Cellular Users Research Articles

Enhancement of Spectral Efficiency and Interference Reduction in D2D Communication

Device-to-Device (D2D) communication is one of the most promising techniques for next-generation wireless networks, including 5G and beyond. It is mainly aimed at minimizing the waste of resources in 5G D2D communication for maximizing the spectral efficiency and minimizing the interference to the original cellular network. Device-to-device (D2D) communication with direct transmission enhances the network performance by reducing the latency. However, it is difficult to allocate resources efficiently while ensuring less interference between D2D links and cellular users. To address this, the machine learning method is adopted focusing on a Random Forest Regressor, which is trained with simulated data to estimate the best resource block allocation. The main parameters comprising data rate, bandwidth, level of interference and power of transmission are taken into account. Extra computations related to spectral efficiency and interference cost drive this optimization process that can vary the allocation of resources for the purpose of throughput maximization. Graphical representations are employed to demonstrate the spectrum-efficiency, bandwidth and interference-cost relationships. In general, the proposed algorithm effectively enhances resource utilization of 5G D2D communication, and the trade-off between the spectrum efficiency and the interference helps optimize the network performance.

Impact of information and communication technology on financial development: An International view

ABSTRACT Any economy’s growth depends on finance sector expansion. Information and Communication technology (ICT) is crucial to a successful financial system in the digital age. In recent years, numerous researchers have empirically examined the relationship between ICT and financial development; however, these studies are confined to certain economies or areas and overlook the multi-layered dimensions of both ICT and FD. An in-depth analysis of ICT’s effect on Financial Development (FD) is done in the present study by utilizing the new additional indicators, considering the depth and efficiency dimensions on the panel data set of 114 economies for 2000–2021 worldwide. Specifically, the most advanced panel data methods, named cross-sectionally autoregressive distributive lag (CS-ÅRDL) and Dumitrescu-Ηurlin panel causality techniques to empirically elaborate on how ICT infrastructure measured by internet users, mobile cellular users, fixed telephone, and broadband subscriptions influence the FD globally. Based on the results of the adopted methodology, there are significant and long-run relationships among the ICT, gross fixed capital formation (GFCF), unemployment (UT), and financial development (FD). To exhibit the CS-ARDL’s resilience concerning the study factors, robustness analysis was also carried out using panel correlated standard errors (PCSE) and feasible generalized least squares (FGLS). Taking into account the empirical findings of the ICT-FD-based study; finally, we proposed several sensible policy implications, their main implementation difficulties, possible cures, and some feasible suggestions for subsequent studies.

Системные эффекты радиочастотных электромагнитных полей (обзор). Часть 1. Железы секреции

Among publications on systemic effects of radio frequency electromagnetic fields (RF EMF), primarily those inherent in cellular communication devices (most often from 900 MHz to 2.5 GHz), as well as Wi-Fi, special attention should be paid to their influence on structural changes in the secretory glands, which are often direct targets for the impact of the corresponding RF EMF (for example, the thyroid gland). Various pathohistological effects of chronic exposure to RF EMF in different modes on the glands of external secretion have been established both in experimental studies on animals and in epidemiological ones. The parotid gland, salivary glands, sweat glands are among those mentioned in them. The endocrine glands can also be affected by RF EMF, which is confirmed by the results of numerous studies on the pineal gland, pituitary gland, thyroid gland, and adrenal glands, in which changes in their structure and functions have been recorded in both experimental animals and humans. At the same time, there is fairly pronounced dependence between resulting effects and exposure and other characteristics of RF EMF. At present, biological effects of RF EMF produced by various frequency ranges (cellular devices and telecommunication masts) have been reliably established. Various histopathological changes have been registered in the glands of mixed secretion such as the liver, pancreas, testicles and ovaries. Serious disorders in the testicles and ovaries revealed in experimental animals are particularly relevant since they undoubtedly lead to reproductive dysfunction. Particular concern is raised by the fact that cellular users of different ages, primarily children and adolescents, carry mobile devices in their trouser pockets, i.e. in close proximity to the sex glands. The present period is also characterized by accumulation of comparative epidemiological data as well as non-invasive measurements of structural and functional changes in the secretory glands in humans. Based on them, an unambiguous conclusion should be made about the need to limit and take precautions when using cellular devices, which is also indicated by some of the works considered in this review.

