Cellular Network Operators Research Articles

Taking Advantage of Spare Battery Capacity in Cellular Networks to Provide Grid Frequency Regulation

The increasing use of renewable energies places new challenges on the balance of the electricity system between demand and supply, due to the intermittent nature of renewable energy resources. However, through frequency regulation (FR) services, owners of battery storage systems can become an essential part of the future smart grids. We propose a thorough first study on the use of batteries associated with base stations (BSs) of a cellular network, to participate in ancillary services with respect to FR services, via an auction system. Trade-offs must be made among the number of participating BSs, the degradation of their batteries and the revenues generated by FR participation. We propose a large-scale mathematical programming model to identify the best participation periods from the perspective of a cellular network operator. The objective is to maximize profit while considering the aging of the batteries following their usage to stabilize the electrical grid. Experiments are conducted with data sets from different real data sources. They not only demonstrate the effectiveness of the optimization model in terms of the selection of BSs participating in ancillary services and providing extra revenues to cellular network operators, but also show the feasibility of ancillary services being provided to cellular network operators.

Core network traffic prediction based on vertical federated learning and split learning

Wireless traffic prediction is vital for intelligent cellular network operations, such as load-aware resource management and predictive control. Traditional centralized training addresses this but poses issues like excessive data transmission, disregarding delays, and user privacy. Traditional federated learning methods can meet the requirement of jointly training models while protecting the privacy of all parties’ data. However, challenges arise when the local data features among participating parties exhibit inconsistency, making the training process difficult to sustain. Our study introduces an innovative framework for wireless traffic prediction based on split learning (SL) and vertical federated learning. Multiple edge clients collaboratively train high-quality prediction models by utilizing diverse traffic data while maintaining the confidentiality of raw data locally. Each participant individually trains dimension-specific prediction models with their respective data, and the outcomes are aggregated through collaboration. A partially global model is formed and shared among clients to address statistical heterogeneity in distributed machine learning. Extensive experiments on real-world datasets demonstrate our method’s superiority over current approaches, showcasing its potential for network traffic prediction and accurate forecasting.

A Unified Prediction Framework for Signal Maps: Not All Measurements are Created Equal

Signal maps are essential for the planning and operation of cellular networks. However, the measurements needed to create such maps are expensive, often biased, not always reflecting the performance metrics of interest, and posing privacy risks. In this paper, we develop a unified framework for predicting cellular performance maps from limited available measurements. Our framework builds on a state-of-the-art random-forest predictor, or any other base predictor. We propose and combine three mechanisms that deal with the fact that not all measurements are equally important for a particular prediction task. First, we design <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">quality-of-service functions (<inline-formula><tex-math notation="LaTeX">$Q$</tex-math></inline-formula>)</i> , including signal strength (RSRP) but also other metrics of interest to operators, such as number of bars, coverage (improving recall by 76%-92%) and call drop probability (reducing error by as much as 32%). By implicitly altering the loss function employed in learning, quality functions can also improve prediction for RSRP itself where it matters ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> MSE reduction up to 27% in the low signal strength regime, where high accuracy is critical). Second, we introduce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">weight functions</i> ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$W$</tex-math></inline-formula> ) to specify the relative importance of prediction at different locations and other parts of the feature space. We propose re-weighting based on importance sampling to obtain unbiased estimators when the sampling and target distributions are different. This yields improvements up to 20% for targets based on spatially uniform loss or losses based on user population density. Third, we apply the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Data Shapley</i> framework for the first time in this context: to assign values ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\phi$</tex-math></inline-formula> ) to individual measurement points, which capture the importance of their contribution to the prediction task. This can improve prediction ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> from 64% to 94% in recall for coverage loss) by removing points with negative values and storing only the remaining data points ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> as low as 30%), which also has the side-benefit of helping privacy. We evaluate our methods and demonstrate significant improvement in prediction performance, using several real-world datasets.

