RIS輔助下的跨模態(tài)通信資源分配

陳鳴鍇; 孫振德; 萬雅芳

doi:10.11999/JEIT240619

RIS輔助下的跨模態(tài)通信資源分配

doi: 10.11999/JEIT240619 cstr: 32379.14.JEIT240619

陳鳴鍇^{1, 2, ,},
孫振德¹,
萬雅芳¹

1.
南京郵電大學(xué)通信與信息工程學(xué)院南京 210003
2.
南京郵電大學(xué)江蘇省通信與網(wǎng)絡(luò)技術(shù)工程研究中心南京 210019

基金項(xiàng)目: 國家自然科學(xué)基金(62001246)，江蘇省重點(diǎn)研發(fā)計(jì)劃項(xiàng)目(BE2023035)，江蘇省通信與網(wǎng)絡(luò)技術(shù)工程研究中心開放課題

詳細(xì)信息

作者簡介:
陳鳴鍇：男，副教授，研究方向?yàn)闊o線通信、信號(hào)處理、多媒體信息處理等

孫振德：男，碩士生，研究方向?yàn)檎Z義通信

萬雅芳：女，碩士生，研究方向?yàn)槎嗝襟w通信

通訊作者:
陳鳴鍇　mkchen@njupt.edu.cn

中圖分類號(hào): TN911
計(jì)量
- 文章訪問數(shù): 136
- HTML全文瀏覽量: 124
- PDF下載量: 31
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2024-07-17
- 修回日期: 2025-02-12
- 網(wǎng)絡(luò)出版日期: 2025-02-21
- 刊出日期: 2025-02-28

Resource Allocation for RIS-aided Cross-Model Communications

1.
School of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
2.
Jiangsu Engineering Research Center of Communication and Network Technology, Nanjing University of Posts and Telecommunications, Nanjing 210019, China

Funds: The National Natural Science Foundation of China (62001246), The Key Reserch and Development Program of Jiangsu Province Key project and topics (BE2023035), Open Research Fundation of Jiangsu Engineering Research Center of Communication and Network Technology, NJUPT

