Cloud Radio Access Network  Research Topics

C-RAN stands for Cloud Radio Access Network, which is a wireless network architecture widely used nowadays for communications and also being the crucial part of future networks. By going through this research you can get a better understanding of the topic in detail.

  1. Define C-RAN

C-RAN can also be called as Centralized-RAN. It has centralized architecture to access the radio networks; it is structured based on the cloud computing architecture. The wireless standards for communication such as 2G, 3G, 4G and the future 5G communications are supported by C-RAN. It serves as a major technology in the future utilization of 5G networks.

  1. What is C-RAN?

C-RAN is a network architecture, which plays a major role in telecommunication and the full form of it is Cloud Radio Access Network. This follows the mechanism of baseband processing, which reaches the cloud or data center from any cites of individual cell. The strengths of C-RAN are cost effective, scalability and flexibility. The significance of the approach is easy maintenance, resource utilization and allows using advanced technologies such as software-defined networking and virtualization of network function. C-RAN is being the most effective architecture in deploying 5G also the future technologies.

  1. Where C-RAN is used?

Major role of C-RAN is played in the industry of telecommunications, specifically in the area of cellular networks such as 5G and 4G LTE. It is mostly used in the region where there is more need of cellular coverage and capacity such as rural, urban and sub urban areas. It is required in the areas which have several cell sites in order to satisfy the data demand of more users and the implementation of this architecture is made by the telecom operators in the places which have more population. In the growing requirements of network in different locations the C-RAN technology being the capable manager for handling radio resources to provide more scalability.

  1. Why C-RAN is proposed? Previous Technology Issues

One of the major problems faced by C-RAN is latency constraints mainly in the multimedia service of 5G. The performance is decreased because of increase in users; it can be overcome by using prominent techniques for allocating resources. When you distribute a dynamic work based on various priorities consider to schedule it. The main limitation occurs when integrating the delay occurred in signal processing and managing resource of network capacity.

Latency Constrains: To reduce the response time in order to achieve reduced need of latency for bringing back the user materials, the adjustments in procedures and approximation is done by the online strategy.

Scaling issues with the user volume: When the user number increases, performance of the algorithm used to allocate resources will decrease, which shows that the strategy which is used till date is not suitable to handle more users.

Dynamic task prioritization: In the dynamic environment where the priorities of work keep on changing, it becomes critical to handle the issues related to offloading of work and to manage the different requirements.

Resource management integration & Network limitations: While integrating the algorithms of resource management, there is a possibility for communication overhead to happen. This includes feedback loops, data flow within pieces of network and coordination techniques which reduces the performance of network. When the channel State Information (CSI) transmits from Remote Radio Heads (RRH) to the Baseband Units (BBU), this can be affected by delays related to signal processing or communication or fronthaul/backhaul capacity due to which data integrity and timelines are affected.

  1. Algorithms / Protocols

The algorithms provided for C-RAN to overcome the previous issues faced by it are: Graph Partition and Joining (GPJ) algorithm, Deep Reinforcement Learning-based Soft Actor-Critic with Alternating Optimization (DRLSAC-AO) algorithm, Adaptive Slicing Allocation with Enhanced Cat Swarm Optimization (ASA-ECSO) algorithm, Backhaul-Aware Cross-Layer Bitrate Allocation (BACLBA) algorithm and Online Approximation (OA) algorithm.

  1. Comparative study / Analysis

In C-RAN the analysis for scheduling 5G is based on the Reinforcement Learning which is listed further:

