Opportunistic scheduling: generalizations to include multiple constraints, multiple interfaces, and short term fairness

  • Authors:
  • Sunil Suresh Kulkarni;Catherine Rosenberg

  • Affiliations:
  • School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN;School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN

  • Venue:
  • Wireless Networks
  • Year:
  • 2005

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Abstract

We consider several scheduling problems for packet based systems with time-varying channel conditions. Designing scheduling mechanisms that take advantage of time-varying channel conditions, which are different for different users, is necessary to improve system performance; however this has to be done in a way that provides some level of fairness among the users. Such scheduling mechanisms are termed opportunistic. We generalize the opportunistic scheduling mechanisms in the literature on three fronts. First, we formulate and solve an opportunistic scheduling problem with multiple general long term QoS constraints and a general system objective function. The solution of this opportunistic scheduling problem is an index policy. Then, we generalize this problem to include multiple interface systems in which several users can be served simultaneously. Apart from the long term QoS constraints specified by each user, multiple interface systems are constrained with other physical limitations imposed by the system. We show that the structure of the optimal opportunistic scheduling policy is carried over to the problem with general constraints and multiple interfaces. We also study the stability of the multiple interface systems and propose a throughput optimal scheduling rule for such systems. We then formulate an opportunistic scheduling problem with short term processor sharing fairness constraints as an optimization problem where fairness is guaranteed over a finite time window. In its most general form, this problem cannot be solved analytically. Hence observing the form of the optimal policies for special cases, we propose a heuristic scheduling policy. We illustrate the effectiveness of the policies via simulation.