Channel Usage Research Articles

Deep Adaptive Learning‐Based Beam Combining Framework for 5G Millimeter‐Wave Massive 3D‐MIMO Uplink Systems

ABSTRACTIn today's wireless communication systems, the integration of 5G millimeter‐wave (mmWave) Massive Multiple Input–Multiple Output (M‐MIMO) technology offers significant advancements and capabilities that address the growing demand for higher data rates, increased capacity, and improved user experiences. In three‐dimensional (3D) environments, the design of beamforming for uplink multi‐user M‐MIMO relies on accurate uplink channel state information (CSI) at the transmitter/receiver. In fact, it is difficult for the Base Station (BS) and the User Equipment (UE) to obtain beam patterns due to computational complexity, multiple 3D beams, and regulating the weight of antenna elements, which leads to significantly low sum‐rate. Hence, a robust, deep adaptive learning framework in the case of 3D beamforming is needed. This paper proposes a deep adaptive learning‐based beam combining framework using User‐Wise Attention‐Assisted Deep Adaptive Neural Network (UWA‐DANN) for mmWave‐3DM‐MIMO systems. In this, a UWA mechanism learns a set of beam features and corresponding weights for each user. This mechanism allows the system to focus on different users independently and adapt the beamforming process accordingly. Also, it allows the network to dynamically focus on relevant information from the input channels and user constraints. To this end, a dynamic beam pattern is adapted using the DANN model to learn user positions, channel measurements, and beamforming weights. This approach learns to map input parameters such as user positions, channel measurements, and corresponding beam patterns to extract relevant features for beam pattern adaptation. Thus, the UWA‐DANN approach provides higher data rates, low complexity, and improved link stability for users. Experimental results show that the proposed UWA‐DANN model obtains robust performance over existing schemes in terms of achievable rate and sum‐rate under field trial sites in urban scenarios.

A Q-Learning-Based Approach to Design an Energy-Efficient MAC Protocol for UWSNs Through Collision Avoidance

Deploying and effectively utilizing wireless sensor networks (WSNs) in underwater habitats remains a challenging task. In underwater wireless sensors networks (UWSNs), the availability of a continuous energy source for communicating with nodes is either very costly or is prohibited due to the marine life law enforcement agencies. So, in order to address this issue, we present a Q-learning-based approach to designing an energy-efficient medium access control (MAC) protocol for UWSNs through collision avoidance. The main goal is to prolong the network’s lifespan by optimizing the communication methods, specifically focusing on improving the energy efficiency of the MAC protocols. Factors affecting the energy consumption in communication are adjustments to the interference ranges, i.e., changing frequencies repeatedly to obtain optimal communication; data packet retransmissions in case of a false acknowledgment; and data packet collision occurrences in the channel. Our chosen protocol stands out by enabling sensor (Rx) nodes to avoid collisions without needing extra communication or prior interference knowledge. According to the results obtained through simulations, our protocol may increase the network’s performance in terms of network throughput by up to 23% when compared to benchmark protocols depending on the typical traffic load. It simultaneously decreases end-to-end latency, increases the packet delivery ratio (PDR), boosts channel usage, and lessens packet collisions by over 38%. All these gains result in minimizing the network’s energy consumption, with a proportional gain.

