雙RIS輔助的MISO系統(tǒng)吞吐量最大化研究

謝文武; 張沁可; 梁錫濤; 劉晨宇; 余超; 王驥

doi:10.11999/JEIT240612

雙RIS輔助的MISO系統(tǒng)吞吐量最大化研究

doi: 10.11999/JEIT240612

1.
湖南理工學院信息科學與工程學院岳陽 414006
2.
華中師范大學物理科學與技術學院武漢 430079

基金項目: 國家自然科學基金 (62472169)，湖南省自然科學基金(2023JJ50045, 2024JJ7218, 2024JJ7219)，湖南省教育廳項目(22B0676, 23C0217)，湖南省大學生創(chuàng)新創(chuàng)業(yè)項目(S202410543061)

詳細信息

作者簡介:
謝文武：男，副教授，研究方向為5G&6G基帶算法、NFC等

張沁可：女，碩士生，研究方向為RIS、凸優(yōu)化

梁錫濤：男，碩士生，研究方向為5G/6G無線通信

劉晨宇：男，碩士生，研究方向為RIS、凸優(yōu)化

余超：男，講師，研究方向為NOMA、凸優(yōu)化

王驥：男，副教授，研究方向為5G/6G無線通信

通訊作者:
余超　12012017@hnist.edu.cn

中圖分類號: TN911
計量
- 文章訪問數(shù): 127
- HTML全文瀏覽量: 52
- PDF下載量: 22
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2024-07-17
- 修回日期: 2024-11-15
- 網(wǎng)絡出版日期: 2024-11-29

Throughput Maximization for Double RIS-Assisted MISO Systems

1.
School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang 414006, China
2.
College of Physical Science and Technology, Central China Normal University, Wuhan 430079, China

Funds: The National Natural Science Foundation of China (62472169), Hunan Provincial Natural Science Foundation (2023JJ50045, 2024JJ7218, 2024JJ7219), The Project of Education Bureau of Hunan Province (22B0676, 23C0217), Hunan Provincial College Students Innovation and Entrepreneurship-Project (S202410543061)

