TL;DR
This paper develops an analytical framework for channel estimation and data detection in massive MIMO systems with 1-bit ADCs, providing explicit performance expressions and revealing a fundamental SNR trade-off.
Contribution
It offers closed-form expressions for MSE and symbol detection metrics in 1-bit massive MIMO, enabling precise performance analysis and system optimization.
Findings
Explicit MSE expressions for channel estimation
Closed-form detection performance metrics
Identification of a key SNR trade-off
Abstract
We present an analytical framework for the channel estimation and the data detection in massive multiple-input multiple-output uplink systems with 1-bit analog-to-digital converters (ADCs) and i.i.d. Rayleigh fading. First, we provide closed-form expressions of the mean squared error (MSE) of the channel estimation considering the state-of-the-art linear minimum MSE estimator and the class of scaled least-squares estimators. For the data detection, we provide closed-form expressions of the expected value and the variance of the estimated symbols when maximum ratio combining is adopted, which can be exploited to efficiently implement minimum distance detection and, potentially, to design the set of transmit symbols. Our analytical findings explicitly depend on key system parameters such as the signal-to-noise ratio (SNR), the number of user equipments, and the pilot length, thus enabling…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
