[Paper Review] Analysis of Wireless Communications with Finite Blocklength and Imperfect Channel Knowledge.

Sebastian Schiessl, Hussein Al-Zubaidy+2 more|arXiv (Cornell University)|Aug 30, 2016

Advanced MIMO Systems Optimization14 references3 citations

TL;DR

This paper proposes a closed-form approximation for transmission error probability in wireless systems under finite blocklength and imperfect channel state information (CSI), enabling accurate rate adaptation. The model reveals that jointly optimizing training sequence length and code rate significantly improves reliability under strict delay constraints, outperforming fixed-rate systems despite training overhead.

ABSTRACT

With the rise of critical machine-to-machine (M2M) applications, next generation wireless communication systems must be designed with strict constraints on the latency and the reliability. A key enabler for designing low-latency systems is the availability of accurate and tractable analytic models. Unfortunately, many performance models do not account for the effects of channel coding at finite blocklength and imperfect channel state information (CSI) due to time-limited channel training. These models are therefore inaccurate for low-latency systems. In this work, we derive a closed-form approximation for the transmission error probability while considering both of these effects. The approximation provides an inverse mapping from the error probability to the achievable rate. Using this approximation, we analyze the queuing delay of the system through stochastic network calculus. Our results show that even though systems with rate adaptation must spend resources on channel training, these systems perform better than systems operating at fixed rate. The enhancement becomes even greater under stricter delay constraints. Moreover, we find that adapting both the training sequence length and the code rate to the delay constraints is crucial for achieving high reliability.

Motivation & Objective

• Address the lack of accurate analytic models for low-latency wireless systems due to finite blocklength and imperfect channel state information (CSI).
• Develop a tractable, closed-form approximation for transmission error probability that accounts for both finite blocklength and CSI estimation errors.
• Enable performance analysis of queuing delay using stochastic network calculus under realistic system constraints.
• Investigate the trade-off between training overhead and system reliability in rate-adaptive transmission schemes.
• Determine optimal adaptation strategies for training sequence length and code rate under strict delay requirements.

Proposed method

• Derive a closed-form approximation for the error probability in wireless systems with finite blocklength and imperfect CSI from time-limited channel training.
• Use the error probability approximation to establish an inverse mapping from error rate to achievable spectral efficiency (rate).
• Apply stochastic network calculus to analyze the queuing delay performance of the system under the derived rate adaptation model.
• Model rate adaptation as a function of both channel quality and delay constraints, incorporating dynamic training sequence length and code rate selection.
• Evaluate system performance under varying levels of CSI accuracy and blocklength, comparing fixed-rate and adaptive-rate transmission schemes.
• Optimize the joint design of training sequence length and code rate to minimize error probability under delay constraints.

Experimental results

Research questions

• RQ1 How does imperfect channel state information from limited training affect error probability in finite blocklength wireless systems?
• RQ2 What is the impact of rate adaptation on system reliability and queuing delay when accounting for finite blocklength and CSI estimation errors?
• RQ3 How does the performance of adaptive-rate systems compare to fixed-rate systems under strict delay constraints?
• RQ4 What is the optimal trade-off between training overhead and spectral efficiency in low-latency wireless systems?
• RQ5 How do joint optimizations of training sequence length and code rate influence system reliability under stringent delay requirements?

Key findings

• Systems with rate adaptation outperform fixed-rate systems despite the resource cost of channel training, particularly under strict delay constraints.
• The error probability approximation enables accurate performance evaluation of low-latency wireless systems through stochastic network calculus.
• Adapting both training sequence length and code rate is essential for achieving high reliability in low-latency applications.
• The performance gain from rate adaptation increases with tighter delay constraints, highlighting the importance of dynamic adaptation.
• Even with imperfect CSI, the proposed model provides a reliable framework for designing next-generation M2M communication systems.
• The closed-form error probability approximation allows for efficient inverse mapping to achievable rates, enabling practical system design.