Data-Driven Spectrum Partition for Multiplexing URLLC and eMBB

Peng, Haoran; Wang, Li-Chun; Jian, Zhuofu

doi:10.1109/tccn.2022.3231690

Cited by 5 publications

(4 citation statements)

References 42 publications

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“…This study aims to analyze the delay and backlog violation behaviors of the URLLC and eMBB multiplexing system supported by RIS or DF relays. As multiplexing methods for URLLC and eMBB have already been investigated in [4], the focus of this study is on the performance analysis of URLLC and eMBB multiplexing systems, rather than proposing any multiplexing approaches. Various scheduling schemes for URLLC and eMBB multiplexing were investigated.…”

Section: Contributions and Organizationsmentioning

confidence: 99%

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A Generalized Delay and Backlog Analysis for Multiplexing URLLC and eMBB: Reconfigurable Intelligent Surfaces or Decode-and-Forward?

Peng,

Hsia,

Han

et al. 2024

IEEE Trans. Wireless Commun.

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By creating multipath backscatter links and amplify signal strength, reconfigurable intelligent surfaces (RIS) and decode-and-forward (DF) relaying are shown to degrade the latency of the ultrareliable low-latency communications (URLLCs) and enhanced mobile broadband (eMBB) multiplexing system. This study investigates the delay and backlog violation behavior of URLLCs and eMBB multiplexing systems supported by different technologies, e.g. RIS and DF relay, for different scheduling policies of static priority, nonpreemption, and earliest deadline first. A tight analysis approach based on the Martingale theory was proposed to evaluate the serviceability of URLLC and eMBB multiplexing systems. On this basis, the Martingale theory analyzes the delay and backlog bounds by transforming the arrival and service processes into exponential forms of the moment generating function. Furthermore, this study derives the closed-form expression of delay and backlog bound for the URLLCs and eMBB multiplexing in two-hop heterogeneous communication networks. Numerical results demonstrate that the proposed Martingale-based tightly analytical method outperforms the state-of-the-art classic stochastic network calculus for evaluating delay and backlog violation in URLLC and eMBB multiplexing systems.

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Section: Contributions and Organizationsmentioning

confidence: 99%

“…works (5G) new radio (NR) is to efficiently support multiplexing ultrareliable low-latency communications (URLLC) and enhanced mobile broadband (eMBB) [1]- [4]. URLLC traffic aims to achieve an extremely low latency (0.25-0.3 msec/packet) while guaranteeing high reliability of the 99.999% packet success probability [5].…”

mentioning

confidence: 99%

A Generalized Delay and Backlog Analysis for Multiplexing URLLC and eMBB: Reconfigurable Intelligent Surfaces or Decode-and-Forward?

Peng,

Hsia,

Han

et al. 2024

IEEE Trans. Wireless Commun.

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“…A spectrum partition method is applied to analytically investigate the eMBB and URLLC co-existence system [31]. A joint optimization problem is formulated to maximize the eMBB data rate while providing the strict URLLC to the users.…”

Section: Resource Allocations For Embb and Urllc Co-existencementioning

confidence: 99%

Resource Allocation for Co-Existence of eMBB and URLLC Services in 6G Wireless Networks: A Survey

Adhikari,

Jaseemuddin,

Anpalagan

2024

IEEE Access

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Next generation of wireless networks are characterized by two main features named Enhanced Mobile Broadband (eMBB) and Ultra Reliable Low Latency Communications (URLLC). These two services can be accommodated in the same wireless infrastructure so that wide range of users, demanding either massive throughput or extremely low latency and high reliability requirements, are directly benefited for providing various mission critical services. Co-existence of eMBB and URLLC services, however, demand highly efficient and less complex resource allocation schemes. In this paper, various resource allocation techniques are studied for the co-existence of eMBB and URLLC traffic to meet the heterogeneous specifications of each class of users. A detailed study on existing resource allocation schemes for simultaneous transmission of eMBB and URLLC services based on network slicing, flexible Transmit Time Interval (TTI), scheduling and distributed and federated learning are provided. Moreover, Machine Learning (ML) aided and Reconfigurable Intelligent Surface (RIS) and UAV assisted resource allocation techniques are also studied in detail. Additionally, this paper identifies some challenges for eMBB and URLLC service accommodation in the same wireless architecture and proposes their possible solution approaches.INDEX TERMS eMBB and URLLC, multiplexing, resource allocation, slicing, transmit time interval, machine learning.

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“…The authors utilized a Difference of Convex (DC) programming and a Successive Convex Approximation (SCA) to solve the scheduling problem of power allocation and user selection, respectively. In [37], the authors proposed a spectrum partition scheme for maximizing the eMBB data rate and satisfying the URLLC latency and reliability. In [38], the authors proposed a precoding design in order to minimize the transmit power of a Base Station (BS) while satisfying the QoS of eMBB and URLLC users based on imperfect CSI.…”

Section: B Dynamic Resource Block Structurementioning

confidence: 99%

AI-Assisted Dynamic Frame Structure With Intelligent Puncturing Schemes for 5G Networks

Abedi,

Javan,

Pourghasemian

et al. 2023

IEEE Access

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The dynamic resource block structure (D-RBS) allows for flexible allocation of radio resources. This flexibility (potentially) enables efficient utilization of available resources and adaptability to changing network conditions. In this context, managing resource contention and optimizing allocation decisions become increasingly challenging. In this research paper, we introduce a new approach for D-RBS for re-allocation of enhanced Mobile Broadband (eMBB) and massive Machine Type Communication (mMTC) resource blocs (RBs) to URLLC users. Our scheme leverages artificial intelligence (AI) to support the three main services of 5th generation networks. To efficiently allocate resources for eMBB/mMTC and URLLC services, we propose an intelligent puncturing scheme. Additionally, we formulate an optimization problem that aims to minimize resource and transmit power usage while meeting the requirements of all three services. Since this problem is non-convex and involves multiple optimization variables, we utilize deep reinforcement learning as a solution algorithm. We then compare our proposed intelligent allocation (IA) scheme with two other schemes: random allocation (RaA) and overallocation (OA), which have lower complexity and overhead. Performance and complexity analyses are conducted in a multi-cell scenario with interference. Our results demonstrate that the IA scheme outperforms RaA and OA, achieving an energy efficiency gain of 40% and 15% respectively. However, it is worth noting that IA has a 36.3% higher complexity in terms of action selection compared to RaA and OA.INDEX TERMS Dynamic RB structure, URLLC puncturing, eMBB and URLLC multiplexing, MA-DRL.

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Data-Driven Spectrum Partition for Multiplexing URLLC and eMBB

Cited by 5 publications

References 42 publications

A Generalized Delay and Backlog Analysis for Multiplexing URLLC and eMBB: Reconfigurable Intelligent Surfaces or Decode-and-Forward?

A Generalized Delay and Backlog Analysis for Multiplexing URLLC and eMBB: Reconfigurable Intelligent Surfaces or Decode-and-Forward?

Resource Allocation for Co-Existence of eMBB and URLLC Services in 6G Wireless Networks: A Survey

AI-Assisted Dynamic Frame Structure With Intelligent Puncturing Schemes for 5G Networks

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