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, vol. 9, no. 18, pp. 17741-17755, 15 Sept.15, 2022, doi: 10.1109/JIOT.2022.3157956
Effective energy efficiency and statistical QoS provisioning under Markovian arrivals and finite blocklength regime
|Author:||Qasmi, Fahad1; Shehah, Mohammad1; Alves, Hirley1;|
1Centre for Wireless Communications (CWC), Univer- sity of Oulu, Finland
|Online Access:||PDF Full Text (PDF, 0.9 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022090156962
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2022-09-01
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.
IEEE internet of things journal
|Pages:||17741 - 17755|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
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 (Academy of Finland Funding decision)
307492 (Academy of Finland Funding decision)
318927 (Academy of Finland Funding decision)
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.