星地协同通信场景下智能超表面多维资源非正交动态匹配算法设计

梁微; 李奥莹; 罗薇; 李立欣; 林文晟; 李旭; 卫保国

doi:10.11999/JEIT250078

星地协同通信场景下智能超表面多维资源非正交动态匹配算法设计

doi: 10.11999/JEIT250078 cstr: 32379.14.JEIT250078

梁微^{1, 2, ,},
李奥莹¹,
罗薇¹,
李立欣^{1, 2},
林文晟^{1, 2},
李旭¹,
卫保国¹

1.
西北工业大学电子信息学院西安 710129
2.
西北工业大学深圳研究院深圳 518057

基金项目: 深圳市科技计划(GJHZ20220913143203006)

详细信息

作者简介:
梁微：副教授，博士生导师，研究方向为5G\6G关键技术、智能超表面、多址接入技术等

李奥莹：硕士生，研究方向为智能超表面、多址接入技术等

罗薇：硕士生，研究方向为智能超表面、多址接入技术、太赫兹通信等

李立欣：教授，博士生导师，研究方向为智能通信技术等

林文晟：副教授，硕士生导师，研究方向为语义通信等

李旭：副教授，硕士生导师，研究方向为智能信号处理等

卫保国：副教授，硕士生导师，研究方向为深度学习、信号处理等

通讯作者:
梁微　liangwei@nwpu.edu.cn

中图分类号: TN927
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- 被引次数: 0
出版历程
- 收稿日期: 2025-02-12
- 修回日期: 2025-06-06
- 网络出版日期: 2025-06-21

Resource Allocation in Reconfigurable Intelligent Surfaces Assisted NOMA Based Space-Air-Ground Integrated Network

LIANG Wei^{1, 2
, ,},
LI Aoying¹,
LUO Wei¹,
LI Lixin^{1, 2},
LIN Wensheng^{1, 2},
LI Xu¹,
WEI Baoguo¹

1.
School of Electronic Information, Northwestern Polytechnical University, Xi’an 710000, China
2.
Shenzhen Research Institute, Northwestern Polytechnical University, Shenzhen 518057, China

Funds: Shenzhen Science and Technology Program (GJHZ20220913143203006)

摘要

摘要: 由于地面用户与基站之间有障碍物遮挡，致使直连链路被阻断，该文考虑基于双联路通道的空天地一体化网络(SAGIN)，利用空中智能超表面(ARIS)辅助地面用户与基站间的通信，以及高空平台(HAP)辅助低轨卫星(LEO)与地面基站间的通信。具体而言，在第1段上行链路传输中，地面用户将利用ARIS作为无源中继传输信息至基站。在第2段上行链路传输中，LEO作为通信用户先将信号发射至HAP，再由HAP放大信号转发至地面基站，其中HAP和地面基站通信链路可能存在云层阻挡仍需依靠ARIS辅助通信。综上所述，由于地面用户数量远远大于ARIS数量，该文以最大化系统能效为目标，利用1对多双边匹配论算法对地面用户们进行分组，且组内用户采用非正交多址接入(NOMA)方式进行传输，组间用户则采用频分多址接入(FDMA)方式。进一步，在考虑地面用户分组、地面用户功率分配、LEO波束赋形、ARIS群波束赋形等约束条件后，该文所提ARIS赋能交替迭代网络效能优化算法(APIA-SAGIN)设计方案，并通过仿真验证了所提算法的可行性。
- 智能超表面 /
- 非正交多址接入 /
- 能效优化 /
- 凸优化 /
- 匹配论 /
- 空天地一体化网络
Abstract: Objective The exponential growth of 6G wireless communication demands has positioned the Space-Air-Ground Integrated Network (SAGIN) as a promising architecture, aiming to achieve broad coverage and adaptive networking. However, complex geographic environments, including building obstructions, frequently hinder direct communication between ground users and base stations, thereby requiring effective relay strategies to maintain reliability. Reconfigurable Intelligent Surfaces (RIS) have attracted considerable attention for their capacity to improve signal coverage through passive beamforming. This study develops an RIS-assisted SAGIN architecture incorporating aerial RIS clusters and High-Altitude Platforms (HAPS) to enable communication between ground users and a Low Earth Orbit (LEO) satellite. To enhance energy efficiency, the system further optimizes user relay selection, power allocation, and beamforming for both LEO and RIS components. Methods The proposed system integrates LEO satellites, HAPS, Unmanned Aerial Vehicles (UAVs) equipped with RIS, and ground users within a three-dimensional communication space. Due to environmental obstructions, ground users are unable to maintain direct links with base stations; instead, RIS functions as a passive relay node. To improve relay efficiency, users are grouped and associated with specific RIS units. The total system bandwidth is partitioned into sub-channels assigned to different user groups. A matching algorithm is designed for user selection, followed by user group association with each RIS. For LEO communications, HAPS serve as active relay nodes that decode and forward signals to ground base stations. The system considers both direct and RIS-assisted communication links. An optimization problem is formulated to maximize energy efficiency under constraints related to user Quality of Service (QoS), power allocation, and beamforming for both LEO and RIS. To solve this, the proposed Alternating Pragmatic Iterative Algorithm in SAGIN (APIA-SAGIN) decomposes the problem into three sub-tasks: user relay selection, LEO beamforming, and RIS beamforming. The Successive Convex Approximation (SCA) and SemiDefinite Relaxation (SDR) methods are employed to transform the original non-convex problem into tractable convex forms for efficient solution.g. Results and Discussions Simulation results confirm the effectiveness of the proposed APIA-SAGIN algorithm in optimizing the energy efficiency of the RIS-assisted SAGIN. As shown in (Fig. 5), increasing the number of RIS elements and LEO antennas markedly improves energy efficiency compared with the random phase shift algorithm. This demonstrates that the proposed algorithm enables channel-aware control by aligning RIS beamforming with the ground transmission channel and jointly optimizing LEO beamforming, RIS beamforming, and LEO-channel alignment. As illustrated in (Fig. 6), both energy efficiency and the achievable rate of the LEO link increase with transmission power. However, beyond a certain power threshold, energy consumption rises faster than the achievable rate, leading to diminishing or even negative returns in energy efficiency. (Fig. 7) shows that higher power in ground user groups leads to increased achievable rates. Nonetheless, expanding the number of RIS elements proves more effective than increasing transmission power for enhancing user throughput. As shown in (Fig. 8), a higher number of RIS elements leads to simultaneous improvements in energy efficiency and achievable rate in the ground segment. Moreover, increasing the number of ground users does not degrade energy efficiency; instead, it results in a gradual increase, suggesting efficient resource allocation. Compared with the random phase shift algorithm, the proposed approach achieves superior performance in both energy efficiency and achievable rate. These findings support its potential for practical deployment in SAGIN systems. Conclusions This study proposes an RIS-assisted SAGIN architecture that utilizes aerial RIS clusters and HAPS to support communication between ground users and LEO satellites. The APIA-SAGIN algorithm is developed to jointly optimize user relay selection, LEO beamforming, and RIS beamforming with the objective of maximizing system energy efficiency. Simulation results demonstrate the effectiveness and robustness of the algorithm under complex conditions. The proposed approach offers a promising direction for improving the energy efficiency and overall performance of SAGIN, providing a foundation for future research and practical implementation.
- Reconfigurable Intelligent Surface (RIS) /
- Non-Orthogonal Multiple Access (NOMA) /
- Energy Efficiency (EE) optimizaion /
- Convex optimization /
- Matching theory /
- Space-Air-Ground Integrated Network (SAGIN)

