QoS aware resource allocation for coexistence mechanisms between eMBB and URLLC: Issues, challenges, and future directions in 5G

Kumar, Rajesh; Sinwar, Deepak; Singh, Vijander

doi:10.1016/j.comcom.2023.10.024

Cited by 3 publications

(3 citation statements)

References 102 publications

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“…eMBB is one of the three main use case classes for 5G (the others being mMTC and uRLLC). eMBB focuses on delivering faster data speeds, increased network capacity and improved mobility in order to support a wide range of applications and services, including high-de inition video streaming, virtual reality (VR) and augmented reality (AR) (Dangi et al, 2021;Kumar et al, 2024).…”

Section: Enhanced Mobile Broadband (Embb)mentioning

confidence: 99%

“…mMTC is designed to facilitate M2M communications and support massive IoT infrastructures, enabling connectivity for a large number of devices. This includes the ability to support at least 1 million IoT connections per square kilometre, with long battery life and extensive wide-area coverage (Kumar et al, 2024).…”

Section: Massive Machine-type Communications (Mmtc)mentioning

confidence: 99%

“…uRLLC is one of the three main use case classes for 5G (the other two being mMTC and eMBB). Important for mission-critical applications that require low latency and high reliability, uRLLC ensures timely and reliable communication for vital operations (Kumar et al, 2024). It thus has enabling potential for connected and V2X (vehicle-to-everything), remote surgery, smart grids, etc.…”

Section: Ultra-reliable and Low Latency Communications (Urllc)mentioning

confidence: 99%

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From Vision to Practice – Insights from Nordic-Baltic 5G applications across sectors

de Jesus,

Melander

2024

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This report builds on the findings of the Nordic-Baltic 5G Monitoring Tool (N-B 5G MT) project ‘Analytical Report’, which focused on mapping 5G activities in the Nordic-Baltic region and analysing their roll-out status. In this follow-up report, we delve deeper into actual 5G applications across different verticals (i.e. sectors), including healthcare, transportation/mobility, industry and media/broadcasting. The report identifies challenges in each sector, such as funding constraints in healthcare, technical hurdles in transportation, market immaturity in industry, and infrastructure investment needs in media, highlighting the complex landscape of 5G deployment. Key cross-cutting challenges include uncertain business cases due to lack of clear benefits and empirical validation, financial barriers from high deployment costs and insufficient early-stage funding, technical and infrastructural limitations, especially in rural areas, regulatory constraints including spectrum allocation and data privacy, security and privacy concerns necessitating a comprehensive approach for compliance, acceptability and usability issues that require simplifying 5G for broader adoption, and collaboration challenges highlighting the need for forums, dialogue sessions, and partnerships to facilitate idea exchange and project advancements. Overall, the report emphasizes the need for a systemic approach to addressing these challenges. This includes clarifying the business value of 5G; fostering ecosystems for collaboration; and ensuring that policy and regulatory frameworks support the innovative, equitable deployment of 5G technology. Overcoming these hurdles will require concerted efforts from all stakeholders, including governments, industry and the wider community. Only then will the transformative potential of 5G for society be fully realized.

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Section: Enhanced Mobile Broadband (Embb)mentioning

confidence: 99%

Section: Massive Machine-type Communications (Mmtc)mentioning

confidence: 99%

Section: Ultra-reliable and Low Latency Communications (Urllc)mentioning

confidence: 99%

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From Vision to Practice – Insights from Nordic-Baltic 5G applications across sectors

de Jesus,

Melander

2024

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A robust OFDM IM-QAM NOMA scheme for URLLC and eMBB downlink service coexistence

Iossifides,

Lioumpas,

Yioultsis

2024

Physical Communication

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Ambient Backscatter Communication System Empowered by Matching Game and Machine Learning for Enabling Massive IoT Over 6G HetNets

Khalifa,

El-Haleem,

Elmesalawy

et al. 2024

IEEE Access

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Ambient backscatter communication (ABC) is considered as a promising paradigm for meeting the 6G massive Internet of Things (IoT) requirements which is expected to revolutionize our world. In this paper, a new multimode matching game and machine learning-based IoT ambient backscatter communication scheme is proposed to maximize the ABC system rate and capacity over the LTE and Wi-Fi multi-RAT heterogeneous network, thereby supporting the 6G green massive IoT communication. The proposed algorithm is designed to support different rate and capacity requirements for different massive Machine Type Communication (mMTC) use cases such as sensor networks, smart grid, agriculture and low data rate Ultra Reliable Low Latency Communication (URLLC) use cases such as Tactile Interaction. The proposed optimization algorithm runs into two phases, the first one is a matching game-based algorithm that selects the optimum association between the IoT tags and the primary users (PU) downlink signals from a specific base station which maximizes the IoT tags rate while minimizing the resulting interference to the PU. Each IoT tag can ride the PU downlink signal using one of three different riding modes according to the required IoT ABC system rate and capacity, whereas mode 1 allows multiple IoT tags to ride the whole PU downlink signal resource blocks, in mode 2 each IoT tag can ride only one subcarrier from the PU downlink signal resource blocks, while in mode 3 multiple IoT tags can ride the same subcarrier from the PU downlink signal resource blocks. In addition, unmanned aerial vehicles (UAVs) flying HetNodes equipped with LTE and Wi-Fi receivers are used as backscatter receivers to receive the IoT tags uplink backscattered signals, so the second optimization phase is formulated to maximize the total sum rate of the ABC system by dividing its service area into clusters using the enhanced unsupervised k-means algorithm, also the enhanced k-means algorithm finds the optimum location of each cluster's serving UAV flying HetNode that maximizes the channels gain between the IoT tags and the serving UAV flying HetNode in order to maximize the total system rate. The system model was implemented within the MATLAB environment where simulations across the various scenarios are conducted to assess the effectiveness of the proposed algorithm. Simulation results and the performance analysis demonstrated that the proposed algorithm can support the required rate for the most mMTC and low data rate URLLC IoT applications with average IoT tag rates in the range of 15 Kbps to 115 Kbps, and outperforms the algorithm-free riding technique in the case of massive IoT applications. The proposed mode 2 (first enhanced mode) achieves the best performance in terms of the average IoT tags rate and the total system rate with the lowest interference to the primary system users, on the other hand, mode 3 (second enhanced mode) improves the system capacity with maximum IoT tags satisfaction ratio. The capacity and satisfaction ratio of the proposed mode 3 outp...

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QoS aware resource allocation for coexistence mechanisms between eMBB and URLLC: Issues, challenges, and future directions in 5G

Cited by 3 publications

References 102 publications

From Vision to Practice – Insights from Nordic-Baltic 5G applications across sectors

From Vision to Practice – Insights from Nordic-Baltic 5G applications across sectors

A robust OFDM IM-QAM NOMA scheme for URLLC and eMBB downlink service coexistence

Ambient Backscatter Communication System Empowered by Matching Game and Machine Learning for Enabling Massive IoT Over 6G HetNets

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