Variation of Distributed Power Control Algorithm in Co-Tier Femtocell Network

The wireless communication network has seen rapid growth, especially with the widespread use of smartphones, but resources are increasingly limited, especially indoors. Femtocell, a spectrum-efficient small cellular network solution, faces challenges in distributed power control (DPC) when deployed with distributed users, impacting power levels, and causing interference in the main network. The aim of this research is optimizing user power consumption in co-tier femtocell networks by using the user power treatment. This study proposed the Distributed Power Control (DPC) variation methods such as Distributed Constrained Power Control (DCPC), Half Distributed Constrained Power Control (HDCPC), and Generalized Distributed Constrained Power Control (GDCPC) in co-tier femtocell network. The research examines scenarios where user power converges but exceeds the maximum threshold or remains semi-feasible, considering factors like number of users, distance, channel usage, maximum power values, non-negative power vectors, Signal-to-Interference-plus-Noise Ratio (SINR), and link gain matrix values. In Distributed Power Control (DPC), distance and channel utilization affect feasibility conditions: feasible, semi-feasible, and non-feasible. The result shows that Half Distributed Constrained Power Control (HDCPC) is more effective than Distributed Constrained Power Control (DCPC) in semi-feasible conditions due to its efficient power usage and similar Signal-to-Interference-plus-Noise Ratio (SINR). Half Distributed Constrained Power Control (HDCPC) is also easier to implement than Generalized Distributed Constrained Power Control (GDCPC) as it does not require user deactivation when exceeding the maximum power limit. Distributed Power Control (DPC) variations can shift the power and Signal-to-Interference-plus-Noise Ratio (SINR) conditions from non-convergence to convergence at or below the maximum power level. We concluded that the best performance of Distributed Power Control (DPC) is Half Distributed Constrained Power Control (HDCPC).

Research on Resource Allocation Algorithm for Non-Orthogonal Multiple Access Visible Light Communication

In order to satisfy the large-scale access of visible light communication (VLC) users, as well as the demand for user request rate, the resource allocation problem of visible light communication with drone-assisted non-orthogonal multiple access (NOMA) technique is investigated. An efficient scheme for joint optimization of power allocation and access point (AP) location is proposed. According to the state of user channel information, a user pairing strategy with uniform channel gain difference for any number of users is designed, and an objective function of maximizing the average user data rate with constraints is constructed. For this non-convex NP-hard problem, the optimization problem with constraints is transformed into an optimization problem without constraints by introducing the idea of a penalty function and then solved by the Harris Hawk Optimization (HHO) algorithm based on the nonlinear energy convergence factor, and ultimately, the optimal user power allocation factor, as well as the location of the AP, are found. The simulation results show that the scheme in this paper can improve the average user data rate better compared to other classical schemes. The system performance of the HHO algorithm is improved by about 20.31% compared to the Particle Swarm Optimization (PSO) algorithm. The HHO algorithm based on the nonlinear energy convergence factor improves the convergence speed by about 50% compared to the classical HHO algorithm.

Construction and Application of a Private 5G Standalone Medical Network in a Smart Health Environment: Exploratory Practice From China.

To date, the differentiated requirements for network performance in various health care service scenarios-within, outside, and between hospitals-remain a key challenge that restricts the development and implementation of digital medical services. This study aims to construct and implement a private 5G (the 5th generation mobile communication technology) standalone (SA) medical network in a smart health environment to meet the diverse needs of various medical services. Based on an analysis of network differentiation requirements in medical applications, the system architecture and functional positioning of the proposed private 5G SA medical network are designed and implemented. The system architecture includes the development of exclusive and preferential channels for medical use, as well as an ordinary user channel. A 3-layer network function architecture is designed, encompassing resource, control, and intelligent operation layers to facilitate management arrangements and provide network open services. Core technologies, including edge cloud collaboration; service awareness; and slicing of access, bearer, and core networks, are employed in the construction and application of the 5G SA network. The construction of the private 5G SA medical network primarily involves system architecture, standards, and security measures. The system, featuring exclusive, preferential, and common channels, supports a variety of medical applications. Relevant standards are adhered to in order to ensure the interaction and sharing of medical service information. Security is achieved through mechanisms such as authentication, abnormal behavior analysis, and dynamic access control. Three typical medical applications that rely on the 5G network in intrahospital, interhospital, and out-of-hospital scenarios-namely, mobile ward rounds, remote first aid, and remote ultrasound-were conducted. Testing of the 5G-enabled mobile ward rounds showed an average download rate of 790 Mbps and an average upload rate of 91 Mbps. Compared with 4G, the 5G network more effectively meets the diverse requirements of various business applications in prehospital emergency scenarios. For remote ultrasound, the average downlink rate of the 5G network is 4.82 Mbps, and the average uplink rate is 2 Mbps, with an average fluctuation of approximately 8 ms. The bandwidth, performance, and delay of the 5G SA network were also examined and confirmed to be effective. The proposed 5G SA medical network demonstrates strong performance in typical medical applications. Its construction and application could lead to the development of new medical service models and provide valuable references for the further advancement and implementation of 5G networks in other industries, both in China and globally.

