粗糙集信息系统实现自适应O-OFDM符号分解信号检测研究

贾科军; 车佳祺; 刘佳欣; 缐玉琴; 秦翠翠; 杨博然

doi:10.11999/JEIT240864

粗糙集信息系统实现自适应O-OFDM符号分解信号检测研究

doi: 10.11999/JEIT240864 cstr: 32379.14.JEIT240864

1.
兰州理工大学计算机与通信学院兰州 730050
2.
兰州城市学院教务处兰州 730050
3.
西南交通大学信息科学与技术学院成都 610031

基金项目: 国家自然科学基金(62265010)，甘肃省自然科学基金资助项目(22JR5RA276)，兰州理工大学博士科研启动经费项目(061903)，甘肃省教育厅：研究生“创新之星”项目(2025CXZX-575)

详细信息

作者简介:
贾科军：男，博士，教授，研究方向为可见光通信理论与技术

车佳祺：男，硕士生，研究方向为可见光通信LED非线性抑制、深度学习和粗糙集理论

刘佳欣：女，研究方向为可见光通信LED非线性抑制

缐玉琴：女，硕士生，研究方向为可见光通信LED非线性抑制

秦翠翠：女，硕士，研究方向为可见光通信LED非线性抑制

杨博然：男，博士生，研究方向为可见光通信LED非线性抑制

通讯作者:
贾科军　kjjia@lut.edu.cn

中图分类号: TN929.12
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出版历程
- 收稿日期: 2024-10-14
- 修回日期: 2025-03-25
- 网络出版日期: 2025-04-19
- 刊出日期: 2025-05-01

Research on Signal Detection of Adaptive O-OFDM Symbol Decomposition in Rough Set Information System

1.
School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China
2.
Academic Affairs Office of Lanzhou City University, Lanzhou 730050, China
3.
College of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, China

Funds: The National Natural Science Foundation of China (62265010), The Natural Science Foundation of Gansu Province (22JR5RA276), The Ph.D. Research Funding of Lanzhou University of Technology (061903), Gansu Provincial Department of Education: Graduate Student “Innovation Star” Project (2025CXZX-575)