  1. For allocating storage in cloud networks, Online Approximation (OA) algorithm is used. It is helpful in turning regularization, decomposition techniques and rounding. For deriving ideal solution this algorithm uses heuristics and for snap judgments, partial information is used.
  2. Using Adaptive Slicing Allocation with Enhanced Cat Swarm Optimization (ASA-ECSO) algorithm, complex allocation and slicing of resources is done. This algorithm is mainly used because it integrates optimization capabilities related to ECSO and flexibility related to ASA.
  3. The Deep Reinforcement Learning-based Soft Actor-Critic with Alternating Optimization (DRLSAC-AO) algorithm is used for scheduling tasks because this algorithm maintains a meaningful balance between exploitation and exploration. It is scheduled by considering the different priorities of the users.
  4. This Graph Partition and Joining (GPJ) algorithm which enables transferring of material and data in 5G easily. This algorithm can reduce latency and increase performance by segmenting the subset and then rejoining at end.
  5. Backhaul-Aware Cross-Layer Bitrate Allocation (BACLBA) algorithm is used for easy transfer of data over backhaul infrastructure by increasing backhaul resources and reducing congestion.
  6. Simulation results / Parameters

The approaches which were proposed to overcome the issues faced by C-RAN are tested using different methodologies to analyze its performance. The comparison is done by using metrics like Number of tasks vs. energy consumption (J), Number of users’ vs. throughput, Number of users vs. delay (sec), Number of tasks vs. latency (ms) and Traffic cost.

  1. Dataset LINKS / Important URL

Here are some of the links provided for you below to gain more knowledge about C-RAN which can be useful for you:

  1. C-RAN Applications

C-RAN Technology can be used in several application, some of them are listed here like: Network Function Virtualization (NFV), Latency-Sensitive Applications, Mobile Networks (4G, 5G), Edge Computing, Cost Efficiency, Future Network Evolution, Flexibility and Optimization.

  1. Topology

In C-RAN the Topology for scheduling 5G is based on the Reinforcement Learning which is listed further, like: Resource Allocation, Data Transmission, Storage Allocation and Task Scheduling.

  1. Environment

The use of C-RAN architecture is increased more in the modern telecommunications specifically in 5G and 4G communication networks. Distributed unit (DU) and the Centralized unit (CU) are the two main sections in the modern C-RAN Technology which is different from the usual function of base station.

The C-RAN environment involves many key elements such as: Fronthaul Links, High-Speed Connectivity, Network Management and Orchestration, Centralized Unit (CU), Distributed Unit (DU) and Virtualization and Cloud Infrastructure.

  1. Simulation Tools

Here we provide some simulation software for previous works, which is established with the usage of python language and C++ along with tools like Network simulator version 3.26 or above version

  1. Results

After going through this research based on C-RAN 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.

Cloud Radio Access Network  Research Ideas

The following are the C-RAN based research topics which provide lot of information and we utilize this to clarify the doubts and these are helpful when we go through the explanations or descriptions:

