Resource Allocation Scheme of eMBB URLLC Research Topics

Resource allocation in URLLC and in eMMB is used for assigning certain network resources to a system in order to effectively increase its performance. By going through this research you can get a better understanding of the topic in detail. Continue reading this research paper to gain more knowledge about this topic.

  1. Define Resource allocation in eMMB and URLLC services

This involves allocating resources such as time slots, bandwidths and some extra network resources for service like enhanced Mobile Broadband (eMMB) and Ultra-Reliable Low Latency Communication (URLLC), so that the reliability and data rate can be increased for broadband services like eMMB video streaming and low-latency communication in URLLC. Effective allocation of resource is very important for providing quality services and optimal performance in 5G networks or more than that.

  1. What is Resource allocation in eMMB and URLLC services?

The resource allocation service of eMMB and URLLC do a process of effectively distributing resources like time slots, computing capacity and bandwidth among networks. The main goal of eMMB is to provide maximum of data rate for video streaming and of URLLC, it is to enhance low-latency communication in applications like real time control and industrial automation.

  1. Where Resource allocation in eMMB and URLLC services is used?

In this section we are going to discuss about the uses of allocating resource in services like URLLC and eMMB. They can be used in 5G networks and any other network greater than that. This has been used in the architecture of a network core network of elements or base stations to improve allocation of computational resources, bandwidth and time slots. This method can be used in various services and applications like autonomous vehicles, industrial automation and healthcare, from application of high ranging data rate broadband to low latency services.

  1. Why Resource allocation in eMMB and URLLC services is proposed? Previous Technology Issues

Moving on to the next section, here we are going to discuss about the challenges faced by this resource allocating services. The earlier works has challenges in reliability, scalability and performance in resource allocation. Some other serious issues faced by this system are:

Energy consumption: The earlier work has problem like energy constrains in edge servers. This should be overcome so that the performance of system can be enhanced.

Cross-domain resource allocation optimization: To enhance the existing method, issues in resource allocation within network architecture should be optimized. In the globalized business happening in real world, the cross-domain issues like scheduling, harmonizing interest on multiple users and providers, resource management and load balancing should be addressed. The solution for these problems should mainly focus on creating a comprehensive or singular framework for problem solving.

Computational scalability: Due to increase in the size of matrix for transition probability, the earlier methods had limitations. Training the model within a particular time period was a tough task because of increase in actions and states.

Monitoring and System resilience: The model monitoring technique used in earlier methods can be improved with the help of auxiliary devices like Unnamed Aerial Vehicles (UAV). After creating; the system resilience and robustness should be tested for handling disaster scenarios and real-world environment.

  1. Algorithms / Protocols

After knowing about the technology, uses of it and the issues faced by them in the earlier stage, now we are going to learn about the algorithms used for this technology. The algorithms provided for resource allocating services to overcome the previous issues faced by it are: “Genetic Algorithm”, “Generalized Processor Sharing” (GPS) and “Deep Q Network” (DQN), “Mixed Integer Linear Programming (MILP) with Ant Colony Optimization” (ACO) and “Particle swarm optimization” (PSO).

  1. Simulation results / Parameters

Here in this section we are going to compare different parameters related to this study of resource allocating services in order to find the best one. For analyzing them some of the metrics are required, they are: Edge server Coverage (km) Vs. Total energy consumption (kWh), Network Nodes vs. Latency (ms), Time (s) vs. Packet arrival rate and Time (s) vs. Resource Utilization (%)

  1. Dataset LINKS / Important URL

Here are some of the links provided for you below to gain more knowledge about resource allocating services which can be useful for you:

  1. Resource allocation in eMMB and URLLC services Applications

The eMMB resource allocation can be applicable in streaming video with high definition and also in VR/AR for optimizing the bandwidth. URLLC can be applied in critical real-time control applications like industrial automation, autonomous vehicles and V2V communication also in healthcare for precise communication.

  1. Topology

Topology refers to the architecture of network. Here in this study, the resource allocation follows topology of “Cloud Radio Access Network” (C-RAN) which allows centralized processing; it helps in effective resource allocation for 5G networks.

  1. Environment

The environment needed for this method to function properly in a 5G environment of wireless network, uses the algorithms of deep learning to optimize resource allocation and to provide better performance in both eMMB and URLLC services.

  1. Simulation Tools

Here we provide some simulation software for resource allocation with eMMB and URLLC, which is established with the usage of tools like “Objective Modular Network” (OMNET++) and along with Network simulator version 3.26 or above to enhance its performance.

  1. Results

After going through this research based on resource allocation Technology, you can understand in detail about this technology, applications of this technology, different topologies of it, algorithms followed by it also about the limitations and how it can be overcome.