Mass Media Exposure Moderates the Association of Education and Wealth with Enrollment in Health Insurance Among Older Adults Aged 60 Years and Older in India

ABSTRACT Mass media exposure (MME) plays an important role in changing health-related behavior or decision-making. This study aimed to examine the association of MME with enrollment in health insurance and its moderating effect on the associations of education and wealth with enrollment in health insurance among older adults in India. The data of 29,935 older adults aged 60 years and older from the first wave (baseline) of the Longitudinal Ageing Study in India (LASI-2017/18) were utilized. Descriptive statistics, bivariate analysis, multivariable logistic regression models, and Fairlie decomposition were used. The findings revealed that 18.3% of older adults had MME and enrollment in health insurance in India. Older adults with MME were 1.42 times more likely to be enrolled in health insurance than those with no MME, and years of schooling increased the probability of enrolling in health insurance compared to those who did not have formal schooling. With increasing monthly per capita expenditure (MPCE), the probability of enrolling in health insurance is lower than the poorest. Further, MME moderates the effects of education and MPCE on enrollment in health insurance and increases the likelihood of enrollment in health insurance. The study’s findings imply strategic communication plans to encourage health insurance enrollment in India should take into account the usage of these communication channels.

Hierarchical MTC User Activity Detection and Channel Estimation With Unknown Spatial Covariance

Hierarchical MTC User Activity Detection and Channel Estimation With Unknown Spatial Covariance

Promotional Press Releases and Investor Processing Costs

Press releases are a primary disclosure channel firms use to communicate with investors. However, firms can also use press releases to self-promote to improve their public image. Promotional press releases are, on average, significantly less value relevant than those focused toward investors. They also contain more positive and emotional language as well as less specific and financially oriented language. Based on theories of disclosure processing costs and investor attention, we predict and find that firms’ recent high usage of this disclosure channel to self-promote is associated with increased investor attention and more efficient reactions to subsequent investor-focused disclosures. This effect is concentrated in less visible firms, whereas promotional press releases appear to have no effect on the pricing efficiency of more visible firms. We also provide evidence consistent with media attention being one channel through which these effects operate. Our findings deepen our understanding of how firms use press releases as a disclosure channel and contribute to the literature on disclosure processing costs. This paper was accepted by Eric So, accounting. Funding: This work was supported by resources at the Arkansas High Performance Computing Center. C. Rawson gratefully acknowledges financial support from the Sam M. Walton College of Business. B. J. Twedt gratefully acknowledges financial support from the Mays Business School. J. C. Watkins gratefully acknowledges financial support from the Mendoza College of Business.. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.01903 .

Analysis of delays in medium access control devices in cognitive radio systems with correlation of time intervals in request flows

This article is about the development of a method for analyzing the average waiting time of a request in a queue with correlated inter-arrival and service times. The ON-OFF process is often used to model the primary user channel occupancy in different questions related to cognitive radio, such as resource allocation, medium access control protocols, spectrum handoff and others. It is assumed that the time intervals of the ON- and OFF- periods are not correlated. More realistic models of primary user activity require the presence of correlation not only between consequent periods, but also the presence of correlation between channel occupancy and other quantities interpreted as service time in models based on queuing systems. As a result, here is proposed a variant of constructing the probability density function of a random variable which is the difference between the service time of the i-th request and the time interval between the moments of arriving of the i-th and (i-1)-th requests with correlated inter-arrival and service times. Solution based on the use of a two-dimensional characteristic function, so in this case it is possible to more purposefully specify the components of the statistical connection of previously described sequences. These components are the cumulants of the described distribution. For a queuing system with hyperexponential distribution of inter-arrival and service intervals, an analytical expression for the average waiting time of a request in a queue is found.