摘要

摘要: 自适应光正交频分复用符号分解串行传输(O-OFDM-ASDST)可以有效抑制可见光通信(VLC)中发光二极管(LED)的非线性限幅失真，然而，O-OFDM-ASDST在接收端合并分解符号时会导致加性高斯白噪声(AWGN)叠加，从而引起误码率(BER)恶化。为此，该文基于人工智能粒计算的粗糙集理论(RST)信息系统与不可分辨关系，提出一种O-OFDM-ASDST信号检测算法。首先，将接收端时域抽样值作为论域，并将信号特征作为条件属性构建信息系统。通过决策规则对接收的分解符号进行分类，尽可能分类决策出原本等于门限值和零值的时域抽样值；然后，根据制定的决策规则导出不可分辨关系，并通过属性约简提取处于门限值之间的时域抽样值并进行重构，以达到降低重构算法复杂度的目的；最后，采用蒙特卡罗仿真方法，验证检测算法的性能。结果表明，与对比信号检测算法相比，该文检测算法在房间中心位置的BER性能可获得5.4 dB和1 dB的信噪比(SNR)增益；即使在房间边缘位置，也可以获得5.5 dB和0.8 dB的SNR增益。此外，检测算法的复杂度仅为对比信号检测算法的1/10。综上所述，所提检测算法能够有效抑制AWGN，提高BER性能，并显著降低算法复杂度和处理时延。
- 可见光通信 /
- O-OFDM符号分解 /
- 信号检测 /
- 粗糙集理论 /
- 属性约简
Abstract: Objective Adaptive Optical Orthogonal Frequency Division Multiplexing Symbol Decomposition with Serial Transmission (O-OFDM-ASDST) effectively suppresses the nonlinear clipping distortion of Light-Emitting Diodes (LEDs) in Visible Light Communication (VLC). However, incorporating decomposition symbols in the O-OFDM-ASDST system incorporates Additive White Gaussian Noise (AWGN), which degrades the Bit Error Rate (BER). To address this issue, this study proposes an O-OFDM-ASDST signal detection algorithm based on the Rough Set Theory (RST) information system and the indiscernibility relation of artificial intelligence particle computing. Methods An O-OFDM-ASDST signal detection algorithm is proposed based on the RST information system and the indiscernibility relation of artificial intelligence particle computing. The algorithm consists of two stages: RST preprocessing and attribute reduction reconstruction. In the first stage, the RST information system is constructed by using preprocessed time-domain sampled values as theoretical domain data. The signal characteristics of these sampled values are converted into symbolic attributes, serving as the conditional attributes of the RST information system, while the upper and lower amplitude thresholds are designated as decision attributes. The RST attribute dependence formula, combined with an attribute importance-based addition and deletion method, is applied to establish decision rules and classify the information system. In the second stage, the indiscernibility relation is derived from the decision rules, and attribute reduction is performed on the constructed information system. This reduction process is applied to the time-domain sampled values within the upper and lower thresholds, followed by reconstruction. Results and Discussions The performance of the proposed detection algorithm is verified using the Monte Carlo simulation method. The results demonstrate that this algorithm effectively suppresses AWGN at the O-OFDM-ASDST receiver, enhances BER performance, and significantly reduces computational complexity and processing delay. For instance, when the PhotoDetector (PD) is positioned at the center of the room [3,3,0.85], the ACO-OFDM-ASDST system achieves Signal-to-Noise Ratio (SNR) gains of approximately 1 dB and 1.2 dB under 4QAM and 16QAM modulation, respectively, at a BER of 10^–5. The DCO-OFDM-ASDST system achieves SNR gains of approximately 1 dB and 2 dB under the same conditions (Fig. 7). Similarly, when the PD is located at the edge of the room [0.5,0.5,0.85], the ACO-OFDM-ASDST system achieves SNR gains of approximately 0.8 dB and 1.1 dB for 4QAM and 16QAM, respectively, at a BER of 10^–5, while the DCO-OFDM-ASDST system achieves SNR gains of approximately 2.5 dB and 3.2 dB (Fig. 8). The proposed detection algorithm also maintains favorable BER performance under different DC bias levels. For example, under 16QAM modulation with DC bias values of 0.3 V, 0.4 V, and 0.6 V, the DCO-OFDM-ASDST system achieves SNR gains of approximately 2.2 dB, 2 dB, and 0.8 dB, respectively, at a BER of 10^–5 (Fig. 9). Furthermore, the complexity of the proposed detection algorithm in the ACO-OFDM-ASDST system is only one-tenth that of the contrast signal detection algorithm (Fig. 12). As the number of symbol decompositions increases, the proposed algorithm requires fewer computing resources compared to the contrast detection algorithm. For instance, in the ACO-OFDM-ASDST system with 4QAM, 16QAM, and 64QAM modulation, when the number of symbol decompositions is 4, the computational resources required by the contrast detection algorithm amount to 4096, whereas those required by the proposed detection algorithm are 408, 600, and 736, respectively. This corresponds to reductions in computational resource consumption by factors of 1/10, 1/7, and 1/6, respectively (Fig. 13). Additionally, the proposed detection algorithm exhibits lower processing latency. Conclusions The O-OFDM-ASDST signal detection algorithm is implemented using the RST information system and the indiscernibility relation, effectively suppressing AWGN in decomposed symbols. The simulation results confirm the effectiveness of the proposed algorithm, demonstrating superior BER performance compared to other signal detection methods. Notably, high BER performance is maintained even at the room’s edge, highlighting the algorithm’s reliability, coverage, and robustness. Additionally, the proposed algorithm exhibits low complexity and reduced processing delay. It not only mitigates LED nonlinear distortion but also effectively suppresses AWGN in decomposition symbols, thereby enhancing BER transmission performance and improving the overall O-OFDM system performance.
- Visible Light Communication (VLC) /
- O-OFDM symbol decomposition /
- Signal detection /
- Rough Set Theory (RST) /
- Attribute reduction

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图 1 RST-ASDST系统原理图

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图 2 DCO-OFDM-ASDST分解符号

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图 3 不同决策规则正确率对比

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图 4 不同决策规则召回率对比

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图 5 室内VLC几何场景分布

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图 6 LED与PD之间的多径信道增益

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图 7 PD位于中心位置时ACO-OFDM-ASDST和DCO-OFDM-ASDST系统BER性能

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图 8 PD位于边缘位置时ACO-OFDM-ASDST和DCO-OFDM-ASDST系统BER性能

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图 9 16QAM下DCO-OFDM-ASDST不同直流偏置BER性能

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图 10 不同直流偏置下接收端分解符号

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图 11 不同信号检测算法BER分布

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图 12 ACO-OFDM-ASDST重构算法复杂度分析

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图 13 ACO-OFDM-ASDST和DCO-OFDM-ASDST系统实值计算资源分析

