Cross-layer Scheduling Algorithm Research For LTE System

With the rapid development of radio network technology, comes the explosive growth of service requirement, service mode gradually moves to high-bandwidth multimedia service, which puts the defect of resource allocation strategy based on wired network's layered architecture under the highlight. Due to the degraded QoS and radio resource usage ratio provided by the layered resource allocation scheme, cross-layer resource allocation method was promoted, it considers the separated layers as a whole, useful information such as channel condition, traffic's QoS requirement are shared among layers, each layer adjusts its behavior according to those information so as to optimize the system performance.These years, extensive attention has been received by the LTE, which is considered to be the most potential 4G standard. The cross-layer resource allocation in LTE is still an open issue, in this paper we focus on the problem that how to improve the QoS in cross-layer design. For the delay optimization in QoS-guarantee, we propose a cross-layer resource schedule algorithm which unites physical layer and link layer. This algorithm is based on queue theory and puts physical channel condition, link layer's queue state, modulation and coding scheme and available transmit power into consideration, establishing a restraint utility function as the criterion of resource allocation to minimize the average packet delay. Delay jitter is another important aspect of QoS-guarantee, it has a serious influence on TCP's estimation of round-trip delay. Too much delay jitter can lead to TCP's false estimation and a large retransmission timeout timer, resulting in the increment of retransmission waiting time. We propose a PHY-MAC cross-layer resource allocation strategy, which introduces delay jitter factor into the control of user priority, to reduce the average delay jitter. The user schedule priority is updated according to the current delay jitter condition. According to the simulation results of the two algorithms, both of the algorithms improve the performance compared to the traditional algorithm.