Mobile Broadband Networks Research Articles

Space-time domain equalization algorithm based on complex-valued neural network in a long-haul photonic-aided MIMO THz system.

The urgent demand for high-bandwidth wireless services in enhanced mobile broadband networks needs innovative solutions for mobile front-haul systems. The terahertz (THz) band offers a promising candidate for ultrahigh-capacity data transmission. This study investigates the integration of photonics-aided THz signal generation with MIMO and PDM technologies. We proposed a novel, to the best of our knowledge, space-time domain equalization algorithm based on MIMO-complex-valued neural networks (CVNN), which can preserve the signal phase and the relation between the X- and Y-polarization. We experimentally demonstrate the transmission of 60-GBaud PDM-QPSK and 30-GBaud PDM-16QAM signals over a 100-m 2 × 2 wireless MIMO link at 320 GHz with BER below 3.8 × 10-3 and 1.56 × 10-2 for QPSK and 16QAM signals, respectively. Compared with the MIMO-Volterra, our MIMO-CVNN has an advantage in terms of calculation complexity and decision accuracy due to its effective handling of phase information and inter-polarization relationships simultaneously.

Advancing 6G-IoT networks: Willow catkin packet transmission scheduling with AI and bayesian game-theoretic approach-based resource allocation.

The rapid expansion of mobile broadband networks and the proliferation of Internet of Things (IoT) applications have substantially increased data transmission and processing demands. However, the application domains of IoT-enabled models often face resource limitations, requiring rapid responses, low latency, and large bandwidth, surpassing their inherent capabilities. To address these challenges, we propose a fishnet approach-based packet scheduling and resource allocation system, termed Fishnet-6G, to optimize network resource allocation in the proposed 6G networks. Initially, we constructed a Sierpinski Triangle-based network in a 6G-IoT environment, enhancing device connectivity. We utilize the Quantum Density Peak Clustering (QDPC) algorithm to perform clustering for IoT devices, establishing Cluster Head (CH) and Substitute CH (SUB CH) based on actual metrics. Furthermore, traffic prediction is achieved through two processes, grouping, and fair queue status, using the Improved Deep Deterministic Policy Gradient (IMPDDPG) algorithm with a variable sampling rate, resulting in well-organized packet scheduling. Subsequently, we perform optimal packet scheduling by employing the Willow Catkin Optimization (WCO) algorithm, and the scheduled packets are managed within a Fishing Net Topology to reduce energy consumption and system complexity. Finally, we allocate the scheduled packets to the desired resource blocks using the Bayesian Game-Theoretic Approach (BGTA). The proposed approach is implemented using Network Simulator-3.26, and the performance of the Fishnet-6G model is evaluated based on time, transmission rate, energy efficiency, average throughput, latency, and Packet loss rate. Numerical analysis demonstrates that Fishnet-6G outperforms existing approaches across these metrics, showcasing its effectiveness in addressing the challenges of 6G-IoT networks.

Analisis Transformasi Digital Pelayanan Publik Menggunakan Metode Gartner Analytic Asceedency (Studi Kasus : Pelayanan Perizinan Pemerintah Daerah Kabupaten Majene

Public service is one of the activities carried out to fulfill the need for services to the community or individuals. With the outbreak of the Covid-19 virus some time ago, it demands public service delivery to innovate so that public services can run well and optimally even in a situation of physical distancing. One of the innovations was utilizing technology or digital transformation in public services. The aim of this study is to analyze the role and influence of digital transformation in improving licensing services in Majene Regency through the use of the Online Single Submission (OSS) application and the SiCantik Cloud application. The method used in this study is explanatory research with analytical techniques using Gartner Analytic Ascendency. The results of this study indicate that the Regional Government of Majene Regency needs to take persuasive approaches to remove existing obstacles to the use of digitalization in licensing services, such as regulating the use of regional digital-based licensing services in a binding regulation, conducting thorough education for users of licensing services, conduct outreach regarding the importance of this digital-based licensing service and expand the reach of fixed broadband and mobile broadband networks in Majene Regency. These things can be done by involving the cooperation of related agencies such as the Investment and One-Stop Integrated Services (DPMPTSP) and Communication, Information, Statistics and Coding Services (Dinas Kominfo) of Majene Regency.