Resource Allocation Scheme of eMBB URLLC Research Ideas

  1. Resource Allocation and Slicing Puncture in Cellular Networks With eMMB and URLLC Terminals Coexistence
  2. Deep Reinforcement Learning Based Resource Allocation for URLLC User-Centric Network
  3. Resource Allocation for URLLC Service in Relay-Assisted Smart Grid System
  4. Energy-Efficient Optimization via Joint Power and Subcarrier Allocation for eMMB and URLLC Services
  5. Resource Allocation in MU-MISO Rate-Splitting Multiple Access With SIC Errors for URLLC Services
  6. Optimal Uplink Resource Allocation for Single-User eMMB and URLLC Coexistence
  7. Resource Allocation of eMMB and URLLC Traffic using Pre-emption Mechanism
  8. Resource Allocation for Co-Existence of eMMB and URLLC Services in 6G Wireless Networks: A Survey
  9. Coordinated Resource Allocations for eMMB and URLLC in 5G Communication Networks
  10. EMMB and URLLC Service Multiplexing Based on Deep Reinforcement Learning in 5G and Beyond
  11. Dynamic Traffic Scheduling Strategy for the Coexistence of URLLC and eMMB Services in Power Communication
  12. Learning-Based Cooperative Multiplexing Mode Selection and Resource Allocation for eMMB and uRLLC
  13. Joint User Association and Resource Allocation in Multi-IRSs URLLC Systems
  14. Puncturing-Based Resource Allocation for URLLC and eMMB services via Unsupervised Deep Learning
  15. 5G Multi-rats URLLC and eMMB Dynamic Task Offloading with MEC Resource Allocation Using Distributed Deep Reinforcement Learning
  16. Improved Grant-Free Access for URLLC via Multi-Tier-Driven Computing: Network-Load Learning, Prediction, and Resource Allocation
  17. Hybrid Puncturing and Superposition Scheme for Multiplexing uRLLC and eMMB Services Based on Deep Reinforcement Learning
  18. Dynamic Resource Block Allocation Techniques for Simultaneous EMBB and URLLC Traffic
  19. Resource Slicing for eMMB and URLLC Services in Radio Access Network Using Hierarchical Deep Learning
  20. QoS Guaranteed Resource Allocation for Coexisting eMMB and URLLC Traffic in 5G Industrial Networks
  21. Joint Uplink and Downlink Resource Allocation toward Energy-Efficient Transmission for URLLC
  22. beyond 5G Resource Slicing with Mixed-Numerologies for Mission Critical URLLC and eMMB Coexistence
  23. Intelligent Resource Management for eMMB and URLLC in 5G and Beyond Wireless Networks
  24. Joint α-Fair Allocation of RAN and Computing Resources to Vehicular Users with URLLC Traffic
  25. Risk-Resistant Resource Allocation for eMMB and URLLC Coexistence under M/G/1 Queuing Model
  26. Dynamic SDN-Based Radio Access Network Slicing with Deep Reinforcement Learning for URLLC and eMMB Services
  27. Joint Resource Allocation and Phase Shift Optimization for RIS-Aided eMMB/URLLC Traffic Multiplexing
  28. Anticipatory Slice Resource Reservation for 5G Vehicular URLLC Based on Radio Statistics
  29. Cellular Offloading of eMMB and URLLC Services in Multiple UAV-aided Communication Networks
  30. Attention-Aware Resource Allocation and QoE Analysis for Metaverse URLLC Services
  31. Codebook Based Two-Time Scale Resource Allocation Design for IRS-Assisted eMMB-URLLC Systems
  32. Dynamic Resource Allocation for URLLC in UAV-Enabled Multi-access Edge Computing
  33. Resource Allocation for URLLC Service in In-Band Full-Duplex-Based V2I Networks
  34. Predictive Resource Allocation for URLLC using Empirical Mode Decomposition
  35. URLLC Edge Networks With Joint Optimal User Association, Task Offloading and Resource Allocation: A Digital Twin Approach
  36. Coexistence of eMMB and URLLC in Open Radio Access Networks: A Distributed Learning Framework
  37. Resource Allocation for URLLC-Oriented Two-Way UAV Relaying
  38. Resource Allocation for IRS-Enabled Secure Multiuser Multi-Carrier Downlink URLLC Systems
  39. Optimal Resource Allocation for Multi-User OFDMA-URLLC MEC Systems
  40. Resource Allocation Design for Spectral-Efficient URLLC Using RIS-Aided FD-NOMA System
  41. Resource Allocations for Coexisting eMMB and URLLC Services in Multi-UAV Aided Communication Networks for Cellular Offloading
  42. Joint URLLC Traffic Scheduling and Resource Allocation for Semantic Communication Systems
  43. Energy-Efficient Resource Allocation in Ultra-Dense Networks with EMBB and URLLC Users Coexistence
  44. Dynamic resource allocation schemes for eMMB and URLLC services in 5G wireless networks
  45. Resource Scheduling for eMMB and URLLC Multiplexing in NOMA-Based VANETs: A Dual Time-Scale Approach
  46. Resource Allocation for Intelligent Reflecting Surface-Assisted Cooperative NOMA-URLLC Networks in Smart Grid
  47. Resource Allocation for Cell-Free Massive MIMO-Enabled URLLC Downlink Systems
  48. Joint α-Fair Allocation of RAN and Computing Resources to URLLC Users in 5G
  49. Composite Robot Aided Coexistence of eMMB, URLLC and mMTC in Smart Factory
  50. Intelligent Energy Efficient Resource Allocation for URLLC Services in IoT Networks