Systemic effects of radiofrequency electromagnetic fields (review). Part 1. Secretion glands

Among publications on systemic effects of radio frequency electromagnetic fields (RF EMF), primarily those inherent in cellular communication devices (most often from 900 MHz to 2.5 GHz), as well as Wi-Fi, special attention should be paid to their influence on structural changes in the secretory glands, which are often direct targets for the impact of the corresponding RF EMF (for example, the thyroid gland). Various pathohistological effects of chronic exposure to RF EMF in different modes on the glands of external secretion have been established both in experimental studies on animals and in epidemiological ones. The parotid gland, salivary glands, sweat glands are among those mentioned in them. The endocrine glands can also be affected by RF EMF, which is confirmed by the results of numerous studies on the pineal gland, pituitary gland, thyroid gland, and adrenal glands, in which changes in their structure and functions have been recorded in both experimental animals and humans. At the same time, there is fairly pronounced dependence between resulting effects and exposure and other characteristics of RF EMF. At present, biological effects of RF EMF produced by various frequency ranges (cellular devices and telecommunication masts) have been reliably established. Various histopathological changes have been registered in the glands of mixed secretion such as the liver, pancreas, testicles and ovaries. Serious disorders in the testicles and ovaries revealed in experimental animals are particularly relevant since they undoubtedly lead to reproductive dysfunction. Particular concern is raised by the fact that cellular users of different ages, primarily children and adolescents, carry mobile devices in their trouser pockets, i.e. in close proximity to the sex glands. The present period is also characterized by accumulation of comparative epidemiological data as well as non-invasive measurements of structural and functional changes in the secretory glands in humans. Based on them, an unambiguous conclusion should be made about the need to limit and take precautions when using cellular devices, which is also indicated by some of the works considered in this review.

Enhanced Security and Reliability in Messaging Systems through Real-Time SMS Spam Filtering with Machine Learning

Abstract— SMS spam has emerged as a major issue for cellular users, which leads to annoyance and inconvenience. Machine learning has been effective in filtering out spam SMS. Yet, applying these techniques in actual real-time situations poses special challenges. A newly published study seeks to tackle these challenges by building an efficient real-time. SMS spam filtering system based on machine learning. The main contribution of this work is to improve the performance of the system in real-time classification by focusing on data preparation, feature engineering, algorithm selection, and model deployment. Keywords—SMS spam filtering, Real-time classification, Machine Learning

Resource Allocation Algorithm for UAV Aided Symbiotic Radio Communication System

Aiming at the issue of how to improve the system transmission rate in a multiple Internet of things (IoT) device application scenario, we propose the resource allocation algorithm of the symbiotic radio communication system under multiple backscatter devices (BDs) assisted by unmanned aerial vehicle (UAV). We formulate the optimization problem of maximizing BDs’ sum rate by jointly optimizing the time allocation, BDs’ reflection coefficient and UAV location under constraints of BD’s harvested energy, quality of service (QoS) of cellular user and UAV. Since the problem is non-convex, it is difficult to solve directly. Therefore, the iterative algorithm based on block coordinate descent (BCD) method can be adopted, which decomposes the optimization problem into three sub-problems: time allocation, reflection coefficients of BDs and UAV location. For non-convex sub-problems, we utilize the successive convex approximation (SCA) technique to transform it into convex optimization problems, and we prove that the conversion is convex optimization problems. Simulation results show that our proposed algorithm converges fast, and significantly improves the system transmission rate compared with other schemes.

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.

Energy efficient resource and power allocation for uplink underlay D2D communication in HetNet-based 5G network

Device-to-Device (D2D) communication is a revolutionary technique to enhance the data rate, spectrum efficiency and energy efficiency in wireless network. It is important to have this technique due to the high demand of users and spectrum scarcity. Each D2D users can share the resource of the cellular user and transmit power of the user can create an interference to the user sharing the same resource. So, there needs a mechanism to govern the power and effectively allocate the resource to mitigate the interference created by sharing of resource between users. In this paper, D2D communication and small cells are introduced to form the Heterogeneous Network (HetNet). We propose a Sequential High Throughput Claim algorithm (SHTCA) to allocate resources to the D2D users such that Quality of Service (QoS) of the cellular users are maintained by applying the interference threshold to the cellular users. This provides the best throughput to the D2D users. Transmit power of the D2D and cellular users are optimized using the Genetic Algorithm (GA) such that interference between D2D and cellular users is effectively handled. The proposed work is differentiated over other existing algorithms and outcomes are compared in terms of energy efficiency, throughput and battery utilization in the network.

