5G NR Radio Interface

Bertényi, Balázs; Nagata, Satoshi; Kooropaty, Havish; Zhou, Xutao; Chen, Wanshi; Kim, Younsun; Dai, Xizeng; Xu, Xiaodong

doi:10.13052/jicts2245-800x.613

Cited by 29 publications

(16 citation statements)

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“…However, a cooperative HARQ which sets up a virtual radio reception channel array among gNBs is proposed to guarantee reliability under extreme shadowing and fading conditions [33]. A PHY DL control channel (PDCCH) is utilized for dynamic scheduling of DL control information (DCI) in 5G NR, which incorporates the data required for the UE to process the scheduled information [74]. To schedule a DL data, the DCI scheduling also incorporates channel resource information of the HARQ-ACK feedback.…”

Section: A Hybrid Automatic Repeat Request (Harq) Feedbackmentioning

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: A Hybrid Automatic Repeat Request (Harq) Feedbackmentioning

confidence: 99%

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

et al. 2021

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“…3). The three main characteristics of the parity check matrix H are: base graph, lifting size and cyclic shifts which are applied to the edges of the graph [8]. In the case of NR, two base graphs are defined [23]: Base graph 1, whose size is 46 x 68, and Base graph 2, with a size of 42 x 52.…”

Section: A Ldpc Codementioning

confidence: 99%

“…Having in mind that there is no single channel coding candidate able to meet all 5G requirements and that not choosing channel coding technique correctly lead to poor mobile network performances in terms of coverage, data rates, capacity, and QoS [7,8], the research devoted to the selection and implementation of appropriate channel coding techniques is crucial. Hence, this paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of 5G Channel Coding Techniques

Čarapić

Maksimović

2020

IJEEC

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The rapid, reliable, and secure data transmission in everyday life and numerous applications is one of the crucial demands of modern society. Mobile wireless communications have advanced significantly in recent decades. From the first (1G) to fifth-generation (5G) of mobile communications, the realization of fast and secure communication has always been challenging as data transfer happens in an imperfect channel environment where noise due to amplification, distortion, and other impairments is present. Channel coding is key to establishing fast communication with low error probability, implying that choosing the proper channel coding scheme is a challenging and crucial task. Higher flexibility and reliability, and low computational complexity, latency, and costs are desired coding technique characteristics. This paper focuses on two 5G channel coding techniques, Low-Density Parity-Check (LDPC) and Polar codes. These codes have been examined in the case of variable message sizes and for a wide range of code rates. In addition, different Polar decoding algorithms have been investigated. Simulations results have confirmed that there is no single channel coding scheme able to meet all 5G requirements as well as the superiorities of LDPC codes in case of long messages and Polar codes for short messages. The ability to support a wide range of code lengths and code rates and excellent Bit Error Rate (BER) performances, justify the utilization of LDPC and Polar codes in 5G communication systems.

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“…The Fifth Generation (5G) mobile networks hold enormous potential for innovations across vertical industries given its promised multi-Gbps speed, 1 ms latency, and massive connectivity. Among the three important use cases of 5G systems are: enhanced Mobile BroadBand (eMBB), massive Machine Type Communications (mMTC), and mission critical MTC (cMTC) [1], which include applications with Ultra-Reliable Low Latency Communication (URLLC) requirements with a target error rate of less than 10 −5 and 1 ms end-to-end latency [2]. The cMTC exemplary scenarios that can only be enabled by URLLC [1]: are factory automation through network controlled systems, autonomous roads, platooning, haptic communications and many others.…”

Section: Introductionmentioning

confidence: 99%

Analytical Model and Feedback Predictor Optimization for Combined Early-HARQ and HARQ

et al. 2021

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In order to fulfill the stringent Ultra-Reliable Low Latency Communication (URLLC) requirements towards Fifth Generation (5G) mobile networks, early-Hybrid Automatic Repeat reQuest (e-HARQ) schemes have been introduced, aimed at providing faster feedback and thus earlier retransmission. The performance of e-HARQ prediction strongly depends on the classification mechanism, data length, threshold value. In this paper, we propose an analytical model that incorporates e-HARQ and Hybrid Automatic Repeat reQuest (HARQ) functionalities in terms of two phases in discrete time. The model implies a fast and accurate way to get the main performance measures, and apply optimization analysis to find the optimal values used in predictor’s classification. We employ realistic data for transition probabilities obtained by means of 5G link-level simulations and conduct extensive experimental analysis. The results show that at false positive probability of 10−1, the e-HARQ prediction with the found optimal parameters can achieve around 20% of gain over HARQ at False Negative (FN) of 10−1 and around 7.5% at FN of 10−3 in terms of a mean spending time before successful delivery.

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5G NR Radio Interface

Cited by 29 publications

References 0 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

A Comparison of 5G Channel Coding Techniques

Analytical Model and Feedback Predictor Optimization for Combined Early-HARQ and HARQ

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