Effect of PNBP Policy on The financial Performance of Cellular Mobile Network Operators

Digital transformation is one of the programs initiated by the Government, which in practice requires the support of healthy mobile network operators through financial performance indicators, to fulfill the development of telecommunications infrastructure in all regions. Mobile network operators require significant capital to provide telecommunications infrastructure, including payment of Non-Tax State Revenue (PNBP) to the government every year, which continues to increase due to frequency allocation and telecommunications activities. The aim of this research is to measure the estimated influence of PNBP policy on the financial performance of mobile network operators in Indonesia, while considering several other control variables such as investment costs (CAPEX), number of subscribers, liabilities, number of base transceiver stations (BTS), and number of mobile network operators. This research uses panel data regression with a fixed effect model approach, covering four mobile network operators from 2010 to 2022. The results show that PNBP, sourced from the obligation to pay Spectrum Frequency Usage Fee, has a significant negative impact on financial performance. These findings are also consistent with the research conducted by Katz and Jung (2023)

SWIPT-Aided Device-to-Device Communications for Massive IoT Networks: A Novel Resource Allocation With Sparse Code Multiple Access

Spectral efficiency (SE) and energy efficiency (EE) are crucial for next-generation wireless Internet of things networks. Device-to-device (D2D) communications and sparse code multiple access (SCMA) are promising solutions to meet the high quality of service. This requires a proper interference management for an ultra-dense user system. Considering both resource and power allocation, we herein aim to optimize the SE and improve the EE. First, rate fairness is considered to assign SCMA codewords to both cellular and D2D users. Subsequently, we propose a beamforming design and power allocation algorithm for D2D communications underlaying downlink and uplink operation of cellular networks, wherein energy harvesting is involved as a simultaneous wireless information and power transfer to assist an SCMA network.

A quality of experience model for live video in first-person-view drone control in cellular networks

Several upcoming 5G and 6G services will rely on unmanned aerial vehicles (UAV) sending live information to remote terminals. Thus, understanding the traffic flows that might influence end-user experience in these services is key for cellular network operators. One of these UAV-based services is first person view (FPV) drone control, consisting on the remote control of the UAV in Beyond Visual Line of Sight scenarios using only the live video visualized in a ground control station. This work focuses on the networking aspects of this service by presenting the assembly, integration and evaluation methodology of an UAV quadrotor teleoperated via FPV through a Long Term Evolution (LTE) network or WiFi radio access link. To assess system performance, three different connectivity schemes between UAV and ground control station are tested, namely server-based connection via LTE, direct LTE, and peer-to-peer WiFi connection. Then, several experiments are carried out in the testbed to characterize telemetry, control and video traffic for FPV service in the above schemes. Later, a methodology is defined to estimate Quality of Experience (QoE) for FPV service based on image quality and video latency measurements collected at network and application level. Results show that the QoE model for live video introduced in this work can be the basis of more sophisticated models for cellular FPV services.

The Impact of Denial-of-Service Attacks and Queue Management Algorithms on Cellular Networks

In today's digital landscape, Distributed Denial of Service (DDoS) attacks stand out as a formidable threat to organisations all over the world. As known technology gradually advances and the proliferation of mobile devices, cellular network operators face pressure to fortify their infrastructure against these risks. DDoS incursions into Cellular Long-Term Evolution (LTE) networks can wreak havoc, elevate packet loss, and suboptimal network performance. Managing the surges in traffic that afflict LTE networks is of paramount importance. Queue management algorithms emerge as a viable solution to wrest control over congestion at the Radio Link Control (RLC) layer within LTE networks. These algorithms work proactively, anticipating, and mitigating congestion by curtailing data transfer rates and fortifying defences against potential DDoS onslaughts. In the paper, we delve into a range of queue management methods Drop-Tail, Random Early Detection (RED), Controlled Delay (CoDel), Proportional Integral Controller Enhanced (PIE), and Packet Limited First In, First Out queue (pFIFO). Our rigorous evaluation of these queue management algorithms hinges on a multifaceted assessment that encompasses vital performance parameters. We gauge the LTE network's resilience against DDoS incursions, measuring performance based on end-to-end delay, throughput, packet delivery rate (PDF), and fairness index values. The crucible for this evaluation is none other than the NS3 simulator, a trusted platform for testing and analysis. The outcomes of our simulations provide illuminating insights. CoDel, RED, PIE, pFIFO, and Drop-Tail algorithms emerge as top performers in succession. These findings underscore the critical role of advanced queue management algorithms in fortifying LTE networks against DDoS attacks, offering robust defences and resilient network performance.

