Energy and Bursty Packet Loss Tradeoff Over Fading Channels: A System-Level Model

Abstract

Abstract-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 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 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 energyperformance trade-off numerically and show the effect of channel uncertainty on the packet scheduler design.Index Terms-Energy efficiency, Markov chain, opportunistic scheduling, radio resource allocation, green communications, cross layer design.

On Optimizing Power Allocation For Reliable Communication Over Fading Channels With Uninformed Transmitter

“…It is therefore, important to save transmit energy as much as possible. Depending on the application, the UE has two constraints on the reliability of data packet transfer [26], [27 2) Maximum number of packets dropped successively. This is called bursty packet drop constraint.…”

“…In the optimization problem in (27), average packet drop rate γ, successive packet loss constraint N and outage probability out affect the average power consumption. It is worth noting that parameter γ is the only parameter that controls the 'quantity' of data loss.…”

“…Simulated Annealing (SA) is another similar stochastic optimization algorithm with the distinct feature that it helps avoid the solution to get stuck in local minima by introducing a probabilistic process called 'muting' as explained later in this section. The algorithm was originally introduced in statistical mechanics, and has been applied successfully to networking problems [26], [27]. Based on its ability to compute global minima with high probability, we use SA algorithm to solve optimization problem in (15) and (27).…”

“…The algorithm was originally introduced in statistical mechanics, and has been applied successfully to networking problems [26], [27]. Based on its ability to compute global minima with high probability, we use SA algorithm to solve optimization problem in (15) and (27).…”

“…However, these works do not characterize the effect of average and bursty packet loss on the consumed energy at link level. Some recent studies in [1], [26], [27] characterize the effect of packet loss burstiness on average system energy for a multiuser wireless communication system where the transmit channel state information (CSIT) is fully available or erroneous.…”