Soft Interference Cancellation for Random Coding in Massive Gaussian Multiple-Access

05/07/2020
by   Ralf Müller, et al.
0

We utilize recent results on the exact block error probability of Gaussian random codes in additive white Gaussian noise to analyze Gaussian random coding for massive multiple-access at finite message length. Soft iterative interference cancellation is found to closely approach the performance bounds recently found in [1]. The existence of two fundamentally different regimes in the trade-off between power and bandwidth efficiency reported in [2] is related to much older results in [3] on power optimization by linear programming. Furthermore, we tighten the achievability bounds of [1] in the low power regime and show that orthogonal constellations are very close to the theoretical limits for message lengths around 100 and above.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
02/20/2018

Achievability Bounds for T-Fold Irregular Repetition Slotted ALOHA Scheme in the Gaussian Multiple Access Channel

We address the problem of massive random access for an uncoordinated Gau...
research
07/06/2019

Error Probability Bounds for Gaussian Channels under Maximal and Average Power Constraints

This paper studies the performance of block coding on an additive white ...
research
01/10/2018

Rate Selection and Power Adaptation using Maximal Ratio Combining for the Random Access Gaussian Channel

With the emergence of machine-driven communi- cation, there is a renewed...
research
05/09/2021

Feedback Gains for Gaussian Massive Multiple-Access Channels

Feedback is shown to increase the sum-rate capacity of K-user Gaussian m...
research
02/09/2021

Near-Optimal Coding for Massive Multiple Access

We study the Gaussian multiple access channel (MAC) in the asymptotic re...
research
06/22/2023

Rate-Splitting Multiple Access for 6G Networks: Ten Promising Scenarios and Applications

In the upcoming 6G era, multiple access (MA) will play an essential role...

Please sign up or login with your details

Forgot password? Click here to reset