User Density Research Articles

Development of Adaptive Resource Allocation and Interference Mitigation for Spectrum Sharing in D2D-Enabled 5G Heterogeneous Networks: A Case Study of Urban Microcell Environments

Device-to-device (D2D) communication in heterogeneous networks (HetNets) poses significant challenges in resource allocation and interference management, especially within 5G networks where spectrum sharing between cellular users (CUEs) and D2D user equipment (DUEs) is critical. This study developed an adaptive resource allocation framework using Long Short-Term Reinforcement Learning (LSRL), which integrated Long Short-Term Memory (LSTM) networks with Deep Reinforcement Learning (DRL) technique. The proposed approach addressed the dynamic nature of interference in urban microcell environments by leveraging a Hierarchical Data Format (HDF5) dataset generated from network simulations. These simulations incorporate diverse scenarios, including varying user densities, transmission power levels, and interference conditions. The LSRL-based scheme was evaluated against conventional DRL methods, demonstrating notable improvements in network performance. Specifically, the proposed framework achieved up to a 6.67% increase in sum throughput and an 8.2% enhancement in power efficiency, even under dense user conditions. Additionally, the LSRL model proved resilient to variations in D2D pair distances, maintaining robust spectral efficiency and quality of service (QoS). These findings underscore the potential of the LSRL-based adaptive approach for improving resource management in 5G HetNets, particularly in dense urban deployments, and provide valuable insights for optimizing next-generation wireless communication systems.

User Association, Power Allocation, and Aerial Base Stations Placement in Hybrid Networks: A Deep Reinforcement Learning Approach

Relevance. With the development of information technologies and the Internet of Things, the demand for more efficient and flexible mobile networks is increasing. Future wireless systems must not only ensure high speed and reliability of connections but also enable quick recovery of communication in emergency situations. Ground base stations (GBS) are typically installed stationary and are designed for long-term service, which limits their efficiency during sudden increases in traffic or infrastructure damage. In such conditions, aerial base station (ABS) emerge as a promising solution. Due to their mobility, affordability, and the ability to deploy quickly, they can support the operation of ground stations in high user density conditions or in emergencies when GBS are damaged or destroyed. This makes them an essential element of future communication networks.Problem Statement. Development of methods for the placement of ABS in three-dimensional space and the distribution of users and power among users with the goal of maximizing the data transmission speed of the systems.Goal of the work. Increase the data transmission speed of systems using ABS to support GBS through optimal three-dimensional positioning of ABS, distribution of users between ABS and GBS, and power allocation among users.Methods. The research was conducted using a dynamic approach, in which the coverage radius of the GBS is gradually reduced, along with the reinforcement learning algorithm. The analysis of the results showed the high effectiveness of the proposed method and allowed for a significant increase in data transmission speed within the framework of the task.Scientific novelty. The scientific novelty of the proposed solution lies in the joint optimization of the placement of ABS and power allocation under resource constraints, which revealed a dependency between the coverage radius of GBS and the flight altitude of ABS. Specifically, as the coverage radius of GBS increases, the optimal flight altitude of ABS decreases, and vice versa.Practical significance. The practical significance lies in the possibility of developing a methodology for planning public communication networks using ABS to support GBS under resource constraints. This approach makes it possible to ensure a high total data transmission rate and improve the reliability of network operation.

A beam-selection-based statistical beamforming algorithm for FDD massive MIMO systems with dense users

A beam-selection-based statistical beamforming algorithm for FDD massive MIMO systems with dense users

Bio inspired multi agent system for distributed power and interference management in MIMO OFDM networks

