M. Neely, E. Modiano, and C. Rohrs, "Power and server allocation in a multi-beam satellite with time varying channels," in Proceedings of IEEE INFOCOM '02, New York, NY, June 2002.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....proposed by Tse in [18] and is known as Proportional Fair. A variation of Proportional Fair was presented in [12] The case in which the channel conditions and arrival process can be modeled by ergodic Markov chains has been extensively studied. In this setting stable algorithms include MAX WEIGHT [5, 4, 11], MAX DE LAY [5, 4] and EXP [14, 15] MAX WEIGHT selects the user that maximizes q i (t)r i (t) whereas MAX DELAY selects the user that maximizes Delta i (t)r i (t) where Delta i (t) denotes the Head of Line delay for user i at time t. EXP is a more complex algorithm that keeps the user delays ....

M. Neely, E. Modiano, and C. Rohrs. Power and server allocation in a multi-beam satellite with time varying channels. In Proc of IEEE INFOCOM '02, New York, NY, June 2002.

....over the state of the queues. In [20] we show the same policy is stabilizing for Markov modulated input and channel dynamics (see simulation results in Section VII) 1063 6692 03 17.00 2003 IEEE Previous work on queue control problems for satellite and wireless applications is found in [1] [8] 14] 15] 18] and [20] In [2] a parallel queue system with a single server is examined, where every timeslot the transmit channels of the queues vary between ON and OFF states and the server selects a queue to service from those that are ON. The capacity region of the system is ....

M. J. Neely, E. Modiano, and C. E. Rohrs, "Power and server allocation in a multibeam satellite with time-varying channels," in Proc. IEEE INFOCOM, vol. 3, 2002, pp. 1451--1460.

....has far fewer channels available for transmissions than the number of users to be served. Again, this raises a nearly identical problem of allocating channels to the di#erent users. This scheduling problem has received attention recently in the context of next generation wireless data systems [2, 3, 4, 5, 6, 7, 8, 10]. We model the system as a discrete time queueing system, with arrivals and channel states described by independent Bernoulli processes. More specifically, we assume that # This research is supported in part by NSF Grant No. NCR 9627610 and by DARPA under the Next Generation Internet Initiative ....

....withdrawals, when the system is in state (b, g) i.e, U(b, g) # if g i =0then u i =0; i=1 u i C . The Most Balanced policy chooses a u U(b, g)sothat i u i is as large as possible, and in addition, so that it minimizes max i:g i =1 (b i u i ) For example, if b =[5, 4, 3, 2, 6, 1], g = 1, 1, 1, 1, 0, 0] and C =2,amost balanced policy will let u = 1.5, 0.5, 0, 0, 0, 0] resulting in the configuration b u = 3.5, 3.5, 3, 2, 6, 1] It is not hard to show that the most balanced policy is uniquely defined. Finally, we let be the set of all functions f : ###that are ....

M. Neely, E. Modiano and C. Rohrs, "Power and Server Allocation in a Multi-Beam Satellite with Time Varying Channels," IEEE INFOCOM 2002.

....The strategy involves solving an optimization problem every timeslot. We implement centralized and decentralized approximations of the algorithm for an adhoc wireless network, where channel variations are due to user mobility. Previous work on power control for wireless systems is found in [1 7], 23] 25 27] In [1] a stabilizing power allocation strategy is developed for a satellite downlink with random inputs and time varying channels. Routing over finite buffer downlinks is considered in [2] In [3,4] optimal power allocation policies are developed for minimizing the energy ....

....solving an optimization problem every timeslot. We implement centralized and decentralized approximations of the algorithm for an adhoc wireless network, where channel variations are due to user mobility. Previous work on power control for wireless systems is found in [1 7] 23] 25 27] In [1], a stabilizing power allocation strategy is developed for a satellite downlink with random inputs and time varying channels. Routing over finite buffer downlinks is considered in [2] In [3,4] optimal power allocation policies are developed for minimizing the energy expended to transmit data ....

[Article contains additional citation context not shown here]

M.J. Neely, E. Modiano, and C.E.Rohrs, "Power and Server Allocation in a Multi-Beam Satellite with Time Varying Channels," IEEE Proceedings of INFOCOM 2002.

No context found.

M. Neely, E. Modiano, and C. Rohrs, "Power and server allocation in a multi-beam satellite with time varying channels," in Proceedings of IEEE INFOCOM '02, New York, NY, June 2002.

No context found.

M. Neely, E. Modiano, and C. Rohrs. Power and server allocation in a multi-beam satellite with time varying channels. In Proc of IEEE INFOCOM '02, New York, NY, June 2002.

No context found.

M. Neely, E. Modiano, and C. Rohrs, "Power and server allocation in a multi-beam satellite with time varying channels," in Proceedings of IEEE INFOCOM '02, New York, NY, June 2002.

No context found.

M. Neely, E. Modiano, and C. Rohrs. Power and server allocation in a multi-beam satellite with time varying channels. In Proceedings of IEEE INFOCOM '02, New York, NY, June 2002.

No context found.

M. Neely, E. Modiano, and C. Rohrs, "Power and server allocation in a multi-beam satellite with time varying channels," in Proceedings of IEEE INFOCOM '02, New York, NY, June 2002.

No context found.

M. Neely, E. Modiano and C. Rohrs, "Power and server allocation in a multi-beam satellite with time varying channels," IEEE INFOCOM, New York, NY, June, 2002

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