Ant-Colony-Optimization-Based Scheduling Algorithm for Uplink CDMA Nonreal-Time Data

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.