Millimeter wave Network research Topics

mmWave networks Research Topics is one of the recently enhanced techniques that are utilized to transfer the data information without any connections. Here we provide some important information about Wireless Communication and where to use this, applications, metrics that performed are specified below:

1. Define mmWave networks.

In the beginning of the research we first see the definition for our proposed Millimeter-wave (mm Wave) networks. It is a wireless communication system which works in the range of frequency in about 30GHz to 300GHz. This technology attained crucial considerations for future wireless communication systems, containing 5G and beyond, because of its possibility to supply very fast data rates, low latency and massive bandwidths. These networks employ very high–frequency radio waves, represented by small wavelengths and increased data transmission rates. Some of the components for mmWave networks are Backhaul infrastructure, Network Management and Orchestration, Antennas, Transceivers, Small cells, Network core, User equipment and Base stations.

2. What is mmWave network?

Subsequent to the definition, next we understand the comprehensive explanation for mmWave network. It is also known as the Extremely High Frequency (EHF) band by the International Telecommunication Union. These networks have a decreased coverage range but it provides very high bandwidth and high speeds. It has a range of electromagnetic frequencies among infrared and microwaves which are employed for wireless high-speed communications. There are many kinds of mmWave networks that will comprise of LP: Supports mobility, High Power (HP): Supports static use cases and Sub-6: Supports mobility.

3. Where mmWave networks used?

Afterwards the comprehensive explanation we discuss where to utilize this mmWave network. It is used in places like Automotive radars, Imaging, Remote sensing, Radio astronomy and Military applications.

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

We proposed the mmWave network technology that overcomes several problems in the previous technologies. The problems that turn around the requirement to improve the speed and constancy of interaction with one another network, particularly in relation to the developing communication technology like 5G. The digital economy is always increasing and enhancing, there emerges a challenge for more innovative communication networks which will assist to improve the mobile broadband, massive machine-type interaction and ultra-low latency reliable communication.

5. Algorithms / protocols

For this research the mmWave networks we overcome some of the existing technology issues by this proposed technology. Some of the algorithms or methods to be used for this research are ULLRC (Ultra Low Latency Reliable Communication), eMBB (Enhanced mobile broadband) and MMTC (Massive Machine Type Communication).

6. Comparative study / Analysis

In this research the mmWave network is proposed and it tackles some of the existing technology issues. Moreover, we compared several methods to be used in this research to obtain the best possible findings:

• By creating the hybrid algorithm, to enhance the efficiency of the distribution system’s performance over the particular region.
• The essential step to perform the target is to generate an algorithm which manages the movement of subscriber nodes. For securing the quality of service and network coverage, the algorithm possibly ensures that the subscriber nodes remain equally away from the base stations.
• To strongly control the location of the node in a 5G environment. Create the algorithms or methods to frequently control and optimize the network node positions are possibly engaged. Resource allocation and network management will depend on this.

7. Simulation results / Parameters

Now we see the metrics or parameters to be utilized for this research. The metrics that we employed for this research are Throughput, Fairness and Delay. These metrics were used to enhance the best findings for our research.

8. Dataset LINKS / Important URL

Here we provide few important links which are incorporated to obtain the details to be required to overview the concepts or details related to our proposed technique the mmWave Networks:

• https://ieeexplore.ieee.org/abstract/document/9222019/
• https://ieeexplore.ieee.org/abstract/document/9773317/
• https://ieeexplore.ieee.org/abstract/document/9515585/
• https://ieeexplore.ieee.org/abstract/document/10114524/
• https://ieeexplore.ieee.org/abstract/document/10286299/

9. mmWave networks Applications

Our proposed technique mmWave network is now being employed in various applications. Some of the applications utilized are Lane change assistance, Cross traffic warning, Self-parking, Adaptive cursive control, Collision avoidance, Parking assistance and Blind spot detection.

10. Topology for mmWave networks

The topology for mmWave network contains a gathering of base stations that are beneficially deployed along an urban landscape. These base stations are generally attached on high-rise buildings or other high frameworks to make sure the broad coverage and line-of-sight that links in the deep urban environment. The network that utilizes dense spatial reuse, which defines the multiple base stations that assists the nearby users at the same time by using the narrow directional beams that is allowed by innovative beamforming technologies.

11. Environment for mmWave networks

Let’s see the environment to be used for this mmWave network, it is a dense urban environment which is taken into account, and then is described by the high-rise buildings and the dense population focus. The network utilizes a hybrid beamforming scheme, using digital as well as analog beamforming techniques to achieve the spatial diversity and perform effective multi-user communication. The Dynamic Spectrum Access (DSA) tool is employed to take advantage by using frequency bands, improving spectral efficiency.

12. Simulation tools

The following are the software requirements that are used for the wireless networks for this research. The development tool that is required for this research is NS 3. Then the programming language that is employed to implement this research is Python. Moreover the operating system that is required for executing is Ubuntu 16.06 [LTS].

13. Results

As a result the proposed mmWave networks give the best performance as compared to the various metrics. Our proposed system overcomes several previous technology issues by using this technology and is executed in a language like Python to achieve best findings for this research.

