Optimization of cooperative secondary users in cognitive radio networks

In this paper, we identified the optimal number of secondary users in a cooperative spectrum sensing by maximizing the energy efficiency. We obtain the mathematical expressions and simulation results for the optimal number of secondary users using OR and AND fusion rules. We conducted the simulation for both OR and AND rules in two categories, One by keeping signal to noise ratio constant and second by keeping the detection threshold constant. Based on the analysis we showed that the performance obtained for OR rule is better than the AND rule. We hope that our results will be useful for improving the energy efficiency in identifying the un-utilized spectrum. Keywords: Cognitive radio, Cooperative spectrum sensing, Optimization, Energy efficienc

A Survey on 5G Coverage Improvement Techniques: Issues and Future Challenges

Fifth generation (5G) is a recent wireless communication technology in mobile networks. The key parameters of 5G are enhanced coverage, ultra reliable low latency, high data rates, massive connectivity and better support to mobility. Enhanced coverage is one of the major issues in the 5G and beyond 5G networks, which will be affecting the overall system performance and end user experience. The increasing number of base stations may increase the coverage but it leads to interference between the cell edge users, which in turn impacts the coverage. Therefore, enhanced coverage is one of the future challenging issues in cellular networks. In this survey, coverage enhancement techniques are explored to improve the overall system performance, throughput, coverage capacity, spectral efficiency, outage probability, data rates, and latency. The main aim of this article is to highlight the recent developments and deployments made towards the enhanced network coverage and to discuss its future research challenges

Comparison of UMi, UMa, and RMa Path Loss Models of 5G mmWave Communication System

The signal power in wireless communication systems is influenced by its surroundings; primarily, it will be affected by the path difference, operating frequency, and environmental effects. This makes it extremely challenging to plan any communication system that will provide better signal strength. Therefore, large-scale path loss models are considered to estimate the path loss at various frequencies, distances, and in various environments. In this paper, we considered UMi, UMa, and RMa environments to estimate the LOS and NLOS path loss for frequencies from 0.5 to 100 GHz. In the millimeter wave frequency range, a comparison is made between the path loss observed and the path loss models created by different standard organizations. The simulation results demonstrate that the 5GCM model is an optimized path loss model in the urban micro-environment, similarly 3GPP model is an optimized path loss model in UMa and RMa environments. These optimized models produce enhanced path loss compared to the other path loss models. These optimized models could be used by the service providers to enhance the quality of service in 5G wireless networks

Enhanced Indoor Path Loss and RSRP of 5G mmWave Communication System with Multi-objective Genetic Algorithm

The signal strength in 5G mobile communication systems is signifcantly infuenced by the surroundings, with key factors including the path diference, operating frequency, and obstructions at specifc locations. Consequently, planning a communication system that can deliver improved signal strength becomes highly challenging. To address this issue, indoor path loss models are employed to estimate signal loss in diferent environments, frequencies, and distances. This paper introduces an intelligent multi-objective genetic algorithm aimed at enhancing path loss and received signal power. A comparative analysis is conducted to evaluate the performance of the proposed intelligent optimization algorithm against the traditional approach. The path loss and received power of various scenarios are estimated using various path loss models. The 5GCM indoor ofcce, 5GCM InH shopping mall, 3GPP TR 38.91 InH ofce, mmMAGIC InH ofce, METIS InH shopping mall, and IEEE 802.11 ad InH ofce indoor path loss models estimates the path loss of 62.37 dB, 62.15 dB, 63.12 dB, 50 dB, 55.18 dB, and 52.89 dB in traditional approach and 36. 87 dB, 35.86 dB, 36.84 dB, 68.80 dB, 36.23 dB and 33.94 dB using GA approach and received powers of −12.17 dBm,−11.37 dBm,−12.17 dBm,−5.80 dBm, −12.24 dBm and −8.68 dBm in traditional approach and 26.13 dBm, 27.14 dBm, 26.15 dBm, −5.80 dBm, 26.75 dBm and 29.05 dBm using GA approach repectively. The 5GCM and 3GPP models produces the path loss diference above 25 dB and mmMAGIC, METIS and IEEE models produces a path loss below 19 dB. Except mmMAGIC model, all models produces the recceiver power diference above 37 dBm. Therefore, the highest path loss diference of 26 dB is observed in 5GCM InH shopping mall model and the highest reccieved power difference of 39.01 dBm is observed in METIS InH shopping mall model. The results clearly demonstrate that the proposed intelligent optimization approach outperforms the traditional approach across various indoor scenarios

Performance Analysis of a Millimeter Wave Communication System in Urban Micro, Urban Macro, and Rural Macro Environments

