TY - GEN
T1 - Two-sample tests for validating the UL-DL conjecture in FDD systems
AU - Rizzello, Valentina
AU - Turan, Nurettin
AU - Joham, Michael
AU - Utschick, Wolfgang
N1 - Publisher Copyright:
© 2021 IEEE
PY - 2021/9/6
Y1 - 2021/9/6
N2 - In this work, we present a two-sample tests analysis based on the maximum mean discrepancy metric to validate the recently proposed uplink-downlink conjecture for frequency division duplex systems. This novel concept shows that a neural network trained with uplink channel data can adequately generalize to downlink channel data. With this paper, we focus on a particular application of this idea, namely an autoencoder neural network, which has been introduced lately to generate channel feedback, without requiring any training effort at the mobile terminals. Simulation results with several datasets demonstrate that application-based low-dimensional representations for two-sample testing give a deeper insight into the similarities and dissimilarities between the uplink and downlink data distributions and are in accordance with the performance of the neural network that is applied to the respective datasets.
AB - In this work, we present a two-sample tests analysis based on the maximum mean discrepancy metric to validate the recently proposed uplink-downlink conjecture for frequency division duplex systems. This novel concept shows that a neural network trained with uplink channel data can adequately generalize to downlink channel data. With this paper, we focus on a particular application of this idea, namely an autoencoder neural network, which has been introduced lately to generate channel feedback, without requiring any training effort at the mobile terminals. Simulation results with several datasets demonstrate that application-based low-dimensional representations for two-sample testing give a deeper insight into the similarities and dissimilarities between the uplink and downlink data distributions and are in accordance with the performance of the neural network that is applied to the respective datasets.
KW - Autoencoder neural networks
KW - Deep learning
KW - FDD systems
KW - Machine learning
KW - Maximum mean discrepancy
UR - http://www.scopus.com/inward/record.url?scp=85118122593&partnerID=8YFLogxK
U2 - 10.1109/ISWCS49558.2021.9562147
DO - 10.1109/ISWCS49558.2021.9562147
M3 - Conference contribution
AN - SCOPUS:85118122593
T3 - Proceedings of the International Symposium on Wireless Communication Systems
BT - 2021 17th International Symposium on Wireless Communication Systems, ISWCS 2021
PB - VDE VERLAG GMBH
T2 - 17th International Symposium on Wireless Communication Systems, ISWCS 2021
Y2 - 6 September 2021 through 9 September 2021
ER -