摘要

摘要: 針對(duì)視頻和觸覺業(yè)務(wù)共存的跨模態(tài)業(yè)務(wù)場景，該文構(gòu)建了可重構(gòu)智能表面(RIS)輔助的共存網(wǎng)絡(luò)切片系統(tǒng)，用以提高視頻業(yè)務(wù)和觸覺業(yè)務(wù)的傳輸速率和可靠性。同時(shí)，為了有效降低觸覺業(yè)務(wù)通過穿孔帶給視頻業(yè)務(wù)的資源損耗，提出了動(dòng)態(tài)被動(dòng)波束賦形方案，允許RIS在不同時(shí)隙進(jìn)行動(dòng)態(tài)調(diào)整。基于上述方案，該文在確保觸覺業(yè)務(wù)傳輸?shù)臅r(shí)延和可靠性滿足約束的同時(shí)，構(gòu)建最大化視頻業(yè)務(wù)傳輸速率的優(yōu)化問題，以滿足跨模態(tài)業(yè)務(wù)共存需求，實(shí)現(xiàn)資源的合理分配。為求解此優(yōu)化問題，該文將其建模為一個(gè)馬爾可夫決策過程(MDP)，通過深度確定性策略梯度(DDPG)算法來進(jìn)行視頻數(shù)據(jù)和觸覺數(shù)據(jù)傳輸資源的聯(lián)合優(yōu)化。仿真結(jié)果顯示，與現(xiàn)有方案相比，所提方案具有一定的優(yōu)越性，在保證傳輸觸覺業(yè)務(wù)可靠性的前提下，提高了約66.67%的視頻業(yè)務(wù)和速率。
- 跨模態(tài)通信 /
- 可重構(gòu)智能表面 /
- 網(wǎng)絡(luò)切片 /
- 資源分配
Abstract: Objective The rapid development of digital and intelligent technologies has driven the increasing demand for cross-modal communication systems to support a wide range of applications, such as high-bandwidth video streaming, ultra-reliable low-latency haptic interactions, and immersive virtual reality experiences. These applications require the concurrent transmission of heterogeneous services, each with distinct and often conflicting resource demands. For instance, video services necessitate high data rates and large bandwidth allocations for smooth playback, while haptic services require ultra-low latency (<0.3 ms) and high reliability (>99.999%) for real-time interaction. Existing resource allocation schemes, typically designed for single-service scenarios or static optimization, do not effectively address the dynamic nature of wireless channels or the stringent requirements of multi-service coexistence. This paper proposes a dynamic resource allocation framework that utilizes Reconfigurable Intelligent Surfaces (RIS) to optimize the transmission efficiency of video services and the reliability of haptic services, thereby enhancing spectrum utilization and improving the Quality of Experience (QoE) in cross-modal communication systems. Methods To address the resource competition between video and haptic services, this paper proposes an RIS-aided network slicing architecture. The RIS dynamically adjusts its phase shifts to reshape the wireless propagation environment, improving channel gain and reducing interference. A puncturing-based resource sharing mechanism is introduced, enabling haptic traffic to temporarily use resources allocated to video services during burst arrivals. This mechanism ensures the stringent latency and reliability requirements of haptic services are met without significantly affecting video service performance. The optimization problem is formulated as a Mixed-Integer Nonlinear Programming (MINLP) task, with the objective of maximizing the video service rate while satisfying the constraints of haptic services. To tackle the complexity of joint RIS phase optimization and resource allocation, the problem is modeled as a Markov Decision Process (MDP) with continuous state and action spaces. A Deep Deterministic Policy Gradient (DDPG) algorithm is employed, integrating actor-critic networks, experience replay, and target networks to learn optimal policies. The actor network generates decisions regarding resource block allocation, RIS phase shifts, and puncturing ratios, while the critic network evaluates the long-term reward, defined as the weighted sum of video throughput and haptic service satisfaction. Results and Discussions Simulation results demonstrate the effectiveness of the proposed scheme. Compared to the HMSA scheme, the proposed method significantly improves the total transmission rate for users, particularly under varying Base Station (BS) power levels (Fig. 4). The RIS phase optimization scheme outperforms both the random phase and no-RIS scenarios, highlighting the importance of dynamically adjusting RIS reflection coefficients to enhance channel gain (Fig. 5). Furthermore, the average delay of haptic data packets decreases as the number of RIS reflection units increases, and higher BS transmit power further reduces latency, confirming the synergy between RIS deployment and power allocation (Fig. 6). The user sum rate declines as the arrival rate of haptic data packets increases, due to intensified resource competition. However, deploying additional RIS reflection units mitigates this degradation, demonstrating the robustness of RIS-aided resource allocation (Fig. 7). The convergence behavior of the DDPG algorithm is analyzed, showing faster convergence in low-SNR environments (e.g., P = 0 dBm) compared to high-SNR scenarios (e.g., P = 30 dBm), where reward fluctuations are more pronounced (Fig. 8). Additionally, the learning rate is identified as a key hyperparameter, with a value of 0.001 providing the optimal balance between convergence speed and stability (Fig. 9). These results confirm that the proposed framework enhances video service throughput while ensuring the stringent reliability and low-latency requirements of haptic services, enabling efficient cross-modal resource coexistence. Conclusions This work presents an RIS-assisted dynamic resource allocation framework for cross-modal communication systems, effectively addressing the coexistence challenges of video and haptic services. Key innovations include the integration of RIS phase optimization with puncturing-based resource sharing and the application of DDPG to solve high-dimensional MINLP problems. The proposed scheme significantly enhances video throughput and haptic reliability, demonstrating its potential for 6G-enabled immersive applications. Future research will extend this framework to mobile user scenarios, multi-RIS collaborative systems, and multi-service coexistence environments with diverse QoS requirements. Specifically, the study will examine the impact of user mobility on RIS configuration and resource allocation strategies. Additionally, the benefits of deploying multiple RIS units in a coordinated manner will be explored to further enhance system performance and coverage. Finally, the framework will be expanded to support a broader range of services with varying latency, reliability, and bandwidth demands, paving the way for more versatile and efficient cross-modal communication systems.
- Cross-model communication /
- Reconfigurable Intelligent Surfaces (RIS) /
- Network slicing /
- Resource allocation

HTML全文

圖 1 基于穿孔方案的RIS輔助跨模態(tài)通信系統(tǒng)架構(gòu)

