TY - JOUR
AU1 - Ahmed Ouameur, Messaoud
AU2 - Lê, Anh Duong Tuấn
AU3 - Massicotte, Daniel
AB - Deep learning (DL) has been recognized as an instrumental tool for the design of future communication systems. Since it is still not clear whether a fully data-driven end-to-end communication learning approach would eventually outperform the traditional ones in terms of performance and complexity, it is argued that the optimal design needs to be tackled by taking the benefits of both model-based and data-driven approaches and by leveraging the concept of transfer learning. However, the grand question lies in how this can be implemented efficiently. As such, this paper proposes an efficient end-to-end OFDM based receiver learning approach based on distributed data-driven and model-based approaches. The approach relies mainly on augmenting a typical OFDM receiver’s processing blocks with a shallow neural network as a data-driven stub to improve its performance. Relying on a two-phases training approach, the last receiver’s processing stage benefits from the transfer learning approach to improve its performance.Limiting the scope to a typical OFDM transmission where the DL-based methods fail, the proposed model-aided shallow learning receiver shows performance improvements compared to the baseline structure.
TI - Model-aided distributed shallow learning for OFDM receiver in IEEE 802.11 channel model
JF - Wireless Networks
DO - 10.1007/s11276-020-02412-1
DA - 2020-10-29
UR - https://www.deepdyve.com/lp/springer-journals/model-aided-distributed-shallow-learning-for-ofdm-receiver-in-ieee-802-6hET0kjRuv
SP - 5427
EP - 5436
VL - 26
IS - 7
DP - DeepDyve
ER -