HTML全文

图 1 ARIS群辅助空天地一体化网络模型

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图 2 基于参与者更新的双边匹配算法

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图 3 匹配过程流程图

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图 4 针对弱用户匹配算法改进流程图

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图 5 空中RIS元素数目与能效之间关系图

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图 6 LEO发射功率与系统性能之间关系图

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图 7 地面用户小组功率与可达速率之间关系图

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图 8 在不同的用户数目下，空中RIS元素数目与系统性能之间关系图

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1 基于弱用户补偿的中继选择算法

(1) 初始化：
(2) 　地面用户集合$ K $，ARIS集合$ I $，偏好关系序列$ {{{\bf{Pre}}} _K} $和　　　 $ {{{\bf{Pre}}} _I} $；
(3) 　触发阈值$ {\varepsilon _{{\text{th}}}} $、加权比例$ {\theta _{{\text{th}}}} $和加权回合$ {N_{\lim }} $。
(4) 匹配流程
(5) 　Repeat：
(6) 　　每一个未匹配的用户按照偏好关系序列向排名最高的　　　　 ARIS发出匹配请求；
(7) 　　If 收到匹配请求的ARIS未与地面用户建立匹配关系，　　　　 then ARIS接受地面用户匹配
请求；
(8) Else
(9) 　　Repeat:
(10) 　 If 对于ARIS而言，当前匹配请求的用户在偏好关系序列　　　　中更靠前，then ARIS接受当前用户匹配请求，拒绝原匹　　　　配用户；
(11) 　 Else计算效用值提升比例，then
(12) 　　 If当前用户是弱用户，then效用值加权　　　　　 $ x_{{\text{cur}}}^k = x_{{\text{cur}}}^k\left( {1 + {\theta _{{\text{th}}}}} \right) $，$ {N_{\lim }} = {N_{\lim }} - 1 $，再次向被拒　　　　　绝的ARIS发出匹配请求；
(13) 　　 Until 当前用户不是弱用户、当前用户与ARIS建立匹　　　　　配关系或者$ {N_{\lim }} \le 0 $；
(14) 　更新参与者集合和偏好关系序列；
(15) Until 地面用户集合$ K $,ARIS集合$ I $或偏好关系序列为空。

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2 利用APIA-SAGIN求解LEO和ARIS波束赋形