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圖 2 資源塊的說明和提出的動(dòng)態(tài)被動(dòng)波束形成方案

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圖 3 基于actor-critic的DDPG算法框架圖

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圖 4 不同方案下用戶和速率隨著基站功率的變化趨勢

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圖 5 不同方案下用戶和速率隨著RIS反射單元數(shù)量的變化趨勢

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圖 6 不同基站功率和RIS反射單元數(shù)量對(duì)觸覺數(shù)據(jù)包平均時(shí)延的影響

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圖 7 不同觸覺數(shù)據(jù)包到達(dá)速率下用戶和速率的變化情況

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圖 8 不同功率下獎(jiǎng)勵(lì)隨步長的變化

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圖 9 不同學(xué)習(xí)率下平均獎(jiǎng)勵(lì)隨步長的變化

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1 DDPG算法

初始化：${s_1}$，${\theta _a}$，${\theta _c}$，${\theta '_a} \leftarrow {\theta _a}$和${\theta '_c} \leftarrow {\theta _c}$，經(jīng)驗(yàn)回放池$\mathbb{N}$，隨　機(jī)噪聲${{{\boldsymbol{N}}}_t}$
while 迭代回合$ \le $最大迭代回合 do
while $t \le T$ do
? 根據(jù)狀態(tài)${s_t}$和隨機(jī)噪聲${{{\boldsymbol{N}}}_t}$，通過actor網(wǎng)絡(luò)計(jì)算動(dòng)作　　　${{\boldsymbol{a}}_t} = \mu ({{\boldsymbol{s}}_t};{\theta _a}) + {{\boldsymbol N}_t}$
? 執(zhí)行動(dòng)作${{\boldsymbol{a}}_t}$，獲得獎(jiǎng)賞值$r({{\boldsymbol{s}}_t},{{\boldsymbol{a}}_t})$和下一狀態(tài)${{\boldsymbol{s}}_{t + 1}}$
? 將經(jīng)驗(yàn)$({{\boldsymbol{s}}_t},{{\boldsymbol{a}}_t},{r_t},{{\boldsymbol{s}}_{t + 1}})$存儲(chǔ)至經(jīng)驗(yàn)回放池$\mathbb{N}$中
? 從經(jīng)驗(yàn)回放池$\mathbb{N}$中隨機(jī)采樣${N_{{\mathrm{batch}}}}$個(gè)經(jīng)驗(yàn)樣本進(jìn)行神經(jīng)網(wǎng) 　　　絡(luò)訓(xùn)練
? 通過式(26)的近似形式，計(jì)算得到當(dāng)前訓(xùn)練critic網(wǎng)絡(luò)的損　　　失函數(shù)
? 通過損失函數(shù)$L({\theta _c})$關(guān)于${\theta _c}$的梯度更新critic網(wǎng)絡(luò)的參數(shù)
? 通過式(23)更新actor網(wǎng)絡(luò)的參數(shù)${\theta _a}$
? 使用式(29)和式(30)來更新目標(biāo)actor網(wǎng)絡(luò)和目標(biāo)critic網(wǎng)絡(luò) 　　　的參數(shù)${\theta '_a}$和${\theta '_c}$
? $t \leftarrow t + 1$
end while
end while

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表 1 仿真參數(shù)表

參數(shù)意義	設(shè)定數(shù)值
資源塊RB總數(shù)$K$	200
時(shí)隙個(gè)數(shù)$T$	20
一個(gè)時(shí)隙的持續(xù)時(shí)間	1 ms
一個(gè)微小時(shí)隙的持續(xù)時(shí)間$\varDelta $	0.125 ms
一個(gè)時(shí)隙內(nèi)微小時(shí)隙個(gè)數(shù)${M}$	8
RB的頻率帶寬$B$	180 kHz
觸覺數(shù)據(jù)包到達(dá)速率$\lambda $	3
觸覺數(shù)據(jù)包的大小$D_l^{m,t}$	20 Byte
高斯隨機(jī)噪聲功率${\delta ^2}$	–93 dBm
觸覺數(shù)據(jù)包的解碼錯(cuò)誤概率${\varepsilon _l}$	${10^{ - 6}}$

下載: 導(dǎo)出CSV

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