Joint Block Length and Pilot Length Optimization for URLLC in the Finite Block Length Regime

Cao, Jie; Zhu, Xu; Jiang, Yufei; Liu, Yujie; Zheng, Fu-Chun

doi:10.1109/globecom38437.2019.9013958

Cited by 17 publications

(3 citation statements)

References 15 publications

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“…A comparison study was carried out in [6] to find suitable coding techniques that can meet the URLLC requirements. [7] aimed to maximize the URLLC link throughput by jointly optimize the block length and pilot length. [8] provide an overview of 3GPP Releases 15, 16, and 17 from a Physical Layer Design perspective.…”

Section: A Related Workmentioning

confidence: 99%

Swift HARQ Based on Machine Learning for Latency Minimization in URLLC

2023

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Section: A Related Workmentioning

confidence: 99%

Swift HARQ Based on Machine Learning for Latency Minimization in URLLC

2023

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“…Similarly, the blocklength was optimized to satisfy the stringent requirements of the DEP and latency [8]. The DEP of relay-assisted transmission under FBL was analyzed in [9] with perfect channel state information (CSI), which was further extended to the imperfect CSI case in [10].…”

Section: Introductionmentioning

confidence: 99%

Resource Allocation for Uplink Cell-Free Massive MIMO Enabled URLLC in a Smart Factory

Peng

Ren

Pan

et al. 2023

IEEE Trans. Commun.

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Smart factories need to support the simultaneous communication of multiple industrial Internetof-Things (IIoT) devices with ultra-reliability and low-latency communication (URLLC). Meanwhile, short packet transmission for IIoT applications incurs performance loss compared to traditional long packet transmission for human-to-human communications. On the other hand, cell-free massive multipleinput and multiple-output (CF mMIMO) technology can provide uniform services for all devices by deploying distributed access points (APs). In this paper, we adopt CF mMIMO to support URLLC in a smart factory. Specifically, we first derive the lower bound (LB) on achievable uplink data rate under the finite blocklength (FBL) with imperfect channel state information (CSI) for both maximum-ratio combining (MRC) and full-pilot zero-forcing (FZF) decoders. The derived LB rates based on the MRC case have the same trends as the ergodic rate, while LB rates using the FZF decoder tightly match the ergodic rates, which means that resource allocation can be performed based on the LB data rate rather the exact ergodic data rate under FBL. The log-function method and successive convex approximation (SCA) are then used to approximately transform the non-convex weighted sum rate problem into a series of geometric program (GP) problems, and an iterative algorithm is proposed to jointly optimize the pilot and payload power allocation. Simulation results demonstrate that CF mMIMO significantly improves the average weighted sum rate (AWSR) compared to centralized mMIMO. An interesting observation is

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“…Moreover, the blocklength optimization has not been addressed. The problem of finding the optimal blocklength has been mainly studied to minimize instantaneous BLER by dynamically adapting blocklength based on channel realizations [6]- [8].…”

Section: Introductionmentioning

confidence: 99%

Bayesian optimization of Blocklength for URLLC Under Channel Distribution Uncertainty

Zhang

Derakhshani

Khosraviradsup

et al. 2022

2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)

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For block fading channels with uncertainty in channel distribution knowledge, we propose and optimize a statistical measure as a way to surely assess reliability in finite-block communications regime. In particular, the confidence level in guaranteeing average block-error rate lower than a specific target is introduced and maximized to find the optimal blocklength, aiming to meet the strict requirements of ultra-reliable low latency communications (URLLC). In order to compute the confidence level, non-parametric learning algorithms are employed for channel modeling with a limited number of training samples. Bayesian optimization, i.e., the tool for black-box optimization, is applied to solve the problem in the absence of the closed form of the confidence level.

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Joint Block Length and Pilot Length Optimization for URLLC in the Finite Block Length Regime

Cited by 17 publications

References 15 publications

Swift HARQ Based on Machine Learning for Latency Minimization in URLLC

Swift HARQ Based on Machine Learning for Latency Minimization in URLLC

Resource Allocation for Uplink Cell-Free Massive MIMO Enabled URLLC in a Smart Factory

Bayesian optimization of Blocklength for URLLC Under Channel Distribution Uncertainty

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