Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Performance-Complexity Analysis for MAC ML-based Decoding with User
  Selection   This work explores the rate-reliability-complexity limits of the quasi-static
K-user multiple access channel (MAC), with or without feedback. Using high-SNR
asymptotics, the work first derives bounds on the computational resources
required to achieve near-optimal (ML-based) decoding performance. It then
bounds the (reduced) complexity needed to achieve any (including suboptimal)
diversity-multiplexing performance tradeoff (DMT) performance, and finally
bounds the same complexity, in the presence of feedback-aided user selection.
This latter effort reveals the ability of a few bits of feedback not only to
improve performance, but also to reduce complexity. In this context, the
analysis reveals the interesting finding that proper calibration of user
selection can allow for near-optimal ML-based decoding, with complexity that
need not scale exponentially in the total number of codeword bits. The derived
bounds constitute the best known performance-vs-complexity behavior to date for
ML-based MAC decoding, as well as a first exploration of the
complexity-feedback-performance interdependencies in multiuser settings.

32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
22
The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
20
The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
11
The categories of this paper are: hep-lat
16
The categories of this paper are: stat