摘要

摘要: 近年來，有源可重構智能表面(ARIS)技術獲得了學術界的廣泛關注。然而，ARIS在多RIS輔助無線通信系統(tǒng)中的應用還缺乏相關研究。針對此問題，該文提出基于雙RIS輔助的無線通信系統(tǒng)模型。模型假設基站(BS)和用戶之間的直連鏈路受阻，僅通過RIS形成的反射鏈路進行通信。在此基礎上，根據(jù)ARIS與被動RIS(PRIS)的不同組合情況，提出4種RIS組合模型。模型的目標是優(yōu)化基站波束賦形、RIS的相移矩陣和功率分配因子，以最大化系統(tǒng)通信容量。由于該優(yōu)化問題為非凸問題，該文采用了交替優(yōu)化算法(AO)與連續(xù)凸逼近(SCA)對問題進行處理。仿真結果表明，無論基站發(fā)射功率高或低，TAAR組合模型的性能均顯著優(yōu)于傳統(tǒng)單ARIS配置。
- 雙RIS /
- MISO /
- 交替優(yōu)化算法 /
- 凸逼近
Abstract: Objective With the continuous advancement of research on Reconfigurable Intelligent Surfaces (RIS), various application scenarios have emerged. Among these, Active Reconfigurable Intelligent Surfaces (ARIS) attracts significant attention from the academic community. While some studies focus on dual Passive RIS-assisted communication systems, others investigate dual RIS-assisted systems incorporating ARIS. Existing literature consistently demonstrates that dual RIS configurations outperform single RIS setups in terms of achievable Signal-to-Noise Ratio (SNR), power gain, and energy transfer efficiency, with dual RIS systems achieving approximately ten times higher energy transfer efficiency.However, most existing studies on RIS focus on optimizing the performance of reflection coefficients in one or more distributed RIS-aided systems, primarily serving users within their respective coverage areas, without sufficiently addressing the benefits of single-reflection links. While dual RIS systems can effectively mitigate the limitations of antenna numbers and improve transmission reliability and efficiency, single-reflection links can still significantly enhance channel capacity, especially under low transmission power conditions. This paper proposes a novel approach wherein dual-reflection links and two single-reflection links jointly serve users. The goal is to maximize the downlink capacity of dual RIS-assisted Multiple-Input Single-Output (MISO) systems by strategically configuring the interaction between the two RISs. Methods In this paper, four combinatorial models of RIS are investigated: the Transmitter-PRIS PRIS-Receiver (TPPR), Transmitter-ARIS PRIS-Receiver (TAPR), Transmitter-PRIS ARIS-Receiver (TPAR), and Transmitter-ARIS ARIS-Receiver (TAAR). The optimization objective of all models is to maximize the communication rate by optimizing the antenna beamforming vector of the base station and the phase shift matrix of the RIS. Due to the coupling of the three variables in the objective function, the model is non-convex, making it difficult to obtain an optimal solution. To address the coupling problem, the Alternating Optimization (AO) algorithm is employed, where one phase shift vector is fixed while the other is optimized alternately. To tackle the non-convex problem, SCA is applied to iteratively approximate the optimal solution by solving a series of convex subproblems. Results and Discussions Building on the research methods outlined above and employing the SCA and AO algorithms, experimental results are obtained. The system capacity of each combination model increases with rising amplification power (Fig. 2). However, once the amplification factor reaches a certain threshold, the capacity curves of all models begin to flatten due to the constraints imposed by the maximum amplification power.Further demonstration of the system capacity performance of different combination models as transmit power increases is shown in (Fig. 3). Across all dual-RIS combination models, system capacity improves with higher transmit power and outperforms the Single-Active model in all scenarios.In (Fig. 3(a)), under low transmit power conditions, regions of the curves corresponding to higher amplification power overlap due to the constraint of the amplification factor. As transmit power increases, system capacity stabilizes, which can be attributed to the proximity of ARIS to the base station, allowing it to receive stronger signals. Under high transmit power, system capacity continues to improve due to the influence of PRIS. Unlike ARIS, PRIS reflects the optimized signal path without being constrained by amplification power. Consequently, as transmit power increases, the signal strength received by PRIS is enhanced.In (Fig. 3(b)), system capacity increases with transmit power, showing trends similar to those in (Fig. 3(a)). In the TPAR combined model, the amplification factor constraint dominates, causing the system capacity curves to exhibit similar behavior across different amplification power levels. Under low transmit power, the signal strength at ARIS does not exceed the maximum amplification power budget. As transmit power increases, the amplification power constraint increasingly affects system capacity, leading to a gradual slowdown in the curve's upward trend until it flattens. At high transmit power levels, the system capacity curve of the TPAR model levels off due to the low signal strength received by ARIS when it is positioned farther from the base station. This positioning necessitates higher transmit power to overcome the amplified power constraint. Thus, it is recommended that ARIS be deployed as close to the user as possible.In (Fig. 3(c)), the TAAR combined model leverages the characteristics of both ARIS and PRIS in a dual ARIS-assisted scenario. Under low transmit power conditions, significant capacity gains are achieved. However, at high transmit power, the system capacity is constrained by the maximum amplification power of ARIS and eventually levels off. The system capacity trends in (Fig. 3(a)) and (Fig. 3(b)) consistently increase with higher transmit power. This is because both combination models integrate the advantages of PRIS and ARIS, ensuring high performance under both high and low transmit power conditions.In (Fig. 3(d)), where ARIS is positioned on the user side, comparison with (Fig. 3(c)) reveals that, under high transmit power, the system capacity of both combination models is nearly identical, regardless of the amplification power level. This suggests that in strong transmit power scenarios, the additional gains from ARIS are limited. Conclusions This paper provides an in-depth analysis of the optimization of dual RIS-assisted MISO communication systems, confirming their superiority over single RIS configurations. However, several potential research directions remain unexplored. Most current studies assume ideal channel models, whereas real-world applications often involve complex channel conditions that significantly affect system performance. Future research could investigate the performance of dual RIS systems under these practical conditions, paving the way for more robust and applicable solutions.
- Double Reconfiguration Intelligent Surface (RIS) /
- Multiple-Input Single-Output (MISO) /
- Alternating Optimization (AO) algorithm /
- Convex approximation

