Energy Efficient Adaptive beam alignment & beam design for 5G Millimeter-wave networks
Wednesday, June 20, 11:00 AM, room DEI/G 318
Mobile data traffic is expected to increase tremendously over the next decade, and cannot be accommodated by the limited bandwidth availability below 6GHz. On the other hand, the so called millimeter wave frequencies in the 28-100 GHz range promise to overcome these limitations. However, millimeter wave systems require narrow beam communication to achieve high throughput, thus mobile users need to be tightly tracked to provide seamless communication. Such requirement may pose severe challenges in mobile environments and may entail a significant performance degradation due to the associated signaling overhead. In the first part of the talk, we address the energy efficient design of the beam alignment protocol, with the goal of minimizing power consumption under communication constraints. We prove the optimality of a fractional search method, which senses a given fraction of the beam in each slot during beam alignment, and derive the fractional value in closed form. We also investigate the trade-off among beam-alignment, communication and user mobility. Beam alignment is achieved via a proper beam sensing protocol, which specifies how to allocate amplitude and phase at each antenna array element (a codeword) to sense the mobile user's position, through appropriate beam pointing. However, beam imperfections -- such as the presence of side-lobes -- and noise may cause errors in the detection process. Thus, in the second part of the talk, we investigate a Neyman-Pearson codebook design with optimal detection performance. We show that the optimal codebook is the principle eigenvector of a weighted array response matrix, and the dual problem can be solved via semidefinite programming. We show numerically that the proposed design outperforms a state-of-the art algorithm, with improvement up to 33% in detection performance.
About the Speaker:
Dr. Nicolo Michelusi received the B.Sc. degree with honors, M.Sc. degree with honors and Ph.D. degree in Electrical Engineering from University of Padova, Italy, in 2006 and 2009, and 2013 respectively, and the M.Sc. degree in Telecommunication Engineering from Technical University of Denmark in 2009. In 2013-2015, he was a postdoctoral research fellow at the Ming-Hsieh Department of Electrical Engineering, University of Southern California, USA. He is currently an Assistant Professor at the School of Electrical and Computer Engineering, Purdue University, IN, USA, Associate Editor for the IEEE Transactions on Wireless Communications, and Senior Member of IEEE. His research interests lie in the areas of wireless communications, cognitive networks, energy harvesting IoT, 5G millimeter-wave networks, distributed algorithm over wireless networks, and machine learning applied to wireless communications. His research is currently funded by the National Science Foundation under grant CNS-1642982 and by DARPA to compete on the Spectrum Collaboration Challenge.