TY - GEN
T1 - Deep learning for large intelligent surfaces in millimeter wave and massive MIMO systems
AU - Taha, Abdelrahman
AU - Alrabeiah, Muhammad
AU - Alkhateeb, Ahmed
PY - 2019/12
Y1 - 2019/12
N2 - As a promising candidate for future wireless systems, large intelligent surfaces (LISs) recently emerged to serve considerate improvements in both spectral and energy efficiencies. These surfaces consist of large numbers of passive elements capable of intelligently reflecting the incident signals. Since the LIS employs passive elements, critical challenges are inherent in the channel training/estimation process in order to properly design the LIS reflection matrices. One challenge particularly is how to acquire the channel knowledge with low training overhead and power consumption solutions. In this paper, we first propose an energy-efficient novel LIS architecture where all the LIS elements are passive except few non-uniformly distributed active elements (connected to the baseband). Then, we develop an efficient solution to design the LIS reflection matrices, with negligible training overhead, leveraging deep learning tools. Given what we call environment descriptors, the LIS has the ability to learn the optimal LIS reflection matrices. The simulation results show that the developed solution can approach the optimal upper bound, when only a small fraction of the LIS elements are active, yielding a promising solution for LIS systems from both energy efficiency and training overhead perspectives.
AB - As a promising candidate for future wireless systems, large intelligent surfaces (LISs) recently emerged to serve considerate improvements in both spectral and energy efficiencies. These surfaces consist of large numbers of passive elements capable of intelligently reflecting the incident signals. Since the LIS employs passive elements, critical challenges are inherent in the channel training/estimation process in order to properly design the LIS reflection matrices. One challenge particularly is how to acquire the channel knowledge with low training overhead and power consumption solutions. In this paper, we first propose an energy-efficient novel LIS architecture where all the LIS elements are passive except few non-uniformly distributed active elements (connected to the baseband). Then, we develop an efficient solution to design the LIS reflection matrices, with negligible training overhead, leveraging deep learning tools. Given what we call environment descriptors, the LIS has the ability to learn the optimal LIS reflection matrices. The simulation results show that the developed solution can approach the optimal upper bound, when only a small fraction of the LIS elements are active, yielding a promising solution for LIS systems from both energy efficiency and training overhead perspectives.
KW - Beamforming
KW - Deep learning
KW - Large intelligent surface
KW - Millimeter wave
KW - Smart reflect-array
UR - http://www.scopus.com/inward/record.url?scp=85081968686&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85081968686&partnerID=8YFLogxK
U2 - 10.1109/GLOBECOM38437.2019.9013256
DO - 10.1109/GLOBECOM38437.2019.9013256
M3 - Conference contribution
T3 - 2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings
BT - 2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE Global Communications Conference, GLOBECOM 2019
Y2 - 9 December 2019 through 13 December 2019
ER -