A time-scale decomposition approach to optimize wireless packet resource allocation and scheduling

Wireless channels are error-prone and susceptible to several kinds of interference from different time scales. This paper investigates the resource optimization and scheduling of wireless networks through a time-scale decomposition approach. We decompose the dynamics of time-varying wireless channel conditions into two random processes in different time scales: a slow time-varying process in a larger time scale, called frame-scale, and a stationary random process with high variation in a smaller time scale, called slot-scale. This results in two different algorithms dealing with each time-scale: the resource optimizer optimize the sum of utilities in the slowly changed time scale, and the slot scheduler exploits the efficiency in the highly variable time scale. Our scheme can obtain a high utilization while providing service guarantee. Simulation results show our scheme can improve the performance and efficiency substantially.