MIMO–OFDM systems are essential for high-capacity wireless networks, offering improved data throughput and spectral efficiency necessary for dense user environments. Effective power and interference management are pivotal for maintaining signal quality and enhancing resource utilization. Existing techniques for resource allocation and interference control in massive MIMO–OFDM networks face challenges related to scalability, adaptability, and energy efficiency. To address these limitations, this work proposes a novel bio-inspired Termite Colony Optimization-based Multi-Agent System (TCO-MAS) integrated with an LSTM model for predictive adaptability. The deep learning LSTM model aids agents in forecasting future network conditions, enabling dynamic adjustment of pheromone levels for optimized power allocation and interference management. By simulating termite behavior, agents utilize pheromone-based feedback to achieve localized optimization decisions with minimal communication overhead. Experimental analyses evaluated the proposed TCO-MAS across key metrics such as Sum Rate, Energy Efficiency, Spectral Efficiency, Latency, and Fairness Index. Results demonstrate that TCO-MAS outperformed conventional algorithms, achieving a 20% higher sum rate and 15% better energy efficiency under high-load conditions. Limitations include dependency on specific pheromone adjustment parameters, which may require fine-tuning for diverse scenarios. Practical implications highlight its potential for scalable and adaptive deployment in ultra-dense wireless networks, though additional field testing is recommended to ensure robustness in varied real-world environments.

Throughput Performance in D2D Communication Networks: Effects of Power, Density and User Distance

Device-to-Device (D2D) communication plays a vital role in enhancing spectral efficiency and data rates in next-generation wireless networks, but interference management and optimal power control remain key challenges. This study aims to address these gaps by developing a modified Power Control Scheme for D2D communication (mPCS-D2D) using a Hierarchical Clustering Algorithm (HCA) to improve throughput while minimizing interference. The scheme combines proximity-based clustering for general D2D users and social relationship-based clustering for mmWave D2D communication. Simulations were conducted to evaluate the throughput performance of mPCS-D2D under varying D2D transmit power levels, user densities, and inter-user distances. Results showed that the proposed scheme significantly outperformed the baseline PCS-D2D model across multiple scenarios. At 10 m and 15 m distances with a pathloss exponent of 4.5, mPCS-D2D improved throughput by 5.15% and 4.42%, respectively. Under varying user densities and pathloss exponents (3.5 and 4.5), throughput gains ranged from 4.33% to 4.77%, while across 10 m, 15 m, and 20 m distances, it achieved improvements of 4.13% to 5.53%. These findings demonstrate that the proposed mPCS-D2D scheme effectively enhances data transmission rates under diverse network conditions. The study concludes that integrating hierarchical clustering into power control mechanisms can significantly improve D2D communication efficiency. The proposed method offers practical implications for designing scalable, interference-aware D2D systems in future wireless networks.

Impact of Secondary Users density and Clustering Strategies on Detection of Performance of Spectrum Sharing System

Accurate spectrum hole detection is essential for cognitive radio networks to avoid interfering with primary users. However, channel impairments often hinder accurate detection of spectrum hole. This paper examines an energy-efficient cooperative spectrum hole detection method that relies on multiple secondary users and clusters to improve detection rates. The study uses an eigenvalue detector in a combined Rayleigh and log-normal fading environment, employing majority and OR fusion rules for local and global sensing, respectively. Simulations in MATLAB R2023a demonstrate that increasing the number of secondary users and clusters enhances detection probability and spectral efficiency, while reducing sensing time.

Design and implementation of a wireless network with mechanisms that do not violate security to meet the demand of higher education institutions

At the Autonomous University the current wireless network infrastructure is insufficient to meet the growing demand for access, causing failures and intermittencies in the service. Faced with this problem, a thesis proposal has been developed to implement a modern wireless network with high user density, centralized management and improved security schemes. The proposed methodology for the implementation of a high-density wireless network in a higher education institution. In conclusion, the project made it possible to comply with the hypothesis proposed since the implementation of a robust wireless network, with adequate levels of security and profile management, facilitated the ubiquitous access of online academic resources by the student and teaching community of the university. This translates into support for educational quality.

