Multi-Device Low-Latency IoT Networks With Blind Retransmissions in the Finite Blocklength Regime

He, Qinwei; Zhu, Yao; Zheng, Paul; Hu, Yulin; Schmeink, Anke

doi:10.1109/tvt.2021.3120145

Cited by 15 publications

(13 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When G = 32, the former corresponds to the MU-HARQ and the latter to the PL-HARQ. The MU-HARQ configuration (K = 1, C = 32) exhibits the best F performance for t f = 0, but it increases linearly with t f (20). On the other hand, the PL-HARQ configuration (K = 32, C = 1), shows a constant performance, identical to the one of the OS, up to a t f threshold and then degrades linearly with the increase of the delay.…”

Section: A Power Constrained Transmissionsmentioning

confidence: 94%

“…The proposed schemes differ on how they handle possible collisions that may occur on the second phase. In the second phase, collisions can be handled through HARQ signalling [16], [21], successive interference cancellation (SIC) [17]- [20], or have their probability minimized by optimally selecting the number of UEs [20]. In [21], a collision free GCG schemes optimized for latency performance was proposed.…”

Section: A Related Workmentioning

confidence: 99%

“…This work considers a SISO uplink AWGN channel, where a group of G UEs, with similar traffic characteristics and channel statistics, is formed. In this work, and similarly to [17], [20], it is assumed that the group is formed using an appropriate classification procedure for group formation [24], [25].…”

Section: System Modelmentioning

confidence: 99%

“…The PL-MU-HARQ is a combination between MU-HARQ and PL-HARQ. The motivation to pair these schemes arises from the fact that MU-HARQ is able to obtain a better latency performance than a OS scheme for t f = 0, even when using the same transmission power, but its latency performance is rapidly degraded by t f (20). The PL-HARQ can never achieve a latency lower than the one-shot scheme when using the same transmission power, however, contrary to MU-HARQ its latency performance is resilient against t f .…”

Section: B Pl-mu-harqmentioning

confidence: 99%

See 3 more Smart Citations

Pipelined Multi-User IR-HARQ Scheme for Improved Latency Performance in URLLC

Santos,

Castanheira,

Silva

et al. 2024

IEEE Access

View full text Add to dashboard Cite

The demand for ultra-reliable low-latency communications (URLLC) has led to the adoption of grant-free (GF) access techniques by the 5G NR, with the goal of reducing uplink access time. When GF access is employed, the base station (BS) preallocates multiple transmission opportunities (TOs) that can be utilized by the user equipment (UE) as needed. However, this approach results in inefficient resource utilization as unused TOs are wasted. To overcome this inefficiency, the 5G NR allows the assignment of configured grants (CG) to a group of UEs instead of a single one. This development has led research into group-based CG (GCG) schemes, whose reliance on shared resources can result in collisions. The collisions can be prevented by the use of stop-and-wait IR-HARQ schemes. Nevertheless, the delay caused by feedback latency is also undesirable as it severely affects latency performance. This work proposes two new IR-HARQ GCG schemes to efficiently handle feedback latency. The first one is able to eliminate the feedback latency overhead and is proven to simultaneously achieve the latency of a one-shot transmission and the energy efficiency of IR-HARQ, even in the presence of non-instantaneous feedback signaling. The second one features both a feedback latency protection mechanism, similar to the first scheme, and a mechanism specifically designed to further reduce latency. The performance of the proposed schemes is compared with scenarios where each UE uses either an individual one-shot or an IR-HARQ scheme. These comparisons encompass scenarios with either power or energy constraints. The results have shown that the second scheme always outperforms the IR-HARQ scheme and that it is able to outperform the one-shot scheme on a wide interval of feedback latency values, achieving a lower latency both for power and energy constrained cases.

show abstract

Section: A Power Constrained Transmissionsmentioning

confidence: 94%

Section: A Related Workmentioning

confidence: 99%

Section: System Modelmentioning

confidence: 99%

Section: B Pl-mu-harqmentioning

confidence: 99%

See 2 more Smart Citations

Pipelined Multi-User IR-HARQ Scheme for Improved Latency Performance in URLLC

Santos,

Castanheira,

Silva

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…As a powerful channel coding scheme, LDPC codes are designed to support high throughput and variable code rate in addition to their powerful error correcting capability [16]. Many IoT applications including industrial automation, haptic feedback in virtual/augmented reality and the tactile internet require URLLC [18]. Given the low latency requirements of such IoT applications, in this paper we consider the use of short block length LDPC coded system.…”

