论文标题
NOMA光学无线网络中的随机线性网络编码
Random Linear Network Coding in NOMA Optical Wireless Networks
论文作者
论文摘要
光学无线通信(OWC)有可能提供高通信速度,以支持在不久的将来预期的大量使用Internet。在OWC中,将光接入点(AP)部署在电池上,以服务于多个用户。在这种情况下,需要有效的多个访问方案才能在用户之间共享资源并对齐多用户干扰。最近,已经研究了非正交多访问(NOMA),以同时使用相同的资源为多个用户提供服务,而将不同的功率级别分配给每个用户。尽管NOMA的表现可接受,但由于使用连续的干扰取消(SIC),用户可能会因高噪声而遭受高包装损失。在这项工作中,提出了随机线性网络编码(RLNC),以增强光学无线网络中NOMA的性能,在该网络中,将用户分为多播组,并且每个组都包含频道增长略有不同的用户。此外,在这些群体中考虑了固定的功率分配(FPA)策略,以避免复杂性。根据数据包成功概率评估了建议方案的性能。结果表明,与其他基准方案(例如传统的NOMA和正交传输方案)相比,所提出的方案更适合考虑的网络。此外,在所有情况下,分配给每个组的功率水平的数据包成功概率都受到了高度影响。
Optical wireless communication (OWC) has the potential to provide high communication speeds that support the massive use of the Internet that is expected in the near future. In OWC, optical access points (APs) are deployed on the celling to serve multiple users. In this context, efficient multiple access schemes are required to share the resources among the users and align multi-user interference. Recently, non-orthogonal multiple access (NOMA) has been studied to serve multiple users simultaneously using the same resources, while a different power level is allocated to each user. Despite the acceptable performance of NOMA, users might experience a high packet loss due to high noise, which results from the use of successive interference cancelation (SIC). In this work, random linear network coding (RLNC) is proposed to enhance the performance of NOMA in an optical wireless network where users are divided into multicast groups, and each group contains users that slightly differ in their channel gains. Moreover, a fixed power allocation (FPA) strategy is considered among these groups to avoid complexity. The performance of the proposed scheme is evaluated in terms of total packet success probability. The results show that the proposed scheme is more suitable for the network considered compared to other benchmark schemes such as traditional NOMA and orthogonal transmission schemes. Moreover, the total packet success probability is highly affected by the level of power allocated to each group in all the scenarios.