M. Shehab, H. Alves, E. A. Jorswieck, E. Dosti and M. Latva-Aho, "Effective Energy Efficiency of Ultrareliable Low-Latency Communication," in IEEE Internet of Things Journal, vol. 8, no. 14, pp. 11135-11149, 15 July15, 2021, doi: 10.1109/JIOT.2021.3052965
Effective energy efficiency of ultrareliable low-latency communication
|Author:||Shehab, Mohammad1; Alves, Hirley1; Jorswieck, Eduard A.2;|
1Centre for Wireless Communications (CWC), University of Oulu, Finland
2Department of Information Theory and Communication Systems, Technische UniverstÃt Braunschweig, German
3Department of Signal Processing and Acoustics, Aalto University, Finland
|Online Access:||PDF Full Text (PDF, 0.7 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2021101150545
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2021-10-11
Effective capacity (EC) defines the maximum communication rate subject to a specific delay constraint, while effective energy efficiency (EEE) indicates the ratio between EC and power consumption. We analyze the EEE of ultrareliable networks operating in the finite-blocklength regime. We obtain a closed-form approximation for the EEE in quasistatic Nakagami- m (and Rayleigh as subcase) fading channels as a function of power, error probability, and latency. Furthermore, we characterize the quality-of-service constrained EEE maximization problem for different power consumption models, which shows a significant difference between finite and infinite-blocklength coding with respect to EEE and optimal power allocation strategy. As asserted in the literature, achieving ultrareliability using one transmission consumes a huge amount of power, which is not applicable for energy limited Internet-of-Things devices. In this context, accounting for empty buffer probability in machine-type communication (MTC) and extending the maximum delay tolerance jointly enhances the EEE and allows for adaptive retransmission of faulty packets. Our analysis reveals that obtaining the optimum error probability for each transmission by minimizing the nonempty buffer probability approaches EEE optimality, while being analytically tractable via Dinkelbach’s algorithm. Furthermore, the results illustrate the power saving and the significant EEE gain attained by applying adaptive retransmission protocols, while sacrificing a limited increase in latency.
IEEE internet of things journal
|Pages:||11135 - 11149|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work is partially supported by Academy of Finland 6Genesis Flagship (Grant no. 318927), Aka Project EE-IoT (Grant no. 319008). The work of E. Jorswieck is partly funded by the German Research Foundation (DFG, Deutsche Forschungsgemeinschaft) as part of Germany’s Excellence Strategy - EXC 2050/1 - Project ID 390696704 - Cluster of Excellence "Centre for Tactile Internet with Human-in-the-Loop" (CeTI) of Technische Universität Dresden.
|Academy of Finland Grant Number:||
318927 (Academy of Finland Funding decision)
319008 (Academy of Finland Funding decision)
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