TL;DR
This paper introduces TASER, a novel approximate semidefinite relaxation algorithm for high-throughput, low-error data detection in large multi-antenna wireless systems, suitable for practical FPGA and ASIC implementations.
Contribution
The paper proposes TASER, a new low-complexity, near-ML error-rate data detection algorithm for large MU-MIMO and SIMO systems, with convergence guarantees and hardware architecture designs.
Findings
TASER achieves near-ML error-rate performance.
TASER operates with low complexity using semidefinite relaxation.
Hardware implementations demonstrate high throughput and efficiency.
Abstract
Practical data detectors for future wireless systems with hundreds of antennas at the base station must achieve high throughput and low error rate at low complexity. Since the complexity of maximum-likelihood (ML) data detection is prohibitive for such large wireless systems, approximate methods are necessary. In this paper, we propose a novel data detection algorithm referred to as Triangular Approximate SEmidefinite Relaxation (TASER), which is suitable for two application scenarios: (i) coherent data detection in large multi-user multiple-input multiple-output (MU-MIMO) wireless systems and (ii) joint channel estimation and data detection in large single-input multiple-output (SIMO) wireless systems. For both scenarios, we show that TASER achieves near-ML error-rate performance at low complexity by relaxing the associated ML-detection problems into a semidefinite program, which we…
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.