Analytical framework for UAV-enabled wireless communication with D2D networks

This study analyzes the performance of Device-to-Device (D2D) communication in drone-assisted cellular networks, focusing on coverage probability, system throughput, and energy efficiency. Simulation results reveal that increasing UAV altitude improves the probability of line-of-sight (LoS) communication, enhancing coverage probability up to 92% at optimal altitudes. However, higher altitudes also increase path loss, leading to a 15% reduction in total system throughput. Additionally, an optimal D2D user density of 50 users per km2 is identified to balance interference and resource utilization effectively. Uplink and downlink scenarios show that interference significantly affects the success transmission probability, which declines by 25% when the SINR threshold increases from 0 to 10 dB. In public safety scenarios, underlay in-band communication enables D2D users to establish reliable indirect links via cellular users, ensuring critical message delivery with energy efficiency improvements of up to 18%. These findings provide actionable insights for optimizing UAV deployment and resource allocation in D2D networks.

Energy Consumption Minimization for UAV-Assisted Network in Hotspot Area

Unmanned aerial vehicles (UAVs) play a crucial role in enhancing network coverage and capacity, especially in areas with high user density or limited infrastructure. This paper proposes an effective UAV-assisted offloading framework to minimize the energy consumption of both users and UAVs in an air-to-ground (A2G) network. First, UAVs are deployed by jointly considering the user distribution and guaranteeing the quality of service (QoS) of users. Further, user association, power control, and bandwidth allocation are jointly optimized, aiming to minimize the power consumption of users. Considering user mobility, the positions of UAVs are continuously refined using the double deep Q-network (DDQN) algorithm to reduce the weighted energy consumption of users and UAVs. Simulation results show that the proposed algorithm has better performance in reducing the total energy consumption compared with benchmark schemes.

Modelling A 5 Km² rural area with deployment of IoT devices and user equipment

The growing presence of wireless communication in rural regions calls for the enhancement of network performance to provide stable connectivity to user equipment (UEs) and Internet of Things (IoT) devices. This study presents a simulation-based approach to modelling a 5 km² rural environment, evaluating key performance metrics such as antenna gain, path loss, and signal-to-noise ratio (SINR). Using the WINNER II radio propagation model and MATLAB simulation tools, the research simulates the spatial distribution of IoT devices and UEs, calculates received power levels, and determines the optimal placement of devices by selecting the top 50% of cells based on received power, gains, and SINR. The work also considers terrain features, atmospheric conditions, and user density for better predictive accuracy. The results provide valuable insights into rural wireless network optimization via enhanced coverage, interference minimization, and connectivity maximization for low-power IoT devices and mobile users. The results translate to better network planning and deployment strategies, facilitating efficient and cost-effective rural wireless communication systems

NOMA Performance Improvement with Downlink Sectorization

This study tackles the growing challenge of inter-user interference in Non-Orthogonal Multiple Access (NOMA) systems, particularly as user density increases in modern communication networks. The primary objective is to improve system performance by implementing a downlink sectorization strategy, which groups users into distinct sectors to manage interference and optimize resource allocation. A Sequential Power Allocation (SePA) algorithm was introduced to enhance power distribution within sectors, aiming to maximize both user capacity and overall sum rate. The methods employed included detailed simulations comparing the performance of traditional NOMA systems and those incorporating sectorization. The results demonstrate that sectorization can significantly boost the system’s sum rate by up to 25% and reduce decoding errors by as much as 51%, particularly when the number of users per sector is kept under 20. However, performance saturation occurs beyond this threshold, where additional users do not contribute to further improvements. The novelty of this research lies in applying spatial sectorization to NOMA, showing that spatial sectorization can minimize intra-sector interference, improve power efficiency, and maintain reliable communication in high-demand environments such as the Internet of Things (IoT). This study provides valuable insights for optimizing NOMA systems, crucial for next-generation wireless networks. Doi: 10.28991/ESJ-2025-09-01-017 Full Text: PDF