Feasibility Analysis of GSM-R System for Railways in Indonesia: Case Study of Jakarta Bandung High Speed Train

High-speed railway technology development necessitates the development of a communication system that supports various components of train operation facilities, such as train control, safety communication, and others. The construction of fhigh-speed railway in Indonesia in the Jakarta-Bandung area uses the Chinese Train Control System (CTCS) Level 3 signaling system, which makes the Global System for Mobile Communication-Railway (GSM-R) a wireless signaling network system. Because there are already existing GSM-based public telecommunications services, known as public GSM, on the same frequency used by a number of cellular operators in Indonesia, the use of GSM-R poses implementation challenges. This will result in reduced service quality and revenue for protected cellular network operators due to the use of these frequency bands to support the operation of high-speed railway services in Indonesia. In this study, an evaluation was carried out to look for other potentials from using the GSM 900 MHz frequency band, which can be used as an option to be implemented on high-speed trains in Indonesia. The research begins with calculating the estimated potential loss of revenue from using telecommunications network operators. This data will be analyzed in terms of the economic feasibility of several other frequency band choices for implementing the GSM 900 MHz frequency band on high-speed trains in Indonesia, so that it is hoped that this will become a new consideration that can become a solution and recommendation in the application and use of GSM-R technology in Indonesia.

MPKIX: Towards More Accountable and Secure Internet Application Services via Mobile Networked Systems

Nowadays, both Internet Application Service (IAS) providers and users face various security threats and legal issues. Due to the lack of reliable user information verification mechanisms, adversaries can abuse IASs to launch various cyberattacks, such as misinformation distributing and phishing, by using fake user accounts. IAS providers may thus inadvertently offer inappropriate content to restricted users, thereby suffering a serious risk of prosecution under local or international laws. Also, IAS users may suffer from nefarious ID theft attacks. In this paper, we proposed a novel security framework, MPKIX, designated as Mobile-assisted PKIX (Public-Key Infrastructure X.509). MPKIX secures both IAS providers and users by leveraging the broadly used PKIX services and mobile networked systems. It not only provides IAS providers with a reliable user verification mechanism while simultaneously enabling cross-IAS user privacy protection, but also largely mitigates the possibility of ID theft attacks and benefits other involved parties, such as cellular network operators and PKIX service providers. We further conduct a security analysis of MPKIX and implement an MPKIX prototype. The evaluation results based on the prototype confirm the effectiveness and efficiency of MPKIX with low overhead.

Feasibility Analysis Of Gsm-R System For Railways In Indonesia: Case Study Of Jakarta Bandung High Speed Train

High-speed railway technology development necessitates the development of a communication system that supports various components of train operation facilities, such as train control, safety communication, and others. The construction of fhigh-speed railway in Indonesia in the Jakarta-Bandung area uses the Chinese Train Control System (CTCS) Level 3 signaling system, which makes the Global System for Mobile Communication-Railway (GSM-R) a wireless signaling network system. Because there are already existing GSM-based public telecommunications services, known as public GSM, on the same frequency used by a number of cellular operators in Indonesia, the use of GSM-R poses implementation challenges. This will result in reduced service quality and revenue for protected cellular network operators due to the use of these frequency bands to support the operation of high-speed railway services in Indonesia. In this study, an evaluation was carried out to look for other potentials from using the GSM 900 MHz frequency band, which can be used as an option to be implemented on high-speed trains in Indonesia. The research begins with calculating the estimated potential loss of revenue from using telecommunications network operators. This data will be analyzed in terms of the economic feasibility of several other frequency band choices for implementing the GSM 900 MHz frequency band on high-speed trains in Indonesia, so that it is hoped that this will become a new consideration that can become a solution and recommendation in the application and use of GSM-R technology in Indonesia.

