Joint Resource Configuration and MCS Selection Scheme for Uplink Grant-Free URLLC

Jacobsen, Thomas; Abreu, Renato; Berardinelli, Gilberto; Pedersen, Klaus I.; Kovács, István Z.; Mogensen, Preben

doi:10.1109/glocomw.2018.8644377

Cited by 12 publications

(15 citation statements)

References 17 publications

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“…2) Functionality for High Reliability: Three important functionalities for high reliability are discussed in the following. a) Low Spectrum-Efficient MCS/CQI: To guarantee reliable data transmission, optimal modulation and coding scheme (MCS) based on the channel quality indication (CQI) should be employed according to a look-up table [73]. In URLLC, reliability can be improved at the expense of low spectrum efficiency.…”

Section: A Basic Urllc Functionalitymentioning

confidence: 99%

Towards Enabling Critical mMTC: A Review of URLLC Within mMTC

et al. 2020

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Massive machine-type communication (mMTC) and ultrareliable and low-latency communication (URLLC) are two key service types in the fifth-generation (5G) communication systems, pursuing scalability and reliability with low-latency, respectively. These two extreme services are envisaged to agglomerate together into critical mMTC shortly with emerging use cases (e.g., wide-area disaster monitoring, wireless factory automation), creating new challenges to designing wireless systems beyond 5G. While conventional network slicing is effective in supporting a simple mixture of mMTC and URLLC, it is difficult to simultaneously guarantee the reliability, latency, and scalability requirements of critical mMTC (e.g., < 4ms latency, 106 devices/km 2 for factory automation) with limited radio resources. Furthermore, recently proposed solutions to scalable URLLC (e.g., machine learning aided URLLC for driverless vehicles) are ill-suited to critical mMTC whose machine type users have extremely limited energy budget and computing capability that should be tightly optimized for given tasks. In view of this, this paper aims to characterize promising use cases of critical mMTC and search for their possible solutions. To this end, we first review the state-of-theart (SOTA) technologies for separate mMTC and URLLC services and then identify key challenges from conflicting SOTA requirements, followed by potential approaches to prospective critical mMTC solutions at different layers. Index Terms-Ultra reliable low latency communication (URLLC), massive machine type communications (mMTC), critical mMTC, 5G, beyond 5G.

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Section: A Basic Urllc Functionalitymentioning

confidence: 99%

Towards Enabling Critical mMTC: A Review of URLLC Within mMTC

et al. 2020

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“…For UL packet transmissions, we assume configured grant (CG) transmission (aka grant-free) with fixed MCS per UE [32], The use of CG means that as soon as a packet arrives at the UE, it is immediately prepared for UL transmission, and transmitted at the first coming UL TTI opportunity. Each CG transmission includes a robust preamble, so the receiving BS is able to detect from which UE the transmission is coming.…”

Section: A System Modelmentioning

confidence: 99%

“…The CG parameterization is such that UEs with high path-loss are transmitting on the full bandwidth with a conservative MCS corresponding to QPSK rate 1 /8, such that one URLLC payload of 32 bytes can be transmitted. In line with [32], UEs with better path-loss conditions are configured to transmit on one quarter of the carrier bandwidth with MCS QPSK rate 1 /2. Such UE classification, of high or low path-loss conditions, is based on a predefined coupling gain threshold c. The UL transmit power Σ [dBm] is configured to equal…”

Section: A System Modelmentioning

confidence: 99%

“…where Σ max is the max UE transmit power, P 0 is the target power spectral density, ℘ is the number of granted UL PRBs, α and ð denote the path-loss compensation factor and pathloss, respectively. ∇ MCS is an UL power boost factor where ∇ MCS = 10 dB for QPSK1/2 and ∇ MCS = 0 dB for QPSK1/8 in line with [32]. As CG transmissions from multiple users may occur at the same time on overlapping resources, uplink transmissions from UEs are subject to potential intra-cell interference, which only to a certain extent can be combated by the a linear BS multi-antenna receiver.…”

