Transmission Scheduling in Wireless Networks with SINR Constraints

Klerides, Evelina; Charalambous, Themistoklis

doi:10.1109/icns.2009.61

Cited by 3 publications

(1 citation statement)

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“…This problem is notoriously difficult to solve, even in a centralised manner. In , this line of research is followed and the transmission scheduling problem for minimising the total number of slots (channel or time) for variable power levels is formulated. However, in this work, they considered non‐problem‐specific analytic solutions that are computationally expensive.…”

Section: Introductionmentioning

confidence: 99%

On the minimum latency transmission scheduling in wireless networks with power control under SINR constraints

Charalambous

Klerides

Wiesemann

et al. 2013

Trans Emerging Tel Tech

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In order to alleviate interference and contention in a wireless network, we may exploit the existence of multiple orthogonal channels or time slots, thus achieving a substantial improvement in performance. In this paper, we study a joint transmission scheduling and power control problem that arises in wireless networks. The goal is to assign channels (or time slots) and transmitting powers to communication links such that all communication requests are processed correctly, specified quality-of-service requirements are met, and the number of required time slots is minimised. First, we formulate the problem as a mixed-integer linear programming. Then, we show that the problem considered is non-deterministic polynomial-time hard, and subsequently, we propose non-trivial bounding techniques to solve it. Optimisation methods are also discussed, including a column generation approach, specifically designed to find bounds for the transmission scheduling problem. Moreover, we develop optimisation techniques in which the bounding techniques are integrated in order to derive the optimal solution to the problem faster. We close with an extensive computational study, which shows that despite the complexity of the problem, the proposed methodology scales to problems of non-trivial size. Our algorithms can therefore be used for static wireless networks where propagation conditions are almost constant and a centralised agent is available (e.g. cellular networks where the base station can act as a centralised agent or wireless mesh networks), and they can also serve as a benchmark for the performance evaluation of heuristic, approximation or distributed algorithms that aim to find near-optimal solutions without information about the whole network.

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Section: Introductionmentioning

confidence: 99%