mmWave 5G Network Project Ideas

mmWave Networks Research Topics is one of the crucial topics that are utilized to transmit the data. It is widely utilized in most of the applications, fields or domains. This network enables very fast wireless communication among data. Here we provide the details related to this proposed technology.

1. Define mmWave Networks.

In the beginning of the research we look for the definition for this proposed research. It is a kind of radio wave which is employed for the transmission of data over 5G networks. MmWave commonly defines 24-100 GHz, and will take a large amount of data. When it is combined with the improvements in coding techniques, mmWave will take thousands of times as much data as a low band signal. It is a range of electromagnetic frequencies among infrared and microwaves, and is also referred to as Extremely High Frequency (EHF) band.

2. What is mmWave Networks?

Thereafter the definition we see the brief description for this proposed research. The mmWave networks are cellular technologies which utilizes the high-frequency electromagnetic waves to offer large capacity and bandwidth in frequency bands greater than 24 GHz.

3. Where mmWave Networks used?

Next to the brief description we discuss where to use this proposed mmWave network. It is used in many applications like imaging, telecommunications and radar systems. Its frequencies will range from about 30-300 GHz. Here are some uses for this mmWaves are: Autonomous vehicles, Telecommunications and Radar systems.

4. Why mmWave Networks technology proposed? , previous technology issues

We proposed the mmWave network technology that is used to tackle the issues in the previous technology. To overcome the issues we propose this technology. Some of the issues in the previous technologies are Increasing the density of blockages deteriorates, User clustering problems, inefficiency in scheduler, mmWave backhaul link blockage and Impact on Computation offloading.

5. Algorithms / protocols

For this proposed mmWave networks technology we utilize some of the methods or techniques to overcome the issues in the existing technologies. The methods that we employed are: Q-learning agent with Particle Swarm Optimization (IQ-LA-PSO) for distributed scheduling approach, K-means clustering for User clustering, Markov Stochastic Process with the Phase-Type Distribution and Dingo Optimisation Algorithm is employed for optimal routing, DQN for Hybrid beamforming, Deep Reinforcement Learning (DRL) for mmWave Backhaul Link Blockage Mitigation and State Action Reward State Action with Genetic Algorithm (SARSA-GA) is proposed as a mechanism to optimize computation offloading decisions.

6. Comparative study / Analysis

Our proposed mmWave networks technology compares some of the methods and then utilizes it to enhance the findings for this research. The methods that we compared are as follows:

• K-means clustering technique is utilized when the users are grouped into clusters on the basis of spatial proximity and channel conditions.
• Due to some environmental impacts, the mmWave backhaul is susceptible to blockages. We overcome the impact of these blockages by using Deep Reinforcement Learning (DRL).
• To enhance the achievement of hybrid beamforming, we recommend Dual Agent DQN, where in DQN comprises two agents.
• The SARSA-GA is proposed as a technique to optimize computation offloading decisions, taking into account the tradeoffs among quality of Service (QoS) constraints and Energy reduction.
• IQ-LA-PSO technique is utilized to allow scheduling decisions and adaptive power allocations on the basis of local explanations, leading to effective interference management and best network achievement.
• For optimal routing, packet delivery ratio and improving the throughput MSP-PTD-DOA technique is used.

7. Simulation results / Parameters

In this research we proposed the mmWave network technology that is utilized to enhance the findings of the research when compared to the existing technologies. The proposed work’s achievement is estimated by comparing the following parameters. NMSE, Spectral efficiency, Bit Error Rate, versus achievable rate and MSE with SNR and the Sum Rate (bit/s/Hz) with No of users and the Weighted sum Rate with No. of Reflecting Elements.

8. Dataset LINKS / Important URL

The following are some of the important links that we offered to go through the concepts or details that are relevant to this proposed technique. And these links will clear the doubts on the basis of this proposed technique.

• https://ieeexplore.ieee.org/abstract/document/9264161/
• https://ieeexplore.ieee.org/abstract/document/10153707/
• https://ieeexplore.ieee.org/abstract/document/9803864/

9. mmWave Networks Applications

Now we see the applications for mmWave networks, it allows very fast wireless communication, important for 5G and beyond, supporting high-speed data transfer for the applications like smart infrastructure, augmented reality and autonomous vehicles. With this capacity to send big volumes of data through short distances, mmWave networks are transforming industries like manufacturing, healthcare, and entertainment, encouraging improved efficacy and connectivity in different settings.

10. Topology for mmWave Networks

The topology defines the arrangement of nodes and connections in a network and defines how the data flows across the devices.

11. Environment for mmWave Networks

For this research we utilize the environment that contains virtual and physical factors like interference, security protocols and layout, which is important for describing consistency and achievement.

12. Simulation tools

The succeeding are the software requirements that are used for this proposed mmWave networks technology. The development tool that is required for this research is NS 3.26 with python. Moreover the operating system that is required for executing is Ubuntu 16.04 LTS.

13. Results

Our proposed mmWave networks technology will overcome the issues in the existing technology. It also compares some of the parameters or metrics with the previous technologies to achieve the best accuracy for this research when compared to others. The tool that is used for developing the research is NS 3.26 with python.

mmWave Networks Research Ideas:

Below we offered the research topics that are based on the Millimeter Wave networks or mmWave networks which are used to clear the doubts or queries relevant to this proposed research.