HTML全文

圖 1 雙RIS輔助單用戶場景下無線通信系統(tǒng)模型

下載: 全尺寸圖片幻燈片

圖 2 不同組合模型下不同放大功率$a_{\max }^2$的系統(tǒng)容量

下載: 全尺寸圖片幻燈片

圖 3 不同發(fā)射功率$P$的系統(tǒng)容量

下載: 全尺寸圖片幻燈片

1 基于AO算法的信道對齊流程

(1) 初始化參數(shù)${{\theta}} _1^{(0)}$, ${{\theta}} _2^{(0)}$, ${{\mathbf{\omega }}^{(0)}}$迭代數(shù)$n = 0$，收斂門限$\varepsilon $
(2) 利用初始參數(shù)計算$ \gamma _{{\text{TPPR}}}^n $的值
(3) 迭代開始：
(4) 　固定${{\theta} _2}$, ${\mathbf{\omega }}$，求解式(15)得到${{\theta} _1}$
(5) 　固定${{\theta} _1}$, ${\mathbf{\omega }}$，求解式(16)得到${{\theta} _2}$
(6) 　固定${{\theta} _1}$,${{\theta} _2}$，求解式(17)得到${\mathbf{\omega }}$
(7) 　使用${{\theta} _1}$,${{\theta} _2}$, ${\mathbf{\omega }}$，計算$\gamma _{{\text{TPPR}}}^{n + 1}$的值
(8) 　　判斷$\left\| {\gamma _{{\text{TPPR}}}^{n + 1} - \gamma _{{\text{TPPR}}}^n} \right\| \le \varepsilon $或$n \ge 100$是否成立，若不滿　　　　足條件，$n = n + 1$返回步驟4
(9) 結束循環(huán)
(10) 得到最優(yōu)解${{\theta} _1}$, ${{\theta} _2}$, ${\mathbf{\omega }}$

下載: 導出CSV

2 基于SCA的AO算法流程

(1) 初始化參數(shù)${\theta} _1^{(0)}$, ${\theta} _2^{(0)}$, ${{\mathbf{\omega }}^{(0)}}$, ${\tau _0}$, ${\kappa _0}$，迭代數(shù)$n = 0$，收斂　門限$\varepsilon $
(2) 計算$\gamma _{{\text{TAPR}}}^n$的值
(3) 迭代開始:
(4) 　固定${{\theta} _2}$, ${\mathbf{\omega }}$，求解問題(P2.1)，將解進行高斯隨機化后得到　　　 ${\theta} _1^{n + 1}$
(5) 　固定${{\theta} _1}$, ${\mathbf{\omega }}$，求解問題(P2.2)，將解進行高斯隨機化后得到　　　 ${\theta} _2^{n + 1}$
(6) 　固定${{\theta} _1}$, ${{\theta} _2}$，求解問題(P2.3)得到${{\mathbf{\omega }}^{n + 1}}$
(7) 　　使用${\theta} _1^{n + 1}$, ${\theta} _2^{n + 1}$,${{\mathbf{\omega }}^{n + 1}}$，計算$\gamma _{{\text{TAPR}}}^{n + 1}$的值
(8) 　　　判斷收斂條件$\left\| {\gamma _{{\text{TAPR}}}^{n + 1} - \gamma _{{\text{TAPR}}}^n} \right\| \le \varepsilon $或$n \ge 100$是否成　　　　　立，若滿足條件，迭代結束，否則，$n = n + 1$返回步　　　　　驟4
(9) 得到最優(yōu)解${{\theta} _1}$, ${{\theta} _2}$, ${\mathbf{\omega }}$

下載: 導出CSV

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