Resource Allocation for Multi-User Downlink URLLC-OFDMA Systems

Ghanem, Walid R.; Jamali, Vahid; Sun, Yan; Schober, Robert

doi:10.1109/iccw.2019.8756746

Cited by 43 publications

(40 citation statements)

References 59 publications

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“…Taking into account the results from FBL communication theory, many researches have studied several radio resource management techniques and investigated various URLLC enablers to further boost the 5G performance. Particularly, optimal power allocation and subcarrier assignment policies for DL multi-user scenarios are proposed in [47] and [48], respectively. The authors in [49] adopt multi-class queuing theory to design and analyse the network performance of URLLC.…”

Section: A Brief Overview Of the State Of The Art On Urllcmentioning

confidence: 99%

“…However, applying this does not change the problem formulation and the provided solution of this paper. Performing periodic channel state information (CSI) measurements, we assume that the CSI knowledge is available at both transceivers [33], [47], [48]. CSI acquisition procedure is run in parallel with packet scheduling and tight resolution periodicity (e.g.…”

Section: System Model and Basic Transmission Assumptionsmentioning

confidence: 99%

“…Please refer to Appendix A. To handle the non-convexity of constraints C2Ib-C4Ib, we employ successive convex approximation [48], [81]. Applying Taylor expansion for the convex functions, the first order approximation of e yi with respect to an initial point y 0 i is expressed as…”

Section: A In-resource Control Signallingmentioning

confidence: 99%

See 2 more Smart Citations

On the Multiplexing of Data and Metadata for Ultra-Reliable Low-Latency Communications in 5G

Karimi

Pedersen

Mahmood

et al. 2020

IEEE Trans. Veh. Technol.

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This paper addresses the problem of downlink radio resource management for ultra-reliable low-latency communications (URLLC) in fifth generation (5G) systems. To support low-latency communications, we study performance of two multiplexing schemes namely in-resource control signalling and joint encoding of data and metadata. In the former, the metadata and data are separately encoded and the metadata is sent at the beginning of transmission time prior to the data. Thus, it benefits from a low-complexity receiver structure to decode the data block. The latter takes advantages of transmitting a larger blocklength to enhance the reliability and improve spectrum efficiency by jointly encoding data and metadata as a single codeword. Dealing with small URLLC payloads, the overhead and error of sending metadata are not negligible and have a significant impact on the system performance in terms of resource usage the reliability of transmission. For each scheme, we derive expressions for the outage probability and resource usage by taking into account impacts of the finite blocklength payloads, overhead and error of sending metadata, and probability of error in uplink feedback channel. We propose a novel framework for joint data and metadata link adaptation to minimize the average number of allocated resources, while ensuring the stringent URLLC quality of service requirements. An optimization problem is formulated for each scheme that is mixed-integer nonconvex problem, difficult to solve in polynomial time. Solutions based on successive convex optimization are proposed. Numerical evaluations show that the proposed algorithms perform close to the optimal solution and demonstrate remarkable gains of up to 27% improvement in resource usage. Finally, we present sensitivity analysis of the results for various network parameters.

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Section: A Brief Overview Of the State Of The Art On Urllcmentioning

confidence: 99%

Section: System Model and Basic Transmission Assumptionsmentioning

confidence: 99%

See 1 more Smart Citation

On the Multiplexing of Data and Metadata for Ultra-Reliable Low-Latency Communications in 5G

Karimi

Pedersen

Mahmood

et al. 2020

IEEE Trans. Veh. Technol.

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“…Recently, there are increasing research studying the transmission design based on the short packet transmission capacity formula [8]- [14]. In specific, the effective throughput maximization was studied in [8] for a two-device downlink non-orthogonal multiple access (NOMA) system.…”

mentioning

confidence: 99%

“…Hence, TDMA strategy is not suitable for the applications in industrial applications with extremely stringent latency target. On the other hand, the authors in [13] [14] studied the resource allocation for multiple devices operating under the orthogonal frequency division multiple access (OFDMA) mode. Unfortunately, this requires huge amount of system bandwidth, which is not feasible as some industrial applications operate over unlicensed spectrum [15].…”

mentioning

confidence: 99%

Joint Pilot and Payload Power Allocation for Massive-MIMO-Enabled URLLC IIoT Networks

Ren

Pan

Deng

et al. 2020

IEEE J. Select. Areas Commun.

127

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The Fourth Industrial Revolution (Industrial 4.0) is coming, and this revolution will fundamentally enhance the way the factories manufacture products. The conventional wired lines connecting central controller to robots or actuators will be replaced by wireless communication networks due to its low cost of maintenance and high deployment flexibility. However, some critical industrial applications require ultra-high reliability and low latency communication (URLLC). In this paper, we advocate the adoption of massive multiple-input multiple output (MIMO) to support the wireless transmission for industrial applications as it can provide deterministic communications similar as wired lines thanks to its channel hardening effects. To reduce the latency, the channel blocklength for packet transmission is finite, and suffers from transmission rate degradation and decoding error probability. Thus, conventional resource allocation for massive MIMO transmission based on Shannon capacity assuming the infinite channel blocklength is no longer optimal. We first derive the closed-form expression of lower bound (LB) of achievable uplink data rate for massive MIMO system with imperfect channel state information (CSI) for both maximum-ratio combining (MRC) and zero-forcing (ZF) receivers. Then, we propose novel lowcomplexity algorithms to solve the achievable data rate maximization problems by jointly optimizing the pilot and payload transmission power for both MRC and ZF. Simulation results confirm the rapid convergence speed and performance advantage over the existing benchmark algorithms.

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Resource allocation for a multi‐device URLLC network

Qureshi

Nasir

Masood

et al. 2021

Trans Emerging Tel Tech

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The fifth generation of wireless cellular networks, the 5G New Radio (5G NR), is expected to support ultra‐reliable low latency communications (URLLC) as one of its primary communication services. The URLLC service of the network targets very high levels of reliability, which according to the 3GPP standards, corresponds to a block error rate of 10−9 to 10−5. Simultaneously, the networks need to guarantee very low latency, less than 1 millisecond according to the 3GPP standards. In this article, we propose an algorithm to solve the resource allocation problem, in terms of power and blocklength optimization, for a general multi‐device downlink URLLC in a factory automation scenario. Our objective is to minimize the decoding‐error probability at a target device, while ensuring that the rest of the devices perform under a threshold error probability. To achieve this, we propose a multi‐layered encapsulation method which simplifies the resource allocation problem of multi‐device networks. We also propose a general relay‐assisted strategy in the last step of the multi‐layered encapsulation model, which results in a remarkable improvement in the decoding‐error probability of the target device. Moreover, we also propose a multiple‐antenna beamforming design based resource allocation, which further improves the decoding‐error‐probability at the expense of higher computational complexity.

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Resource Allocation for Multi-User Downlink URLLC-OFDMA Systems

Cited by 43 publications

References 59 publications

On the Multiplexing of Data and Metadata for Ultra-Reliable Low-Latency Communications in 5G

On the Multiplexing of Data and Metadata for Ultra-Reliable Low-Latency Communications in 5G

Joint Pilot and Payload Power Allocation for Massive-MIMO-Enabled URLLC IIoT Networks

Resource allocation for a multi‐device URLLC network

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