Wireless Throughput and Energy Efficiency with Random Arrivals and Statistical Queueing Constraints

TL;DR

This paper analyzes the impact of random data arrivals and statistical queueing constraints on wireless throughput and energy efficiency, providing closed-form expressions and asymptotic insights for various source models.

Contribution

It introduces a comprehensive framework using effective bandwidth and capacity to characterize throughput and energy efficiency under diverse Markovian source models and QoS constraints.

Findings

Closed-form throughput expressions for ON/OFF sources

Analysis of energy efficiency in low SNR regimes

Impact of source and channel characteristics on performance

Abstract

Throughput and energy efficiency in fading channels are studied in the presence of randomly arriving data and statistical queueing constraints. In particular, Markovian arrival models including discrete-time Markov, Markov fluid, and Markov-modulated Poisson sources are considered. Employing the effective bandwidth of time-varying sources and effective capacity of time-varying wireless transmissions, maximum average arrival rates in the presence of statistical queueing constraints are characterized. For the two-state (ON/OFF) source models, throughput is determined in closed-form as a function of the source statistics, channel characteristics, and quality of service (QoS) constraints. Throughput is further studied in certain asymptotic regimes. Furthermore, energy efficiency is analyzed by determining the minimum energy per bit and wideband slope in the low signal-to-noise ratio (SNR) regime. Overall, the impact of source characteristics, QoS requirements, and channel fading correlations on the throughput and energy efficiency of wireless systems is identified.