EngineeringCommunicationComputer ScienceDigital CommunicationInformation TheorySchedulingBroadcast CommunicationChannel CodingCommunication SchedulingCooperative CommunicationMobile CommunicationNetwork Information TheoryComplexityMulticast CommunicationMulticast SchedulingMulticastingOptimal ScalingScaling LawsScheduling AlgorithmsStatic Scheduling
In this paper, we develop an information theoretic framework for analyzing the fundamental tradeoffs of the downlink multicasting channel in a single cell system. We consider three classes of scheduling algorithms with varying complexities. The first class strives for minimum complexity by resorting to a static scheduling strategy along with memoryless decoding. Our analysis for the static scheduling algorithms reveals a fundamental throughput-delay tradeoff. In particular, we establish the existence of a static scheduling policy that achieves the optimal scaling law of the throughput at the expense of a delay that increases exponentially with the number of users. The second scheduling policy resorts to a higher complexity incremental redundancy encoding/decoding strategy to achieve a superior throughput-delay tradeoff. The third, and most complex, scheduling strategy benefits from the cooperation between the different users to minimize the delay while achieving the optimal scaling law of the throughput In particular, the proposed cooperative multicasting strategy is shown to achieve the optimal scaling laws of both throughput and delay. Finally, we present simulation results in certain representative scenarios that validate our theoretical claims.
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Optimal power allocation over parallel Gaussian broadcast channels
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