无线多媒体网络上行混合重传资源分配算法

Resource Allocation Algorithm for Uplink Hybrid Retransmission in Wireless Multimedia Networks

摘要

针对受传输信道及传输队列任务的动态属性影响,以固定方案分配传输资源时,网络上行混合重传的传输速率难以实现高水平稳定输出的问题,提出一种无线多媒体网络上行混合重传资源分配算法.首先,结合Shannon公式计算网络增强移动带宽执行上行混合重传的峰值速率,利用二元变量确定峰值速率下的信道资源分配期望,并构建以传输速率为参量的网络上行混合重传资源分配目标函数,使目标函数包含信道动态属性;其次,对目标函数进行求解时,引入了迭代机制,将约束Lyapunov漂移的资源分配参数作为传输队列当前任务的分配结果,根据传输队列任务递推更新分配方案.实验结果表明,该算法对网络上行混合重传资源的分配结果有较高的公平性,在实现高吞吐量的基础上,传输速率始终稳定在较高水平.

Abstract

Due to the dynamic properties of transmission channels and transmission queue tasks, it is difficult to achieve high-level stable output of network uplink hybrid retransmission transmission rate using a fixed resource allocation scheme is challenging. Therefore, research on wireless multimedia network uplink hybrid retransmission resource allocation algorithm has been carried out. Combining Shannon’s formula to calculate the peak rate of network enhanced mobile bandwidth for uplink hybrid retransmission, using binary variables to determine the expected channel resource allocation at the peak rate, and constructing a network uplink hybrid retransmission resource allocation objective function with transmission rate as a parameter, so that the objective function includes channel dynamic properties; when solving the objective function, an iterative mechanism is introduced to use the resource allocation parameters that constrain Lyapunov drift as the allocation result of the current task in the transmission queue, and update the allocation scheme recursively based on the transmission queue task. The test results demonstrate that the application design algorithm achieves high fairness in allocating network uplink hybrid retransmission resources, and the transmission rate remains stable at a high level while delivering high throughput.

关键词

无线多媒体网络 / 上行混合重传 / 资源分配 / 峰值速率 / 目标函数 / 迭代机制 / Lyapunov漂移

Key words

wireless multimedia network / upstream mixed retransmission / resource allocation / peak rate / objective function / iterative mechanism / Lyapunov drift

参考文献

[1] 崔馨月, 富爽. 基于DEABC算法的EH-CRAIoT的资源分配算法研究[J]. 现代电子技术, 2024, 47(13):55-60.

[2] (Cui X Y, Fu S. DEABC-Based Cognitive Radio Agriculture IoT Resource Allocation Algorithm with Function of Energy Harvesting[J]. Modern Electronics Technique, 2024, 47(13): 55-60.)

[3] 黄燕辉, 张陆洋, 吴旭阳, 等. 协作传输下的RRH分簇映射与资源分配策略[J]. 重庆邮电大学学报(自然科学版), 2024, 36(5):879-887.

[4] (Huang Y H, Zhang L Y, Wu X Y, et al. RRH Clustering Mapping and Resource Allocation Strategy for Cooperative Transmission[J]. Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition), 2024, 36(5): 879-887.)

[5] 王果信, 方旭明, 何蓉. 同步多链路Wi-Fi网络基于DDQN的资源分配和优化[J]. 重庆邮电大学学报(自然科学版), 2024, 36(6):1110-1119.

[6] (Wang G X, Fang X M, He R. DDQN-Based Resource Allocation and Optimization for Synchronous Multilink Wi-Fi Networks[J]. Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition), 2024, 36(6): 1110-1119.)

[7] 钮金鑫. 基于射频能量收集的无线中继网络资源分配[J]. 电讯技术, 2024, 64(1):98-105.

[8] (Niu J X. Resource Allocation in RF Energy Harvesting Based Wireless Relay Networks[J]. Telecommunication Engineering, 2024, 64(1): 98-105.)

[9] 刘英杰, 刘士虎, 高海燕, 等. 结合邻居影响和资源分配的链路预测算法[J]. 郑州大学学报(理学版), 2024, 56(1):53-59.

[10] (Liu Y J, Liu S H, Gao H Y, et al. Link Prediction Algorithm Combining Neighbor Influence and Resource Allocation[J]. Journal of Zhengzhou University(Natural Science Edition), 2024, 56(1): 53-59.)

[11] 马礼智, 唐睿, 张睿智, 等. 基于无线能量传输的物联网数据采集系统中资源分配机制的设计[J]. 信息与控制, 2023, 52(2):220-234.

[12] (Ma L Z, Tang R, Zhang R Z, et al. Design of Resource Allocation Mechanisms for Wireless Power Transfer-Based Internet-of-Things Data Collection System[J]. Information and Control, 2023, 52(2): 220-234.)

[13] 王艳阁, 奚建清. 具有隐私保护的车联网边缘计算任务卸载资源分配策略[J]. 计算机工程与设计, 2023, 44(2):372-378.

[14] (Wang Y G, Xi J Q. Privacy Preserving Resource Allocation Strategy for Edge Computing Task Offloading in Internet of Vehicles[J]. Computer Engineering and Design, 2023, 44(2): 372-378.)

[15] 杨霁, 张林, 向菲, 等. 基于无线能量传输的移动边缘计算联合资源分配[J]. 计算机应用与软件, 2023, 40(2):151-157.

[16] (Yang J, Zhang L, Xiang F, et al. Joint Resource Allocation for Mobile Edge Computing Based on Wireless Energy Transfer[J]. Computer Applications and Software, 2023, 40(2): 151-157.)

[17] 康万杰, 潘有顺. 光纤网络中云计算资源联合最优分配方法[J]. 激光与红外, 2022, 52(11):1689-1694.

[18] (Kang W J, Pan Y S. The Joint Optimal Allocation Method of Cloud Computing Resources in Optical Fiber Network[J]. Laser & Infrared, 2022, 52(11): 1689-1694.)

[19] 魏会娜, 翟明岳. 面向边缘计算的电力线通信资源分配优化方法[J]. 激光杂志, 2022, 43(5):121-127.

[20] (Wei H N, Zhai M Y. Power Line Communication Resource Allocation Optimization Method for Edge Computing-Oriented[J]. Laser Journal, 2022, 43(5): 121-127.)

[21] 陈发堂, 杨玲, 夏苗苗. 超密集网络中基于MEC的任务卸载和资源分配[J]. 南京邮电大学学报(自然科学版), 2022, 42(2):22-30.

[22] (Chen F T, Yang L, Xia M M. Task Offloading and Resource Allocation Based on MEC in Ultra-dense Networks[J]. Journal of Nanjing University of Posts and Telecommunications(Natural Science Edition), 2022, 42(2): 22-30.)

[23] 许方敏, 伍丽娇, 王翔, 等. 5G上行链路中基于预测的紧急资源分配方法研究[J]. 电子与信息学报, 2022, 44(2):611-619.

[24] (Xu F M, Wu L J, Wang X, et al. Research on Prediction Based Emergency Resource Allocation in 5G Uplink[J]. Journal of Electronics & Information Technology, 2022, 44(2): 611-619.)

基金资助

全国教育科学“十四五”规划教育部重点项目(DIA220367)