Power Control and Resource Allocation Strategy for Information Freshness Guarantee in Internet of Vehicles

YANG Peng; KANG Yiming; YANG Jing; TANG Tong; ZHU Zhiyuan; WU Dapeng

doi:10.11999/JEIT240698

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YANG Peng, KANG Yiming, YANG Jing, TANG Tong, ZHU Zhiyuan, WU Dapeng. Power Control and Resource Allocation Strategy for Information Freshness Guarantee in Internet of Vehicles[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240698

Citation:

YANG Peng, KANG Yiming, YANG Jing, TANG Tong, ZHU Zhiyuan, WU Dapeng. Power Control and Resource Allocation Strategy for Information Freshness Guarantee in Internet of Vehicles[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240698

Citation:

YANG Peng, KANG Yiming, YANG Jing, TANG Tong, ZHU Zhiyuan, WU Dapeng. Power Control and Resource Allocation Strategy for Information Freshness Guarantee in Internet of Vehicles[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240698

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Power Control and Resource Allocation Strategy for Information Freshness Guarantee in Internet of Vehicles

doi: 10.11999/JEIT240698

YANG Peng^{1, 2, 3},
KANG Yiming¹,
YANG Jing¹,
TANG Tong¹,
ZHU Zhiyuan¹,
WU Dapeng^{1
,
,}

1.
School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
China Academy of Information and Communications Technology, Beijing 100191, China
3.
Institute of Information and Communications Technology Innovation Co., Ltd., Xi’an 710116, China

Funds: The National Natural Science Foundation of China (U24A20211, 62271096, U20A20157), The Natural Science Foundation of Chongqing (CSTB2023NSCQ-LZX0134, CSTB2024NSCQ-LZX0124), The University Innovation Research Group Project of Chongqing (CXQT20017), The Youth Innovation Group Support Program of ICE Discipline of CQUPT (SCIE-QN-2022-04)

Received Date: 2024-08-06
Rev Recd Date: 2025-01-02

Available Online: 2025-01-17

Abstract

Abstract

Objective In the Internet of Vehicles (IoV), where differentiated services coexist, the system is progressively evolving towards safety and collaborative control applications, such as autonomous driving. Current research primarily focuses on optimizing mechanisms for high reliability and low latency, with Quality of Service (QoS) parameters commonly used as benchmarks, while the timeliness of vehicle status updates receives less attention. Merely optimizing metrics like transmission delay and throughput is insufficient for ensuring that vehicles obtain status information in a timely manner. For example, in security-critical IoV applications, which require the exchange of state information between vehicles, meeting only the constraints of delay interruption probability or data transmission interruption does not fully address the high timeliness requirements of security services. To tackle this challenge and meet the stringent timeliness demands of security and collaborative applications, this paper proposes a user power control and resource allocation strategy aimed at ensuring information freshness. Methods This paper investigates user power control and resource allocation strategies to ensure information freshness. First, the problem of maximizing the Quality of Experience (QoE) for Vehicle-to-Infrastructure (V2I) users under the constraint of freshness in Vehicle-to-Vehicle (V2V) status updates is formulated based on the system model. Then, by incorporating the queue backlog constraint, equivalent to the Age of Information (AoI) violation constraint, the extreme value theory is applied to optimize the tail distribution of AoI. Furthermore, using the Lyapunov optimization method, the original problem is transformed into minimizing the Lyapunov drift plus a penalty function, based on which the optimal user transmission power is determined. Finally, a resource allocation strategy based on Genetic Algorithm improved Particle Swarm Optimization (GA-PSO) is proposed, leveraging a hypergraph structure to determine the optimal user channel reuse mode. Results and Discussions Simulation analysis indicates the following: 1. The proposed algorithm employs a channel gain differential partitioning method to cluster V2V links, effectively reducing intra-cluster interference. By integrating GA-PSO, it accelerates the search for the optimal channel reuse pattern in three-dimensional matching, minimizing signaling overhead and avoiding local optima. Compared with benchmark algorithms, the proposed approach increases V2I channel capacity by 7.03% and significantly improves the average QoE for V2I users (Fig. 4). 2. As vehicle speed increases, the distance between vehicles also grows, leading to higher transmission power for V2V communication to maintain link reliability and timeliness. This power increase results in reduced V2I channel capacity, subsequently lowering the average QoE for V2I users. Simulation results show a nearly linear relationship between vehicle speed and average QoE for V2I users, suggesting a relatively uniform effect of speed on V2I link capacity (Fig. 5). 3. Under varying Vehicle User Equipment (VUE) densities, the extreme event control framework is used to compare the conditional Complementary Cumulative Distribution Function (CCDF) of AoI and V2V link beacon backlog. The equivalent queue constraint, derived using extreme value theory, effectively controls the occurrence of extreme AoI violations. The simulations show improved AoI tail distribution across different VUE densities (Fig. 6 and Fig. 7). 4. With decreasing vehicle speed, the CCDF tail distribution of AoI improves (Fig. 8). Reduced speed shortens the transmission distance, decreasing V2V link path loss. This lower path loss, combined with less restrictive VUE transmission power limits, increases the V2V link transmission rate. As beacon transmission rate increase, beacon backlog is reduced, and the probability of exceeding a fixed AoI threshold decreases, ensuring the freshness of V2V beacon transmissions. 5. A comparison of curves under identical beacon reach rate (Fig. 9) reveals that worst-case AoI consistently increases with rising beacon reach rate. At low beacon arrival rate, the average AoI is high. However, once the V2V beacon queue accumulates beyond a certain threshold, further increases in the update arrival rate also raise the average AoI. In summary, the proposed scheme optimizes both the AoI tail distribution and the QoE for V2I users. Conclusions This paper investigates resource allocation and power control in vehicular network communication scenarios. By simultaneously considering the constraints of transmission reliability and status update timeliness in V2V links, restricted by the Signal-to-Interference-plus-Noise Ratio (SINR) threshold and the AoI outage probability threshold, the proposed strategy ensures both link reliability and information freshness. An extreme control framework is applied to minimize the probability of extreme AoI outage events in V2V links, ensuring the timeliness of transmitted information and meeting service requirements. The Lyapunov optimization method is then used to transform the original problem, yielding the optimal transmission power for both V2I and V2V links. Additionally, a GA-PSO-based three-dimensional matching algorithm is developed to determine the optimal spectrum sharing scheme among V2I, V2V, and subchannels. Numerical results demonstrate that the proposed scheme optimizes the AoI tail distribution while enhancing the QoE for all V2I users.
- Resource allocation,
- Power control,
- Age of Information (AoI),
- Extreme events

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References(24)

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