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.
- 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.
- 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.
- 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.
- 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.
- 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).
- 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 (%)
- 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:
- https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10159393
- https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10005293
- 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.
- 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.
- 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.
- 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.
- 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
- Resource Allocation and Slicing Puncture in Cellular Networks With eMMB and URLLC Terminals Coexistence
- Deep Reinforcement Learning Based Resource Allocation for URLLC User-Centric Network
- Resource Allocation for URLLC Service in Relay-Assisted Smart Grid System
- Energy-Efficient Optimization via Joint Power and Subcarrier Allocation for eMMB and URLLC Services
- Resource Allocation in MU-MISO Rate-Splitting Multiple Access With SIC Errors for URLLC Services
- Optimal Uplink Resource Allocation for Single-User eMMB and URLLC Coexistence
- Resource Allocation of eMMB and URLLC Traffic using Pre-emption Mechanism
- Resource Allocation for Co-Existence of eMMB and URLLC Services in 6G Wireless Networks: A Survey
- Coordinated Resource Allocations for eMMB and URLLC in 5G Communication Networks
- EMMB and URLLC Service Multiplexing Based on Deep Reinforcement Learning in 5G and Beyond
- Dynamic Traffic Scheduling Strategy for the Coexistence of URLLC and eMMB Services in Power Communication
- Learning-Based Cooperative Multiplexing Mode Selection and Resource Allocation for eMMB and uRLLC
- Joint User Association and Resource Allocation in Multi-IRSs URLLC Systems
- Puncturing-Based Resource Allocation for URLLC and eMMB services via Unsupervised Deep Learning
- 5G Multi-rats URLLC and eMMB Dynamic Task Offloading with MEC Resource Allocation Using Distributed Deep Reinforcement Learning
- Improved Grant-Free Access for URLLC via Multi-Tier-Driven Computing: Network-Load Learning, Prediction, and Resource Allocation
- Hybrid Puncturing and Superposition Scheme for Multiplexing uRLLC and eMMB Services Based on Deep Reinforcement Learning
- Dynamic Resource Block Allocation Techniques for Simultaneous EMBB and URLLC Traffic
- Resource Slicing for eMMB and URLLC Services in Radio Access Network Using Hierarchical Deep Learning
- QoS Guaranteed Resource Allocation for Coexisting eMMB and URLLC Traffic in 5G Industrial Networks
- Joint Uplink and Downlink Resource Allocation toward Energy-Efficient Transmission for URLLC
- beyond 5G Resource Slicing with Mixed-Numerologies for Mission Critical URLLC and eMMB Coexistence
- Intelligent Resource Management for eMMB and URLLC in 5G and Beyond Wireless Networks
- Joint α-Fair Allocation of RAN and Computing Resources to Vehicular Users with URLLC Traffic
- Risk-Resistant Resource Allocation for eMMB and URLLC Coexistence under M/G/1 Queuing Model
- Dynamic SDN-Based Radio Access Network Slicing with Deep Reinforcement Learning for URLLC and eMMB Services
- Joint Resource Allocation and Phase Shift Optimization for RIS-Aided eMMB/URLLC Traffic Multiplexing
- Anticipatory Slice Resource Reservation for 5G Vehicular URLLC Based on Radio Statistics
- Cellular Offloading of eMMB and URLLC Services in Multiple UAV-aided Communication Networks
- Attention-Aware Resource Allocation and QoE Analysis for Metaverse URLLC Services
- Codebook Based Two-Time Scale Resource Allocation Design for IRS-Assisted eMMB-URLLC Systems
- Dynamic Resource Allocation for URLLC in UAV-Enabled Multi-access Edge Computing
- Resource Allocation for URLLC Service in In-Band Full-Duplex-Based V2I Networks
- Predictive Resource Allocation for URLLC using Empirical Mode Decomposition
- URLLC Edge Networks With Joint Optimal User Association, Task Offloading and Resource Allocation: A Digital Twin Approach
- Coexistence of eMMB and URLLC in Open Radio Access Networks: A Distributed Learning Framework
- Resource Allocation for URLLC-Oriented Two-Way UAV Relaying
- Resource Allocation for IRS-Enabled Secure Multiuser Multi-Carrier Downlink URLLC Systems
- Optimal Resource Allocation for Multi-User OFDMA-URLLC MEC Systems
- Resource Allocation Design for Spectral-Efficient URLLC Using RIS-Aided FD-NOMA System
- Resource Allocations for Coexisting eMMB and URLLC Services in Multi-UAV Aided Communication Networks for Cellular Offloading
- Joint URLLC Traffic Scheduling and Resource Allocation for Semantic Communication Systems
- Energy-Efficient Resource Allocation in Ultra-Dense Networks with EMBB and URLLC Users Coexistence
- Dynamic resource allocation schemes for eMMB and URLLC services in 5G wireless networks
- Resource Scheduling for eMMB and URLLC Multiplexing in NOMA-Based VANETs: A Dual Time-Scale Approach
- Resource Allocation for Intelligent Reflecting Surface-Assisted Cooperative NOMA-URLLC Networks in Smart Grid
- Resource Allocation for Cell-Free Massive MIMO-Enabled URLLC Downlink Systems
- Joint α-Fair Allocation of RAN and Computing Resources to URLLC Users in 5G
- Composite Robot Aided Coexistence of eMMB, URLLC and mMTC in Smart Factory
- Intelligent Energy Efficient Resource Allocation for URLLC Services in IoT Networks