Nonlinear 1-Bit Precoding for Massive MU-MIMO with Higher-Order Modulation

Christoph Studer; Giuseppe Durisi; Mikael Coldrey; Sven Jacobsson; Tom Goldstein

Nonlinear 1-Bit Precoding for Massive MU-MIMO with Higher-Order Modulation

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1612.02685 v1 pith:5OAVR7TN submitted 2016-12-08 cs.IT math.IT

Sven Jacobsson , Giuseppe Durisi , Mikael Coldrey , Tom Goldstein , Christoph Studer This is my paper

classification cs.ITmath.IT

keywords algorithmsprecodingdacsmassivemodulationmu-mimoerrorhigher-order

verification ladder T0 review T1 audit T2 compute T3 formal

0 comments

read the original abstract

Massive multi-user (MU) multiple-input multiple- output (MIMO) is widely believed to be a core technology for the upcoming fifth-generation (5G) wireless communication standards. The use of low-precision digital-to-analog converters (DACs) in MU-MIMO base stations is of interest because it reduces the power consumption, system costs, and raw baseband data rates. In this paper, we develop novel algorithms for downlink precoding in massive MU-MIMO systems with 1-bit DACs that support higher-order modulation schemes such as 8-PSK or 16-QAM. Specifically, we present low-complexity nonlinear precoding algorithms that achieve low error rates when combined with blind or training-based channel-estimation algorithms at the user equipment. These results are in stark contrast to linear-quantized precoding algorithms, which suffer from a high error floor if used with high-order modulation schemes and 1-bit DACs.

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

What to Keep and What to Drop: Adaptive Table Filtering Framework
cs.CL 2025-06 conditional novelty 5.0 of 10

ATF prunes table columns and rows with LLM scoring plus retrieval, cutting cells by about 70% and improving out-of-domain TableQA accuracy, while hurting in-domain QA and fact verification.

Pith tools