University of Oulu

F. Qasmi, M. Shehab, H. Alves and M. Latva-aho, "Effective Energy Efficiency and Statistical QoS Provisioning under Markovian Arrivals and Finite Blocklength Regime," in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2022.3157956

Effective energy efficiency and statistical QoS provisioning under Markovian arrivals and finite blocklength regime

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Author: Qasmi, Fahad1; Shehah, Mohammad1; Alves, Hirley1;
Organizations: 1Centre for Wireless Communications (CWC), Univer- sity of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.9 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2022090156962
Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2022-09-01
Description:

Abstract

In this paper, we evaluate the Effective Energy Efficiency (EEE) and propose delay-outage aware resource allocation strategies for energy-limited IoT (Internet of Things) devices under the finite blocklength (FBL) regime. The EEE is a cross-layer model, measured by the ratio of Effective Capacity to the total consumed power. To maximize the EEE, there is a need to optimize transmission parameters such as transmission power and rate efficiently. Whereas it is quite complex to study the impact of transmission power, or rate alone, the complexity is aggravated by the simultaneous consideration of both variables. Hence, we formulate power allocation (PA) and rate allocation (RA) optimization problems individually and jointly to maximize EEE. Furthermore, we investigate the performance of the EEE under constant and random arrivals, where statistical QoS constraints are imposed on buffer overflow probability. Using effective bandwidth and effective capacity theories, we determine the arrival rate and the required service rate that satisfy the QoS constraints. After that, we compare the performance of different iterative algorithms such as Dinkelbach’s and Cross Entropy, which guarantee the convergence for the optimal solution. By numerical analysis, the influence of source characteristics, fixed transmission rate, error probability, coding blocklength, and QoS constraints on the throughput are identified. Our analysis reveals that the joint PA and RA is the optimal resources allocation strategy for maximizing the EEE in the presence of constant and random data arrivals. Finally, the results illustrate that the modified Dinkelbach’s algorithm has high performance and low complexity compared to others.

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Series: IEEE internet of things journal
ISSN: 2372-2541
ISSN-E: 2327-4662
ISSN-L: 2327-4662
Issue: Online first
DOI: 10.1109/jiot.2022.3157956
OADOI: https://oadoi.org/10.1109/jiot.2022.3157956
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
EEE
IoT
MTC
Funding: This research has been financially supported by Academy of Finland, 6Genesis Flagship (Grant n.318937) and ee-IoT (Grant n.319008), and Academy Professor (Grant n.307492).
Academy of Finland Grant Number: 319008
307492
Detailed Information: 319008 (Academy of Finland Funding decision)
307492 (Academy of Finland Funding decision)
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