Resource allocation scheme for eMBB and uRLLC coexistence in 6G networks

Al-Ali, Muhammed; Yaacoub, Elias

doi:10.1007/s11276-023-03328-2

Cited by 8 publications

(2 citation statements)

References 35 publications

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“…Latency is an important factor to consider, especially when the number of users increases. It refers to the time it takes for a packet of data to move from one place to another within a network [15]. Table 1 From the results, it can be analyzing that OMA's dedicated resource allocation leads to constant or even declining spectral efficiency for both eMBB and uRLLC users as the total number of users increases.…”

Section: Simulation Resultsmentioning

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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Section: Simulation Resultsmentioning

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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“…These scenarios include the use of massive number of uRLLC devices. When these large number of devices will be operated under the massive uRLLC (mURLLC) use case scenario, then the resource allocations to meet the diverse requirements of each use cases will be more challenging [7]. As the number of uRLLC devices is expected to increase massively in few years, resource allocation needs to be optimized to lower the impact on the QoS of eMBB and mMTC users in the same network while meeting the latency and reliability requirements of massive uRLLC users.…”

Section: Introductionmentioning

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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6G Self‐Evolution Network for IoT Using Rainbow Deep Q‐Network Based on Decision‐Making

Reddy,

Chintapalli,

Alkhayyat

et al. 2024

Internet Technology Letters

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In recent trends, the development of 6G technology has provided a critical focus on its core network architecture which facilitates the robust foundation for entire network. A solution of self‐evolving networks for 6G is under consideration, in which networks develop their autonomy as a key perspective in the decision‐making process. Till now, various decision‐making mechanisms are identified, such as their key features, individual theoretical analyzes, and customized for 6G networks. To reduce the network load and improve system utilization, a decision‐making based on Rainbow Deep Q‐Network (RDQN) is proposed in this research. This methodology considers distributed decision‐making scenarios in which IoT devices enhance the quality of experience (QoE) with low training. Furthermore, it aims at a community of self‐evolving networks to show the improvements in 6G technologies and accelerates the learning rate. From the result analysis, it evidently displays that proposed RDQN has better training rate than other algorithms and requires fewer rounds of approximately 20 for a high number of episodes (500). Similarly, while evaluating QoE utility value, the proposed RDQN demonstrates a higher value of 100 when the number of target customers is large which outperforms the other existing models. The above results prove that the proposed RDQN is more efficient in IoT traffic monitoring and more suitable for the 6G environment.

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Resource allocation scheme for eMBB and uRLLC coexistence in 6G networks

Cited by 8 publications

References 35 publications

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

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

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

6G Self‐Evolution Network for IoT Using Rainbow Deep Q‐Network Based on Decision‐Making

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