RT Conference Proceedings
T1 Hardware Evaluation of Interference Alignment Algorithms Using USRPs for Beyond 5G Networks
A1 Urquiza Villalonga, David Alejandro
A1 Lopez Barrios, Alejandro
A1 Fernández-Getino García, María Julia
AB Network densification is a key technology to achieve the spectral efficiency (SE) expected in 5G wireless networks and beyond. However, the proximity between transmitters and receivers increases the interference levels, becoming a major drawback. To overcome this problem, several interference management techniques have been proposed to increase the signal-to-interference-plus-noise ratio (SINR). Interference alignment (IA) algorithms have been extensively studied due to their capability to achieve optimal degrees of freedom (DoFs) in interference channels (ICs). Nevertheless, most of the works are limited to a purely theoretical analysis based on non-realistic assumptions such as perfect channel state information (CSI) and the synchronization of all nodes in the network. To the best of our knowledge, only a few articles address the IA implementation using reconfigurable hardware. To cover this lack, this paper proposes a practical design of the IA algorithm based on the SINR maximization, known as MAX-SINR, considering a multi-user IC. Each transmitter and receiver is implemented on the National Instruments USRP-2942. A practical solution for the channel estimation and synchronization stages in an IC, that are usually omitted in theoretical works, is developed. The performance of the proposed implementation is shown in terms of the SINR gain, SE, and bit error rate (BER). Unlike previous works, all the results are based on real measurements providing valuable insights into the performance of IA algorithms.
PB IEEE
SN 978-1-6654-6397-3
YR 2023
FD 2023-07-06
LK https://hdl.handle.net/10016/38380
UL https://hdl.handle.net/10016/38380
LA eng
NO Proceedings of the 20th IEEE Region 8 EUROCON Conference, EUROCON 2023, 6-8 July 2023, Turín, Italy
NO This work has received funding from the European Union (EU) Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie ETN TeamUp5G, grant agreement No. 813391. Also, this work has been partially funded by the Spanish National project IRENE-EARTH (PID2020- 115323RB-C33 / AEI / 10.13039/501100011033)
DS e-Archivo
RD 1 jul. 2024