Millimeter wave Network research Ideas

Below we offer several research topics on the basis of mmWave networks; we go through the topics to know the details about this proposed technique mmWave networks and such other relevant Information:

1. Coverage and Rate Analysis for mmWave-Enabled Aerial and Terrestrial Heterogeneous Networks
2. Joint User Association and Resource Allocation for mmWave Communication: A Neural Network Approach
3. Network Planning and Performance Analysis for 5G mmWave in Urban Areas
4. DC-DLLR: A MAC Layer Approach for Reliable and Blockage Tolerant mmWave Indoor Networks
5. Performance Analysis of Non-ideal Wireless PBFT Networks with mmWave and Terahertz Signals
6. Phase Modulation-based Fronthaul Network for 5G mmWave FR-2 Signal Transmission over Hybrid Links
7. mmWave path loss modeling for urban scenarios based on 3D-convolutional neural networks
8. A Measurement Study of TCP Performance over 60GHz mmWave Hybrid Networks
9. Multi-Agent Reinforcement Learning-Based Cooperative Beam Selection in mmWave Vehicular Networks
10. Cooperative Gigabit Content Distribution With Network Coding for mmWave Vehicular Networks
11. A Review on Recent Approaches in mmWave UAV-aided Communication Networks and Open Issues
12. Using Fulkerson-Ford Algorithm for UE – AP Association in mmWave Cellular Networks
13. Multimodal Fusion Assisted Mmwave Beam Training in Dual-Model Networks
14. Heterogenous Public Safety Wireless Networks with mmWave Small Cells: An IAB-based Approach
15. Reliably Route IoT Packets in Software Defined mmWave Mesh Networks
16. Angular Distance-Based Performance Analysis of mmWave Cellular Networks
17. A Statistical Investigation of Spatial Consistency and Human Blockage Consideration based mmWave Channel Modeling for 5G Back-Haul Networks
18. Subscriber Location in 5G mmWave Networks – Machine Learning RF Pattern Matching
19. Radio Frequency Pattern Matching – Smart Subscriber Location in 5G mmWave Networks
20. Deep Reinforcement Learning Based Joint Beam Allocation and Relay Selection in mmWave Vehicular Networks
21. Distributed Generative Adversarial Networks for mmWave Channel Modeling in Wireless UAV Networks
22. KF-LSTM Based Beam Tracking for UAV-Assisted mmWave HSR Wireless Networks
23. Intelligent Dual Active Protocol Stack Handover Based on Double DQN Deep Reinforcement Learning for 5G mmWave Networks
24. Wiretapping or Jamming: On Eavesdropper Attacking Strategy in mmWave Ad Hoc Networks
25. Demystifying Resource Allocation Policies in Operational 5G mmWave Networks
26. Evaluation of Power Consumption in 5G Networks at sub-6 GHz and mmWave
27. Blockage Prediction Using Exhaustive Beam-Pair Scan in mmWave Networks: An Experimental Study
28. NS-3 simulations on Smart Grid Networks performance adopting mmWave communications
29. A Comprehensive Study on Statistical Channel Modeling for Outdoor to Indoor (O2I) Penetration Concern of 5G mmWave Access Networks
30. Predictive Data Replication for XR Applications in Multi-Connectivity Enabled mmWave Networks
31. DRL Based Beam Management for Joint Sensing and Communications in HSR mmWave Wireless Networks
32. Clustering-Assisted 3D Beamforming for Throughput Maximization in mmWave Networks
33. Deep Reinforcement Learning-based User Association in Sub6GHz/mmWave Integrated Networks
34. Energy-Efficient Resource Allocation Scheme in Hybrid Heterogeneous Networks with mmWave Base Stations
35. Enabling Efficient Blockage-Aware Handover in RIS-Assisted mmWave Cellular Networks
36. Adaptive Beam Tracking based on Recurrent Neural Networks for mmWave Channels
37. Fast Initial Access with Deep Learning for Beam Prediction in 5G mmWave Networks
38. A Novel Channel Model and Optimal Power Control Schemes for Mobile mmWave Two-Tier Networks
39. A Smart Handover Strategy for 5G mmWave Dual Connectivity Networks
40. User Pairing and Beamforming Design in mmWave CR-NOMA Networks
41. Unified Analysis of Coordinated Multipoint Transmissions in mmWave Cellular Networks
42. RIS-Aided mmWave Network Planning Toward Connectivity Enhancement and Minimal Electromagnetic Field Exposure
43. Leveraging the Coupling of Radio Access Network and mmWave Backhaul Network: Modeling and Optimization
44. A Directional TDMA Protocol for High Throughput URLLC in mmWave Vehicular Networks
45. Efficient Beam Scheduling for Half-Duplex mmWave Relay Networks
46. Integrated Beamforming and Resource Allocation in RIS-Assisted mmWave Networks based on Deep Reinforcement Learning
47. IFNet: Imaging and Focusing Network for handheld mmWave Devices
48. Blockage Avoidance Based Sensor Data Dissemination in Multi-Hop MmWave Vehicular Networks
49. Dual Band (28/38 GHz) Antenna Design for 5G mmWave Communication Networks
50. Sensing of Side Lobes Interference for Blockage Prediction in Dense mmWave Networks