Section: A System Modelmentioning

confidence: 99%

“…If the BS fails to correctly decode a CG transmission from an UL UE, it immediately sends an uplink scheduling grant for the UE in the next coming DL TTI, issuing an UL HARQ re-transmission from the UE in the next UL TTI. The UL HARQ re-transmission is sent using the same configuration (bandwidth and MCS configurations) as the original transmission, but with a +3 dB transmission power boost to enhance the probability of decoding the HARQ re-transmission at the BS [32].…”

Section: A System Modelmentioning

confidence: 99%

See 2 more Smart Citations

Online Radio Pattern Optimization Based on Dual Reinforcement-Learning Approach for 5G URLLC Networks

2020

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The fifth generation (5G) radio access technology is designed to support highly delaysensitive applications, i.e., ultra-reliable and low-latency communications (URLLC). For dynamic time division duplex (TDD) systems, the real-time optimization of the radio pattern selection becomes of a vital significance in achieving decent URLLC outage latency. In this study, a dual reinforcement machine learning (RML) approach is developed for online pattern optimization in 5G new radio TDD deployments. The proposed solution seeks to minimizing the maximum URLLC tail latency, i.e., min-max problem, by introducing nested RML instances. The directional and real-time traffic statistics are monitored and given to the primary RML layer to estimate the sufficient number of downlink (DL) and uplink (UL) symbols across the upcoming radio pattern. The secondary RML sub-networks determine the DL and UL symbol structure which best minimizes the URLLC outage latency. The proposed solution is evaluated by extensive and highly-detailed system level simulations, where our results demonstrate a considerable URLLC outage latency improvement with the proposed scheme, compared to the state-of-the-art dynamic-TDD proposals.

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Multi-Cell Reception for Uplink Grant-Free Ultra-Reliable Low-Latency Communications

et al. 2019

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The fifth-generation (5G) radio networks will support ultra-reliable low-latency communications (URLLC). In the uplink, the latency can be reduced by removing the time-consuming and error-prone scheduling procedure and, instead, using the grant-free (GF) transmissions. Reaching the strict URLLC reliability requirements with GF transmissions is, however, particularly challenging due to the wireless channel uncertainties and interference from other URLLC devices. As a consequence, the supported URLLC capacity and, hence, the spectral efficiency are typically low. Multi-cell reception, i.e., joint reception and combining by multiple base-stations (BS), is a technique known from long-term evolution (LTE), with the potential to greatly enhance the reliability. This paper proposes the use of multi-cell reception to increase the URLLC spectral efficiency while satisfying the strict requirements using GF transmissions in a 5G new radio (NR) scenario. We evaluate the achievable URLLC capacity for an elaborate multi-cell reception parameter space and multi-cell combining techniques. In addition, we demonstrate that rethinking of the radio resource management (RRM) in the presence of multi-cell reception is needed to unleash the full potential of multi-cell reception in the context of UL GF URLLC. It is observed that multi-cell reception, compared to a single-cell reception, can provide URLLC capacity gains from 205% to 440% when the BSs are equipped with two receive antennas and 53% to 22% when BSs are equipped with four receive antennas, depending on whether the retransmissions are enabled. INDEX TERMS 5G new radio, grant-free, multi-cell reception, radio resource management, system level simulation, ultra-reliable low latency communications, uplink, URLLC.

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Joint Resource Configuration and MCS Selection Scheme for Uplink Grant-Free URLLC

Cited by 12 publications

References 17 publications

Towards Enabling Critical mMTC: A Review of URLLC Within mMTC

Towards Enabling Critical mMTC: A Review of URLLC Within mMTC

Online Radio Pattern Optimization Based on Dual Reinforcement-Learning Approach for 5G URLLC Networks

Multi-Cell Reception for Uplink Grant-Free Ultra-Reliable Low-Latency Communications

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