反向散射NOMA辅助的直传与中继协同互惠传输方法

许尧; 胡荣飞; 贾少波; 李博; 王钢; 张治中

doi:10.11999/JEIT250405

反向散射NOMA辅助的直传与中继协同互惠传输方法

doi: 10.11999/JEIT250405 cstr: 32379.14.JEIT250405

许尧^1, ,,
胡荣飞¹,
贾少波²,
李博³,
王钢⁴,
张治中¹

1.
南京信息工程大学电子与信息工程学院南京 210044
2.
郑州大学电气与信息工程学院郑州 450001
3.
哈尔滨工业大学(威海)信息科学与工程学院威海 264209
4.
哈尔滨工业大学通信技术研究所哈尔滨 150001

基金项目: 国家自然科学基金(62301268, 62301502)，江苏省自然科学基金(BK20220438)，南京信息工程大学引进人才科研启动专项经费(2023r015)，江苏省重点研发计划(BE2023088)，江苏省双创团队((2023)200008)，河南省优秀青年科学基金(252300421224)

详细信息

作者简介:
许尧：男，讲师，博士，研究方向为非正交多址接入、正交时频空间调制等

胡荣飞：男，硕士生，研究方向为反向散射通信、非正交多址接入等

贾少波：男，副教授，博士，研究方向为反向散射通信、物理层安全等

李博：男，教授，博士，博士生导师，研究方向为空天地网络、飞行自组织网络、通信感知一体化

王钢：男，教授，博士，博士生导师，研究方向为物理层网络编码、通信网理论与技术、数据通信

张治中：男，教授，博士，博士生导师，研究方向为移动通信、通信网络与测试技术等

通讯作者:
许尧　yaoxu@nuist.edu.cn

中图分类号: TN929.5
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出版历程
- 收稿日期: 2025-05-13
- 修回日期: 2025-08-20
- 网络出版日期: 2025-09-01

Mutualistic Backscatter NOMA Method for Coordinated Direct and Relay Transmission System

1.
School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
2.
School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China
3.
School of Information Science and Engineering, Harbin Institute of Technology at Weihai, Weihai 264209, China
4.
Communication Research Center, Harbin Institute of Technology, Harbin 150001, China

Funds: The National Natural Science Foundation of China (62301268, 62301502), The Natural Science Foundation of Jiangsu Province of China (BK20220438), The Startup Foundation for Introducing Talent of NUIST (2023r015), Jiangsu Provincial Key Research and Development Program (BE2023088), Jiangsu Provincial Innovation and Entrepreneurship Team ((2023)200008), Henan Natural Science Foundation for Excellent Young Scholar (252300421224)

