# V2X Communications in Highway Environments: Scheduling Challenges and Solutions for 6G Networks
## 6 Conclusions and Future Work  
The highway network configuration is a less explored topology than urban and city scenarios. The main highway traffic characteristic of high speed can lead to delayed data transmission and processing, reducing the effectiveness of V2X UCs, as a delay of even milliseconds can be crucial in preventing accidents. Managing traffic data flow relies on accurate and timely information from many vehicles under dynamic conditions. Highway traffic conditions can change rapidly due to sudden stops, construction zones, or accidents, so inconsistent or outdated data can lead to ineffective traffic management strategies, exacerbating congestion rather than alleviating it. Advanced UCs like ToD demand low-latency communication, which can be affected by factors such as signal loss or interference and therefore leads to safety risks, as misjudged distances or speeds can cause collisions. The efficient managing, processing, and interpreting of data related to V2X UCs is critical as inadequate data handling can lead to slow responses to critical events, reducing the overall effectiveness of V2X applications.  
To overcome these challenges, the paper highlighted the performance of the SOVANET+ scheduler. The new scheduler addresses V2N communication for delay-critical automated driving services of a high SAE automation driving level in highway environments. The distinction between the PF and other suggested schemes for autonomous driving can be summarized in three main aspects:  
* SOVANET+ adopts a novel metric that facilitates vehicles with high and moderate MCS.
* SOVANET+ acknowledges the overall network load and adjusts the resources so that the performance on the UL and the DL is equally balanced.
* SOVANET+ overprovides resources for delay-critical services while reducing resources for non-delay-critical services and temporarily declines the provision on vehicles under poor channel conditions.  
Based on the extensive simulation results presented, SOVANET+ has been shown to outperform both the baseline PF and the SOVANET scheduler. SOVANET+ provides greater gains on both the UL and DL cases for delay-critical and non-critical UCs deployed in a highway scenario.  
The advent of 6G networks is expected to revolutionize wireless communications introducing a new era of connectivity. Future networks are anticipated to provide ultra-high data rates, ultra-low latencies, massive connectivity, and immersive services, whilst functioning in a denser radio access network environment, characterized by high speeds, dynamic channel conditions, and diverse QoS requirements. To tackle these challenges, the next step in our work is using packet scheduling techniques that leverage machine learning and artificial intelligence. Additionally, we strive to design predictive fast handover protocols enabling seamless handovers.  
Conceptualization, A.K. ([PERSON]); methodology, A.K. ([PERSON]); software, A.K. ([PERSON]); validation, A.K. ([PERSON]) and A.K. ([PERSON]); formal analysis, A.K. ([PERSON]); investigation, A.K. ([PERSON]); data curation, A.K. ([PERSON]); writing--original draft preparation, A.K. ([PERSON]); writing--review and editing, A.K. ([PERSON]); visualization, A.K. ([PERSON]); supervision, A.K. ([PERSON]); project administration, A.K. ([PERSON]). All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author due to ongoing research.  
The authors declare no conflicts of interest.