Flexible Resource Optimization for D2D XL-MIMO Communication via Adversarial Multi-Armed Bandit

Extremely large-scale multi-input and multi-output (XL-MIMO) communication, compared to conventional massive multi-input multi-output communication, can support more users and higher data throughput, thereby significantly improving its spectral efficiency and spatial multiplexing capabilities. This paper investigates the optimization of resource allocation for device-to-device (D2D) multicast communication in XL-MIMO cellular networks. The “many-to-many” sharing model permits one subcarrier to be shared among multiple D2D groups (DGs) and each DG to reuse multiple subcarriers. The objective is to maximize the total multicast data rate of DGs while meeting the data rate requirements of cellular users. This optimization problem is formulated as a 0–1 mixed-integer nonlinear programming problem, with the challenge lying in the fact that adjusting the subcarriers and the power of the user equipment alters the network’s carrier occupation and interference relationships, thereby increasing computational complexity. To address this challenge, a phased strategy is proposed. Initially, subcarrier allocation and coarse power allocation are conducted for cellular users. Subsequently, an adversarial multi-player multi-armed bandit framework is employed, treating DGs as players and subcarrier and power combinations as arms, to maximize the total multicast data rate. An improved Exp3 algorithm is utilized for selecting the optimal combination of arms. Finally, precise power allocation for cellular users is conducted based on the allocation results of the DGs. A comparative analysis of various simulations confirms the superiority of our algorithm over the established heuristic subcarrier assignment and proposed power allocation (HSAPP) and the channel allocation scheme using full information of device locations (CAFIL) approaches.

Context-Awareness for Device-to-Device Resource Allocation

The paper investigates a context-aware approach to radio resource allocation for device-to-device (D2D) communication, focusing on solutions that leverage information on user equipment location and environmental features, such as building layouts. A system enabling direct communication by sharing uplink resources with cellular users is considered. Such a system introduces mutual interference between direct and cellular communications, posing challenges related to maintaining adequate performance levels. To address these challenges, various context-based resource allocation methods are analyzed, aiming to optimize spectral efficiency and minimize interference. The study explores the impact that different D2D device densities exert on overall network performance measured by means of spectral efficiency and the signal-to-interference ratio.

A Cellular Network Resource Allocation Strategy Based on D2D Communication

ABSTRACTTo address the problems of system capacity reduction due to high system interference in highway vehicular communication scenarios, a resource allocation strategy for cellular vehicular communication based on device‐to‐device (D2D) communication is proposed. A mathematical model is constructed with CUE user channel capacity as the optimization objective, considering resource sharing between device‐to‐device users (DUE) and cellular users (CUE), and the model is solved through three stages. Firstly, it is proposed to calculate the user channel gain to construct the DUE user channel gain matrix and compare the total channel gain after user multiplexing to manage the user clustering to reduce the interference between users. Secondly, the elite reverse learning strategy and Lévy flight strategy were introduced to improve the sparrow search algorithm, which increased the convergence speed and the ability to escape from the local optimal solution and optimized the channel matching problem. Finally, the water injection algorithm is invoked to solve the power allocation problem to maximize the channel capacity of CUE. Simulation experimental results show that the strategy achieves a system capacity of 340 bps/Hz while ensuring that the system communication is basically stable.

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.

