面向信息新鮮度保障的車聯(lián)網(wǎng)功率控制和資源分配策略

楊鵬; 康一銘; 楊靜; 唐桐; 祝志遠(yuǎn); 吳大鵬

doi:10.11999/JEIT240698

面向信息新鮮度保障的車聯(lián)網(wǎng)功率控制和資源分配策略

doi: 10.11999/JEIT240698 cstr: 32379.14.JEIT240698

楊鵬^{1, 2, 3},
康一銘¹,
楊靜¹,
唐桐¹,
祝志遠(yuǎn)¹,
吳大鵬^1, ,

1.
重慶郵電大學(xué)通信與信息工程學(xué)院重慶 400065
2.
中國信息通信研究院北京 100191
3.
信通院(西安)科技創(chuàng)新有限公司西安 710116

基金項(xiàng)目: 國家自然科學(xué)基金(U24A20211, 62271096, U20A20157)，重慶市自然科學(xué)基金(CSTB2023NSCQ-LZX0134, CSTB2024NSCQ-LZX0124)，重慶市高校創(chuàng)新研究群體項(xiàng)目(CXQT20017)，重郵信通青創(chuàng)團(tuán)隊(duì)支持計(jì)劃(SCIE-QN-2022-04)

詳細(xì)信息

作者簡介:
楊鵬：男，高級工程師，研究方向?yàn)闃?biāo)識解析、工業(yè)軟件數(shù)據(jù)集成

康一銘：男，碩士生，研究方向?yàn)檐嚶?lián)網(wǎng)資源管理

楊靜：女，高級工程師，研究方向?yàn)闊o線通信網(wǎng)絡(luò)、物聯(lián)網(wǎng)技術(shù)等

唐桐：男，講師，研究方向?yàn)橐曨l編碼傳輸?shù)?/p>
祝志遠(yuǎn)：男，講師，研究方向?yàn)榭尚胚吘売?jì)算

吳大鵬：男，教授，研究方向?yàn)榉涸跓o線網(wǎng)絡(luò)、社會(huì)計(jì)算等

通訊作者:
吳大鵬　wudp@cqupt.edu.cn

中圖分類號: TN929.5
計(jì)量
- 文章訪問數(shù): 319
- HTML全文瀏覽量: 149
- PDF下載量: 41
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2024-08-06
- 修回日期: 2025-01-02
- 網(wǎng)絡(luò)出版日期: 2025-01-17
- 刊出日期: 2025-02-28

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

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)

摘要

摘要: 在差異化服務(wù)共存的車聯(lián)網(wǎng)場景中，針對基于平均信息年齡(AoI)優(yōu)化無法降低極端事件發(fā)生概率的問題，該文提出一種信息新鮮度保障的用戶功率控制和資源分配策略。首先，根據(jù)系統(tǒng)模型刻畫出車輛到車輛(V2V)用戶狀態(tài)更新信息新鮮度約束下最大化車輛到基站(V2I)用戶體驗(yàn)質(zhì)量(QoE)的問題。然后，結(jié)合與AoI中斷約束等價(jià)的隊(duì)列積壓約束，并引入極值理論以優(yōu)化AoI尾部分布。接著，基于李雅普諾夫優(yōu)化方法將原問題轉(zhuǎn)化最小化李雅普諾夫漂移加懲罰函數(shù)的問題，在此基礎(chǔ)上求解最優(yōu)的用戶發(fā)射功率。最后，在構(gòu)建超圖的基礎(chǔ)上，提出了一種基于遺傳算法改進(jìn)粒子群算法(GA-PSO)的資源分配策略確定最優(yōu)的用戶信道復(fù)用方式。仿真結(jié)果表明，相比于基準(zhǔn)方案，所提方案能夠在降低V2V鏈路AoI中斷的極端事件發(fā)生概率的同時(shí)，提高約7.03%的V2I鏈路信道容量，實(shí)現(xiàn)V2I用戶平均QoE提升。
- 資源分配 /
- 功率控制 /
- 信息年齡 /
- 極端事件
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

HTML全文

圖 1 系統(tǒng)模型

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圖 2 V2I-Channel-V2V 3維匹配圖

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圖 3 GA-PSO算法流程

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圖 4 不同算法下所有V2I用戶平均QoE的CDF對比

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圖 5 不同車速下所有V2I用戶的平均QoE對比

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圖 6 所提算法與基準(zhǔn)算法AoI的CCDF對比

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圖 7 所提算法與基準(zhǔn)算法信標(biāo)積壓的CCDF對比

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圖 8 不同車速下AoI 的CCDF對比

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圖 9 狀態(tài)更新到達(dá)率與AoI的權(quán)衡

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表 1 仿真參數(shù)

參數(shù)	取值
載波頻率	2 GHz
帶寬	10 MHz
基站覆蓋半徑	500 m
基站與公路距離	35 m
車道寬度	4 m
車輛天線高度	1.5 m
車輛天線增益	3 dBi
基站接收機(jī)噪聲	5 dB
車輛接收機(jī)噪聲	9 dB
V2I鏈路最大發(fā)射功率	23 dBm
V2V鏈路最大發(fā)射功率	23 dBm
V2V車輛收發(fā)距離	15 m
噪聲功率	–114 dBm
車速	60 km/h
時(shí)隙長度	3 ms
最大可容忍AoI	60 ms
AoI中斷概率閾值	0.001
狀態(tài)更新到達(dá)率	125 update/s
每個(gè)信標(biāo)數(shù)據(jù)量大小	500 Byte

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