RT Journal Article
T1 Efficient evaluation of the error probability for pilot-assisted URLLC with massive MIMO
A1 Kislal, A. Oguz
A1 Lancho Serrano, Alejandro
A1 Giuseppe, Durisi
A1 Ström, Erik G.
AB We propose a numerically efficient method for evaluating the random-coding union bound with parameter s on the error probability achievable in the finite-blocklength regime by a pilot-assisted transmission scheme employing Gaussian codebooks and operating over a memoryless block-fading channel. Our method relies on the saddlepoint approximation, which, differently from previous results reported for similar scenarios, is performed with respect to the number of fading blocks (a.k.a. diversity branches) spanned by each codeword, instead of the number of channel uses per block. This different approach avoids a costly numerical averaging of the error probability over the realizations of the fading process and of its pilot-based estimate at the receiver and results in a significant reduction of the number of channel realizations required to estimate the error probability accurately. Our numerical experiments for both single-antenna communication links and massive multiple-input multiple-output (MIMO) networks show that, when two or more diversity branches are available, the error probability can be estimated accurately with the saddlepoint approximation with respect to the number of fading blocks using a numerical method that requires about two orders of magnitude fewer Monte-Carlo samples than with the saddlepoint approximation with respect to the number of channel uses per block.
PB IEEE
SN 0733-8716
YR 2023
FD 2023-07
LK https://hdl.handle.net/10016/38368
UL https://hdl.handle.net/10016/38368
LA eng
NO An earlier version of this paper was presented in part at the Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA, November 2022.
NO This work was supported in part by the Swedish Research Council under Grant 2018-04359 and in part by the National Science Foundation under Grant CCF-2131115. The work of Alejandro Lancho was supported by the European Union's Horizon 2020 Research and Innovation Programme through the Marie Sklodowska-Curie under Grant 101024432.
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RD 7 jun. 2024