Optimization of Robust and Secure Transmit Beamforming for Dual-Functional MIMO Radar and Communication Systems

This paper investigates a multi-antenna, multi-input multi-output (MIMO) dual-functional radar and communication (DFRC) system platform. The system simultaneously detects radar targets and communicates with downlink cellular users. However, the modulated information within the transmitted waveforms may be susceptible to eavesdropping. To ensure the security of information transmission, we introduce non-orthogonal multiple access (NOMA) technology to enhance the security performance of the MIMO-DFRC platform. Initially, we consider a scenario where the channel state information (CSI) of the radar target (eavesdropper) is perfectly known. Using fractional programming (FP) and semidefinite relaxation (SDR) techniques, we maximize the system’s total secrecy rate under the requirements for radar detection performance, communication rate, and system energy, thereby ensuring the security of the system. In the case where the CSI of the radar target (eavesdropper) is unavailable, we propose a robust secure beamforming optimization model. The channel model is represented as a bounded uncertainty set, and by jointly applying first-order Taylor expansion and the S-procedure, we transform the original problem into a tractable one characterized by linear matrix inequalities (LMIs). Numerical results validate the effectiveness and robustness of the proposed approach.

A Semi-Distributed Scheme for Mode Selection and Resource Allocation in Device-to-Device-Enabled Cellular Networks Using Matching Game and Reinforcement Learning

Device-to-Device (D2D) communication is a promising technological innovation that is significantly considered to have a substantial impact on the next generation of wireless communication systems. Modern wireless networks of the fifth generation (5G) and beyond (B5G) handle an increasing number of connected devices that require greater data rates while utilizing relatively low power consumption. In this study, we present joint mode selection, channel assignment, and power allocation issues in a semi-distributed D2D scheme (SD-scheme) that underlays cellular networks. The objective of this study is to enhance the data rate, Spectrum Efficiency (SE), and Energy Efficiency (EE) of the network while maintaining the performance of cellular users (CUs) by creating a threshold of data rate for each CU in the network. Practically, we propose a centralized approach to address the mode selection and channel assignment problems, employing greedy and matching algorithms, respectively. Moreover, we employed a State-Action-Reward-State-Action (SARSA)-based reinforcement learning (RL) algorithm for a distributed power allocation scheme. Furthermore, we suggest that the sub-channel of the CU is shared among several D2D pairs, and the optimum power is determined for each D2D pair sharing the same sub-channel, taking into consideration all types of interferences in the network. The simulation findings illustrate the enhancement in the performance of the proposed scheme in comparison to the benchmark schemes in terms of data rate, SE, and EE.

An Effective Resource Allocation Framework for D2D Communication in 5G Wireless Personal Area Network With Hybrid Optimizer

ABSTRACTAllocating resources in an energy‐effective method for the development of 5G networks. Energy efficiency in D2D communication is another challenge. Less energy usage is needed for devices to communicate with one another. D2D communication frequently uses more energy than conventional communication techniques. This is one of the major challenges of D2D transmission that needs to be resolved. To address these challenges, a new energy‐effective resource allocation system for D2D transmission in 5G WPAN is presented in this paper. The primary goal of the presented task is to enhance the average power effectiveness of the entire links of D2D, thus ensuring the service quality of the cellular user equipments (CUEs) and the constraints of energy harvesting (EH) in the D2D connections. Problems such as D2D user equipments (DUEs), spectrum, resource block allocation, and time slot allocation are used in D2D communication. To rectify these problems, this work derived an average energy efficiency issue. Moreover, this work joins the spectrum and power block allocation. Then, the EH time slot allocation is executed based on the Hybrid Gazelle with Walrus Optimization (HGWO). The extensive analysis outcomes illustrate that the implemented HGWO optimization‐aided model attains more energy efficacy for distinct network attribute settings.

Joint Transmit Power and Power-Splitting Optimization for SWIPT in D2D-Enabled Cellular Networks with Energy Cooperation

In this paper, we propose a joint optimization scheme for a transmit power and power-splitting ratio in device-to-device (D2D)-enabled simultaneous wireless information and power transfer (SWIPT) cellular networks, considering energy signal transmission. This energy signal facilitates the energy cooperation between the D2D transmitter (DT) and the CU. Under the proposed scheme, the D2D rate is maximized while guaranteeing that the cellular user (CU) achieves the same performance as in scenarios without D2D communications. In order to solve the formulated nonconvex problem, we leverage the monotonically increasing property of logarithmic functions to transform it into an equivalent convex problem. As a result, we obtain the optimal solution in closed form. Also, the optimal D2D performance is analyzed, and useful insights into the performance improvements achievable through the proposed scheme are obtained. Numerical results demonstrate that the proposed scheme significantly outperforms the baseline scheme.