The Development of Marketing Plan for a Virtual Cycling Application: A Case Study of China (Beijing)

Background and Aims: Due to the COVID-19 pandemic, people's social activities have been greatly affected, so online activities have seen rapid development and user growth. become the norm. In cycling, the China Cycling Association canceled or postponed several races, affecting cyclists across the country. This is because the mass gatherings and high crowd density associated with such events increase the risk of virus transmission. Because of this, bicycle virtual riding software has developed rapidly, and users have increased rapidly. In addition, the evolving metaverse concept has prompted increased investment in bicycle virtual riding software development. This study explores and analyzes the operation plan of bicycle virtual riding software in China (Beijing) and analyzes the specific needs of Chinese people. Methodology: The main research methods include SWOT analysis, user stories, and population research. Use competitive product analysis such as SWOT analysis to study the specific reasons why other products cannot be promoted and popular in China (Beijing). Collect the main channels for users to obtain information and determine product promotion plans. And conduct user demand research, establish user portraits, and finally propose feasible solutions. Results: The main findings are the difficulties in promoting virtual bicycle riding software in Beijing, China, the reasons why the currently popular software is difficult to promote, the travel habits and consumption willingness of residents, and a complete promotion plan. Conclusion: The study sheds light on factors impeding the adoption of popular software already in use and clarifies the travel habits and consumption attitudes of the locals in Beijing, China. It also highlights challenges in promoting virtual bicycle riding software in Beijing. It provides insights into creating a strong promotion strategy to overcome the obstacles found and increase the adoption of virtual bicycle riding technology through in-depth analysis.

Bayesian 3D User Localization and Channel Reconstruction with Planar Extremely Large-Scale Antenna Array

An extremely large-scale antenna array (ELAA) can potentially provide significantly increased spatial multiplexing and beamforming gains, as well as enhanced localization capability. While presenting new potential, its near-field propagation and spatial non-stationary properties also impose a great challenge on the receiver design. This paper focuses on the receiver design in an uplink orthogonal frequency-division multiplexing system with a planar ELAA deployed at the base station. To solve the challenging problem of 3D user localization and channel estimation with the planar ELAA, a space-frequency user localization and channel reconstruction (SF-ULCR) receiver is proposed. Under the Bayesian framework, an extended probability model is first established, to capture the channel structural information comprehensively, based on which an iterative receiver consisting of three modules is derived: element-wise line spectrum estimation (ELSE), distance parameter estimation (DPE), and near-field localization (NFL). In particular, the ELSE module handles the line spectrum relationships among multiple subcarriers in the frequency domain, the DPE module extracts and integrates the distance information from the line spectrum parameters, and the NFL module utilizes the messages of distances for user localization based on near-field spatial characteristics. Our numerical results demonstrate that the proposed SF-ULCR algorithm outperforms existing baselines in terms of channel estimation and localization performance, and that it approaches the Cramèr–Rao bound.

Anti-Jamming Resource-Allocation Method in the EH-CIoT Network through LWDDPG Algorithm.

In the Energy-Harvesting (EH) Cognitive Internet of Things (EH-CIoT) network, due to the broadcast nature of wireless communication, the EH-CIoT network is susceptible to jamming attacks, which leads to a serious decrease in throughput. Therefore, this paper investigates an anti-jamming resource-allocation method, aiming to maximize the Long-Term Throughput (LTT) of the EH-CIoT network. Specifically, the resource-allocation problem is modeled as a Markov Decision Process (MDP) without prior knowledge. On this basis, this paper carefully designs a two-dimensional reward function that includes throughput and energy rewards. On the one hand, the Agent Base Station (ABS) intuitively evaluates the effectiveness of its actions through throughput rewards to maximize the LTT. On the other hand, considering the EH characteristics and battery capacity limitations, this paper proposes energy rewards to guide the ABS to reasonably allocate channels for Secondary Users (SUs) with insufficient power to harvest more energy for transmission, which can indirectly improve the LTT. In the case where the activity states of Primary Users (PUs), channel information and the jamming strategies of the jammer are not available in advance, this paper proposes a Linearly Weighted Deep Deterministic Policy Gradient (LWDDPG) algorithm to maximize the LTT. The LWDDPG is extended from DDPG to adapt to the design of the two-dimensional reward function, which enables the ABS to reasonably allocate transmission channels, continuous power and work modes to the SUs, and to let the SUs not only transmit on unjammed channels, but also harvest more RF energy to supplement the battery power. Finally, the simulation results demonstrate the validity and superiority of the proposed method compared with traditional methods under multiple jamming attacks.

