting them are published through standard documents 3-7. Currently, V2X applications are becoming increasingly advanced and diversified as vehicular communication technologies are evolving.

Wireless access in vehicular environments (WAVE) and long‐term evolution (LTE) are the key technologies in V2X communication in the unlicensed 5.9‐GHz band for an intelligent transportation system (ITS) 8. WAVE is a short‐range wireless communication technology that supports V2X in a high‐speed driving vehicle environment and follows the IEEE802.11 standard as well as the IEEE802.11p, which is a physical layer for vehicle radio access. This includes the IEEE1609.x series for security, networking, and multi‐channel support, and this is the only V2X communication technology currently available for deployment 9-12. WAVE technology performance has been verified through several projects and pilot services in the US, Europe, and Korea. 3rd Generation Partnership Project (3GPP) has recently standardized LTE Release 14 for cellular V2X (C‐V2X) technology that is capable of inter‐vehicle communication and continues to standardize 5G technologies to meet the objectives of a peak data rate of 20 Gbps, mobility of 500 km/h, latency of 1 ms, connection density of 106/km2, five times spectrum efficiency, and user experienced data rate of 1 Gbps 13-15.

Recently, as shown in Figure 1, both V2X using WAVE and C‐V2X using LTE have been demonstrated as network solutions for ITS service in the 5.9‐GHz band. The WAVE system uses V2X technology to deliver information between the Road Side Unit (RSU) and vehicle using vehicle‐to‐infrastructure (V2I) and between two vehicles, whereas LTE provides the same service as WAVE through the PC5 interface including vehicle‐to‐pedestrian (V2P) and connects to a cellular network called evolved Node B through the Uu interface using vehicle‐to‐network (V2N). WAVE technology has MAC/PHY (medium access control/physical layer) characteristics that are advantageous for short‐range wireless communication such as ad hoc communication. LTE technology is superior to long‐distance wireless communication owing to its technical features that can restore signals at a lower level. Therefore, it can be stated that WAVE and LTE are mutually complementary. In addition, the technical verification of WAVE technology has been completed through various pilot projects, and WAVE technology is currently in the process of commercialization; however, it has the disadvantage of significantly large infrastructure construction costs. However, LTE can use the existing infrastructure to a certain extent, although the greatest problems are securing technologies until the commercial stage and a lack of verification testing. Currently, the US, Europe, and South Korea have announced the use of the 70‐MHz band from 5.855 GHz to 5.925 GHz for an ITS service, which can cause the problem of adjacent channel interference (ACI) when both WAVE and LTE share this band. The radio frequency (RF) requirements between the two heterogeneous networks should be satisfied to avoid interference between them. The same ACI problem can occur between homogeneous communication systems. This paper presents an analysis of the RF requirements for solving the ACI problems.

Figure 1 Open in figure viewer PowerPoint Coexistence of two V2X communication networks for ITS services

Recently, several studies have been conducted to solve the problem of ACI mainly for the LTE system. Among the studies conducted on ACI within an LTE network, Son and Kim 16 show that an adjacent channel interference ratio (ACIR) of at least 46 dB is required to prevent the occurrence of interference between the uplink channel from a mobile station and the downlink channel from a base station. To secure the reliability between vehicle‐to‐vehicle (V2V) communication devices using LTE, it has been proposed that the interference can be reduced through the scheduling of the resource block, transmission power, and modulation scheme as described in 17. In both wireless local area network (LAN) and LTE environments, the same interference channel problem exists. The throughput capacity is improved by the scheduling algorithm using non‐overlapping channels, power tuning, and partially overlapping channels, as described in 18. Research 19 has been conducted to improve the throughput of the entire network by employing an effective downlink scheduling algorithm when LTE and dedicated short‐range communication (DSRC) networks are used together for V2V and vehicle‐to‐infrastructure (V2I) communications. The ACI model was studied in 20 by simulating the channel interference between the service channel and the control channel according to the number of vehicles in a multichannel vehicular ad hoc networks environment.

By examining studies on ACI between LTE systems and heterogeneous systems, a mutual frame synchronization scheme and a new uplink scheduling scheme that coexist with TD‐LTE and WiMAX systems are proposed in 21. A performance enhancement was demonstrated in 22 using smart antenna beamforming techniques and guard bands to resolve the channel interference between M‐WiMAX TDD and WCDMA FDD systems. Taking into consideration various interference factors, the beamforming schemes for maximizing the data rate and providing the required signal‐to‐noise ratio for high‐mobility users have been studied in 23, 24. In 25, a risk‐informed interference assessment was conducted to solve the frequency‐sharing problem of Wi‐Fi in an unlicensed band. In 26, the authors showed that the appropriate antenna setting and filtering techniques are effective in solving the channel interference problem of a TV receiver caused by the coexistence of a digital video broadcasting‐terrestrial network. In addition, unsolved technical coexistence problems in the 5‐GHz band observed in a network combination scenario analysis are mentioned in 27, including mobile edge computing, latency, and cross‐frequency scheduling issues under a coexistence between LTE and DSRC systems. In the study by Voicu and others 25, the outage probability for the LTE downlink was analyzed using the proposed ACI model when IEEE 802.11C causes ACI to the LTE system.

An accurate ACI model is required when analyzing the performance of ACI through a numerical analysis or simulation. The ACI model derivation and performance analysis has been presented in 16, 23, 28-37. Firstly, in the study of the ACI model using the distribution functions 16, 29, the signal‐to‐interference‐plus‐noise ratio (SINR) equation was obtained by considering the co‐channel and adjacent channel interference signals from multiple terminals using the same LTE network, and the ACI model was derived by using the probability density function (PDF) that was derived from the SINR equation. In order to solve the error problem of the PDF formula using the lognormal distribution when the difference in the distance between the desired path and the interference signal path is large, Kim and otherds 28 presented a method of assigning appropriate weights to each PDF by dividing the radius. In the study by Heath 30, the ACI model is derived using the Poisson point process for co‐channel interference signals using the Gamma distribution with second‐order moment matching. However, there was no study on various communication performance analysis including ACI model and throughput performance in heterogeneous networks between LTE and WAVE networks. Secondly, the ACI model that takes into consideration the channel characteristics was studied. In Kim et al's., study, the channel attenuation characteristics and channel rejection factor were considered for deriving the interference signal model. In 32-34, the leakage power of the adjacent channel was calculated by numerically analyzing the spectral mask characteristics of the transmitter and receiver and the frequency response of the filter for deriving the ACI factor. Lastly, considering the mutual characteristics of heterogeneous networks, 23, 35, 36 applied the ACI model while taking into consideration the interfering time, transmission period, and MAC scheme in the co‐channel and adjacent channels as well as the characteristics of the interference channel. To improve the path loss model as well as the ACI model, 37 proposed the application of the optimal parameters to the path loss model according to the distance between the transmitter and receiver.

Thus far, there has been a lack of ACI research related to the coverage and distribution of the desired and interfering V2X signals in the 5.9‐GHz band for ITS. The communication range changes depending on various use cases and communication environments. Therefore, the interference analysis is important when adjacent channels have various communication ranges. In this study, we analyze the performance based on the communication range, distribution, and RF characteristics of vehicles existing in two adjacent channels. The ACI system modeling is described in Section 2, and the PDF for the system modeling is derived in Section 3. The performance of the V2X communication system with ACI is analyzed in Section 4. Finally, some concluding remarks are presented in Section 5.