光学MIMO-NOMA-VLC单载波传输实验验证

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"这篇研究论文探讨了光学MIMO(多输入多输出)NOMA(非正交多址接入)与单载波传输在可见光通信(VLC)中的实验演示,展示了NOMA技术如何在VLC系统中提高系统容量和用户公平性,同时保持较低的峰均功率比。" 在无线通信领域,尤其是在光通信,特别是可见光通信(Visible Light Communications, VLC)中,研究人员不断寻求提高通信效率和系统容量的方法。这篇发表在《Optics Communications》上的研究论文关注的是NOMA技术在光学MIMO VLC系统中的应用。NOMA是一种先进的多址接入技术,它允许多个用户在同一时间和频率资源上共享信道,通过功率域或码域的非正交性来实现多用户分离。 传统的正交多址接入(Orthogonal Multiple Access, OMA)技术如频分多址(FDMA)、时分多址(TDMA)和码分多址(CDMA),每个用户占据独立的频谱、时间或码字资源,而NOMA则通过在功率域或码域实现用户间的区分,从而提高了频谱效率。在VLC系统中,这种技术尤其重要,因为可见光频谱资源有限,且对功率效率有较高要求。 该实验采用了单载波传输,相比于多载波调制(如OFDM),单载波调制具有较低的峰均功率比(Peak-to-Average Power Ratio, PAPR),这对于光发射器的功耗控制和器件设计更为有利。同时,结合MIMO技术,能够利用空间分集和多径传播来提高系统的抗干扰能力和容量。 文章中提到的频率域连续干扰消除(Frequency Domain Successive Interference Cancellation, FDSIC)是NOMA的一种解调策略,它允许在一个接收端逐层解码多个用户的信号,有效地减少了不同用户间信号的干扰。 实验结果表明,这种NOMA-MIMO VLC系统不仅提高了系统容量,还能在多个用户之间实现更好的公平性,这意味着所有用户都能获得相对均衡的服务质量。这在高密度用户环境中尤为重要,例如在公共场所的室内照明通信系统中。 总结来说,这篇论文为光学通信领域带来了新的洞察,它提出并实验验证了一种能有效提升可见光通信性能的技术组合,即NOMA与MIMO的融合,以及单载波传输与FDSIC的采用。这种方法在保证服务质量和效率的同时,也考虑了实际应用中的功率和成本效益,对于推动VLC技术的进一步发展具有重要意义。

4 Experiments This section examines the effectiveness of the proposed IFCS-MOEA framework. First, Section 4.1 presents the experimental settings. Second, Section 4.2 examines the effect of IFCS on MOEA/D-DE. Then, Section 4.3 compares the performance of IFCS-MOEA/D-DE with five state-of-the-art MOEAs on 19 test problems. Finally, Section 4.4 compares the performance of IFCS-MOEA/D-DE with five state-of-the-art MOEAs on four real-world application problems. 4.1 Experimental Settings MOEA/D-DE [23] is integrated with the proposed framework for experiments, and the resulting algorithm is named IFCS-MOEA/D-DE. Five surrogate-based MOEAs, i.e., FCS-MOEA/D-DE [39], CPS-MOEA [41], CSEA [29], MOEA/DEGO [43] and EDN-ARM-OEA [12] are used for comparison. UF1–10, LZ1–9 test problems [44, 23] with complicated PSs are used for experiments. Among them, UF1–7, LZ1–5, and LZ7–9 have 2 objectives, UF8–10, and LZ6 have 3 objectives. UF1–10, LZ1–5, and LZ9 are with 30 decision variables, and LZ6–8 are with 10 decision variables. The population size N is set to 45 for all compared algorithms. The maximum number of FEs is set as 500 since the problems are viewed as expensive MOPs [39]. For each test problem, each algorithm is executed 21 times independently. For IFCS-MOEA/D-DE, wmax is set to 30 and η is set to 5. For the other algorithms, we use the settings suggested in their papers. The IGD [6] metric is used to evaluate the performance of each algorithm. All algorithms are examined on PlatEMO [34] platform.

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