Machine Learning with Adaptive Time Stepping for Dynamic Traffic Load Prediction in 6G Satellite Networks

The rapid development of sixth-generation (6G) mobile broadband networks and Internet of Things (IoT) applications has led to significant increases in data transmission and processing, resulting in severe traffic congestion. To better allocate network resources, predicting network traffic has become crucial. However, satellite networks face global imbalances in IoT traffic demand, with substantial variations in satellite density and load distribution within the same constellation. These disparities render traditional traffic prediction algorithms inadequate for dynamically changing satellite network topologies. This paper thoroughly examines the impact of adaptive time stepping on the prediction of dynamic traffic load. Particularly, we propose a high-speed traffic prediction method that employs machine learning and recurrent neural networks over the 6G Space Air Ground Integration Network (SAGIN) structure. In our proposed method, we first investigate a variable step size-normalized least mean square (VSS-NLMS) adaptive prediction method for transforming time series prediction datasets. Then, we propose an adaptive time stepping-Gated Recurrent Unit (ATS-GRU) algorithm for real-time network traffic prediction. Finally, we compare the prediction accuracy of the ATS-GRU algorithm with that of the fixed time stepping-Gated Recurrent Unit (FTS-GRU) algorithm and compared the prediction results of three different step sizes (FSS, VSS, and ATS) based on normalized least mean square (NLMS). Numerical results demonstrate that our proposed scheme can automatically choose a suitable time stepping to track and predict the traffic load curve with acceptable accuracy and reasonable computational complexity, as its time stepping dynamically adjusts with the traffic.

Национальное стратегическое планирование цифровой экономики: конкурентный анализ на материале Китая и России

At present, the wave of digital economy is sweeping the world. Countries all over the world are competing for the strategic commanding height in digital economy. In order to firmly seize this major historical opportunity and to transform and upgrade its economic structure faster, Russia keeps on strengthening the core capabilities of the digital economy such as information infrastructure, new generation information technology, ICT industry, human resources, information security, and digital environment supervision. Research purpose: a competitive analysis of the national strategic planning for the digital economies in China and Russia. Research objectives: 1) to provide definition for the digital economy; 2) to show the current improvement of the digital economy; 3) to study Russia’s digital economy; 4) to explore China’s digital economy. Research methodology: study of theoretical literature, data collection and analysis. Research results. “Strategy of the Information Society Development in the Russian Federation for 2017–2030” (2017) implies creating conditions for developing large companies in the information and communications technology industry in order to protect national interests in the digital economy. In 2021, Russia’s investment in the development of the digital economy reached the total of 4.094 trillion rubles. It represents an increase of 7.8 % compared to 2020 and amounts to 3.7 % of GDP. Over the past decade, China’s digital economy has made world-renowned development achievements. The overall scale has been the second best in the world for many years. The construction of information and communication networks is a global leader in terms of scope. The "Broadband China" strategy has been diligently implemented. The country has built the world’s largest fiber optic and mobile broadband network. The length of optical cable lines increased 2.7 times from 14.79 million km in 2012 to 54.81 million km in 2021. By June 2022, the total number of data center racks in China exceeded 5.9 million standard racks, and 153 national green data centers were built. Key core technologies have experienced breakthroughs. Investment in research and development of digital technologies is increasing every year. The level of digitalization in the service sector has increased significantly. The digital transformation of agriculture is making steady progress. In China, the deep implementation of the development strategy focused on innovation, promoting key technology research, accelerating the forging of long boards, making up for short boards, and building an independent and controlled industrial ecology.

Unleashing the potential of mobile broadband: Evidence from Indonesia's underdeveloped regions on its role in reducing income inequality

Although numerous studies have explored the relationship between information and communication technology (ICT) and socioeconomic development, limited research has focused on the impact of ICT infrastructure expansion on income distribution. This study aims to fill this research gap by investigating the effect of mobile broadband network coverage on income inequality in underdeveloped regions of Indonesia using unbalanced panel data encompassing 122 second-level subnational regions over the period from 2018 to 2021. This study utilizes map charts to visually depict the disparities in mobile broadband coverage and income inequality across the selected regions and ordinary least squares (OLS) regression on the pooled panel data to estimate the relationship between the two variables. The findings reveal an inverted U-shaped relationship between mobile broadband coverage and income inequality, indicating an adverse effect on inequality during the early stages of network deployment that persists until coverage reaches approximately 60 percent of the population area. Beyond this threshold, the effect becomes favorable in reducing inequality. The findings of this study remain robust even after accounting for potential biases, utilizing fixed and random effect models, and disaggregating the data for the pre- and during-pandemic periods. However, a minor discrepancy in statistical significance is observed when the dataset is disaggregated by the level of regional development. The implications of these findings are discussed, providing theoretical insights and policy/practical implications for addressing income inequality.

