The IEEE Wireless Communications and Networking Conference (WCNC) is one of the largest conferences of the IEEE Communications Society and was held this year in Dubai. With over 900 participants and the wide-ranging programme, it offers a rich platform for scientific exchange and further development of wireless technologies. Over 600 research contributions such as workshops, technical papers, tutorials and keynotes provide an insight into communication systems of the future.
Two INÜ PhD candidates attended the conference from 21–24 April 2024 and presented the papers:
- Deep Learning Based Adaptive Joint mmWave Beam Alignment
- Optimized Sequences for Estimation of Power Amplifier Nonlinearity in 802.11 Wireless LAN
Contribution to Beam Alignment in Millimeter Wave Wireless Systems:
Current cellular mmWave systems utilize predefined beam-codebooks and fixed beam-sweeps in their initial access phase, which can incur high alignment overhead and relatively low achievable beamforming gain. In order to combat these shortcomings, learnable beam-codebooks and adaptive, codebook-free initial access beam alginment schemes have been proposed. In this work, these different lines of research are combined into a novel, efficient beam alignment scheme aiming to utilize both the advantages that learnable beam-codebooks and adaptive, codebook-free beam alignment offer.
Contribution to Estimation Sequence Design in WLAN Systems:
Nonlinear distortions from power amplifiers of wireless transmitters cause several undesired effects such as reduced signal quality and out-of-band emissions. To combat these effects, a robust and accurate estimation of the nonlinearity is required. Estimation sequences of current 802.11 standards are insufficient for nonlinearity estimation, which motivates the work of this paper to find optimized sequences for nonlinearity estimation. In the paper, optimization objectives and constraints are introduced and an algorithm is provided to find sequences that are compatible with the 802.11 standard family.