Harnessing Intelligent RIS for Optimized Capacity and Latency in 6G Cooperative NOMA Systems

This article examines the performance of cooperative NOMA systems in a massive MIMO configuration inside the sixth-generation (6G) network, including scenarios with and without reconfigurable intelligent surfaces (RIS). The emphasis is on comprehending the effects of implementing static, dynamic, and intelligent RIS on the capacity of the cooperative NOMA system with differing user quantities. The study examines how different user loads and RIS densities affect average latency and effective area spectral efficiency (EASE). Integration of a proposal system in a novel way with water-filling in the multi-user channel logarithm to enhance capacity, average latency, and EASE for diverse user densities. The results show that RIS significantly improves capacity, average latency, and EASE, especially with intelligent RIS. All scenarios, in conjunction with the suggested algorithm, markedly improve network performance, particularly under conditions of increased user demand. Additionally, it enhances the system's capability and spectrum efficiency, especially when deployment numbers rise.

Relational Analysis of User Density, Number of Train Services and Population around Rail Stations on Local Railways in Japan

Relational Analysis of User Density, Number of Train Services and Population around Rail Stations on Local Railways in Japan

3-D Placement of Aerial Base Stations to Support Terrestrial Network Based on Adaptive Particle Swarm Optimization

Problem statement. With the increasing load on terrestrial communication networks, it is necessary to implement new solutions to ensure stable and high-quality user service. One of the promising approaches is the use of aerial base stations (ABS), which can be rapidly deployed in areas with high user density or during emergency situations. This paper addresses the problem of three-dimensional deployment of ABS to support terrestrial communication networks, where the number of users exceeds the capacity of the current ground-based system. The main goal of this work is to determine the minimum number of ABS and their optimal placement while meeting user service requirements. However, this is an NP-hard problem (nondeterministic polynomial-time hardness), so to solve it, we propose using Adaptive Particle Swarm Optimization. Scientific novelty: the proposed approach consists in developing a method for optimizing three-dimensional ABS placement using an adaptive particle swarm algorithm aimed at minimizing the number of ABS while maintaining high quality of service to users. Unlike classical PSO, the proposed algorithm allows connecting more users to ABS due to more efficient resource allocation and selection of ABS location. Simulation results show that the proposed algorithm ensures high coverage performance in environments with various user distributions. Theoretical / practical significance: In practice, the results can be used in planning and deploying ABS in congestion or emergency situations to ensure sustainable coverage.

Use of Low-Cost Technologies for Monitoring User Density on Beaches: A Case Study of Guarita Beach, Southern Brazil

Use of Low-Cost Technologies for Monitoring User Density on Beaches: A Case Study of Guarita Beach, Southern Brazil

Summer outdoor thermal risk area mapping on a university campus in Auckland, New Zealand

Outdoor thermal risks in urban areas are increasingly critical due to climate change and urbanization. This study identifies high-risk areas at Auckland University of Technology, New Zealand, using a multi-layered approach integrating hazard, exposure, and vulnerability. Locations with Physiologically Equivalent Temperature (PET) exceeding 23°C were analyzed alongside user density and survey-based vulnerability assessments, pinpointing two high-risk zones. Future projections for 2050 and 2080 (RCP 4.5 and RCP 8.5 scenarios) indicate rising PET levels, amplifying thermal discomfort. Mitigation strategies, including green walls and tree planting, demonstrated PET reductions of 2°C and 3°C, respectively, under current conditions. These findings underscore the critical role of greenery in enhancing outdoor thermal comfort and resilience. The study’s replicable methodology offers urban planners a practical framework for addressing thermal risks and adapting outdoor spaces to climate change impacts, fostering urban livability.