Special Issue on Approximate Computing: Challenges, Methodologies, Algorithms, and Architectures for Dependable and Secure Systems

The current trend in energy resources shapes how computing systems will address new challenges in the following years. As a matter of fact, following the current trend, by 2040, computers will require more energy than the world’s resources can generate. In the very upcoming future, by 2025, data centers alone will consume 20% of all available electricity. A similar trend already impacts the communications side where, for example, energy consumption in mobile broadband networks and mobile terminals is comparable to data centers. These trends can only accelerate by broadening the spectrum of possible mobile applications to the Internet of Things (IoT), which will connect 50 billion devices through wireless connections to the cloud infrastructure within a few years.

Evaluating the effects of ICT core elements on CO2 emissions: Recent evidence from OECD countries

This research aimed to inform policymakers about the actual climate relevance of the information and communication technology (ICT) ecosystem, which includes data centers and content provision, broadband networks, and consumer devices. To provide robust empirical evidence on the net environmental impact of these core ICT elements, we used OECD panel data for 34 Member States for the years 2002–2019 and panel fixed-effects regression models. In contrast to previous literature, we measured basic broadband and new fiber-based wireline broadband and 3G/4G mobile broadband network deployment. We also captured variations in end-user devices, ICT affinity, and famous online content. We found that, on average, the CO2-reducing effects appear to outweigh the CO2-increasing effects. In particular, we found that, in addition to the reduction effect associated with the use of basic broadband connections, there is a further, albeit smaller, reduction effect associated with new fiber-based broadband connections. Other ICT elements showed insignificant coefficient estimates, suggesting largely offsetting environmental impacts. Our main findings were subject to various robustness checks and were broadly consistent with previous literature using data from developed countries. Our results suggested that old and new broadband networks as enabling technology could generate positive environmental effects for society.

Measurements and Analyses of 4G/5G Mobile Broadband Networks: An Overview and a Case Study

Mobile data has become an integral part of everyday life, making Internet trends more and more profound and deeply embedded in life and the future. Mobile broadband (MBB) is one of the main aims of fifth-generation (5G) networks due to unprecedented growth in data demand. However, 5G networks are not yet deployed in many countries, whereas existing MBB networks, such as third generation (3G) and fourth generation (4G), are available in most world countries. As a result, there is a need to monitor and troubleshoot end-to-end network performance and ensure a better user experience. This paper provides an overview of MBB networks in terms of deployment environments, performance metrics, and implementation scenarios. It highlights the most important criteria for assessing MBB performance and the applications and measurement methods utilized, such as external and internal measurements and the classification of geographical areas. In addition, it provides measurement-based performance analyses of two existing mobile network operators (MNOs) (Omantel and Ooredoo) in the Sultanate of Oman. This performance analysis uses data measurements in various indoor and outdoor environments in five cities (Muscat, Ibra, Sur, Bahla, and Al kamil Wal Wafi). Several performance metrics are considered, such as signal level and quality, throughput, ping rate, and handover. Experimental results demonstrate that the 4G networks were the dominant networks in all measured locations for indoor and outdoor scenarios. Moreover, 5G data measurements are also recorded in the capital city of Oman, Muscat. The results of the 5G measurements show that both MNOs achieved a higher data rate with a lower ping rate.