Section: Introductionmentioning

confidence: 99%

Machine learning assisted adaptive LDPC coded system design and analysis

Xie,

El‐Hajjar,

2023

IET Communications

View full text Add to dashboard Cite

This paper proposes a novel machine learning (ML) assisted low‐latency low density parity check (LDPC) coded adaptive modulation (AM) system, where short block‐length LDPC codes are used. Conventional adaptive modulation and coding (AMC) system includes fixed look‐up table method, which is also called inner loop link adaptation (ILLA) and outer loop link adaptation (OLLA). For ILLA, the adaptive capability is achieved by switching the modulation and coding modes based on a look‐up table using signal‐to‐noise ratio (SNR) thresholds at the target bit error rate (BER), while OLLA builds upon the ILLA method by dynamically adjusting the SNR thresholds to further optimize the system performance. Although both improve the system overall throughput by switching between different transmission modes, there is still a gap to optimal performance as the BER is comparatively far away from the target BER. Machine learning (ML) is a promising solution in solving various classification problems. In this work, the supervised learning based k‐nearest neighbours (KNN) algorithm is invoked for choosing the optimum transmission mode based on the training data and the instantaneous SNR. This work focuses on the low‐latency communications scenarios, where short block‐length LDPC codes are utilized. On the other hand, given the short block‐length constraint, we propose to artificially generate the training data to train our ML assisted AMC scheme. The simulation results show that the proposed ML‐LDPC‐AMC scheme can achieve a higher throughput than the ILLA system while maintaining the target BER. Compared with OLLA, the proposed scheme can maintain the target BER while the OLLA fails to maintain the target BER when the block length is short. In addition, when considering the channel estimation errors, the performance of the proposed ML‐LDPC‐AMC maintains the target BER, while the ILLA system's BER performance can be higher than the target BER.

show abstract

Multi-User IR-HARQ Latency and Resource Optimization for URLLC

Santos,

Castanheira,

Silva

et al. 2023

IEEE Access

View full text Add to dashboard Cite

The ultra-reliable low-latency communications (URLLC) tight latency requirements paired with transmission of small payload packets motivates the development of techniques that reduce or eliminate the need for dynamic scheduling. This justifies the study of grant free (GF) leveraged techniques in order to reduce both the latency and control signaling overhead. Previous works considered preallocating resources not only for the first transmission, but also for all possible IR-HARQ transmissions, effectively reducing the scheduling latency and control signaling overhead. However, this has several drawbacks, as it translates into wasted resources. To address these issues, we propose a group-based preallocation method combined with IR-HARQ. Initially, a pool of preallocated resources is assigned to a group of users, which then cooperatively use IR-HARQ feedback signals to distribute, on the fly, the resources amongst them without collisions. The proposed method has two phases: a preallocation phase that takes place once at the group formation stage and a transmission phase which happens at each uplink transmission. The transmission parameters for all possible transmission scenarios are selected at the preallocation stage, with the goal of reducing the latency under reliability and energy constraints. The transmission parameters are obtained through a constrained latency optimization procedure, which considers the stochastic nature of the underlying process. We prove that, asymptotically, the proposed scheme is able to reduce the latency, at least, down to the average latency of any single user (SU) HARQ. The numerical results show that the latency and resources wastage is significantly reduced comparatively to single user IR-HARQ with preallocated resources.

show abstract

Multi-Device Low-Latency IoT Networks With Blind Retransmissions in the Finite Blocklength Regime

Cited by 15 publications

References 25 publications

Pipelined Multi-User IR-HARQ Scheme for Improved Latency Performance in URLLC

Pipelined Multi-User IR-HARQ Scheme for Improved Latency Performance in URLLC

Machine learning assisted adaptive LDPC coded system design and analysis

Multi-User IR-HARQ Latency and Resource Optimization for URLLC

Contact Info

Product

Resources

About