Wireless Traffic Prediction Based on a Gradient Similarity Federated Aggregation Algorithm

Wireless traffic prediction is critical to the intelligent operation of cellular networks, such as load balancing, congestion control, value-added service promotion, etc. However, the BTS data in each region has certain differences and privacy, and centralized prediction needs to transmit a large amount of traffic data, which will not only cause bandwidth consumption, but may also cause privacy leakage. Federated learning is a kind of distributed learning method with multi-client joint training and no sharing between clients. Based on existing related research, this paper proposes a gradient similarity-based federated aggregation algorithm for wireless traffic prediction (Gradient Similarity-based Federated Aggregation for Wireless Traffic Prediction) (FedGSA). First of all, this method uses a global sharing enhanced data strategy to overcome the data heterogeneity challenge of multi-client collaborative training in federated learning. Secondly, the sliding window scheme is used to construct the dual channel training data to improve the feature learning ability of the model; In addition, to improve the generalization ability of the final global model, a two-layer aggregation scheme based on gradient similarity is proposed. The personalized model is generated by comparing the gradient similarity of each client model, and the central server aggregates the personalized model to finally generate the global model. Finally, the FedGSA algorithm is applied to wireless network traffic prediction. Experiments are conducted on two real traffic datasets. Compared with the mainstream Federated Averaging (FedAvg) algorithm, FedGSA performs better on both datasets and obtains better prediction results on the premise of ensuring the privacy of client traffic data.

Evaluasi Kebijakan tentang Penyelenggaraan Menara Telekomunikasi terhadap Penyedia Menara Telekomunikasi

Along with the growth of cellular phone subscribers in Indonesia, cellular network operators continue to strive to build infrastructure to establish broader service coverage areas, thus, increasing the quality of service. Regional Rules &amp; Regulations in DKI Jakarta are intended to regulate the BTS towers within an area. However, they become a barrier for Telecommunications operators to meet the needs of good networks/signals. Changes in the zoning of telecommunication towers can meet the needs of operators and increase customer satisfaction. This study uses qualitative method analysis with an interview. The results show that policy evaluation regarding the implementation of telecommunication towers has yet to meet the needs of telecommunication tower providers (Tower Providers) and has been shown to cause areas that operator signal networks cannot cover. This situation impacts the satisfaction of particular customers and can threaten the business continuity of the operator. Conclusion of the Policy of the Governor of the Special Capital Region of Jakarta Number 14 of 2014, concerning the Implementation of Telecommunication Towers for Telecommunication Tower Providers, the annexse to the zoning of telecommunication towers needs to be reviewed because it has not been able to meet the needs of telecommunication tower providers and can pose a threat to business continuity.

Exploiting Short Block and Concatenated Codes for Reliable Communications Within the Coexistence of 5G-NR-U and WiFi

Unlicensed spectrum offers opportunities for cellular mobile network operators, where traffic can be offloaded from licensed to unlicensed bands. Modern heterogeneous technologies such as 5G and WiFi can simultaneously operate on ISM and UNII bands under strict coexistence rules. While satisfying harmonious coexistence has largely been studied in LTE/4G, an emerging issue for future deployment is to reduce latency and to guarantee reliable communications, in the direction of achieving URLLC. This work mainly focuses on the critical issues arising in the coexistence of LBT systems, sharing common infrastructure of 5G NR-U and WiFi when operating under imperfect energy detection sensing. We consider the application of short, concatenated and product erasure correcting codes to recover missing data due to collisions in LBT-based systems. The goal is to enhance spectrum utilization with reduced delay and to achieve reliable communications under various sensing impairments, with code parameters such as block length, rate and minimum distance. Using concatenation, we construct a series of sophisticated erasure block codes with less decoding complexity. Furthermore, we derive a closed-form expression using Gaussian approximation of spectrum utilization efficiency. The results are compared with the various LDPC correcting ensembles under various levels of user activities. We provide detailed comparisons of global throughput and failure probability of codes under varied number of coexisting users. Our results show that the proposed codes can achieve over 20% higher efficiency at 0.1 false alarm probability and 30% less failure probability under high contention scenarios.

A Review Paper on Big Data Analytics in Mobile Networks

Mobile cellular networks have evolved into both data producers and data carriers. Big data analytics can enhance the operation of mobile cellular networks while increasing operator income. We present a unified data model based on random matrix theory and machine learning in this study. Following that, we provide an architectural framework for implementing big data analytics in mobile cellular networks. Furthermore, we discuss numerous illustrative cases in mobile cellular networks, such as huge signalling data, big traffic data, big location data, big radio waveforms data, and big heterogeneous data. Finally, we outline many open research problems in big data analytics in mobile cellular networks.

Strategi Industri Perbankan Syariah dalam Menghadapi Era Digital

Industrial revolution 4.0 brings various kinds of changes in the order of people's lives. The &#x0D; changes felt in the current digital era have affected several aspects, including the financial &#x0D; industry in Islamic banking. The purpose of this study is to determine the strategy of the Islamic &#x0D; banking industry in facing the digital era. Data analysis using descriptive qualitative. The &#x0D; results of the study indicate that the strategy of the Islamic banking industry in facing the digital &#x0D; era can be done by providing a form of good service and attention to customers. In addition to &#x0D; various services that make it easier for customers, such as Internet Banking, various other &#x0D; services can help. Second, phone banking is an important tool for engaging with constituents. &#x0D; Third, there is a way to go to the bank using text messages. Fourth, is the presence of mobile &#x0D; banking services, in this case, banks can cooperate with other cellular network operators, so &#x0D; that a SIM card (cellular chip card) and Global for Mobile Communications (GSM) are &#x0D; installed with a special program to be able to conduct banking transactions, thus making all &#x0D; activities customer transaction operations are much easier, more practical and safer.&#x0D; Keywords : Strategy, Digital era, Digital Banking.

Segmented Spline Curve Neural Network for Low Latency Digital Predistortion of RF Power Amplifiers

At present, we are the dawn of a new era for wireless communication systems. The new dynamic approach to the operation of cellular networks requires an extension of the essential input–output hardware mechanisms, to include intelligence. Traditional neural network structures can be used to embed artificial intelligence into cellular network base stations; however, standard network structures are not an optimal solution for these use cases. In this article, we present a neural network structure, which is specifically designed to provide a more accurate and computationally efficient solution compared with the previous neural network solutions for predistortion of RF power amplifiers (PAs). The proposed network structure is directly compared with alternative neural network solutions, which have been successfully employed for digital predistortion (DPD). The operation of this network is validated for DPD with experimental measurements with wideband signals using the latest generation of commercially available RF hardware. The novel network structure proposed in this work is demonstrated, in practice, to have better performance for a normalized mean square error (NMSE), an adjacent channel leakage ratio (ACLR), and an error vector magnitude (EVM) compared with the most popular previously published neural network.

Security of Open Radio Access Networks

The Open Radio Access Network (O-RAN) is a promising radio access network (RAN) architecture aimed at reshaping the RAN industry toward an open, adaptive, and intelligent RAN. In this paper, we perform a comprehensive security analysis of O-RANs. Specifically, we review the architectural blueprint designed by the O-RAN Alliance, leader in the cellular ecosystem. As part of the security analysis, we provide a detailed overview of the O-RAN architecture; present an ontology for evaluating the security of a system that is currently at an early development stage; identify O-RAN’s high-risk areas; enumerate O-RAN’s threat actors; and model potential threats to O-RAN. The significance of this work is in providing an updated attack surface to cellular network operators. Based on the attack surface, cellular network operators can carefully deploy the appropriate countermeasures to improve O-RAN’s security.

QoS Guaranteed Network Slicing Orchestration for Internet of Vehicles

To support the diversified Quality of Service (QoS) requirements of application scenarios, network slicing has been introduced in the mobile cellular network. It allows mobile cellular network operators to accomplish the creation of multiple logically isolated networks on common network infrastructure flexibly depending on specified demands. Meanwhile, in Internet of Vehicles (IoV), it is very intractable to supply a stable QoS for the vehicles, especially for the dynamic vehicular environments. Thus, we investigate the IoV slicing problem in this article, and propose a QoS guaranteed network slicing orchestration, namely, the long short-term memory-based deep deterministic policy gradient algorithm (LSTM-DDPG), to ensure the stable performance for the slices. Specifically, we first decouple the resource allocation problem into two subproblems. After that, the deep learning and reinforcement learning (RL) are used to allocate resources collaboratively to solve these two questions. We use deep learning LSTM to track the characteristic of the long-term vehicular environment changing, and the RL algorithm DDPG is utilized for online resource tuning. Extensive simulations have proved the effectiveness of the LSTM-DDPG, which can offer stable QoS to the vehicles with a probability greater than 92%. We also demonstrated the adaptiveness of the proposed orchestration with different slicing environments, and the performance is always optimal compared to that of other algorithms.

A Novel, Efficient, Green and Real-Time Load Balancing Algorithm for 5G Network Measurement Report Collecting Clusters

With the wide application of data mining and deep learning in mobile cellular network operation and maintenance, network measurement report (MR) plays an increasingly important role in artificial intelligence for IT operations (AIOps). For the integrity of MR reported by the operation and maintenance (OM) proxy of base station, existing collecting methods are typically based on static distributed clustering. Due to the lack of effective load balancing scheme, nevertheless, these methods typically result in some issues, e.g., low collecting efficiency, poor scalability, and excessive number of servers. Thus, in this work, leveraging the historical law of uploading MR for load forecasting, we propose the weighted least-connection load balancing algorithm (LPWLC) based on load forecasting. First, the historical law of reported MR is utilized to predict the load. Second, using the strategy of static binding and dynamic load adjustment, we bind OM with the assigned server in one cycle, calculate the server load in real-time, and evaluate the server weight by the load of each server. Finally, real-time load adjustment is carried out in line with the number of request connections and the weight of servers. Compared with the existing ones, the proposed algorithm could remove backup servers, thereby effectively reducing the cost and power consumption. Compared with the existing methods, this method has improved the load balancing degree by 28%, and reduced the energy consumption by 104[Formula: see text]W per hour.

Carbon-Neutral Cellular Network Operation Based on Deep Reinforcement Learning

With the exponential growth of traffic demand, ultra-dense networks have been proposed to cope with such demand. However, the increase of the network density causes more power use, and carbon neutrality becomes an important concept to decrease the emission and production of carbon. In cellular networks, emission and production can be directly related to power consumption. In this paper, we aim to achieve carbon neutrality, as well as maximize network capacity with given power constraints. We assume that base stations have their own renewable energy sources to generate power. For carbon neutrality, we control the power consumption for base stations by adjusting the transmission power and switching off base stations to balance the generated power. Given such power constraints, our goal is to maximize the network capacity or the rate achievable for the users. To this end, we carefully design the objective function and then propose an efficient Deep Deterministic Policy Gradient (DDPG) algorithm to maximize the objective. A simulation is conducted to validate the benefits of the proposed method. Extensive simulations show that the proposed method can achieve carbon neutrality and provide a better rate than other baseline schemes. Specifically, up to a 63% gain in the reward value was observed in the DDPG algorithm compared to other baseline schemes.