Quantum Computing-Assisted Channel Estimation for Massive MIMO mmWave Systems
Vlachos, Evangelos, Blekos, Kostas
2022 IFIP/IEEE 30th International Conference on Very Large Scale Integration (VLSI-SoC)
Abstract
Quantum computing (QC)-assisted algorithms promise exponential increase in the computational efficiency, enabling instant solution of large systems of equations. Mas- sive multiple-input multiple-output (MIMO) millimeter-wave (mmWave) systems pose extraordinary demands in computa- tional complexity, due to the usage of huge antenna arrays, massive number of users, and ultra low-latency requirements. This work provides an initial discussion on how QC-based algorithms for solving linear systems of equations could be utilized for assisting the basic operations of the transceivers physical-layer, such as the channel estimation. We identify the connections between the amplitude encoding in the quantum domain and the recovery of the channel information.
Type
1
Publication
2022 IFIP/IEEE 30th International Conference on Very Large Scale Integration (VLSI-SoC)
Date
January 2022
Key Contributions
- Proposes a quantum computing-assisted framework for channel estimation in massive MIMO mmWave systems, leveraging quantum algorithms to address classical challenges.
- Introduces a hybrid approach combining quantum computing with classical signal processing techniques to enhance estimation accuracy and reduce computational complexity.
- Demonstrates the potential of quantum algorithms, such as HHL, to solve linear systems arising in channel estimation more efficiently than classical methods.
- Provides insights into the practical implementation of quantum-assisted channel estimation, highlighting key performance gains and scalability considerations.