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表 1 ACO-OFDM-ASDST信息系统

符号时域抽样值	条件属性C			决策属性D
U	1	2	3	${\varepsilon _{{\text{top}}}}$	${\varepsilon _{{\text{bottom}}}}$	$ {\varepsilon _{{\text{bottom}}}} < {\boldsymbol y}_{{\text{del-DC}}}^{\left( i \right)}\left( k \right) < {\varepsilon _{{\text{top}}}} $
$ {{\boldsymbol y}_{{\text{del-DC}}}^{\left( 1\right)}\left( k \right)} $	Y	N	N	T	F	F
${{\boldsymbol y}_{{\text{del-DC}}}^{\left( 2\right)}\left( k \right)} $	N	Y	N	F	T	F
${{\boldsymbol y}_{{\text{del-DC}}}^{\left( 3\right)}\left( k \right)} $	N N	Y N	N Y	F F	T F	F T
${{\boldsymbol y}_{{\text{del-DC}}}^{\left( 3\right)}\left( k \right)} $	N N	Y N	N Y	F F	T F	F T
…	… Y	… N	… N	… T	… F	… F
${{\boldsymbol y}_{{\text{del-DC}}}^{\left( l\right)}\left( k \right)} $	… Y	… N	… N	… T	… F	… F

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表 2 ACO-OFDM-ASDST与DCO-OFDM-ASDST决策规则

$\varphi \left( n \right)$	决策规则
O-OFDM-ASDST	ACO-OFDM-ASDST	DCO-OFDM-ASDST
$\varphi \left( 1 \right)$	${\phi _1}:\left( {1 = Y} \right) \wedge \left( {2 = N} \right),{\phi _2}:\left( {2 = Y} \right) \wedge \left( {1 = N} \right)$	${\phi _1}:\left( {1 = Y} \right) \wedge \left( {2 = N} \right)$,${\phi _2}:\left( {2 = Y} \right) \wedge \left( {1 = N} \right)$,${\phi _3}:\left( {3 = Y} \right) \wedge \left( {2 = N} \right)$
$\varphi \left( 2 \right)$	${\phi _1}:\left( {1 = Y} \right) \vee \left( {2 = Y} \right),{\phi _2}:\left( {2 = Y} \right) \vee \left( {1 = Y} \right)$	${\phi _1}:\left( {1 = Y} \right) \vee \left( {2 = Y} \right)$,${\phi _2}:\left( {2 = Y} \right) \vee \left( {1 = Y} \right)$, ${\phi _3}:\left( {3 = Y} \right) \vee \left( {2 = Y} \right)$
$\varphi \left( 3 \right)$	${\phi _1}:\left( {1 = N} \right) \wedge \left( {2 = N} \right),{\phi _2}:\left( {2 = N} \right) \wedge \left( {1 = N} \right)$	${\phi _1}:\left( {1 = N} \right) \wedge \left( {2 = N} \right)$,${\phi _2}:\left( {2 = N} \right) \wedge \left( {1 = N} \right)$, ${\phi _3}:\left( {3 = N} \right) \wedge \left( {2 = N} \right)$

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表 3 仿真参数

参数	值
LED位置坐标[x, y, z] (m)	[3,3,3.5]
PD位置坐标[x, y, z] (m)	[3,3,0.85] / [0.5,0.5,0.85]
PD视场角${\psi _{{\text{FOV}}}}$ (°)	70
LED半功率角${\tau _{{1 \mathord{\left/ {\vphantom {1 2}} \right. } 2}}}$ (°)	60
PD的面积A_R (cm²)	1
墙面反射率${\rho _i}$	0.8
反射单元面积$ \Delta A $ (m²)	0.01

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表 4 不同重构算法复杂度

算法	复杂度
文献[22]重构算法	$ O\left( {l \times {N_{\text{S}}}} \right) $
改进重构算法	$ O\left( {N_{\text{S}}^{\left( 1 \right)} + N_{\text{S}}^{\left( 2 \right)} + \cdots + N_S^{\left( l \right)}} \right) $

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表 5 ACO-OFDM-ASDST与DCO-OFDM-ASDST系统实值计算资源

O-OFDM-ASDST系统	文献[22]重构算法	改进重构算法
ACO-OFDM-ASDST系统	$O\left( {4\left( {l \times {N_{\text{S}}}} \right)} \right)$	$O\left( {4N_{\text{S}}^{\left( l \right)}} \right)$
DCO-OFDM-ASDST系统	$O\left( {2\left( {l \times {N_{\text{S}}}} \right)} \right)$	$O\left( {2N_{\text{S}}^{\left( l \right)}} \right)$

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