Cloud Radio Access Networks virtualization Research Project Topics

C-RAN stands for Cloud Radio Access Network, which is a wireless network architecture widely used nowadays in the telecommunication industry. In this research paper you can learn about this technology and how it is different from the traditional one and also the algorithms used in it.

1. What is C-RAN?

C-RAN is Cloud RAN, and it is otherwise known as Virtualized RAN (V-RAN) Centralized-RAN (C-RAN). This is a new cellular architecture which is based on the principles of cloud computing. This follows a little different architecture from regular RAN architecture which is mentioned below:

• Centralization: The C-RAN centralizes the function of radio access network in the cloud data center which improves scalability and flexibility. This will easily allow expanding and managing the network without using any extra dedicated hardware.
• Virtualization: This technology is used by C-RAN to run several RAN functions over single server, which will result in reduced dedicated hardware and increase in utilization of resources.
• Openness: The open standards and interfaces which are encouraged by C-RAN will allow more participants for deploying and developing technologies. This will further increase the innovation and competing among users.

2. Previous Technology Issues in C-RAN

One of the major problems faced by C-RAN is latency constraints when data is transmitting within data center and cell sites. This technology includes more complexity, so it should be carefully planned and implemented. C-RAN also has some issues related to security which has to be solved.

3. Algorithms / Protocols

The algorithms provided for C-RAN in Multimedia applications to overcome the previous issues faced by it are: “Modified Energy Efficient VM Placement with Extended Flower Pollination algorithm” (MEEVMP-EFPA), “K-means clustering method, Energy-efficient Trajectory Optimization of UAV–IoT using genetic algorithm” (GA) (EETO-GA), “Multi-agent DRL Approach with Proposed Resource Allocation Approach” (M-AC-VNE-PW-DRL), “Buffer length based priority queue” (PQ), “Weighted round-robin” (WRR) and “Deficit round-robin (DRR)”.

4. Comparative study / Analysis
5. The integration of “Modified Energy Efficient VM Placement with Extended Flower Pollination algorithm” (MEEVMP-EFPA) is made to reduce the energy consumption of data center with the help of ideal and effective strategy for placement of virtual machine.
6. “Multi-agent DRL Approach withProposed Resource Allocation Approach” (M-AC-VNE-PW-DRL) framework is used for resource allocation with the help of online time-sequential resource.
7. The algorithms such as “Deficit round-robin (DRR)”, “weighted round-robin” (WRR) and “Buffer length based priority queue” (PQ) are used for scheduling task when there is a usage of applications which has minimal latency.
8. The “K-means clustering method” is created to solve the problems like ILP issues which arise in bigger networks.
9. “The Energy-efficient Trajectory Optimization of UAV–IoT using genetic algorithm” (GA) (EETO-GA) is designed to monitor the energy spent, jobs completed and to find the devices which can do service with the help of GA.
10. Simulation results / Parameters

The approaches which were proposed to overcome the issues faced by C-RAN Multimedia Applications are tested using different methodologies to analyze its performance. The comparison is done by using metrics like Execution time Vs. Number of virtual machine(VM), Number of RRH (Remote Radio Head) Vs. Cost (million $), Power consumption Vs. Size of datacenter, Transmission time interval Vs. Markov process state, Energy consumption Vs. Network size (no. of nodes), Time[s] Vs. Throughput rate (Mbps), Transmission time interval Vs. DTT(delay-throughput-tolerant) transmission rate.

6. 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:

• https://link.springer.com/article/10.1007/s00607-023-01152-2
• https://link.springer.com/article/10.1007/s00500-022-07578-8
• https://ieeexplore.ieee.org/abstract/document/10302364
• https://ieeexplore.ieee.org/abstract/document/9771597
• https://www.mdpi.com/1424-8220/22/23/9394
• https://www.mdpi.com/2076-3417/13/4/2535

7. Simulation Tools

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

8. Results

After going through in this paper you can understand about the C-RAN technology, the evolution of this technology from the traditional one, the algorithms used in it also about the tools required for simulating it.