NOMA-Based Rate Optimization for Multi-UAV-Assisted D2D Communication Networks

With the proliferation of smart devices and the emergence of high-bandwidth applications, Unmanned Aerial Vehicle (UAV)-assisted Device-to-Device (D2D) communications and Non-Orthogonal Multiple Access (NOMA) technologies are increasingly becoming important means of coping with the scarcity of the spectrum and with high data demand in future wireless networks. However, the efficient coordination of these techniques in complex and changing 3D environments still faces many challenges. To this end, this paper proposes a NOMA-based multi-UAV-assisted D2D communication model in which multiple UAVs are deployed in 3D space to act as airborne base stations to serve ground-based cellular users with D2D clusters. In order to maximize the system throughput, this study constructs an optimization problem of joint channel assignment, trajectory design, and power control, and on the basis of these points, this study proposes a joint dynamic hypergraph Multi-Agent Deep Q Network (DH-MDQN) algorithm. The dynamic hypergraph method is first used to construct dynamic simple edges and hyperedges and to transform them into directed graphs for efficient dynamic coloring to optimize the channel allocation process; subsequently, in terms of trajectory design and power control, the problem is modeled as a multi-agent Markov Decision Process (MDP), and the Multi-Agent Deep Q Network (MDQN) algorithm is used to collaboratively determine the trajectory design and power control of the UAVs. Simulation results show the following: (1) the proposed algorithm can achieve higher system throughput than several other benchmark algorithms with different numbers of D2D clusters, different D2D cluster communication spacing, and different UAV sizes; (2) the proposed algorithm designs UAV trajectory optimization with a 27% improvement in system throughput compared to the 2D trajectory; and (3) in the NOMA scenario, compared to the case of no decoding order constraints, the system throughput shows on average a 34% improvement.

A Method for Localization of Cellular Users From Call Detail Records

A Method for Localization of Cellular Users From Call Detail Records

Cell Zooming With Energy‐Efficient Offloading in Cellular Network: An Optimization‐Assisted Model

ABSTRACTWith the speedy development of cellular users, conventional mobile network communications could not satisfy the requirements of rising traffic. The design of the user association approach should have the ability to balance the energy saving and traffic hotspot providence from the traffic cell sleeping system. Nevertheless, with the gradually rising denser deployment of small cells (SCs), the HetNets face novel challenges. This work aims to introduce a cell zooming (CZ) scheme that admits the EE traffic offloading. Thereby, the given model includes two phases, (i) energy efficient offloading: In this phase, appropriate traffic flow is predicted for the hybrid deep learning model that includes deep Q network (DQN) and reinforcement learning (RL). This prediction is related to the offload traffic from macrocell to small cell based on constraints like QoS and Energy, respectively. (ii) Cell zooming: After the prediction, adaptive CZ is preceded by an improved CZ factor with optimal threshold selection. For optimization, a new scaled bald eagle search optimization (SBESO) is used. The betterment of SBESO is established on throughput, SINR, and so forth.

Machine Learning-Based Resource Allocation Algorithm to Mitigate Interference in D2D-Enabled Cellular Networks

Mobile communications have experienced exponential growth both in connectivity and multimedia traffic in recent years. To support this tremendous growth, device-to-device (D2D) communications play a significant role in 5G and beyond 5G networks. However, enabling D2D communications in an underlay, heterogeneous cellular network poses two major challenges. First, interference management between D2D and cellular users directly affects a system’s performance. Second, achieving an acceptable level of link quality for both D2D and cellular networks is necessary. An optimum resource allocation is required to mitigate the interference and improve a system’s performance. In this paper, we provide a solution to interference management with an acceptable quality of services (QoS). To this end, we propose a machine learning-based resource allocation method to maximize throughput and achieve minimum QoS requirements for all active D2D pairs and cellular users. We first solve a resource optimization problem by allocating spectrum resources and controlling power transmission on demand. As resource optimization is an integer nonlinear programming problem, we address this problem by proposing a deep Q-network-based reinforcement learning algorithm (DRL) to optimize the resource allocation issue. The proposed DRL algorithm is trained with a decision-making policy to obtain the best solution in terms of spectrum efficiency, computational time, and throughput. The system performance is validated by simulation. The results show that the proposed method outperforms the existing ones.