1. Analysis of Transport Layer Congestion Control Algorithms over 5G Millimeter Wave Networks
2. A Multi-Connectivity Architecture with Data Replication for XR Traffic in mmWave Networks
3. Deep Learning and Image Super-Resolution-Guided Beam and Power Allocation for mmWave Networks
4. Dynamic Base Station Clustering in User-Centric mmWave Networks: Performance Analysis and Optimization
5. A Defensive Strategy Against Beam Training Attack in 5G mmWave Networks for Manufacturing
6. Joint Rate and Energy Coverage of User-Centric SWIPT-Enabled Millimeter Wave Networks
7. IMPRESS: Indoor Mobility Prediction Framework for Pre-Emptive Indoor-Outdoor Handover for mmWave Networks
8. Simultaneous Wireless Information and Power Transfer in mmWave Networks Under User-Centric Base Station Clustering
9. GBLinks: GNN-Based Beam Selection and Link Activation for Ultra-Dense D2D mmWave Networks
10. High Accuracy Device Localization in Indoor Mmwave Networks Exploiting Channel Sparsity and Virtual Anchor Mapping
11. A Novel Approach for Inter-User Distance Estimation in 5G mmWave Networks Using Deep Learning
12. Intelligent Dual Active Protocol Stack Handover Based on Double DQN Deep Reinforcement Learning for 5G mmWave Networks
13. Coverage Analysis of Multiple Transmissive RIS-Aided Outdoor-to-Indoor mmWave Networks
14. Intelligent Reflecting Surface and UAV Assisted Secrecy Communication in Millimeter-Wave Networks
15. Blockage Prediction Using Exhaustive Beam-Pair Scan in mmWave Networks: An Experimental Study
16. RIS-Assisted mmWave Networks With Random Blockages: Fewer Large RISs or More Small RISs?
17. Predictive Data Replication for XR Applications in Multi-Connectivity Enabled mmWave Networks
18. Full-Duplex Cooperative NOMA-based mmWave Networks with Fluid Antenna System (FAS) Receivers
19. Efficient Exploration Through Bootstrapped and Bayesian Deep Q-Networks for Joint Power Control and Beamforming in mmWave Networks
20. Joint Video Streaming and Hybrid Beamforming in Multi-connectivity Enabled mmWave Networks
21. Joint User Scheduling and Hybrid Beamforming Design for Cooperative mmWave Networks
22. How Sufficient is TCP When Deployed in 5G mmWave Networks Over the Urban Deployment?
23. RIS-Aided mmWave Network Planning Toward Connectivity Enhancement and Minimal Electromagnetic Field Exposure
24. Joint optimal multi-connectivity enabled user association and power allocation in mmWave networks
25. Integrated Beamforming and Resource Allocation in RIS-Assisted mmWave Networks based on Deep Reinforcement Learning
26. BOOST: A User Association and Scheduling Framework for Beamforming mmWave Networks
27. Enhancing XR Application Performance in Multi-Connectivity Enabled mmWave Networks
28. BEAMWAVE: Cross-layer Beamforming and Scheduling for Superimposed Transmissions in Industrial IoT mmWave Networks
29. User-Centric Association in Ultra-Dense mmWave Networks via Deep Reinforcement Learning
30. Multi-Agent Reinforcement Learning With Measured Difference Reward for Multi-Association in Ultra-Dense mmWave Network
31. Energy-efficient multi-connectivity enabled user association and downlink power allocation in mmWave networks
32. Sequential Beam, User, and Power Allocation for Interference Management in 5G mmWave Networks
33. A Case for Line-Of-Sight Blockage Detection as a Primitive in Millimeter-Wave Networks
34. Performance Analysis of Indoor mmWave Networks With Ceiling-Mounted Access Points
35. DRL-based Joint Beamforming and BS-RIS-UE Association Design for RIS-Assisted mmWave Networks
36. Capacity Maximization of the mmWave Networks Deploying Intelligent Reflecting Surfaces: An Enhancement Approach for 6G Internet of Things
37. Holistic Enlightening of Blackspots With Passive Tailorable Reflecting Surfaces for Efficient Urban mmWave Networks
38. Analysis of Cell Association in mmWave Networks based on Euclidean and Angular Distances
39. Beam Management in Ultra-Dense mmWave Network via Federated Reinforcement Learning: An Intelligent and Secure Approach
40. Toward Energy Efficient and Balanced User Associations and Power Allocations in Multiconnectivity-Enabled mmWave Networks
41. Stochastic Arrow-Hurwicz Algorithm for Path Selection and Rate Allocation in Self-Backhauled mmWave Networks
42. Beamformed Energy Detection in the Presence of an Interferer for Cognitive mmWave Network
43. Sensing-Assisted Distributed User Scheduling and Beamforming in Muli-Cell mmWave Networks
44. Proactive Cell Switching for mmWave Networks with Hybrid Beamforming and Dynamic Blockers
45. Prediction of Maximum Expected Delay using Enhanced Link Adaptation for 5G mm-Wave Network
46. Performance Analysis of Minimum Hop Count-Based Routing Techniques in Millimeter Wave Networks: A Stochastic Geometry Approach
47. Association and Caching in Relay-Assisted mmWave Networks: A Stochastic Geometry Perspective
48. Impact of Finite-Resolution Precoding and Limited Feedback on Rates of IRS Based mmWave Networks
49. A Novel Fairness Allocation Strategy With Minimum Mainlobe Interference for mmWave Networks
50. WIP: Multi-connectivity user associations in mmWave networks: a distributed multi-agent deep reinforcement learning method.