Resource Allocation for RIS-aided Cross-Model Communications

CHEN Mingkai; SUN Zhende; WAN Yafang

doi:10.11999/JEIT240619

Volume 47 Issue 2

Feb. 2025

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2025 > 47(2): 363-374

CHEN Mingkai, SUN Zhende, WAN Yafang. Resource Allocation for RIS-aided Cross-Model Communications[J]. Journal of Electronics & Information Technology, 2025, 47(2): 363-374. doi: 10.11999/JEIT240619

Citation:

CHEN Mingkai, SUN Zhende, WAN Yafang. Resource Allocation for RIS-aided Cross-Model Communications[J]. Journal of Electronics & Information Technology, 2025, 47(2): 363-374. doi: 10.11999/JEIT240619

CHEN Mingkai, SUN Zhende, WAN Yafang. Resource Allocation for RIS-aided Cross-Model Communications[J]. Journal of Electronics & Information Technology, 2025, 47(2): 363-374. doi: 10.11999/JEIT240619

Citation:

PDF( 4295 KB)

Resource Allocation for RIS-aided Cross-Model Communications

doi: 10.11999/JEIT240619 cstr: 32379.14.JEIT240619

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

Received Date: 2024-07-17
Rev Recd Date: 2025-02-12

Available Online: 2025-02-21

Publish Date: 2025-02-28

Abstract

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

FullText(HTML)

References(25)

