Ant-Colony-Optimization-Based Scheduling Algorithm for Uplink CDMA Nonreal-Time Data
Ant-Colony-Optimization-Based Scheduling Algorithm for Uplink CDMA Nonreal-Time Data
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DOI：
10.1109/tvt.2008.924983
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发表时间：
2009
影响因子：
6.8
通讯作者：
Jun-Bo Wang;Ming Chen;Xili Wan;Chengjian Wei
中科院分区：
文献类型：
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作者：
Jun-Bo Wang;Ming Chen;Xili Wan;Chengjian Wei
调度在确定码分多址（CDMA）系统的整体性能方面起着重要作用。本文主要研究CDMA非实时数据的上行调度。在实际的CDMA系统中，数据只能以几个固定的传输速率进行传输。此外，为了保证接收精度，期望实际接收信号功率与干扰加噪声功率比（SINR）不小于目标SINR值。使用Heaviside单位阶跃函数，在所提出的系统模型中描述了实际SINR值和实际可用最大传输速率之间的关系。基于所提出的系统模型，制定了整数优化问题以同时最大化吞吐量和调度效率。特别是，提出了一种基于蚁群优化（ACO）的调度算法来解决所提出的优化问题。计算复杂度分析表明，所提出的基于 ACO 的调度算法在运行时间和存储空间方面都具有计算效率。此外，数值结果表明，所提出的优化问题能够更有效地指导上行CDMA非实时数据调度算法的开发。此外，所提出的基于ACO的调度算法在质量、运行时间和稳定性方面都表现良好。
Scheduling plays an important role in determining the overall performance of code-division multiple-access (CDMA) systems. This paper is focused on the uplink scheduling of CDMA nonreal-time data. In practical CDMA systems, data can only be transmitted with a few fixed transmission rates. Moreover, to guarantee receiving accuracy, the actual received signal-power-to-interference-plus-noise-power ratio (SINR) is expected to be no less than the target SINR value. Using Heaviside unit step functions, the relationship between the actual SINR value and the actual available maximum transmission rate is described in the proposed system model. Based on the proposed system model, an integer optimization problem is formulated to simultaneously maximize the throughput and the scheduling efficiency. Particularly, an ant-colony-optimization (ACO)-based scheduling algorithm is proposed to solve the proposed optimization problem. The computational complexity analysis indicates that the proposed ACO-based scheduling algorithm is computationally efficient in terms of both running time and storage space. In addition, the numerical results show that the proposed optimization problem is more efficient at guiding the development of scheduling algorithms for uplink CDMA nonreal-time data. Moreover, the proposed ACO-based scheduling algorithm performs quite well in terms of quality, running time, and stability.