  1. Design and Analysis of Radio over Fiber for Fronthaul Connection of High-Speed C-RAN
  2. Energy-Efficient Hybrid Powered Cloud Radio Access Network (C-RAN) for 5G
  3. A Bidirectional WDM-PON Free Space Optical (FSO) System for Fronthaul 5 G C-RAN Networks
  4. A 51 GB/s Reconfigurable mm Wave Fiber-Wireless C-RAN Supporting 5G/6G MNO Network Sharing
  5. Distributed Multi-Pair Computation for Intra C-RAN Bidirectional Communications
  6. Adaptive C-RAN Architecture Using Crowd sourced Radio Units for Smart City
  7. Influence of Frequency Mapping on Intermodulation Distortion in an SOA-Based Optical Fronthaul C-RAN Architecture for 5G Communications
  8. Active IRS-Assisted Integrated Sensing and Communication in C-RAN
  9. Radio Resource Allocation in Low-to Medium-Load Regimes for Energy Minimization with C-RAN
  10. End-to-End Network SLA Quality Assurance for C-RAN: A Closed-Loop Management Method Based on Digital Twin Network
  11. Robust Design for IRS-Aided C-RAN with Constrained Wireless Fronthaul Links
  12. C-RAN at Millimeter Wave and Above: Full-Beamspace Radio Access Architecture
  13. Allocation of Resources for HARQ Retransmission in Mobile Networks Based on C-RAN
  14. PV Panel/Battery Sizing and Resource Allocation for Smart-Grid Powered C-RAN
  15. Joint Optimization of Transmit Beamforming and Base Station Cache Allocation in Multi-Cell C-RAN
  16. C-RAN Zero-Forcing With Imperfect CSI: Analysis and Precode & Quantize Feedback
  17. Robust Beamforming Design for Cache-Enabled C-RAN with Fronthaul Multicast
  18. Toward Manageable Cost-Effective 5G C-RAN: Semi-Active Front-Haul by Multi-Carrier Pilot-Tone OAM and MWDM
  19. A Task Offloading Method Based on User Satisfaction in C-RAN with Mobile Edge Computing
  20. Computation Offloading and Resource Allocation in C-RAN Supporting Wireless Charging
  21. Remote mm W photonic local oscillator delivery for uplink down-conversion in DML-based optical hybrid C-RAN fronthaul
  22. Cost-Effective Joint Establishment of Fronthaul and Virtual Base Stations in a Stochastic C-RAN
  23. Joint Optimization of Computation Offloading and Resource Allocation in C-RAN with Mobile Edge Computing Using Evolutionary Algorithms
  24. Optimizing Caching in a C-RAN with a Hybrid Millimeter-Wave/Microwave Fronthaul Link via Dynamic Programming
  25. Enhancing Performance of Downlink NOMA-Based C-RAN Topology through Optimal User Pairing and Dynamic Power Allocation Scheme
  26. HW/SW Development of Cloud-RAN in 3D Networks: Computational and Energy Resources for Splitting Options
  27. Distributed Machine-Learning for Early HARQ Feedback Prediction in Cloud RANs
  28. Fronthaul latency and capacity constrained cost-effective and energy-efficient 5G C-RAN deployment
  29. Downlink power control in C-RAN enabled full duplex cellular networks
  30. Optimizing communication and computational resource allocations in network slicing using twin-GAN-Based DRL for 5G hybrid C-RAN
  31. Colorless WDM-PON fronthaul topology for beyond 5G C-RAN architectures
  32. Minimizing traffic cost of content distribution and storage allocation in cloud radio access networks
  33. Ergodic Capacity of the Cloud Radio Access Network: A General Solution
  34. Energy Efficient Resource Allocation in Cloud Radio Access Network – A Survey
  35. Green Federated Learning over Cloud-RAN with Limited Fronthaul and Quantized Neural Networks
  36. Multi-Objective Energy Efficient Resource Allocation in Massive Multiple Input Multiple Output-Aided Heterogeneous Cloud Radio Access Networks
  37. Latency Guarantee for Task Computation in Wireless-Powered Cloud Radio Access Networks
  38. Learning-Based Fronthaul Compression for Uplink Cloud Radio Access Networks
  39. An Enhanced Deep Reinforcement Learning-based Slice Acceptance Control System (EDRL-SACS) for Cloud–Radio Access Network
  40. Minimizing energy consumption by joint radio and computing resource allocation in Cloud-RAN
  41. A Combination of Cloud Radio Accessing Networks and Mobile Cloud Computing for Remote Cloud Computing Services
  42. An AI-Driven Intelligent Traffic Management Model for 6G Cloud Radio Access Networks
  43. UAV-Assisted Heterogeneous Cloud Radio Access Network with Comprehensive Interference Management
  44. TRASH: Traffic Aware Hybrid-CRAN Scheme for V2I Connectivity Enhancement
  45. FA-CRAN: a Firefly Algorithm for dynamic BBU-RRH mapping in Cloud/Centralized Radio Access Networks
  46. Secrecy Wireless Information and Power Transfer in Ultra-Dense Cloud-RAN with Wireless Fronthaul
  47. Optimizing Functional Split in 5G Cloud RAN: A Particle Swarm Optimization Approach
  48. Efficient Network Slicing for 5G Services in Cloud Fog-RAN Deployment over WDM Network
  49. Functional Split-Aware Optimal BBU Placement for 5G Cloud-RAN over WDM Access/Aggregation Network
  50. Toward Wireless Fronthaul for Cloud RAN Architectures.