Intelligent Resource Management for eMBB and URLLC in 5G and Beyond Wireless Networks

Sohaib, Rana Muhammad; Onireti, Oluwakayode; Sambo, Yusuf A.; Swash, Mohammad Rafiq; Ansari, Shuja; Imran, Muhammad Ali

doi:10.1109/access.2023.3288698

Cited by 7 publications

(3 citation statements)

References 41 publications

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“…This technology includes new radio (NR), multiple input multiple output (MIMO) antennas, and more [18]. Dangi et al (2021) and Sohaib et al (2023) note three key services brought about by 5G technology: (a) extreme mobile broadband (eMBB), (b) massive machine type communication (mMTC), and (c) ultra-reliable low-latency communication (URLLC). eMBB is primarily responsible for providing consumers with lightning-fast internet connections, allowing them to stream ultra-high-definition (UHD) videos and make use of augmented or virtual reality technologies [17,19,21].…”

Section: Fifth Generation and Broadbandmentioning

confidence: 99%

“…Dangi et al (2021) and Sohaib et al (2023) note three key services brought about by 5G technology: (a) extreme mobile broadband (eMBB), (b) massive machine type communication (mMTC), and (c) ultra-reliable low-latency communication (URLLC). eMBB is primarily responsible for providing consumers with lightning-fast internet connections, allowing them to stream ultra-high-definition (UHD) videos and make use of augmented or virtual reality technologies [17,19,21]. For eMBB to effectively work, it must function in a variety of environments, including indoor settings, urban cores, and sparsely populated rural regions.…”

Section: Fifth Generation and Broadbandmentioning

confidence: 99%

See 1 more Smart Citation

Ohio’s 5G and Broadband Workforce: Assessing the Current Landscape Using Skillshed Analysis

Humeniuk,

Erfanian,

Jolley

2024

Merits

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Ohio’s 5G and broadband industry is rapidly changing. In this study, we assess the current skills and skills gaps of the state’s workforce and the supply and demand of labor within the 5G and broadband industry. We use a skillshed analysis to determine declining occupations with easy and challenging transitions into 5G and broadband occupations. Based on the analysis, we determine where skills gaps exist and where additional resources are needed to prepare the state’s workforce for the 5G and broadband industry.

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Section: Fifth Generation and Broadbandmentioning

confidence: 99%

Section: Fifth Generation and Broadbandmentioning

confidence: 99%

Ohio’s 5G and Broadband Workforce: Assessing the Current Landscape Using Skillshed Analysis

Humeniuk,

Erfanian,

Jolley

2024

Merits

View full text Add to dashboard Cite

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“…This is especially crucial in scenarios where network resources are limited. An intelligent resource management architecture for eMBB and uRLLC coexistence in 5G and beyond networks is proposed in [11], This proposed method utilizes machine learning to predict user demands and network conditions, enabling proactive resource allocation that guarantees QoS for both eMBB and uRLLC services. It also employs isolation techniques to ensure security and prevent interference between slices.…”

Section: Related Workmentioning

confidence: 99%

Optimization of resource allocation in 5G networks: A network slicing approach with hybrid NOMA for enhanced uRLLC and eMBB coexistence

Chandra Sekhar,

Singh

2024

Int J Communication

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SummaryTraditional Orthogonal Multiple Access (OMA) and spectrum sharing methods struggle to provide the diverse quality of service (QoS) demands for enhanced mobile broadband (eMBB), ultra‐reliable low latency communications (uRLLC), and massive machine type communications (mMTC) leading to suboptimal performance and service quality degradation. Single‐carrier‐non‐orthogonal multiple access (SC‐NOMA) appears to be a more optimized solution. It can serve multiple users simultaneously on the same time‐frequency resources. This approach offers both enhanced spectrum efficiency and meets the QoS requirements for the coexistence of eMBB, uRLLC, and mMTC. However, SC‐NOMA has some drawbacks. Decoding a user's signal involves a complex successive interference cancellation (SIC) process that gets harder with more users causing delays and errors. Additionally, strong user signals can interfere with weaker ones, limiting the number of users per channel. In order to overcome the drawbacks associated with OMA and SC‐NOMA, this paper introduces a new method called user‐paired NOMA (hybrid NOMA). Hybrid NOMA adopts a strategic approach, employing two user pairing techniques: near‐far/far‐near (NF‐FN) and near‐near/far‐far (NN‐FF). NF‐FN pairing prioritizes users with similar signal strengths but different distances from the base station. This minimizes interference for the weaker user during SIC. NN‐FF pairing, on the other hand, groups users with similar signal strengths and proximity. This approach further simplifies SIC and minimizes potential interference altogether. The simulation results demonstrate trade‐offs between eMBB and uRLLC performance. OMA suffers with dedicated resource allocation, while SC‐NOMA balances performance but experiences interference. NN‐FF prioritizes eMBB and offers best latency, while NF‐FN prioritizes uRLLC with high spectral efficiency but suffers from higher latency. Finally, by providing a thorough grasp of how hybrid NOMA resource allocation works to improve the performance of various use cases, this research makes a significant contribution to the field of 5G spectrum optimization.

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DRL-based Joint Resource Scheduling of eMBB and URLLC in O-RAN

Sohaib,

Shah,

Onireti

et al. 2024

2024 IEEE International Conference on Communications Workshops (ICC Workshops)

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This work addresses resource allocation challenges in multi-cell wireless systems catering to enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communications (URLLC) users. We present a distributed learning framework tailored to O-RAN network architectures. Leveraging a Thompson sampling-based Deep Reinforcement Learning (DRL) algorithm, our approach provides real-time resource allocation decisions, aligning with evolving network structures. The proposed approach facilitates online decision-making for resource allocation by deploying trained execution agents at Near-Real Time Radio Access Network Intelligent Controllers (Near-RT RICs) located at network edges. Simulation results demonstrate the algorithm's effectiveness in meeting Quality of Service (QoS) requirements for both eMBB and URLLC users, offering insights into optimising resource utilisation in dynamic wireless environments.

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Intelligent Resource Management for eMBB and URLLC in 5G and Beyond Wireless Networks

Cited by 7 publications

References 41 publications

Ohio’s 5G and Broadband Workforce: Assessing the Current Landscape Using Skillshed Analysis

Ohio’s 5G and Broadband Workforce: Assessing the Current Landscape Using Skillshed Analysis

Optimization of resource allocation in 5G networks: A network slicing approach with hybrid NOMA for enhanced uRLLC and eMBB coexistence

DRL-based Joint Resource Scheduling of eMBB and URLLC in O-RAN

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