Abstract:
The physical layer Hybrid Automatic Repeat Request (HARQ) protocol efficiently
achieves low error-rate transmission and high network reliability in the fifth generation
(5G) Ultra Reliable Low Latency Communication (URLLC) network. However, this
retransmission protocol suffers from increased transmission latency resulting mainly
from the delay caused by channel decoding. This problem is caused by the fact that
the sender has to wait for acknowledgement of the transmission which is generated
after the decoding process at the receiver, resulting in increased latency. To address
the latency problem, this study proposed the multistage machine learning Early HARQ
(E-HARQ) which uses machine learning algorithms for predicting the
acknowledgement before the decoding process. Furthermore, the proposed scheme
uses the multistage decision to mitigate the throughput loss resulting from incorrect
predictions of the acknowledgement. The multistage decision controls the
transmission bandwidth in a multilevel manner depending on channel conditions
measured by the Channel State Information (CSI). The study used jupyter notebook
and MATLAB for developing the proposed scheme and then evaluating its
performance. Simulation results show that the proposed scheme improves the
achievable trade-off between the transmission latency and throughput which
contributes to the performance of 5G URLLC networks.