Performance Analysis for LTE Networks with Markov Decision Process

Long Term Evolution (LTE) has been proposed as a promising radio access technology to bring higher peak data rates and better spectral efficiency. However, scheduling and resource allocation in LTE still face huge design challenges due to their complexity. In this paper, the optimization problem of scheduling and resource allocation for separate streams is first formulated. By separating streaming scheduling and packet sorting, the scheduler is aware of probabilistic state information, fairness among the streams, and the frame weight. Our algorithm thus reduces an Markov Decision Processes(MDP) to a fully probabilistic Markov chain on which Statistical Model Checking (SMC) may be applied to give an approximate solution to the problem of checking the probabilistic Bounded Linear Temporal Logic (BLTL) property. We integrate our algorithm in a parallelized modification of the PRISM simulation framework. Extensive validation with both new and PRISM benchmarks demonstrates that the approach scales very well in scenarios where symbolic algorithms fail to do so. Simulations results with video sequences show that significant gains could be observed by our scheme in terms of spectrum efficiency, QoS of packet delay, and video quality while maintaining the fairness among the streams.