Effectiveness of Communication Channels for Maternal Healthcare in Delta State

This study on the effectiveness of communication channels for maternal healthcare is one of great concern with the increased health risks for women in their reproductive years of pregnancy and childbirth. This is especially true in developing countries such as Nigeria where thousands of women die yearly in pregnancy or childbirth. The qualitative and quantitative design method was applied in the study and the questionnaire was used for data collection from 399 respondents. The study found that the level of awareness of the programmes was encouraging for FMHCP and HeFAD; that the communication channels were adequate, and more than half thought they were effective in influencing participation in the maternal healthcare programmes. The findings provide healthcare professionals with an important insight into issues of effective communication therefore, recommend the continuous usage of communication channels to spread awareness of maternal healthcare programmes to enhance the provision of appropriate health information and maternity care to women

A Channel Usage Probability Awareness Anti-Jamming Channel Hopping Protocol for Cognitive Radio Networks

A Channel Usage Probability Awareness Anti-Jamming Channel Hopping Protocol for Cognitive Radio Networks

Hybrid Generalized Approximate Message Passing for Active User Detection and Channel Estimation With Correlated Group-Heterogeneous Activity

Hybrid Generalized Approximate Message Passing for Active User Detection and Channel Estimation With Correlated Group-Heterogeneous Activity

Hiperrealitas Pengaruh Penggunaan TikTok dalam Membentuk Eksistensi Diri Mahasiswa Universitas Jember

Currently, the digital era continues to develop both from information technology and social media has become an inseparable part of everyday life. The Tik Tok application has succeeded in attracting millions of users from all walks of life, from children to adults. The opening of this public space is one of the channels for users to express their opinions and viewpoints freely, which stimulates the growth of lifestyles and behaviors that are different from the reality of their users' lives. The research method used in this type of research is qualitative. The technique of determining informants using purposive sampling technique. The results of this study indicate that the use of TikTok has a significant influence on the formation of a student's self-existence in terms of increasing self-confidence and creating a good self-image in social media.

Self‐attention convolutional neural network optimized with Remora Optimization Algorithm for energy aware resource management in Non‐orthogonal Multiple Access networks

SummaryNon‐orthogonal multiple access (NOMA) is an efficient technology for future wireless communication systems. The resource management on NOMA networks is flattering for improving energy efficiency (EE) of systems. Therefore, self‐attention convolutional neural network (SACNN) optimized with Remora Optimization Algorithm (ROA) for energy aware resource management in NOMA network (SACNN‐ROA‐EE) is proposed in this paper for solving user allocation, channel allocation, and power allocation problems. The Matching‐Coalition approach is considered to face the user allocation issue, and the SACNN is considered to face the channel allocation and power allocation problems. SACNN does not adopt any optimization methods to determine the optimal parameters in channel allocation and power allocation. Therefore, the ROA is applied to optimize the SACNN weight parameters. The proposed technique is activated in MATLAB, and its efficacy is analyzed with under performances metrics, such as minimum user rate, sum rate, energy, and spectral performance. The proposed SACNN‐ROA‐EE attains 24.35%, 27.84%, and 38.58% better sum rate and 27.45%, 43.28%, 30.56% better EE compared with existing methods, such as EE utilizing communication deep neural network optimized with power allocation optimization (PAO‐CDNN‐EE), EE under deep transfer deterministic policy gradient along deep deterministic policy gradient approach (DPPGA‐DTDPPG‐EE), and EE utilizing deep reinforcement learning with baseline algorithms (BA‐DRL‐EE), respectively.