摘要

摘要: 针对现有基于非正交多址接入的直传与中继协同传输(NOMA-CDRT)难以支持海量蜂窝物联网数据融合通信的问题，该文提出一种基于反向散射的互惠NOMA-CDRT方法。首先，借助反向散射调制与功率域叠加编码，构建信息传输和辅助一体化双向通信策略，实现蜂窝用户与物联网设备的频谱共享与互惠共生。其次，在理想和非理想串行干扰消除条件下，推导所提方法遍历和速率(ESR)的闭合表达式，以精确表征其系统性能。在此基础上，进一步设计基于改进粒子群优化算法的功率分配方案，以最大化ESR。仿真结果验证了理论分析及优化方案的有效性，并显示所提方法在ESR性能上显著优于传统NOMA-CDRT与正交多址接入方法。
- 蜂窝物联网 /
- 非正交多址接入 /
- 直传与中继协同传输 /
- 反向散射 /
- 互惠传输
Abstract: Objective The exponential growth in data traffic necessitates that cellular Internet of Things (IoT) systems achieve both ultra-high spectral efficiency and wide-area coverage to meet the stringent service requirements of vertical applications such as industrial automation and smart cities. Non-Orthogonal Multiple Access-based Coordinated Direct and Relay Transmission (NOMA-CDRT) method can enhance both spectral efficiency and coverage by leveraging power-domain multiplexing and cooperative relaying, making it a promising approach to address these challenges. However, existing NOMA-CDRT frameworks are primarily designed for cellular communications and do not effectively support spectrum sharing or the deep integration of cellular and IoT transmissions. To overcome these limitations, this study proposes a Mutualistic Backscatter NOMA-CDRT (MB-NOMA-CDRT) method. This approach facilitates spectrum sharing and mutualistic coexistence between cellular users and IoT devices, while improving the system’s Ergodic Sum Rate (ESR). Methods The proposed MB-NOMA-CDRT method integrates backscatter modulation and power-domain superposition coding to develop a bidirectional communication strategy that unifies information transmission and cooperative assistance, enabling spectrum sharing and mutualistic coexistence between cellular users and IoT devices. Specifically, the base station uses downlink NOMA to serve the cellular center user directly and the cellular edge user via a relaying user. Simultaneously, IoT devices utilize cellular radio frequency signals and backscatter modulation to transmit their data to the base station, thereby achieving spectrum sharing. The backscattered IoT signals act as multipath gains, contributing to improved cellular communication quality. To rigorously characterize the system performance, the squared generalized-K distribution and Meijer-G functions are adopted to derive closed-form expressions for the ESR under both perfect and imperfect Successive Interference Cancellation (SIC). Building on this analytical foundation, a power allocation optimization scheme is developed using an enhanced Particle Swarm Optimization (PSO) algorithm to maximize system ESR. Finally, extensive Monte Carlo simulations are conducted to verify the ESR gains of the proposed method, confirm the theoretical analysis, and demonstrate the efficacy of the optimization scheme. Results and Discussions The performance advantage of the proposed MB-NOMA-CDRT method is demonstrated through comparisons of ESR with conventional NOMA-CDRT and Orthogonal Multiple Access (OMA) schemes (Fig. 2 and Fig. 3). The theoretical ESR results closely match the simulation data, confirming the validity of the analytical derivations. Under both perfect and imperfect SIC, the proposed method consistently achieves the highest ESR. This improvement arises from spectrum sharing between cellular users and IoT devices, where the IoT link contributes multipath gain to the cellular link, thereby enhancing overall system performance. To investigate the influence of power allocation, simulation results illustrate the three-dimensional relationship between ESR and power allocation coefficients (Fig. 4). A maximum ESR is observed under specific coefficient combinations, indicating that optimized power allocation can significantly improve system throughput. Furthermore, the proposed optimization scheme demonstrates rapid convergence, with ESR values stabilizing within a few iterations (Fig. 5), supporting its computational efficiency. Finally, ESR performance is compared among the proposed optimization scheme, exhaustive search, and fixed power allocation strategies (Fig. 6). The proposed scheme consistently yields higher ESR across both perfect and imperfect SIC scenarios, demonstrating its superiority in enhancing spectral efficiency while maintaining low computational complexity. Conclusions This study proposes a MB-NOMA-CDRT method that enables spectrum sharing between IoT devices and cellular users while improving cellular communication quality through the backscatter-assisted reflection link. To evaluate system performance, closed-form expressions for the ESR are derived under both perfect and imperfect SIC. Building on this analytical foundation, a power allocation optimization scheme based on PSO is developed to maximize the system ESR. Simulation results demonstrate that the proposed method consistently outperforms conventional NOMA-CDRT and OMA schemes in terms of ESR, under both perfect and imperfect SIC conditions. The optimization scheme also exhibits favorable convergence behavior and effectively improves system performance. Given its advantages in spectral efficiency and computational efficiency, the proposed MB-NOMA-CDRT method is well suited to cellular IoT scenarios. Future work will focus on exploring the mathematical conditions necessary to fully characterize and exploit the mutualistic transmission mechanism.
- Cellular Internet of Things (IoT) /
- Non-Orthogonal Multiple Access (NOMA) /
- Coordinated Direct and Relay Transmission (CDRT) /
- Backscatter /
- Mutualistic transmission

