QoS-driven energy-efficient power control with random arrivals and arbitrary input distributions

Gozde Ozcan, Mustafa Ozmen, M. Cenk Gursoy

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


This paper studies energy-efficiency (EE) and throughput optimization in the presence of randomly arriving data and quality of service (QoS) constraints. For this purpose, maximum average arrival rates supported by transmitting signals with arbitrary input distributions are initially characterized in closed form by employing the effective bandwidth of time-varying sources (e.g., discrete-time Markov and Markov fluid sources, and discrete-time and continuous-time Markov modulated Poisson sources) and effective capacity of the time-varying wireless channel. Subsequently, EE is formulated as the ratio of the maximum average arrival rate to the total power consumption, in which circuit power is also taken into account. Following these characterizations, the optimal power control policies maximizing the EE or maximizing the throughput under a minimum EE constraint are obtained. Through numerical results, the performance of the optimal power control policies is evaluated for different signal constellations and is also compared with that of constant power transmission. The impact of QoS constraints, source characteristics, circuit power, input distributions on the EE, and the throughput is analyzed.

Original languageEnglish (US)
Article number7728079
Pages (from-to)376-388
Number of pages13
JournalIEEE Transactions on Wireless Communications
Issue number1
StatePublished - Jan 2017


  • Circuit power
  • MMSE
  • Markov arrivals
  • QoS constraints
  • effective bandwidth
  • effective capacity
  • energy efficiency
  • fading channel
  • mutual information
  • optimal power control

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics


Dive into the research topics of 'QoS-driven energy-efficient power control with random arrivals and arbitrary input distributions'. Together they form a unique fingerprint.

Cite this