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针对大量用户接入网络引发的接入冲突问题，提出一种基于非正交多址接入的动态上行链路随机接入方法。首先，引入自适应资源分配机制，根据网络状态，动态调整时隙资源配置并更新接入等级限制因子。在每一次随机接入结束后，统计空闲时隙数、成功时隙数和冲突时隙数。然后，利用这些参数估计剩余用户数，通过迭代执行上述过程，直至确保所有用户成功接入网络。为验证所提方法的有效性，在Nakagami-m衰落信道环境下，建立完整的理论分析框架，推导出用户接入成功概率和系统吞吐量的闭式表达式。仿真实验结果表明，当网络用户数量K=60时，与传统的动态ACB因子非正交多址随机接入算法（traditional dynamic ACB factor-based non-orthogonal random access algorithm,DNRA）和固定ACB因子正交多址随机接入算法（fixed ACB factor-based orthogonal random access algorithm,FORA）相比，所提方法在系统吞吐量方面分别提升了30.41%和48.22%。此外，通过对比不同用户数下的性能表现，所提方法在接入成功概率方面具有显著优势，且在各种网络负载条件下均能维持较高的系统吞吐量。
Abstract：To address access collisions caused by massive user access in networks, a dynamic uplink random access method based on non-orthogonal multiple access(NOMA) is proposed. The method incorporates an adaptive resource allocation mechanism that dynamically adjusts time slot configurations and updates the access class barring(ACB) factor according to network conditions. After each round of random access, the system records the number of idle, successful, and collided time slots. These statistics are then used to estimate the number of remaining users. The process is iteratively repeated until all users successfully access the network. To evaluate the proposed method, a complete theoretical analysis framework is established under the Nakagami-m fading channel environment, from which closed-form expressions for access success probability and system throughput are derived. Simulation results show that, compared with the traditional dynamic ACB-based NOMA random access algorithm(DNRA) and the fixed ACB-based orthogonal random-access algorithm(FORA), the proposed method improves system throughput by 30.41% and 48.22%, respectively. Furthermore, under varying user loads, the method consistently achieves higher access success probabilities and maintains high system throughput across different network conditions.
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基本信息:
DOI：10.12194/j.ntu.20241226001
中图分类号:TN929.5
引用信息:
[1]杨睛,刘雨芯,高锐等.一种基于非正交多址接入的动态上行链路随机接入方法[J].南通大学学报(自然科学版),2025,24(02):39-47.DOI:10.12194/j.ntu.20241226001.
基金信息:
国家自然科学基金青年科学基金项目（61901408）; 西藏自治区科技重大专项（XZ202201ZD0006G03）; 江苏省科技计划（现代农业）重点项目（BE2023340）; 扬州市科技计划-产业前瞻与关键核心技术项目（YZ2023004）