Control of multi-node mobile communications networks with time-varying channels via stability methods

Consider a communications network consisting of mobiles and random external data processes, each destined to a particular destination. Each mobile can serve as a node in the multi-hop path from source to destination. At each mobile the data is queued according to the source destination pair. The quality of the connecting channels are randomly varying. Time is divided into small scheduling intervals. At the beginning of each interval, the channels are estimated and this information is used for the decisions concerning allocation of transmission power and/or time, bandwidth, and perhaps antennas, in a queue and channel-state dependent way. Under a natural (and “almost” necessary) “average flow” condition, stochastic stability methods are used to develop scheduling policies that assure stability. The policies are readily implementable and allow a range of tradeoffs between current rates and queue lengths, under very weak conditions. Because of the non-Markovian nature of the problem, we use the perturbed Stochastic Liapunov function method. The choice of Liapunov function allows a choice of the effective performance criteria. All essential factors are incorporated into a “mean rate” function, so that the results cover many different systems. Extensions concerning acknowledgments, multicasting, non-unique routes, and others are given to illustrate the versatility of the method, and a useful method for getting the a priori routes is discussed.

[1]  W. Grassman Approximation and Weak Convergence Methods for Random Processes with Applications to Stochastic Systems Theory (Harold J. Kushner) , 1986 .

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

[3]  Hong Chen Fluid Approximations and Stability of Multiclass Queueing Networks: Work-Conserving Disciplines , 1995 .

[4]  Sean P. Meyn,et al.  Stability and convergence of moments for multiclass queueing networks via fluid limit models , 1995, IEEE Trans. Autom. Control..

[5]  Harold J. Kushner,et al.  Stochastic Approximation Algorithms and Applications , 1997, Applications of Mathematics.

[6]  Maury Bramson,et al.  Stability of two families of queueing networks and a discussion of fluid limits , 1998, Queueing Syst. Theory Appl..

[7]  Hong Chen,et al.  Stability of Multiclass Queueing Networks Under Priority Service Disciplines , 2000, Oper. Res..

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

[9]  Matthew Andrews,et al.  Providing quality of service over a shared wireless link , 2001, IEEE Commun. Mag..

[10]  Harold J. Kushner,et al.  Control of mobile communications with time-varying channels in heavy traffic , 2002, IEEE Trans. Autom. Control..

[11]  Harold J. Kushner,et al.  Control of mobile communication systems with time-varying channels via stability methods , 2004, IEEE Transactions on Automatic Control.

[12]  G. Michailidis,et al.  Queueing and scheduling in random environments , 2004, Advances in Applied Probability.

[13]  A. Stolyar MaxWeight scheduling in a generalized switch: State space collapse and workload minimization in heavy traffic , 2004 .

[14]  Nicholas Bambos,et al.  Queueing Networks of Random Link Topology: Stationary Dynamics of Maximal Throughput Schedules , 2005, Queueing Syst. Theory Appl..

[15]  Harold J. Kushner Stability of Single Class Queueing Networks , 2005 .

[16]  Ahmed K. Elhakeem,et al.  Performance evaluation of multihop ad hoc WLANs , 2005, IEEE Communications Magazine.

[17]  H.J. Kushner Scheduling and Control of Mobile Communications Networks with Randomly Time Varying Channels by Stability Methods , 2006, Proceedings of the 45th IEEE Conference on Decision and Control.

[18]  Harold J. Kushner Scheduling and Control of Multi-Node Mobile Communications Systems with Randomly-Varying Channels by Stability Methods , 2007 .