Distributed congestion control (DCC) improves system performance by lowering channel congestion in vehicular environments with high vehicle density. The 3rd Generation Partnership Project standard defines the related metrics of channel busy ratio (CBR) and introduces possible rate and power control mechanisms to mitigate channel congestion in cellular vehicle-to-everything (C-V2X) sidelink. However, the DCC of C-V2X is not sufficiently specified to implement these controls. In this letter, we propose a novel DCC algorithm based on deep reinforcement learning (DRL) to improve congestion control performance in C-V2X sidelink. The proposed algorithm allows the DRL agent to observe a CBR state and select the packet transmission rate that can maximize the reward of packet delivery rate (PDR) while maintaining higher channel utilization. Simulation results show that the proposed algorithm provides performance gain in terms of PDR and sidelink throughput compared with the existing DCC method.

Recently, research into autonomous driving and traffic safety has been drawing a great deal of attention. To realize autonomous driving and solve traffic safety problems, wireless access in vehicular environments (WAVE) technology has been developed, and IEEE 802.11p defines the physical (PHY) layer and medium access control (MAC) layer in the WAVE standard. However, the IEEE 802.11p frame structure, which has low pilot density, makes it difficult to predict the properties of wireless channels in a vehicular environment with high vehicle speeds; thus, the performance of the system is degraded in realistic vehicular environments. The motivation for this paper is to improve the channel estimation and tracking performance without changing the IEEE 802.11p frame structure. Therefore, we propose a channel estimation technique that can perform well over the entire SNR range of values by changing the method of channel estimation accordingly. The proposed scheme selectively uses two channel estimation schemes, each with outstanding performance for either high-SNR or low-SNR signals. To implement this, an adaptation algorithm based on a preamble is proposed. The preamble is a signal known to the transmitter–receiver, so that the receiver can obtain channel estimates without demapping errors, evaluating performance of the channel estimation schemes. Simulation results comparing the proposed method to other schemes demonstrate that the proposed scheme can selectively switch between the two schemes to improve overall performance.

Affinity propagation (AP) clustering with low complexity and high performance is suitable for radio remote head (RRH) clustering for real-time joint transmission in the cloud radio access network. The existing AP algorithms for joint transmission have the limitation of high computational complexities owing to re-sweeping preferences (diagonal components of the similarity matrix) to determine the optimal number of clusters as system parameters such as network topology. To overcome this limitation, we propose a new approach in which preferences are fixed, where the threshold changes in response to the variations in system parameters. In AP clustering, each diagonal value of a final converged matrix is mapped to the position (x,y coordinates) of a corresponding RRH to form two-dimensional image. Furthermore, an environment-adaptive threshold value is determined by adopting Otsu’s method, which uses the gray-scale histogram of the image to make a statistical decision. Additionally, a simple greedy merging algorithm is proposed to resolve the problem of inter-cluster interference owing to the adjacent RRHs selected as exemplars (cluster centers). For a realistic performance assessment, both grid and uniform network topologies are considered, including exterior interference and various transmitting power levels of an RRH. It is demonstrated that with similar normalized execution times, the proposed algorithm provides better spectral and energy efficiencies than those of the existing algorithms.

Due to the significant increase in the use of autonomous car technology, it is essential to integrate this technology with high-precision digital map data containing more precise and accurate roadway information, as compared to existing conventional map resources, to ensure the safety of self-driving operations. While existing map technologies may assist vehicles in identifying their locations via Global Positioning System, it is however difficult to update the environmental changes of roadways in these maps. Roadway vision algorithms can be useful for building autonomous vehicles that can avoid accidents and detect real-time location changes. We incorporate a hybrid architectural design that combines unsupervised classification of vision data with supervised joint fusion classification to achieve a better noise-resistant algorithm. We identify, via a deep learning approach, an intelligent hybrid fusion algorithm for fusing multimodal vision feature data for roadway classifications and characterize its improvement in accuracy over unsupervised identifications using image processing and supervised vision classifiers. We analyzed over 93,000 vision frame data collected from a test vehicle in real roadways. The performance indicators of the proposed hybrid fusion algorithm are successfully evaluated for the generation of roadway digital maps for autonomous vehicles, with a recall of 0.94, precision of 0.96, and accuracy of 0.92.

3GPP 5G NR(New Radio)-V2X(Vehicle-to-Everything) 기반으로 자율주행 차량의 센싱 음영 영역까지 인지를 확장할 수 있도록 지원하는 SDSS(Sensor Data Sharing Service)와 SDSM(Sensor Data Sharing Message)가 표준화되고 있다. 본 논문은 RSU(Road Side Unit)가 주변 CV(Connected Vehicle)의 동적 상태 정보를 포함하는 BSM(Basic Safety Message)를 수신 및 융합하여 SDSM을 생성하고, RSU가 CAV(Connected Automated Vehicle)에게 SDSM을 브로드캐스팅하는 센서 정보 공유 시스템을 제안한다. 또한, 제안하는 시스템에 대한 5G NR-V2X 기반의 시스템 레벨 시뮬레이션을 통해 제안 시스템이 수행하는 SDSS를 통해 차량의 동적 상태 정보를 공유할 수 있는 공간적인 범위를 기존 LTE-V2X 기반 BSM 전송 방식보다 확장할 수 있음을 보인다.