Cloud Radio Access Networks Research Topics and Ideas

1. Statistical Multiplexing Gain Analysis Based on Resource Utilization for Practical C-RAN
2. Resource Allocation with Vickrey-Dutch Auctioning Game for C-RAN Fronthaul
3. A scheduling algorithm for Adaptive C-RAN Architecture
4. Optimized Uplink Transmission for C-RAN with Hybrid Analog-digital Beamforming and Resolution-adaptive ADCs
5. A Low Complexity Symbol-Wise ML Detection Algorithm for User-Centric C-RAN
6. Quantization-Aided Secrecy: FD C-RAN Communications with Untrusted Radios
7. A Theoretical Performance Bound for Joint Beam former Design of Wireless Fronthaul and Access Links in Downlink C-RAN
8. Fairness Analysis in IRS assisted C-RAN with Imperfect CSI
9. Field Trial of 300 GB/s 12-Channel Medium Wavelength-Division Multiplexing in Deployed 5G C-RAN Front-haul Network
10. Perspectives on AI-based Algorithms Applied to C-RAN Functional Splitting and Advanced Antenna System Problem
11. Proactive Caching Placement Strategy with End-to-end Delay Reduction in C-RAN Using Queuing Theory
12. Synergistic Benefits in IRS- and RS-Enabled C-RAN with Energy-Efficient Clustering
13. Resource Allocation and Beamforming Optimization for IRS-Assisted OFDMA Uplink in C-RAN
14. Multi-Pair Computation for C-RAN with Intra-Cloud and Inter-Cloud Communications
15. Energy-Effective Offloading Scheme in UAV-Assisted C-RAN System
16. A novel front-hauling architecture under centralized radio access network (C-RAN)
17. Intelligent Analog Radio over Fiber aided C-RAN for Mitigating Nonlinearity and Improving Robustness
18. Equilibrium-driven Dynamic Pricing for Shared Access C-RAN
19. Multi-Agent Deep Reinforcement Learning for Slicing and Admission Control in 5G C-RAN
20. Interference and QoS-Aware Resource Allocation Considering DAS Behavior for C-RAN Power Minimization
21. Development of 6G web by Multilayer Perceptron in C-RAN for VANETs
22. FE-OCDMA applied to C-RAN fronthaul in future mobile networks
23. M3: A Sub-Millisecond Scheduler for Multi-Cell MIMO Networks under C-RAN Architecture
24. Dynamic allocation of resources in a heterogeneous Cloud Radio Access Network
25. Optical Intelligent Reflecting Surface for Mixed Dual-Hop FSO and Beamforming-Based RF System in C-RAN
26. Analytical Modeling of RSMA-Enabled User-Centric RRH Clustering in C-RAN over Generalized Fading Channels
27. Traffic Scheduler for BBU Resource Allocation in 5G CRAN
28. Intra C-RAN Two-Way Multi-Pair Computation under Total Power and Fronthaul Capacity Constraints
29. Securing FD C-RAN with Untrusted Radios: Joint Utilization of Jamming and Fronthaul Quantization
30. Slice-Aware Resource Calendaring in Cloud-based Radio Access Networks
31. Active Cell Outage Detection Algorithm for Broadband Services in 5G Cloud Radio Access Networks
32. Dynamic Caching in a Hybrid Millimeter-wave/Microwave C-RAN
33. Multi-Pair Computation for Two-Way Intra Cloud Radio-Access Network Communications
34. Analysis of Compressing PAPR-Reduced OFDM IQ Samples for Cloud Radio Access Network
35. Performance Analysis of Non-Orthogonal Multiple Access (NOMA) Enabled Cloud Radio Access Networks
36. Sparse Joint Transmission for Cloud Radio Access Networks with Limited Fronthaul Capacity
37. Reconfigurable Fiber Wireless Fronthaul with A-RoF and D-RoF Co-Existence through a Si3N4 ROADM for Heterogeneous Mm wave 5G C-RANs
38. Physical-Layer Security for Cache-Enabled C-RANs via Rate Splitting
39. Maximum Profit of Real-Time IoT Content Retrieval by Joint Content Placement and Storage Allocation in C-RANs
40. Joint User Grouping, Sparse Beamforming, and Subcarrier Allocation for D2D Underlaid Cache-Enabled C-RANs with Rate Splitting
41. Cross-Layer Optimization for Industrial Internet of Things in NOMA-Based C-RANs
42. Assessing Software-Defined Radio Security and Performance in Virtualized Environments for Cloud Radio Access Networks
43. Deployment Guidelines for Cloud-RAN in Future Mobile Networks
44. Resiliency in Open-Source Solutions for Disaggregated 5G Cloud Radio Access and Transport Networks
45. Optimal Slicing of Virtualized Passive Optical Networks to Support Dense Deployment of Cloud-RAN and Multi-Access Edge Computing
46. Traffic-Aware Dynamic Functional Split for 5G Cloud Radio Access Networks
47. Cloud Radio Access Network for Next Generation Mobile Networks; practical implementation benefits
48. Throughput Maximization in Cloud-Radio Access Networks Using Cross-Layer Network Coding
49. A hybrid HARQ feedback prediction approach for Single- and Cloud-RANs in the sub-THz regime
50. Full-Duplex Relaying With Partial CSI in Cloud Radio Access Networks