Hybrid Reflecting Intelligent Surface Assisted Sensing Communication and Computation for Joint Power and Time Allocation in Vehicle Ad-hoc Network

SHU Feng; ZHANG Junhao; ZHANG Qi; YAO Yu; BIAN Hongyi; WANG Xianpeng

doi:10.11999/JEIT240719

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SHU Feng, ZHANG Junhao, ZHANG Qi, YAO Yu, BIAN Hongyi, WANG Xianpeng. Hybrid Reflecting Intelligent Surface Assisted Sensing Communication and Computation for Joint Power and Time Allocation in Vehicle Ad-hoc Network[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240719

Citation:

SHU Feng, ZHANG Junhao, ZHANG Qi, YAO Yu, BIAN Hongyi, WANG Xianpeng. Hybrid Reflecting Intelligent Surface Assisted Sensing Communication and Computation for Joint Power and Time Allocation in Vehicle Ad-hoc Network[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240719

Citation:

SHU Feng, ZHANG Junhao, ZHANG Qi, YAO Yu, BIAN Hongyi, WANG Xianpeng. Hybrid Reflecting Intelligent Surface Assisted Sensing Communication and Computation for Joint Power and Time Allocation in Vehicle Ad-hoc Network[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240719

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Hybrid Reflecting Intelligent Surface Assisted Sensing Communication and Computation for Joint Power and Time Allocation in Vehicle Ad-hoc Network

doi: 10.11999/JEIT240719

1.
School of Information and Communication Engineering, Hainan University, Haikou 570228, China
2.
School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China

Funds: The National Natural Science Foundation of China (U22A2002, 62071234), Hainan Province Science and Technology Special Fund (ZDKJ2021022), The Scientific Research Fund Project of Hainan University(KYQD(ZR)-21008), The Collaborative Innovation Center of Information Technology, Hainan University (XTCX2022XXC07)

Received Date: 2024-08-19
Rev Recd Date: 2025-01-07

Available Online: 2025-01-11

Abstract

Abstract

Objective Vehicular networks, as key components of intelligent transportation systems, are encountering increasing spectrum resource limitations within their dedicated 25 MHz communication band, as well as challenges from electromagnetic interference in typical communication environments. To address these issues, this paper integrates cognitive radio technology with radar sensing and introduces Hybrid-Reconfigurable Intelligent Surfaces (H-RIS) to jointly optimize radar sensing, data transmission, and computation. This approach aims to enhance spectrum resource utilization and the Joint Throughput Capacity (JTC) of vehicular networks. Methods A phased optimization approach is adopted to alternately optimize power allocation, time allocation, and reflection components in order to identify the best solution. The data transmission capacity of secondary users is characterized by defining a performance index for JTP. The problem is tackled through a two-stage optimization strategy where power allocation, time allocation, and reflection element optimization are solved iteratively to achieve the optimal solution. First, a joint optimization problem for sensing, communication, and computation is formulated. By jointly optimizing time allocation, H-RIS reflection element coefficients, and power allocation, the goal is to maximize the joint throughput capacity. The block coordinate descent method decomposes the optimization problem into three sub-problems. In the optimization of reflection element coefficients, a stepwise approach is employed, where passive reflection elements are fixed to optimize active reflection elements and vice versa. Results and Discussions The relationship between joint throughput and the number of iterations for the proposed Alternating Optimization Iterative Algorithm (AOIA) is shown (Figure 4). The results indicate that both algorithms converge after a finite number of iterations. The correlation between the target user's joint throughput and radar power is presented (Figure 5). In the H-RIS-assisted Integrated Sensing Communication and Computation Vehicle-to-Everything (ISCC-V2X) scenario, the joint throughput of the Aimed Secondary User (ASU) is maximized through optimal power configuration (Figure 5). The comparison of the target user joint throughput with radar system power for the proposed algorithm and baseline schemes is shown (Figure 6), demonstrating that the proposed method significantly outperforms random Reconfigurable Intelligent Surfaces (RIS) and No-RIS schemes under the same parameter settings. Furthermore, the proposed H-RIS optimization scheme outperforms both Random H-RIS and traditional passive optimization RIS in terms of joint throughput.The relationship between target user joint throughput and the number of H-RIS reflection elements is illustrated (Figure 7). The results show that the proposed scheme provides a significant performance improvement over both Random RIS and No-RIS schemes under the same parameter settings. The relationship between the transmit power of the target secondary user's joint throughput and the transmit power of the ASU is depicted (Figure 9), highlighting that joint throughput increases with transmit power in all scenarios. The relationship between joint throughput and the number of active reflection elements for the proposed algorithm and other benchmark schemes is shown (Figure 10), demonstrating that joint throughput increases with the number of active reflection elements in H-RIS scenarios, with the proposed scheme exhibiting a faster growth rate than Random H-RIS. The relationship between ASU joint throughput, radar sensing time, and radar power is presented (Figure 11), revealing that an optimal joint time and power allocation strategy exists. This strategy maximizes ASU joint throughput while ensuring H-RIS presence and sufficient protection for the primary user. Conclusion To address the challenges of spectrum resource scarcity and low data transmission efficiency in vehicular networks, this paper focuses on improving the joint throughput of intelligent vehicle users, enhancing spectrum utilization, and achieving efficient data transmission in the H-RIS-assisted ISCC-V2X scenario. A joint optimization method for vehicular network perception, communication, and computation based on H-RIS is explored. The introduction of H-RIS aims to enhance data transmission efficiency while considering the interests of both primary and secondary users. The joint optimization problem for the target secondary user's perception, communication, and computation is analyzed. First, the joint allocation scenario for the H-RIS-assisted ISCC-V2X system is constructed, introducing the signal model, radar perception model, communication model, and computation model. Using these models, a joint optimization problem is formulated. Through alternating optimization, the optimal H-RIS reflection element coefficients, time allocation vector, and power allocation vector are derived to maximize the joint throughput. Simulation results demonstrate that the incorporation of H-RIS significantly improves the joint throughput of the target secondary user. Furthermore, an optimal power allocation scheme is identified that maximizes the joint throughput. When both time allocation and power allocation are considered jointly, simulations show the existence of an optimal scheme that maximizes the joint throughput of the target secondary user.

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References(38)

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