Opportunistic Scheduling with Deadline Constraints in Wireless Networks

Shuman, David I; Liu, Mingyan

doi:10.1007/978-1-4419-0534-5_6

Cited by 2 publications

(4 citation statements)

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“…We compare related work in opportunistic scheduling problems with deadline constraints further in [34]. For more complete surveys of opportunistic scheduling studies in wireless networks, see [35] and [36].…”

Section: A Opportunistic Scheduling and Related Workmentioning

confidence: 99%

“…Of course, when these three conditions are not satisfied, the two problems are quite different. For a more detailed comparison of these two problems, see [34].…”

Section: Sufficient Conditions For Equivalence With the Single Deamentioning

confidence: 99%

“…In summary, both the underflow constraints and the power constraints shift the definition of what constitutes a "good" channel, and how much data to send accordingly. For more detailed comparisons of singlereceiver opportunistic scheduling problems highlighting the role of the deadline constraints, see [34].…”

Section: Intuitive Takeaways On the Role Of The Strict Underflow Cmentioning

confidence: 99%

“…Equation (35) follows from (33) and (37) follows from (34). If β 2 ≥ β 1 , (36) holds with equality.…”

Section: Inductionmentioning

confidence: 99%

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Energy-Efficient Transmission Scheduling With Strict Underflow Constraints

Shuman

Liu

2011

IEEE Trans. Inform. Theory

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We consider a single source transmitting data to one or more receivers/users over a shared wireless channel. Due to random fading, the wireless channel conditions vary with time and from user to user.Each user has a buffer to store received packets before they are drained. At each time step, the source determines how much power to use for transmission to each user. The source's objective is to allocate power in a manner that minimizes an expected cost measure, while satisfying strict buffer underflow constraints and a total power constraint in each slot. The expected cost measure is composed of costs associated with power consumption from transmission and packet holding costs. The primary application motivating this problem is wireless media streaming. For this application, the buffer underflow constraints prevent the user buffers from emptying, so as to maintain playout quality. In the case of a single user with linear power-rate curves, we show that a modified base-stock policy is optimal under the finite horizon, infinite horizon discounted, and infinite horizon average expected cost criteria. For a single user with piecewise-linear convex power-rate curves, we show that a finite generalized base-stock policy is optimal under all three expected cost criteria. We also present the sequences of critical numbers that complete the characterization of the optimal control laws in each of these cases when some additional technical conditions are satisfied. We then analyze the structure of the optimal policy for the case of two users. We conclude with a discussion of methods to identify implementable near-optimal policies for the most general case of M users.

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Section: A Opportunistic Scheduling and Related Workmentioning

confidence: 99%

“…Of course, when these three conditions are not satisfied, the two problems are quite different. For a more detailed comparison of these two problems, see [34].…”

Section: Sufficient Conditions For Equivalence With the Single Deamentioning

confidence: 99%

Section: Intuitive Takeaways On the Role Of The Strict Underflow Cmentioning

confidence: 99%

“…Equation (35) follows from (33) and (37) follows from (34). If β 2 ≥ β 1 , (36) holds with equality.…”

Section: Inductionmentioning

confidence: 99%

See 2 more Smart Citations