Mobile Broadband Adoption, Performance Measurements and Methodology: A Review

Mobile broadband (MBB) penetration has deepened globally over the last twenty years. This is largely due to the adoption of smart devices, improved mobile communications network coverage, and the perpetual drive to develop ever faster mobile and wireless communication technologies. However, information on the quality of service (QoS) delivered by MBB operators to the end users remains an issue of concern. This has driven independent researchers and mobile communication industry regulators to develop methodologies for independent and unbiased evaluation of the QoS offered by MBB networks. This paper provides a detailed review of MBB adoption and penetration across several regions of the world. It also includes the existing methodologies for evaluating the performance of MBB systems as experienced by the end user. Specifically, methodologies such as the drive and walk tests, crowd-sourced mobile device-based methods and the software applications they employ, and the dedicated measurement testbeds are reviewed. Based on this, the challenges of adopting each of the methods are discussed in order to make a case for the development of more robust, partially autonomous and scalable MBB measurement platforms for the future.

Performance analysis of a heterogeneous network employing DAPSK based OFDMA-PON

Recent mobile broadband networks require heterogeneous networks supporting high capacity on demand. A hybrid Fiber-Wireless (Fi-Wi) access network integrating Differential Amplitude and Phase Shift Keying (DAPSK) based — Orthogonal Frequency Division Multiple Access (OFDMA) Passive Optical Network (PON) and 4G wireless network using Radio over Fiber (RoF) technique is proposed. The proposed heterogeneous network is simulated, and the performance is analyzed in terms of Bit Error Rate (BER), Error Vector Magnitude (EVM), Capacity, and Spectral efficiency. The proposed network is simulated considering a higher data rate of 10 and 25 Gbps, and the effect of system parameters like Laser Power, Fiber Length, and Number of users is analyzed. The results show that a higher network capacity of 720 Gbps with an average capacity of about 45 Gbps and a higher spectral efficiency of 4.85 bps/Hz is achieved for the multi-user heterogeneous network link with sixteen users. The minimum value of optical power sufficient to achieve the desired BER is found to be −6 dBm. The suitability of the proposed integrated architecture in supporting multiple services is analyzed by considering different services at each UE. The spectral efficiency of the multi-service link varies from 3 to 4 bps/Hz.

Statistical Characterization and Modeling of Radio Frequency Signal Propagation in Mobile Broadband Cellular Next Generation Wireless Networks

An accurate assessment of the spatial and temporal radio frequency channel characteristics is essential for complex signal processing and cellular network optimization. Current research has employed numerous models to figure out how much signal propagation loss occurs along the propagation paths. However, there are issues in finding the right model for a particular terrain because these models are not universally applicable. By employing the lognormal function and the Maximum Likelihood model, a hybrid probabilistic statistical distribution model was evolved. Three LTE cell site locations in Port Harcourt, Nigeria, were used to create a hybrid model that describes the functional stochastic signal propagation loss in the area. The evaluated Maximum Likelihood model accurately estimates the relevant wireless channel properties based on observed field data. The minor square regression approach and the proposed hybrid parameter estimation methodology are compared. When it comes to estimating standard deviation errors as well as the root mean square errors, the ML‐based approach consistently outperforms the least square regression model. Finally, the proposed hybrid probabilistic statistical distribution model would be useful for mobile broadband network planning in related wireless propagation conditions.

Broadband, effective labor, and economic growth during the COVID-19 pandemic period: evidence from a cross-country study

Abstract Purpose This study attempts to explore the mechanism of how broadband influences economic growth during the COVID-19 pandemic period, and examine different impacts of fixed and mobile broadband on economic growth during this period. Design/methodology/approach The panel data regression method and across-country data are used to estimate the impacts of fixed broadband and mobile broadband on economic growth rate. Findings The mobile broadband penetration rate and the mobile broadband network size have positive and significant correlations with economic growth rate during 2020–2021. Practical implications The findings have the policy implications that governments should promote the diffusion of mobile broadband especially 5G to sustain economic growth during the time of the pandemic. Social implications The diffusion of mobile broadband and 5G further facilitates telework mode. Originality/value This study theorizes the role of broadband in economic growth by proposing a new concept – effective labor – which reflects the extent of labor participation in productive activities when the containment measures of the pandemic are implemented.

