A Large Deviations Analysis of Scheduling in Wireless Networks

We consider a cellular network consisting of a base station and N receivers. The channel to each receiver is assumed to be in one of two states (ON or OFF) and the channel states of the receivers are assumed to be independent of each other. The goal is to compare the throughput of two different scheduling policies given an upper bound on the queue overflow probability or the delay violation probability. The two scheduling policies that we consider are: (i) a greedy scheduling policy which chooses to serve any of the channels in the ON state, and (ii) a queue-length-based policy which serves the longest queue connected to an ON channel. We show that the total network throughput of the queue-length-based policy is no less than that of the greedy policy for all N and is strictly larger than the throughput of the greedy policy for large N. Further, given an upper bound on the delay violation probability, we show that the throughput of the queue-length-based policy is an increasing function of N while the throughput of the greedy policy eventually decreases with increasing N and goes to zero. Given an upper bound on the queue overflow probability, we show that the throughput of the queue-length-based policy is a strictly increasing function of N while the throughput of the greedy policy eventually goes to a constant.

[1]  Philip A. Whiting,et al.  Cdma data qos scheduling on the forward link with variable channel conditions , 2000 .

[2]  R. Srikant,et al.  Stable scheduling policies for fading wireless channels , 2005, IEEE/ACM Transactions on Networking.

[3]  Geir E. Dullerud,et al.  A Large Deviations Analysis of Scheduling in Wireless Networks , 2005, CDC 2005.

[4]  P. Glynn,et al.  Logarithmic asymptotics for steady-state tail probabilities in a single-server queue , 1994, Journal of Applied Probability.

[5]  S. Shakkottai,et al.  Pathwise optimality of the exponential scheduling rule for wireless channels , 2004, Advances in Applied Probability.

[6]  John N. Tsitsiklis,et al.  Asymptotic buffer overflow probabilities in multiclass multiplexers: an optimal control approach , 1998, IEEE Trans. Autom. Control..

[7]  Leandros Tassiulas,et al.  Dynamic server allocation to parallel queues with randomly varying connectivity , 1993, IEEE Trans. Inf. Theory.

[8]  A. Stolyar,et al.  LARGEST WEIGHTED DELAY FIRST SCHEDULING: LARGE DEVIATIONS AND OPTIMALITY , 2001 .

[9]  Eytan Modiano,et al.  Optimal Transmission Scheduling in Symmetric Communication Models With Intermittent Connectivity , 2007, IEEE Transactions on Information Theory.

[10]  D. Stroock,et al.  Probability Theory: An Analytic View , 1995, The Mathematical Gazette.

[11]  Alexander L. Stolyar,et al.  Scheduling for multiple flows sharing a time-varying channel: the exponential rule , 2000 .

[12]  Ness B. Shroff,et al.  Opportunistic transmission scheduling with resource-sharing constraints in wireless networks , 2001, IEEE J. Sel. Areas Commun..

[13]  Atilla Eryilmaz,et al.  Stable scheduling policies for fading wireless channels , 2003, IEEE International Symposium on Information Theory, 2003. Proceedings..

[14]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[15]  David Tse,et al.  Opportunistic beamforming using dumb antennas , 2002, IEEE Trans. Inf. Theory.

[16]  Eytan Modiano,et al.  Power and server allocation in a multi-beam satellite with time varying channels , 2002, Proceedings.Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies.