Designing and Testing of Quadrifilar Helix Antenna for NOAA Weather Satellite

Self Cite

Cognitive Radio Network (CRN) is a favourable technology for the future of wireless networks. It aims to seamlessly exploit spectrum resources by opportunistically using the unused or underused radio spectrum that is licensed. Cognitive Radio Network is a technology that is planned to be used in the 5th Generation of wireless communication. This technology is belied to be the answer to the unused and underused radio spectrum in the Radio environment. Whereas Coordinated Multipoint Joint Transmission (CoMP JT) is a technology that has already been used, first used in 3GPP LTE-Advanced. Coordinated Multipoint Joint Transmission aims to improve the performance of the cellular network. In this report, Cognitive Radio Network is being implemented along with Coordinated Multipoint Joint Transmission techniques. An inclusive and layered architecture of Cognitive Radio Network along with Coordinated multipoint Joint Transmission is being designed in this report, showing different technologies and different steps involved in the concept of this combination. Then the impact of CoMP JT on CRN is being studied. By implementing CoMP JT on CRN, it has been able to show how the performance of CRN is changing when implemented alone and when implemented with CoMP JT and some of the significant results are being depicted to better understand the changes in CRN with CoMP JT. Lastly, a study is performed on the combination of Cognitive Radio Network and Coordinated Multipoint Joint Transmission. As far as the investigation is being performed, two factors have been considered, which is: Signal to Interference plus Noise Ratio (SINR) and Total Average Throughput (TAT). The detailed study is being performed in this report and is being implemented in three different environments. Hence the results are being compared and a conclusion is being drawn based on these results.

Section: Discussionmentioning

confidence: 99%

Performance Improvement of Next-Generation Network with Cognitive Radio Network and Coordinated Multipoint Joint Transmission

Valentin

Gupta

Sharma

2022

Self Cite

“…The authors in [10] proposed a self-organizing load balancing that offers policies for load balancing which is self-optimizing to enhance the adaptation and robustness of cellular networks based on Fixed Relay Station (FRS). A game-theoretical analysis is proposed that significantly increases capacity utilization in the network even when the cells do not behave cooperatively [11]. The quantity of load to be accepted or unloaded shall be deemed independently to be decided by each cell.…”

Section: Prior Workmentioning

confidence: 99%

“…Subsequently, the distance between the two picocells can also be varied. This work can also be modified and explored in different areas of wireless communication by following the mentioned research papers [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27].…”

Section: Conclusion and Future Scopementioning

confidence: 99%

Load Balancing using Fixed Geometric Arrangements of Fixed and Mobile Small Cells in Mili-Meter Network

Verma

Pandey

Khurana

2022

The upcoming generation of cellular networks is going to make extensive use of mmWave for communication. Hence, there will be a need for small cells to counter the loss incurred due to the more energy dissipation of mmWave. Small cells contain transmitters and receivers and hence there will be the need to balance the load efficiently so that no one is overwhelmed with functions to perform while others are relatively idle. In this paper, the performance of the user association algorithm is analysed when it is subjected to different scenarios like micro-urban, macro urban, suburban and rural. these scenarios are subjected to different frequency bands of mmWave and are compared for the values of the load balancing index. The load balancing algorithm is subjected to different small cell deployment techniques and the comparison is made for best deployment strategy among Quadrature based Approach (QBA) and Random deployment. Simulation results show that the sub-urban scenario has the maximum load-balancing index. On comparing QBA and random deployment approach QBA has a higher load balancing index in the suburban and rural scenarios and random deployment has a higher load balancing index in Indoor environments.

“…This work can also be modified and explored in different areas of wireless communication by following the mentioned research papers [46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65].…”

Section: Future Scopementioning

confidence: 99%

Optimization of Cell-Free Massive MIMO System

Sharma

Verma²,

Verma³

2022

As an innovative implementation, Cell-Free Massive Multiple Input Multiple Output (MIMO) has appeared in typical Cellular Massive MIMO Networks. This protocol doesn’t recognize cells, as shown by its name, even though a significant number of APs operate on the same frequency/time resources. Connection from multiple distributed access points through joint signal processing is called Cell-Free Massive MIMO. The Cell-Free Massive MIMO System, a contrast between Cell-Free Massive MIMO Systems and Distributed Massive MIMO, the prime focus in this thesis is on Cell-free Massive MIMO and, along with this discussion, on Cell-free Massive MIMO signal processing, Channel Estimation, Uplink Signal Detection, Cumulative Distribution, Spectral Efficiency & Ubiquitous Cell-Free Massive MIMO Model. Ubiquitous Cell-free Massive MIMO contributes to a Massive MIMO system, a distributed system that implements consistent user-centre distribution to solve that constraint of mobile phone interferences as well as to introduce macro-diversity. We investigated the Cell Radius at different locations in CDF with Spectral Efficiency [bits/s/hertz]. Cell-Free Massive MIMO is an evidence-based preventive of massive MIMOs with distributed high percentage APs that serve even lower margins. The cell-free model is not segregated into cells and any individual is concurrently represented by every Access point.