Television White Space Based Broadband Network’s Coverage and CAPEX Performance for Rural Areas Connectivity in Tanzania

This study compared the CAPEX requirements for a television white space (TVWS) based broadband network and that of deploying the UMTS based mobile broadband networks to deliver broadband connectivity in areas with sparse population which characterize most of Tanzania rural. Like many developing countries, Tanzania has low Internet penetration, with penetration of just over 46% when multiple subscriptions are taken into account. The affected communities are mostly rural due to low population densities which make deployment of the mobile broadband to be much expensive in terms of costs. This limits the economic development potential in these areas given that the broadband connectivity and services is a key driver for the Fourth Industrial Revolution (4IR or Industry 4.0) and modern digital economy. Comparative between the coverage capability of the TVWS and UMTS family of standards by considering the number of base transceiver stations (BTS) is conducted to cover a specific geographic area for three different radio operating environments namely hilly, undulating and flat terrain landscapes. The broadband connectivity data rate is defined at the receiver rate of 2 Mbps or above, and used to determine the number of BTS required for the two technologies. Results show that introducing the TVWS as a middle mile solution, cuts the number of UMTS BTS required to serve the same population by 68%, 66.7%, and 75% for the three canonical environments respectively.

Modification and Validation of an mHealth App Quality Assessment Methodology for International Use: Cross-sectional and eDelphi Studies.

BackgroundOver 325,000 mobile health (mHealth) apps are available to download across various app stores. However, quality assurance in this field of medicine remains relatively undefined. Globally, around 84% of the population have access to mobile broadband networks. Given the potential for mHealth app use in health promotion and disease prevention, their role in patient care worldwide is ever apparent. Quality assurance regulations both nationally and internationally will take time to develop. Frameworks such as the Mobile App Rating Scale and Enlight Suite have demonstrated potential for use in the interim. However, these frameworks require adaptation to be suitable for international use.ObjectiveThis study aims to modify the Enlight Suite, a comprehensive app quality assessment methodology, to improve its applicability internationally and to assess the preliminary validity and reliability of this modified tool in practice.MethodsA two-round Delphi study involving 7 international mHealth experts with varied backgrounds in health, technology, and clinical psychology was conducted to modify the Enlight Suite for international use and to improve its content validity. The Modified Enlight Suite (MES) was then used by 800 health care professionals and health care students in Ireland to assess a COVID-19 tracker app in an online survey. The reliability of the MES was assessed using Cronbach alpha, while the construct validity was evaluated using confirmatory factor analysis.ResultsThe final version of the MES has 7 sections with 32 evaluating items. Of these items, 5 were novel and based on consensus for inclusion by Delphi panel members. The MES has satisfactory reliability with a Cronbach alpha score of .925. The subscales also demonstrated acceptable internal consistency. Similarly, the confirmatory factor analysis demonstrated a positive and significant factor loading for all 32 items in the MES with a modestly acceptable model fit, thus indicating the construct validity of the MES.ConclusionsThe Enlight Suite was modified to improve its international relevance to app quality assessment by introducing new items relating to cultural appropriateness, accessibility, and readability of mHealth app content. This study indicates both the reliability and validity of the MES for assessing the quality of mHealth apps in a high-income country, with further studies being planned to extrapolate these findings to low- and middle-income countries.

The tribal digital divide: Extent and Explanations

This paper documents home Internet access, types of Internet access, connection speeds, and prices for basic home Internet in tribal areas of the United States. We find that the share of households with Internet access is 21 percentage points lower in tribal areas than in neighboring non-tribal areas. When compared to these non-tribal areas, download speeds, whether measured using fixed or mobile broadband networks, are approximately 75% slower in tribal areas, while the lowest price for basic Internet services in tribal areas is 11% higher. Regression techniques reveal that traditional cost factors such as terrain and population density fully explain the price gap but account for only a fraction of the tribal differences in Internet access and connection speeds. Income differences are strong predictors of Internet access but do not affect connection speeds. A sizable amount of the variation in the access and home connection gap between tribal and non-tribal is left unexplained. We conclude with a discussion of how federal broadband programs have penetrated Indian Country, how tribal-specific factors are related to the variation in Internet access within Indian Country, and the potential policy implications of our findings.

