Individual packet deadline delay constrained opportunistic scheduling for large multiuser systems

This work addresses opportunistic distributed multiuser scheduling in the presence of a fixed packet deadline delay constraint. A threshold-based scheduling scheme is proposed which uses the instantaneous channel gain and buffering time of the individual packets to schedule a group of users simultaneously in order to minimize the average system energy consumption while fulfilling the deadline delay constraint for every packet. The multiuser environment is modeled as a continuum of interference such that the optimization can be performed for each buffered packet separately by using a Markov chain where the states represent the waiting time of each buffered packet. We analyze the proposed scheme in the large user limit and demonstrate the delay-energy trade-off exhibited by the scheme. We show that the multiuser scheduling can be broken into a packet-based scheduling problem in the large user limit and the packet scheduling decisions are independent of the deadline delay distribution of the packets

Energy and bursty packet loss tradeoff over fading channels: a system-level model

Energy efficiency and quality of service (QoS) guarantees are the key design goals for the 5G wireless communication systems. In this context, we discuss a multiuser scheduling scheme over fading channels for loss tolerant applications. The loss tolerance of the application is characterized in terms of different parameters that contribute to quality of experience (QoE) for the application. The mobile users are scheduled opportunistically such that a minimum QoS is guaranteed. We propose an opportunistic scheduling scheme and address the cross-layer design framework when channel state information (CSI) is not perfectly available at the transmitter and the receiver. We characterize the system energy as a function of different QoS and channel state estimation error parameters. The optimization problem is formulated using Markov chain framework and solved using stochastic optimization techniques. The results demonstrate that the parameters characterizing the packet loss are tightly coupled and relaxation of one parameter does not benefit the system much if the other constraints are tight. We evaluate the energy-performance tradeoff numerically and show the effect of channel uncertainty on the packet scheduler design