Short-packet Transmission via Variable-Length Codes in the Presence of Noisy Stop Feedback

09/03/2019
by   Johan Östman, et al.
0

We present an upper bound on the error probability achievable using variable-length stop feedback codes, for a fixed size of the information payload and a given constraint on the maximum latency and the average service time. Differently from the bound proposed in Polyanskiy et al. (2011), which pertains to the scenario in which the stop signal is sent over a noiseless feedback channel, our bound applies to the practically relevant setup in which the feedback link is noisy. By numerically evaluating our bound, we illustrate that, for fixed latency and reliability constraints, noise in the feedback link can cause a significant increase in the minimum average service time, to the extent that fixed-length codes without feedback may be preferable in some scenarios.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

03/17/2021

Variable-length Feedback Codes with Several Decoding Times for the Gaussian Channel

We investigate variable-length feedback (VLF) codes for the Gaussian poi...
09/18/2018

Low-Latency Short-Packet Transmissions: Fixed Length or HARQ?

We study short-packet communications, subject to latency and reliability...
01/18/2019

Achievable Error Exponents of One-Way and Two-Way AWGN Channels

Achievable error exponents for the one-way with noisy feedback and two-w...
01/23/2018

On The Reliability Function of Discrete Memoryless Multiple-Access Channel with Feedback

We derive a lower and upper bound on the reliability function of discret...
01/13/2020

Upper Bound Scalability on Achievable Rates of Batched Codes for Line Networks

The capacity of line networks with buffer size constraints is an open, b...
01/16/2018

Bounds on the Effective-length of Optimal Codes for Interference Channel with Feedback

In this paper, we investigate the necessity of finite blocklength codes ...
08/04/2020

Simple Modulo can Significantly Outperform Deep Learning-based Deepcode

Deepcode (H.Kim et al.2018) is a recently suggested Deep Learning-based ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.