An Adaptive Control Scheme for Data-Driven Traffic Migration Engineering on 5G Network

Adaptive control of traffic engineering (TE) based on 5G network function virtualization (NFV) authorizes the efficient and dynamic network resource allocation, whose utilization is increasingly wide and will become more widespread. In this paper, we first devise an adaptive control scheme for data-driven traffic migration engineering (TME) on the 5G virtual network. The proposed TME technology focuses on a 5G enhancing mobile broadband (eMBB) network application scenario and takes the network operating expenditure (OPEX) as the main research target. Firstly, we predict the network traffic of the virtual network through the constructed traffic predicted mathematical model. Then, based on the triangle inequality violation (TIV) theorem, some local network traffic is adaptively migrated when the predicted link traffic exceeds the peak rate. Consequently, the migrations of logical links in the virtual network layer are completed. Finally, our experiments show that the proposed protocol can effectively improve the key performance indicators (KPIs) of the reconfigured network, such as throughput, delay and energy consumption. Furthermore, the Fridman and Holm statistical hypothesis tests are also used to analyze the simulation data, which proves that the proposed approximate TME algorithm has statistical significance.

A reinforcement learning agent for mixed-numerology interference-aware slice spectrum allocation with non-deterministic and deterministic traffic

5G RAN slicing is an essential tool to support the simultaneous coexistence of enhanced mobile broadband (eMBB) and ultra-reliable low-latency communications (URLLC) network slices on a shared mixed-numerology physical layer. Moreover, due to recent advance of the private network paradigm, RAN slicing assumes a central role to provide dedicated radio coverage to industry 4.0 applications as a standalone RAN. Unlike the stochastic traffic behavior characterizing URLLC slices in classical mobile networks, industrial networks support URLLC services with deterministic and periodic traffic patterns. Based on this alternative network characterization, we design a deep reinforcement learning (DRL) agent that simultaneously provides a spectrum allocation fulfilling the eMBB and URLLC service requirements and mitigates the inter-numerology interference (INI). Furthermore, by exploiting the information about the deterministic traffic patterns, we specialize the agent reward function to improve the spectrum allocation effectiveness for URLLC slices deployed in industrial environments. We assess the agent performance with respect to resource allocation schemes that are INI agnostic. Results reveal that the proposed solution outperforms the benchmark schemes in terms of service provisioning performance in both network scenarios (e.g. mobile and industrial) and showcase the benefit of INI mitigation.

Innovative Research on Intelligent Enterprise Management Mode under the Background of Smart City

The widespread application of Internet of Things technology, new broadband mobile networks, cloudy computing, and other new information technologies on the Internet has promoted the development of “smart cities.” The essence of the “smart city” is to make full use of advanced information technology to realize the intelligent management and operation of the city, thereby creating a better life for the urban people and realizing the harmonious and sustainable development of the city. This article is aimed at studying the innovation of intelligent enterprise management models in the context of smart cities. This article first analyzes and briefly describes the theoretical knowledge of the “smart city” and the current domestic development, then proposes the technology of data warehouses, and finally uses the multiple linear regression formula for analysis. The experimental results of this paper show that in the context of smart cities, the intelligent management model of enterprises has played an important role in the development of enterprises, which is 51.7% faster than the traditional model, and the accuracy is as high as 92.3%.

Measuring and Localising Congestion in Mobile Broadband Networks

Mobile broadband networks, although increasingly popular, suffer large fluctuations in performance. Download speeds can drop by 50% or more during peak hours. Hence, understanding and dissecting the causes of these fluctuations is central to improving current and future networks. In this paper, we propose a congestion detection and localisation method, Q-TSLP, that combines and extends the two state-of-the-art congestion detection tools: Q-Probe and TSLP. Q-Probe monitors patterns in packet arrivals, while TSLP tracks shifts in RTT to detect bottleneck at different segments of an end-to-end path. QProbe can attribute congestion, at a very coarse level, to either radio or non-radio related. TSLP on the other hand cannot pinpoint radio related congestion. Q-TSLP provides a per-hop congestion attribution thus addressing these limitations. To this end, we build two small scale LTE testbeds and experiment with a series of congestion scenarios. These controlled experiments show that apart from correct congestion localisation to finer granularity, the detection accuracy improves significantly with Q-TSLP, up to 100% in some cases. We then run a three-month long measurement campaign of congestion over two commercial operators in Norway. Overall, we run 17 million tests from a large number of geographically distributed probes. We find that both operators suffer congestion at different parts of the network. Our findings indicate that apart from mobile radio access, a non-trivial fraction of cases is related to congested mobile operator and Internet paths beyond the mobile network core. These findings hint that operators may need significant infrastructure upgrades to cope with potential 5G traffic volumes.