Optimization of gamified Learning Education Model based on XR + 5G technology

The new intelligent technology represented by 5G and XR has become the core driving force of the fourth industrial revolution, and the integrated application of the two in the field of education is also promoting the continuous transformation of teaching to gamification. Based on XR + 5G fusion technology, this paper proposes the optimization design of a gamified education model. Firstly, the problem of how to improve the network performance through D2D communication in a full-load 5G network is studied, and a D2D communication resource allocation framework based on cross-layer optimization design is proposed. In D2D user access control, a D2D user access criterion based on the distance between the CU user and D2D user receiver is designed according to the QoS requirement, maximum transmit power limit, and user channel information. XR simulation platform was designed based on a 5G network with optimized communication resource allocation. UMa and InH simulation scenarios were set according to XR business model working scenarios, and Wrap Around algorithm was used to model two circles of interference for users, ensuring the reliability of results while improving simulation efficiency. Finally, the platform channel was modeled strictly according to the channel modeling method in technical document 38.901, and the channel was calibrated with the channel provided by 3GPP. Based on the simulation platform, gamified education design is optimized. Including class platform and class platform gamification design, system demand analysis, and database design. In the gamification design, a relatively novel classroom interaction mode is introduced to increase students’ enthusiasm in class. In the demand analysis of the system, the main problems in class are listed, based on which the business and functional requirements of the gamification platform are determined. Experiments show that in the IID scenario, the proposed XR + 5G fusion technology can achieve 1.09 times the training speed and obtain 0.83 % learning accuracy gain compared with the individual learning algorithm. In the Non-IID scenario, the training speed of the proposed fusion technique is 1.03 times that of the individual learning algorithm, and there is a learning accuracy gain of 2.27 %.

Adequacy of Communication Channels for Maternal Healthcare in Delta State

This study examined the adequacy of communication channels available for maternal healthcare in Delta State. Rationalized by the agenda setting theory, the quantitative and qualitative research designs, the questionnaire was used to extract relevant information from a sample size of 399 respondents. The study found that the level of awareness of the programmes was encouraging for FMHCP and HeFAD. The majority of respondents acknowledged that the communication channels were adequate, and more than half thought they were effective in influencing participation in the maternal healthcare programmes. The stakeholders acknowledged the use of multiple communication channels in spreading awareness of the maternal healthcare programmes. Therefore, the study recommends the continuous usage of communication channels to spread awareness of maternal healthcare programmes.

Adaptive coalition with Kullback‐Leibler divergence for cooperative spectrum sensing (KLDCSS) in cognitive radio networks

SummaryEffective, reliable, and rapid spectrum sensing in concordance with maximum throughput efficiency is necessary at the cognitive radio network to maximize the network's utilization. An adaptive coalition using Kullback–Leibler divergence (KLD)‐based cooperative spectrum sensing scheme (KLDCSS) with a nominal sensing time to optimize the spectral efficiency is presented in this paper. In a coalition‐based spectrum sensing scheme, cooperating secondary users (SUs) are allocated to different coalitions to sense different Primary User (PU) channels. The same cognitive users allocated to sense the primary user channels are allotted to detect in different sensing phases. In the proposed sensing scheme, the cooperating SUs are adaptively allocated to different coalitions to maximize the throughput efficiency. Collaborating cognitive users with a standard signal‐to‐noise ratio and distinct energy levels that are characterized by the localized probabilities of detection and false alarm is considered. An adaptive coalition and user allocation algorithm are proposed to optimize the average opportunistic throughput and diminish the sensing overhead. The expression for throughput efficiency in terms of average throughput and sensing accuracy is derived. In this scheme, the SU computes the log‐likelihood ratio (LLR) from the acquired sample and forwards the same to the fusion center. The fusion center then accumulates the LLR values computed by the SU to estimate the likelihood of spectrum availability. Simulation results highlighting the competitive edge of the proposed scheme, with higher throughput efficiency over various numbers of SUs, coalitions, and signal‐to‐noise ratios, are presented.