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Energy Efficiency of Fixed-Rate Transmissions with Markov Arrivals under Queueing Constraints. M ustafa Ozmen M. Cenk Gursoy m ozmen @syr.edu mcgursoy @syr.edu Department of Electrical Engineering and Computer Science Syracuse University, Syracuse, NY.

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Energy Efficiency of Fixed-Rate Transmissions with

Markov Arrivals under Queueing Constraints

Mustafa OzmenM. Cenk Gursoy

[email protected]@syr.edu

Department of Electrical Engineering and Computer Science

Syracuse University, Syracuse, NY

  • Due to rapid growth in mobile wireless applications and systems, which are generally equipped with limited energy resources, and also due to rising energy costs and environmental concerns, energy efficiency in wireless systems attracting much interest recently.
  • Another important consideration in wireless systems is to provide quality of service (QoS) guarantees.
  • Wireless multimedia transmissions, voice over IP (VoIP), and online gaming require certain delay/buffer constraints to be satisfied.
  • We address the impact of source and channel variations upon energy efficiency under QoS constraints.
  • In particular, we model random arrivals by considering a simple ON-OFF discrete-time or continuous-time Markov source.
  • Also, we assume transmission rate is fixed, and transmission over the Rayleigh fading channel is modeled as an ON-OFF Markov fluid process.
  • We demonstrate that channel variations known at the receiver improve the energy efficiency under QoS constraints while source variations or burstiness lead to increased energy requirements.
  • We seek to identify the fundamental limits of energy efficiency in wireless systems operating in the presence of quality of service (QoS) constraints.
  • We consider a practical scenario in which both the channel conditions and source arrival rates vary randomly over time.
  • Under these assumptions, our main goal is to establish the ultimate performance limits by determining the minimum energy per bit and wideband slope expressions in the low-SNR regime.

Introduction

Channel Model and Fixed-Rate Transmissions

Goal

Markov Sources, Queueing Constraints and Energy Efficiency

Energy Efficiency with Markov Fluid Sources

Energy Efficiency with Discrete Markov Sources

Energy Efficiency Metrics

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Nunan Poster Competition, Syracuse University, April 5,2013

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