Throughput Maximization and IR-HARQ Optimization for URLLC Traffic in 5G Systems

Avranas, Apostolos; Kountouris, Marios; Ciblat, Philippe

doi:10.1109/icc.2019.8761154

Cited by 11 publications

(13 citation statements)

References 14 publications

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“…IR-HARQ suggests a sub-optimal HARQ procedure to overcome the complexity. However, their sub-optimal solution is further optimized by [82], in which authors propose a dynamic programming algorithm to maximize the throughput by optimally tuning the IR-HARQ parameters. Le et al [83] highlighted the performance degradation of URLLC traffic with the increase of collision among the UEs and gNBs.…”

Section: Related Contributionsmentioning

confidence: 99%

URLLC for 5G and Beyond: Requirements, Enabling Incumbent Technologies and Network Intelligence

et al. 2021

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The tactile internet (TI) is believed to be the prospective advancement of the internet of things (IoT), comprising human-to-machine and machine-to-machine communication. TI focuses on enabling realtime interactive techniques with a portfolio of engineering, social, and commercial use cases. For this purpose, the prospective 5 th generation (5G) technology focuses on achieving ultra-reliable low latency communication (URLLC) services. TI applications require an extraordinary degree of reliability and latency. The 3 rd generation partnership project (3GPP) defines that URLLC is expected to provide 99.99% reliability of a single transmission of 32 bytes packet with a latency of less than one millisecond. 3GPP proposes to include an adjustable orthogonal frequency division multiplexing (OFDM) technique, called 5G new radio (5G NR), as a new radio access technology (RAT). Whereas, with the emergence of a novel physical layer RAT, the need for the design for prospective next-generation technologies arises, especially with the focus of network intelligence. In such situations, machine learning (ML) techniques are expected to be essential to assist in designing intelligent network resource allocation protocols for 5G NR URLLC requirements. Therefore, in this survey, we present a possibility to use the federated reinforcement learning (FRL) technique, which is one of the ML techniques, for 5G NR URLLC requirements and summarizes the corresponding achievements for URLLC. We provide a comprehensive discussion of MAC layer channel access mechanisms that enable URLLC in 5G NR for TI. Besides, we identify seven very critical future use cases of FRL as potential enablers for URLLC in 5G NR.

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Section: Related Contributionsmentioning

confidence: 99%

URLLC for 5G and Beyond: Requirements, Enabling Incumbent Technologies and Network Intelligence

et al. 2021

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“…In [30], [31], the authors propose a low-complexity algorithm for scheduling URLLC users. The authors in [32] consider the throughput maximization and HARQ optimization for URLLC users. Furthermore, reliable transmission is an important issue of URLLC communications.…”

Section: Related Workmentioning

confidence: 99%

Dynamic power control for time-critical networking with heterogeneous traffic

Fountoulakis¹,

Παππάς²,

Ephremides³

2021

ITU-J FET

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Future wireless networks will be characterized by heterogeneous traffic requirements. Examples can be low-latency or minimum-througput requirements. Therefore, the network has to adjust to different needs. Usually, users with low-latency requirements have to deliver their demand within a specific time frame, i.e., before a deadline, and they coexist with throughput oriented users. In addition, mobile devices have a limited-power budget and therefore, a power-efficient scheduling scheme is required by the network. In this work, we cast a stochastic network optimization problem for minimizing the packet drop rate while guaranteeing a minimum throughput and taking into account the limited-power capabilities of the users. We apply tools from Lyapunov optimization theory in order to provide an algorithm, named Dynamic Power Control (DPC) algorithm, that solves the formulated problem in real time. It is proved that the DPC algorithm gives a solution arbitrarily close to the optimal one. Simulation results show that our algorithm outperforms the baseline Largest-Debt-First (LDF) algorithm for short deadlines and multiple users.

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“…In [29], [30], the authors propose low-complexity algorithm for scheduling URLLC users. The authors in [31] consider the throughput maximization and HARQ optimization for URLLC users. Furthermore, reliable transmission is an important issue of URLLC communications.…”

Section: A Related Workmentioning

confidence: 99%

Dynamic Power Control for Time-Critical Networking with Heterogeneous Traffic

Fountoulakis¹,

Παππάς²,

Ephremides³

2020

Preprint

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Future wireless networks will be characterized by heterogeneous traffic requirements. Such requirements can be low-latency or minimum-throughput. Therefore, the network has to adjust to different needs. Usually, users with low-latency requirements have to deliver their demand within a specific time frame, i.e., before a deadline, and they co-exist with throughput oriented users. In addition, the users are mobile and they share the same wireless channel. Therefore, they have to adjust their power transmission to achieve reliable communication. However, due to the limited-power budget of wireless mobile devices, a power-efficient scheduling scheme is required by the network. In this work, we cast a stochastic network optimization problem for minimizing the packet drop rate while guaranteeing a minimumthroughput and taking into account the limited-power capabilities of the users. We apply tools from Lyapunov optimization theory in order to provide an algorithm, named Dynamic Power Control (DPC) algorithm, that solves the formulated problem in realtime. It is proved that the DPC algorithm gives a solution arbitrarily close to the optimal one. Simulation results show that our algorithm outperforms the baseline Largest-Debt-First (LDF) algorithm for short deadlines and multiple users.

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Throughput Maximization and IR-HARQ Optimization for URLLC Traffic in 5G Systems

Cited by 11 publications

References 14 publications

URLLC for 5G and Beyond: Requirements, Enabling Incumbent Technologies and Network Intelligence

URLLC for 5G and Beyond: Requirements, Enabling Incumbent Technologies and Network Intelligence

Dynamic power control for time-critical networking with heterogeneous traffic

Dynamic Power Control for Time-Critical Networking with Heterogeneous Traffic

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