(1) 完成地面用户中继选择分组，得到I个地面用户分组；
(2) 初始化：
(3) 　　输入ARIS群无源波束赋形$ {{\boldsymbol{\varTheta}}}^{(0)} $，设置外循环迭代次数$ i = 1 $；
(4) 　　设置松弛变量初始值$ \left\{ {{\boldsymbol{w}}_{\text{L}}^{\left( 0 \right)},{t^{\left( 0 \right)}},{\nu ^{(0)}},\phi _{\mathrm{L}}^{\left( 0 \right)},{\delta ^{\left( 0 \right)}},{\theta ^{(0)}}} \right\} $，设置CBS内循环迭代次数$ j = 1 $；
(5) 　　设置松弛变量值$ \left\{ {{V^{(0)}},{X^{(0)}},{Y^{(0)}},{Z^{(0)}}} \right\} $，设置ARIS内循环迭代次数$ l = 1 $。
(6) Repeat:
(7) 　　LEO有源波束赋形优化：
(8) 　　　Repeat：
(9) 　　　　基于给定的$ {\boldsymbol{\varTheta }} $，利用CVX求解优化问题(P3)，得到$ \left\{ {{\boldsymbol{w}}_{{\text{L}},j}^,t_j^,\nu _j^,\phi _{{\text{L}},j}^,\delta _j^,\theta _j^} \right\} $；
(10) 　　 If差值$ \left\| {t_j^* - {t^{\left( {j - 1} \right)}}} \right\| \gt {{\mathrm{var}}} $，then$ \left\{ {{\boldsymbol{w}}_{\mathrm{L}}^{\left( j \right)},{t^{\left( j \right)}},{\nu ^{(j)}},\phi _{\mathrm{L}}^{\left( j \right)},{\delta ^{\left( j \right)}},{\theta ^{(j)}}} \right\} = \left\{ {{\boldsymbol{w}}_{{\mathrm{L}},j}^,t_j^,\nu _j^,\phi _{L,j}^,\delta _j^,\theta _j^} \right\} $，且
$ j{\text{ }} = {\text{ }}j{\text{ }} + {\text{ }}1 $；
(11) 　　 Until $ \left\| {t_j^* - {t^{\left( {j - 1} \right)}}} \right\| \le {{\mathrm{var}}} $，输出$ t_j^* $和$ {\boldsymbol{w}}_{{\text{L}},j}^* $。
(12) 　 I个ARIS无源波束赋形优化：
(13) 　　 Repeat：
(14) 　　基于给定的$ {\boldsymbol{w}}_{{\text{L}},j}^* $，利用CVX求解优化问题(P6)，得到$ \left\{ {{{\boldsymbol{U}}^},V_l^,X_l^,Y_l^,Z_l^*} \right\} $；
(15) 　　 If差值$ \left\| {{Z^{\left( l \right)}} - {Z^{\left( {l - 1} \right)}}} \right\| \gt {{\mathrm{var}}} $，then$ \left\{ {{V^{(l)}},{X^{\left( l \right)}},{Y^{\left( l \right)}},{Z^{\left( l \right)}}} \right\} = \left\{ {V_l^,X_l^,Y_l^,Z_l^} \right\} $，且$ l{\text{ }} = {\text{ }}l{\text{ }} + {\text{ }}1 $；
(16) 　 Until $ \left\| {{Z^{\left( l \right)}} - {Z^{\left( {l - 1} \right)}}} \right\| \le {{\mathrm{var}}} $，输出$ {{\boldsymbol{U}}^*} $。
(17) 利用SVD或者高斯随机化方法从$ {{\boldsymbol{U}}^} $中获得$ {{\boldsymbol{\varTheta}}}^{} $。
(18) 利用$ {{\boldsymbol{\varTheta}}}^{} $和$ {\boldsymbol{w}}_{{\text{L}},j}^ $计算$ {{\mathrm{EE}}^{\left( i \right)}} $。
(19) Until $ \left\| {{{\mathrm{EE}}^{(i)}} - {{\mathrm{EE}}^{(i - 1)}}} \right\| \le \delta $，输出$ {{\mathrm{EE}}^*} = {{\mathrm{EE}}^{(i)}} $。

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表 1 ARIS辅助空天地一体化网络仿真参数设置

参数	取值
LEO飞行高度	300 km
HAP飞行高度	20 km
UAV飞行高度	100～500 m
UAV水平分布范围半径	800 m
地面用户水平分布范围半径	600 m
LEO信道衰减特性	$ \left\{ {m,b,\varOmega } \right\} = \left\{ {10,0.126,0.835} \right\} $
LEO 3 dB角度	$ {0.5^ \circ } $
HAP发射功率	10 W
地面用户总带宽	120 kHz
LEO带宽	500 kHz
地面用户路径损耗指数	0.8
地面用户组内功率之和	1 W
UAV通信功率$ {P_{\text{C}}} $	5 W
噪声谱密度	–90 dB/Hz
加权回合上限$ {N_{\lim }} $	5
收敛阈值	0.001

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