References

[1]	李玉宏, 张朋, 金帝, 等. 应用对未来网络的需求与挑战[J]. 电信科学, 2019, 35(8): 2019203. doi: 10.11959/j.issn.1000-0801.2019203. LI Yuhong, ZHANG Peng, JIN Di, et al. Application's needs and challenges for future networks[J]. Telecommunications Science, 2019, 35(8): 2019203. doi: 10.11959/j.issn.1000-0801.2019203.
[2]	WEI Xin, WU Dan, ZHOU Liang, et al. Cross-modal communication technology: A survey[J]. Fundamental Research, 2023. doi: 10.1016/j.fmre.2023.08.002.
[3]	WEI Xin, ZHANG Meng, and ZHOU Liang. Cross-modal transmission strategy[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2022, 32(6): 3991–4003. doi: 10.1109/TCSVT.2021.3105130.
[4]	陈鸣锴, 柳明浩, 王文俊, 等. 面向6G的跨模态语义编解码技术[J]. 信号处理, 2023, 39(7): 1141–1154. doi: 10.16798/j.issn.1003-0530.2023.07.001. CHEN Mingkai, LIU Minghao, WANG Wenjun, et al. Codec for cross-modal semantic communication in 6G[J]. Journal of Signal Processing, 2023, 39(7): 1141–1154. doi: 10.16798/j.issn.1003-0530.2023.07.001.
[5]	ALTAF KHATTAK S B, NASRALLA M M, and REHMAN I U. The role of 6G networks in enabling future smart health services and applications[C]. Proceedings of 2022 IEEE International Smart Cities Conference, Pafos, Cyprus, 2022: 1–7. doi: 10.1109/ISC255366.2022.9922093.
[6]	李昂, 陈建新, 魏昕, 等. 面向6G的跨模态信号重建技术[J]. 通信学报, 2022, 43(6): 28–40. doi: 10.11959/j.issn.1000-436x.2022093. LI Ang, CHEN Jianxin, WEI Xin, et al. 6G-oriented cross-modal signal reconstruction technology[J]. Journal on Communications, 2022, 43(6): 28–40. doi: 10.11959/j.issn.1000-436x.2022093.
[7]	ZHOU Liang, WU Dan, CHEN Jianxin, et al. Cross-modal collaborative communications[J]. IEEE Wireless Communications, 2020, 27(2): 112–117. doi: 10.1109/MWC.001.1900201.
[8]	STEINBACH E, STRESE M, EID M, et al. Haptic codecs for the tactile internet[J]. Proceedings of the IEEE, 2019, 107(2): 447–470. doi: 10.1109/JPROC.2018.2867835.
[9]	ALSENWI M, TRAN N H, BENNIS M, et al. eMBB-URLLC resource slicing: A risk-sensitive approach[J]. IEEE Communications Letters, 2019, 23(4): 740–743. doi: 10.1109/LCOMM.2019.2900044.
[10]	SUN Haipeng, YANG Jin, SU Junhao, et al. Joint resource scheduling for coexistence of URLLC and eMBB in 5G wireless networks[C]. Proceedings of 2021 Computing, Communications and IoT Applications, Shenzhen, China, 2021: 53–58. doi: 10.1109/ComComAp53641.2021.9653121.
[11]	ZHAO Yunzhi, CHI Xuefen, QIAN Lei, et al. Resource allocation and slicing puncture in cellular networks with eMBB and URLLC terminals coexistence[J]. IEEE Internet of Things Journal, 2022, 9(19): 18431–18444. doi: 10.1109/JIOT.2022.3160647.
[12]	REN Rong, WANG Jie, YU Jingming, et al. Hybrid puncturing and superposition scheme for multiplexing uRLLC and eMBB services based on deep reinforcement learning[C]. Proceedings of the 2022 IEEE 8th International Conference on Computer and Communications, Chengdu, China, 2022: 806–810. doi: 10.1109/ICCC56324.2022.10065784.
[13]	GUO Jiangfeng, NIE Gaofeng, TIAN Hui, et al. Puncture-predictive fairness scheduling scheme for eMBB and URLLC based on TD3 algorithm[C]. Proceedings of 2023 IEEE/CIC International Conference on Communications in China, Dalian, China, 2023: 1–6. doi: 10.1109/ICCC57788.2023.10233289.
[14]	ZHUANSUN Chenlu, YAN Kedong, ZHANG Gongxuan, et al. Hypergraph-based joint channel and power resource allocation for cross-cell M2M communication in IIoT[J]. IEEE Internet of Things Journal, 2023, 10(17): 15350–15361. doi: 10.1109/JIOT.2023.3263567.
[15]	WANG Lei, YIN Anmin, JIANG Xue, et al. Resource allocation for multi-traffic in cross-modal communications[J]. IEEE Transactions on Network and Service Management, 2023, 20(1): 60–72. doi: 10.1109/TNSM.2022.3207776.
[16]	文梦甜. 跨模态通信中传输策略优化研究[D]. [硕士论文], 南京邮电大学, 2023. doi: 10.27251/d.cnki.gnjdc.2023.001196. WEN Mengtian. Research on optimization of transmission strategy in cross-modal communications[D]. [Master dissertation], Nanjing University of Posts and Telecommunications, 2023. doi: 10.27251/d.cnki.gnjdc.2023.001196.
[17]	WU Qingqing and ZHANG Rui. Towards smart and reconfigurable environment: Intelligent reflecting surface aided wireless network[J]. IEEE Communications Magazine, 2020, 58(1): 106–112. doi: 10.1109/MCOM.001.1900107.
[18]	LIASKOS C, NIE Shuai, TSIOLIARIDOU A, et al. A new wireless communication paradigm through software-controlled metasurfaces[J]. IEEE Communications Magazine, 2018, 56(9): 162–169. doi: 10.1109/MCOM.2018.1700659.
[19]	DI RENZO M, DEBBAH M, PHAN-HUY D T, et al. Smart radio environments empowered by reconfigurable AI meta-surfaces: An idea whose time has come[J]. EURASIP Journal on Wireless Communications and Networking, 2019, 2019: 129. doi: 10.1186/s13638-019-1438-9.
[20]	GHANEM W R, JAMALI V, and SCHOBER R. Joint beamforming and phase shift optimization for multicell IRS-aided OFDMA-URLLC systems[C]. Proceedings of 2021 IEEE Wireless Communications and Networking Conference, Nanjing, China, 2021: 1–7. doi: 10.1109/WCNC49053.2021.9417582.
[21]	CAO Xuelin, YANG Bo, HUANG Chongwen, et al. Reconfigurable intelligent surface-assisted aerial-terrestrial communications via multi-task learning[J]. IEEE Journal on Selected Areas in Communications, 2021, 39(10): 3035–3050. doi: 10.1109/JSAC.2021.3088634.
[22]	MELGAREJO D C, KALALAS C, DE SENA A S, et al. Reconfigurable intelligent surface-aided grant-free access for uplink URLLC[C]. Proceedings of the 2020 2nd 6G Wireless Summit, Levi, Finland, 2020: 1–5. doi: 10.1109/6GSUMMIT49458.2020.9083788.
[23]	ALMEKHLAFI M, ARFAOUI M A, ELHATTAB M, et al. Joint resource allocation and phase shift optimization for RIS-aided eMBB/URLLC traffic multiplexing[J]. IEEE Transactions on Communications, 2022, 70(2): 1304–1319. doi: 10.1109/TCOMM.2021.3127265.
[24]	ZHOU Shuangquan, ZHANG Wenbin, XU Fanglei, et al. Energy-efficient resource allocation in DDPG-based integrated satellite-terrestrial network[C]. Proceedings of 2023 IEEE Globecom Workshops, Kuala Lumpur, Malaysia, 2023: 147–152. doi: 10.1109/GCWkshps58843.2023.10464487.
[25]	SUTTON R S, MCALLESTER D, SINGH S, et al. Policy gradient methods for reinforcement learning with function approximation[C]. Proceedings of the 13th International Conference on Neural Information Processing Systems, Denver CO, USA, 1999: 1057–1063.