摘 要:在高密度蜂窝车联网(cellular vehicle-to-everything,C-V2X)中,群集通信终端的有效阵列接入方法是多业务性能保障和有限频谱效率提升的前提。利用蜂窝车联网中终端计算能力,提出了信道自适应的业务接入机制。该机制由基站估计当前区域通信密度,生成通信密度关联的接入类别限制(access class barring, ACB)因子,并在通信区域内广播;随后,车载通信终端根据接收基站广播信号的信干噪比(signal to interference plus noise ratio,SINR)和ACB因子计算自适应信道状态的接入概率,并比较接入概率和ACB因子。当接入概率大于ACB因子时,通信终端以最小接入概率从前导码池中随机选择一个前导码上传到基站,以获得与信道状态匹配的接入机会。仿真结果表明,在高密度通信状态下,与S-ALOHA协议和M2M-OSA方案相比,所提方案平均接入碰撞概率降低了约5%~20%,有效地减小了平均接入时延。
Abstract
In high-density cellular vehicle-to-everything (C-V2X)
effective massive access method of cluster communications is the basis to assure the performance index for different services and enhance the spectrum efficiency under limited resource.An improved access class barring (ACB) scheme was proposed using channel status information based on computing power of communication terminal in C-V2X.The base station estimated the communication density within its cover range
generated the ACB factor using the communication density
and broadcasted the ACB.According to signal to interference plus noise ratio (SINR) and ACB factor of the received broadcast signal
the vehicular communication terminal computed the access probability
and compared with ACB factor.When the access probability was more than the ACB factor
the terminal selected a preamble randomly from the preamble pool with the probability which equal to the access probability or one.The terminal transmited the selected preamble to the base station for finishing the access program.The simulation results show that the scheme proposed has 5%~20% advantage over S-Aloha and machine to machine opportunistic splitting algorithm (M2M-OSA) schemes in average access collision probability.It reduces the average access delay effectively.
关键词
Keywords
references
KIM J , DUGUMA D G , ASTILLO P V , et al . A formally verified security scheme for inter-gNB-DU handover in 5G vehicle-to-everything [J ] . IEEE Access , 2021 ( 9 ): 119100 - 119117 .
DONG Z J , GU Y C , LIANG J , et al . Overview on key technology and solution of C-V2X for internet of vehicles [J ] . Telecommunications Science , 2020 , 36 ( 4 ): 3 - 14 .
ZHOU L J , LI Q Q , TU W . An efficient access model of massive spatiotemporal vehicle trajectory data in smart city [J ] . IEEE Access , 2020 ( 8 ): 52452 - 52465 .
TURAN A L , KOSEOGLU M , SEZER E A . Reinforcement learning based adaptive access class barring for RAN slicing [C ] // Proceedings of 2021 IEEE International Conference on Communications Workshops . Piscataway:IEEE Press , 2021 : 1 - 6 .
DUAN H G , LU S P , WANG L F , et al . A congestion control method based on loading-feedback in MTC [J ] . Telecommunications Science , 2016 , 32 ( 11 ): 26 - 31 .
JANG H S , JIN H , JUNG B C , et al . Resource-optimized recursive access class barring for bursty traffic in cellular IoT networks [J ] . IEEE Internet of Things Journal , 2021 , 8 ( 14 ): 11640 - 11654 .
EL TANAB M , HAMOUDA W . Machine-to-machine communications with massive access:congestion control [J ] . IEEE Internet of Things Journal , 2019 , 6 ( 2 ): 3545 - 3557 .
JIAN X , ZENG X P . Traffic modeling and access control for machine type communications:a comprehensive survey [J ] . Telecommunications Science , 2015 , 31 ( 8 ): 133 - 145 .
KIM T , JANG H S , BANG I , et al . Access priority provisioning based on random access parallelization for prioritized cellular IoT [J ] . IEEE Access , 2021 ( 9 ): 111814 - 111822 .
FIRDAUS K F , WIBOWO S A , ANWAR K . Multiple access technique for IoT networks serving prioritized emergency applications [C ] // Proceedings of 2019 IEEE 89th Vehicular Technology Conference . Piscataway:IEEE Press , 2019 : 1 - 5 .
MIUCCIO L , PANNO D , RIOLO S . Joint control of random access and dynamic uplink resource dimensioning for massive MTC in 5G NR based on SCMA [J ] . IEEE Internet of Things Journal , 2020 , 7 ( 6 ): 5042 - 5063 .
AL KHANSA R , ARTAIL H A , ASSAAD M , et al . A Hybrid Scheduled and group-based random access solution for massive MTC networks [J ] . Computer Networks , 2020 , 50 ( 176 ): 107253 .
YANG A N , LI B , YANG M , et al . Group-based uplink OFDMA random access algorithm for next-generation WLAN [J ] . Journal of Northwestern Polytechnical University , 2020 , 38 ( 1 ): 155 - 161 .
HE H L , DU Q H , SONG H B , et al . Traffic-aware ACB scheme for massive access in machine-to-machine networks [C ] // Proceedings of 2015 IEEE International Conference on Communications . Piscataway:IEEE Press , 2015 : 617 - 622 .
IDE C , DUSZA B , WIETFELD C . Client-based control of the interdependence between LTE MTC and human data traffic in vehicular environments [J ] . IEEE Transactions on Vehicular Technology , 2015 , 64 ( 5 ): 1856 - 1871 .
TELLO-OQUENDO L , VIDAL J R , PLA V , et al . Dynamic access class barring parameter tuning in LTE-A networks with massive M2M traffic [C ] // Proceedings of 2018 17th Annual Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net) . Piscataway:IEEE Press , 2018 : 1 - 8 .