E. Dosti, M. Shehab, H. Alves and M. Latva-Aho, "Ultra Reliable Communication via Optimum Power Allocation for HARQ Retransmission Schemes," in IEEE Access, vol. 8, pp. 89768-89781, 2020, doi: 10.1109/ACCESS.2020.2994277
Ultra reliable communication via optimum power allocation for HARQ retransmission schemes
|Author:||Dosti, Endrit1; Shehab, Mohammad2; Alves, Hirley2;|
1Department of Signal Processing and Acoustics, Aalto University, Finland
2Centre for Wireless Communications (CWC), University of Oulu, Finland
|Online Access:||PDF Full Text (PDF, 1.8 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020052538990
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2020-05-25
In this work, we develop low complexity, optimal power allocation algorithms that would allow ultra reliable operation at any outage probability target with minimum power consumption in the finite blocklength regime by utilizing Karush-Kuhn-Tucker (KKT) conditions. In our setup, we assume that the transmitter does not know the channel state information (CSI). First, we show that achieving a very low packet outage probability by using an open loop setup requires extremely high power consumption. Thus, we resort to retransmission schemes as a solution, namely Automatic Repeat Request (ARQ), Chase Combining Hybrid ARQ (CC-HARQ) and Incremental redundancy (IR) HARQ. Countrary to classical approaches, where it is optimal to allocate equal power with each transmission, we show that for operation in the ultra reliable regime (URR), the optimal strategy suggests transmission with incremental power in each round. Numerically, we evaluate the power gains of the proposed protocol. We show that the best power saving is given by IR-HARQ protocol. Further, we show that when compared to the one shot transmission, these protocols enable large average and maximum power gains. Finally, we show that the larger the number of transmissions is, the larger power gains will be attained.
|Pages:||89768 - 89781|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was supported in part by the Academy of Finland 6G Flagship under Grant 318927, Aka Project Energy Efficient IoT (EE-IoT) under Grant 319008, and Framework for the Identication of Rare Events via MAchine learning and IoT Networks (FIREMAN) project under Grant 326301.
|Academy of Finland Grant Number:||
318927 (Academy of Finland Funding decision)
319008 (Academy of Finland Funding decision)
326301 (Academy of Finland Funding decision)
© The Authors 2020. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.