The signal power in wireless communication systems is influenced by various factors, including the environment. These factors include path differences, operational frequency, and environmental conditions. Consequently, designing a communication system that generates a stronger signal is highly challenging. To address this, large-scale path-loss models are employed to estimate the path loss and signal power across different frequencies, distances, and environments. In this paper, we focused on the urban micro, urban macro, and rural macro environments to estimate path loss and signal power at millimeter wave frequencies. We compared the path loss and received power among different path-loss models developed by standard organizations. Simulation results indicate that the fifth-generation channel model provides enhanced path loss and signal power in urban micro environments, while the third-generation partnership project model performs well in urban macro and rural macro environments when compared to other path-loss models

Path Difference Optimization of 5G Millimeter Wave Communication Networks in Malaysia

The development of intelligent transport systems, mobile cellular networks, microwave links, and vehicle communications has accelerated with the use of wireless connections as a communication channel in 5G wireless technology. Weather, including rain, fog, snow, sand, and dust, impacts wireless communication channels in various ways. These effects are more pronounced at the high frequencies of millimeter-wave bands. Recently, the 5G network has made it possible to support a variety of applications with fast speeds and high-quality content. To facilitate the use of high-millimeter-wave frequencies, a recent study investigated how sand and dust affect the 5G communication channel. In this paper, we consider the impact of frequent and heavy rainfall on millimeter-wave propagation and cross-polarization of the wave at various points along the propagation path caused by rainfall in urban and highway scenarios in Malaysia. We estimate rainfall attenuation, path loss, and link margin at various millimeter-wave frequencies. From our simulation results, it is evident that rainfall attenuation, path loss, and link margin depend on the operating frequency, path difference, and rainfall rate. In this paper, we estimate and compare the optimal path difference values under urban and highway scenarios both with and without rainfall attenuatio

Internet of Things: Agriculture Precision Monitoring System based on Low Power Wide Area Network

Nowadays, many people around the world depend mostly on agriculture for their livelihood. In the majority of countries around the world, it is the most significant occupation for many families. Unfortunately, farmers, particularly in oil palm plantations, continue to rely on age-old practices. One of the key elements in achieving high and long-term oil palm production on peat is the adoption of efficient precision water management. In essence, this means maintaining the water table at the necessary depth. Because of the peat's persistently low water table, oil palm productivity has sharply decreased. In this work, an Internet of Things (IoT) for precision agriculture monitoring is developed using a long-range wide area network (LoRaWAN) algorithm. Based on an approach point of view, a LoRaWAN is a long-range, low-power, low-bitrate wireless telecommunications system meant to be used as part of the Internet of Things architecture. The end devices link to gateways through a single wireless hop using LoRaWAN. These gateways function as transparent bridges, relaying messages from the end devices to a central network server. The ultimate result is the creation of a precision water management assistance algorithm employing LoRaWAN and IoT that is both affordable and effective

Comparative analysis and optimization of path loss models for small cell wireless communication systems

Nowadays, a precise assessment of path loss is essential to the efficient design and function-ing of wireless communication networks. This research explores path loss evaluation andcomparison using models designed for urban-micro scenarios. The article also looks intohow these path loss models might be optimized using a genetic algorithm to more accur-ately simulate actual propagation paths. Analytic path loss measurements are determined bythorough analysis to closely match optimal values for every model, and error statistics areused to evaluate model performance in a comprehensive manner. The root mean squareerror of the study reveals that the 5th Generation Channel Model (5GCM) open square dem-onstrates the most favorable performance, exhibiting the lowest mean square error of2.33 dB and standard deviation of 2.33 dB values. Operators of 5 G small cell networks havethe opportunity to greatly improve service quality by utilizing this optimization technique,especially at millimeter wave frequencies. Such developments have the potential to com-pletely transform the user experience in urban micro-environments in addition to optimizingwireless communication infrastructure

Reinforcement learning-based unmanned aerial vehicle trajectory planning for ground users’ mobility management in heterogeneous networks

The surge of data traffic in wireless networks necessitates the provision of high-quality data services to meet users’ satisfaction levels. However, the limited spectral resources of the current network infrastructures and inherent challenges of achieving reliable line-of-sight (LoS) probability for ground users (GUs) in urban environments often lead to disruption to communication services delivery. This paper aims to address the challenges of frequent handover (HO) failures and disrupted communication services for mobile GUs by deploying an unmanned aerial vehicle as a flying base station (UAV-BS) in heterogeneous networks (HetNets). A channel model is investigated that considers both LoS and non-line-of-sight (NLoS) paths in three-dimensional (3D) air-to-ground (A2G) links using a detailed mathematical model with urban infrastructure parameters like building density and heights. In addition, a reinforcement learning (RL) algorithm is presented in this work to optimize UAV trajectories in response to the dynamic mobility of GUs for enhancing LoS connections. The proposed algorithm dynamically adjusts the UAV positions and enhances transmission channels by identifying both LoS and NLoS paths. Simulation results demonstrate that the proposed algorithm outperforms existing benchmarks through learning-based adaptive control of UAVs’ mobility, ensuring ubiquitous network connectivity for GUs and reducing HO failures in HetNets