Time-Scale Decomposition Resource Allocation And Non-Convex Optimization In Broadband Wireless Networks

In resent years, the research on how to provide quality of service (QoS) guarantees to multimedia applications in broadband wireless networks has been a hot spot. Radio resource allocation and scheduling is one of key functions to provide QoS guarantees in wireless networks. Current research focuses on design of efficient algorithms taking into account the special characteristics of the wireless environment such as time-varying channel capacity and location-dependent errors. These algorithms shall maximize the utilization of the wireless channels and guarantee QoS for the users, while providing certain fairness between users. Many questions remain open and even some definitions are ambiguous in this area. For example, there are several different definitions of fairness addressed in related works, such as time-fraction fairness [1], throughput fairness [2], utility fairness and price fairness [4][5].In this paper, a novel scheme of radio resource allocation and scheduling and its system model is proposed through the time-scale decomposition approach. (1) For wireless physical layers, the efficiency function is defined to quantify the upper layer throughput per unit of wireless resources while maintaining a certain maximum remnant bit error rate subjected to a certain SNR. (2) For multimedia applications, it is advantageous to use the different types of utility functions to express different type of QoS requirements, because it can express two major QoS metrics (bandwidth and delay) simultaneously and be used to represent adaptive QoS requirement. (3) For the wireless channels subject to several types of fading existing in different time scales, the dynamics of channel conditions are decoupled into two random processes with different mathematic properties in different time scales. Two algorithms in this scheme are proposed to dealing with each time scale: the resource optimizer allocates the resource to maximize the total revenue with price fairness and provide QoS guarantees to applications, and the slot scheduler exploits the time variability ofchannel capacities to provide higher throughputs to users than the ones obtained in mean channel conditions.The problem of resource allocation in this scheme is a non-convex non-linear optimization problem. By defining an eigen allocation vector, we propose an algorithm to obtain the global optima or sub-optimal solution. The sufficient condition to obtain the global optima is also presented. In this paper, it is proved that when the condition is not satisfied, the gap between the sub-optima solution and the global optima (expressed by a percentage of the global optima) is no more than the ratio between the left resources of the sub-optimal solution and the total resources, and is no more than the ratio between the cutoff resources obtained by one application and the total resources. Finally, the low-complexity online algorithms are presented for the radio resource allocation and scheduling in this scheme. The results of the computer simulations show that the online resource-allocation algorithm converges to the globe optima. The results also show that our scheme can obtain a 50% gain over the Only Scheduler scheme, while providing QoS guarantees.