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图 1 基于反向散射的互惠NOMA-CDRT系统

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图 2 系统ESR随发送信噪比$ {\rho _{\text{s}}} $的变化曲线

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图 3 蜂窝信号ESR随发送信噪比$ {\rho _{\text{s}}} $的变化曲线

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图 4 非理想SIC下系统ESR与功率分配系数的3维关系图

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图 5 非理想SIC下所提优化方案的系统ESR随迭代次数的变化曲线

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图 6 不同功率分配方案的系统ESR随发送信噪比$ {\rho _s} $的变化曲线

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1 基于改进PSO算法的功率分配方案

初始化：最大迭代次数M，粒子数N_p，惯性参数$ \omega = 0.7 $，学习因子$ {\lambda _1} = 2 $, $ {\lambda _2} = 3 $，第n个粒子初始位置$ {{\mathbf{l}}_n}(0) = [{x_n}(0),{y_n}(0)] $，
初始速度$ {{\mathbf{v}}_n}(0) = [{v_{{x_n}}}(0),{v_{{y_n}}}(0)] $，且$ {x_n}(0) $和$ {y_n}(0) $分别为$ {a_{\text{c}}} $和$ {a_{\text{r}}} $规定范围内的随机数，初始个体最佳位置$ {{\mathbf{l}}_{n,{\text{p}}}}(0) = {{\mathbf{l}}_n}(0) $，全局最佳　位置$ {{\mathbf{l}}_{\text{g}}}(0) = \mathop {\arg \max }\limits_{n \in \{ 1,2, \cdots ,{N_p}\} } ({C_{\text{S}}}({{\mathbf{l}}_n}(0))) $；
(1) while $ 1 \le m \le M $do
(2) 　for $ 1 \le n \le {N_{\text{p}}} $do
(3) 　　更新速度$ {{\mathbf{v}}_n}(m) = \omega {{\mathbf{v}}_n}(m - 1) + {\lambda _1}\zeta _{{v_n}}^1({{\mathbf{l}}_{\text{g}}}(m - 1) - {{\mathbf{l}}_n}(m - 1)) + {\lambda _2}\zeta _{{v_n}}^2({{\mathbf{l}}_{n,{\text{p}}}}(m - 1) - {{\mathbf{l}}_n}(m - 1)) $，其中$ \zeta _{{v_n}}^1 $和$ \zeta _{{v_n}}^2 $为[0,1]的　　　　随机数；
(4) 　　更新位置$ {{\mathbf{l}}_n}(m) = {{\mathbf{l}}_n}(m - 1) + {{\mathbf{v}}_n}(m) $；
(5) 　　判断$ {x_n}(m) $和$ {y_n}(m) $是否在限制范围内，若超出边界值，则将其设置为该边界值；
(6) 　　计算$ {C_{\text{S}}}({{\mathbf{l}}_{n,{\text{p}}}}(m - 1)) $,$ {C_{\text{S}}}({{\mathbf{l}}_{\text{g}}}(m - 1)) $,$ {C_{\text{S}}}({{\mathbf{l}}_n}(m)) $；
(7) 　　更新$ {{\mathbf{l}}_{n,{\text{p}}}}(m) = \arg \max ({C_{\text{S}}}({{\mathbf{l}}_n}(m)),{C_{\text{S}}}({{\mathbf{l}}_{n,{\text{p}}}}(m - 1))) $, $ {{\mathbf{l}}_{\text{g}}}(m) = \arg \max ({C_{\text{S}}}({{\mathbf{l}}_n}(m)),{C_{\text{S}}}({{\mathbf{l}}_{\text{g}}}(m - 1))) $；
(8) 　end for
(9) 　　迭代次数$ m = m + 1 $；
(10) end while
(11) 根据$ {{\mathbf{l}}_{\text{g}}}(M) $，输出功率分配系数$ {a_{\text{c}}} $, $ {a_{\text{r}}} $和$ {a_{\text{e}}} $的最优组合。

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