Optimizing resource allocation for enhanced urban connectivity in LEO-UAV-RIS networks

Optimizing resource allocation for enhanced urban connectivity in LEO-UAV-RIS networks

Development model of a high-performance multiple input multiple output microstrip antenna based on a planar series array with 8×2 elements for 5G communication systems

MIMO (Multiple Input Multiple Output) makes a major contribution to 5G communication systems by increasing network capacity and spectrum efficiency. In 5G, MIMO enables the use of multiple antennas at base stations and user devices, allowing simultaneous sending and receiving of data over multiple paths. This significantly increases data throughput and connection reliability, especially in environments with high user density. In addition, MIMO technology supports the implementation of beamforming, which focuses signals on a specific direction, reduces interference, and improves signal coverage and quality, making it one of the keys to achieving faster and more responsive 5G performance. Therefore, antennas with wide bandwidth, high gain and MIMO performance are crucial for supporting 5G communication systems. This paper proposes a high-performance MIMO microstrip antenna based on a series planar array with 8×2 elements operating at a resonant frequency of 3.5 GHz for 5G communication systems. A spiral stub and a feed inset are proposed to control the reflection coefficient and bandwidth of the antenna while the series planar array is proposed to increase the gain. To support the MIMO communication system, the proposed antenna is separated into two different ports with a certain distance. From the measurement results, the proposed antenna has high performance indicated by a wide bandwidth of 680 MHz (3–3.68 GHz) and a high gain of 17.8 dB at a resonant frequency of 3.5 GHz. In addition, the proposed antenna has high mutual coupling and diversity indicated by ECC and DG of 0.001 and 9.99 dB, respectively. This work provides a solution to design a high-performance microstrip antenna and can be implemented as a receiving antenna for 5G communication systems

The concept of effective coverage radius use of the unlicensed high-frequency range in the operation of the 5G network

Economic expediency encourages mobile operators to deploy 5G networks in places with a high concentration of speed-demanding subscribers. In such conditions, sharp fluctuations in the volume of traffic with regulated requirements for the quality of service are inevitable. Note that 5G operates in the millimeter range. Accordingly, the quality of traffic service is affected both by the number of subscribers simultaneously initiating requests from one sector of coverage, and by the appearance of obstacles opaque to radio radiation in the space between the subscriber device and the base station. Effective smoothing of 5G traffic fluctuations, taking into account these disturbing factors, is an urgent task. The goal of this research is to evaluate the service quality parameters in a target area characterized by a specific user density. It takes into account that if the declared QoS requirements for connection speed for users in a network segment deployed within the licensed frequency range for 5G are not met, they can utilize a network segment deployed in the unlicensed high-frequency range for 5G under conditions of free competition. The metric being studied is the probability of session loss in the licensed network segment and the achievable transmission speed in the unlicensed network segment. Based on this, a method for assessing the density of base station deployment in the unlicensed network segment necessary to support the specified user density in the licensed network segment with defined QoS guarantees in terms of bandwidth is formalized. The experiment results showed that the probability of losing sessions with regulated requirements for the quality of service in both network segments, in addition to the base station placement density and subscriber devices, is significantly affected by the minimum data transfer rate, the intensity of obstacles, and the value of the Contention Window.

Digital Dividends: Exploring The Impact Of Telecommunication Infrastructure On Economic Growth In India

This paper examines the effect telecommunications infrastructure has on the growth of the Indian economy, emphasising the role that telecommunication usage and tools play on the GDP per capita and other economic parameters. It accesses information from different organisations, including the Telecom Regulatory Authority of India and UDISE+, to study the links between wireless and wireline users, user density, and school digital access and their impact on economic indicators. With advanced econometrics models, i.e. OLS regression, correlation analysis, random forests, step-wise regression, granger analysis, adfuller, and others, predictable solid relationships and directional relationships are proved to exist between the telecommunications background and GDP growth variable. The study results indicate that in India, there is a strong correlation between internet access in schools and wireless internet users. Moreover, it proposes policies to catalyse economic growth and digital adoption in India. It is a resource that policymakers can leverage to make the right decisions in harnessing the power of ICT for economic development