Abstract

We study a radio resource allocation problem in mobile communication systems. As the distinct characteristic of this problem, a common data transmission rate is used on all channels allocated to a user. Because the channels differ in their quality, for each user the achievable rate varies by channel. Thus allocating more channels does not necessarily increase the total rate, as the common rate is constrained to be the lowest one supported by the allocated channels. Radio resource allocation subject to the common-rate constraint is of practical relevance, though little attention has been paid to modeling and solving the problem. We take a mathematical optimization perspective with focus on modeling. We first provide a complexity analysis. Next, several integer linear programming (ILP) formulations for the problem, including compact as well as non-compact models, are derived. The bulk of our analysis consists in a rigorous comparative study of their linear programming (LP) relaxations, to reveal the relationship between the formulations in terms of bounding. Computational experiments are presented to illustrate the numerical performance in bounding and LP-assisted problem solving. Our theoretical analysis and numerical results together serve the aim of setting a ground for the next step of developing model-based and tailored optimization methods.

Introduction

Mobile communication systems have been evolving rapidly in the past decades. From an optimization standpoint, a key research topic for mobile networks is radio resource management for utilizing the radio resource efficiently, particularly because the amount of data traffic is constantly growing in the current 4th generation (4G) systems, and the trend will remain or even accelerate in the 5th generation (5G) networks. Along with this growth, 5G networks target a plethora of new services (e.g., vehicular communications), making resource management more challenging than before, as pointed out in Calabrese et al. (2018).

4G/5G networks deploy orthogonal frequency division multiple access (OFDMA). In essence, data transmission takes place in two dimensions, namely time and frequency that are divided into time slots and channels, respectively.1 Resource management for channel allocation in a time slot, as well as that across multiple time slots, is commonly called scheduling. In any time slot, a user may be scheduled to receive transmission from its base station (BS) on multiple channels, and a channel may be scheduled for at most one user of this BS. The latter is what the term “orthogonal” refers to in OFDMA. Among the BSs, the channels are reused, i.e., there is generally no dedicated (sub)set of channels for a BS. As a result, a channel is exposed to interference, if the channel is also scheduled in some nearby BSs in the same time slot. Note that even if interference is not present, the channels are still frequency-selective (Tse and Viswanath, 2005), meaning that they vary in quality and hence the achievable data rate from a user’s perspective.

Conclusions and future work

We have studied mathematical modeling of a resource optimization problem with common-rate channel allocation in mobile communication systems. We have demonstrated that the problem admits an array of integer programming formulations. The study shows that modeling does matter, analytically as well as numerically, in terms of performance in bounding and LP-based problem solving. Moreover, there is a clear correlation between the accuracy of LP and the quality of integer solution derived thereby.

As we have observed, straightforward schemes of constructing integer solutions are not practical in solution time, whereas for our resource allocation problem, the ultimate target is real-time optimization. To approach this goal with model-based optimization, we make several observations. First, to deliver a solution rapidly, the amount of computation has to be very small, and thus the LP relaxation may have to be solved approximately, followed by some primal heuristic (both admitting massive parallel computation). Second, non-compact models are also of interest, as restricting the number of columns or rows leads to approximation of the problem (and the LP relaxation). Finally, with presence of rate lower bound, obtaining solution feasibility is of clear significance, and one may need to trade solution quality against feasibility. Developing and implementing algorithmic notions along these lines form interesting topics for forthcoming research.

Abstract

This article presents a study with feasibility and performance analysis of machine learning (ML) techniques using supervised techniques for anomaly detection problems in a 5G communication network. The proposed ML models (Multilayer Perceptron, Decision Tree, and Support Vector Machine) were used to classify data into anomaly or non-anomaly based on two 5G Open Radio Access Network (O-RAN) datasets with various key performance indicators (KPIs). Furthermore, we propose a strategy that devotes to labeling anomalous situations, leveraging the t-Distributed Stochastic Neighbor Embedding (tSNE) technique atop datasets enclosing multiple KPIs. The results were significant, with an accuracy above 90% for all use cases considered.

IntroductionTo support new 5G cellular network requirements (e.g., data rates exceeding 10 Gbps, network latency under 1 ms, capacity expansion by a factor of 1,000, and energy efficiency gains), vendors have begun investigating new radio access network (RAN) architectures [17, 13, 25, 5, 23].

Open Radio Access Network (O-RAN), suggested by the O-RAN Alliance [15], stands as a promising radio technology that has gained worldwide acceptance. O-RAN is a worldwide community of operators, manufacturers,technology that has gained worldwide acceptance. O-RAN is a worldwide community of operators, manufacturers, and academic institutes [18, 1]. The vision is to rewrite the RAN industry towards establishing an open, adaptable, and intelligent RAN [15]. Artificial intelligence (AI) in machine learning (ML) will play a crucial role in the 5G networkwith particular emphasis on the O-RAN. For example, ML use can drive more efficient enhancements in 5G network planning, automation of network operations (e.g., provisioning, optimization, and fault prediction), network slicing, service quality prediction, and other applications and services [8, 3, 14, 20].

Conclusion

In this work, we present results and analyses of three ML/AI supervised approaches applied to anomaly detection: Multilayer Perceptron, Decision Tree, and Support Vector Machine. The tests were conducted on an emulation testbed concerning a network environment dataset. The unsupervised ML/AI strategy, based on t-Distributed Stochastic Neighbor Embedding (tSNE), was used to create data labels. Results associated with the accuracy of the ML/AI algorithms were obtained, suggesting an excellent performance with an accuracy of above 90% for all cases.

Abstract

Document Sections

5G Cellular Mobile Technology

Recent Updates of Health Safety Recommendations

Human Eye and Skin Tissues

Biological Interactions With Mm-Waves

Anomalies in Recently Updated Safety Recommendations

Abstract:

The rollout of 5G cellular communication technology is well underway worldwide. The advocates of 5G mobile technology hail it as a faster and more secure technology than its predecessor, 3G and 4G systems. The major enabling infrastructure uses millimeter-wave (mm-wave) and phased-array technology to achieve line-of-sight directivity, high data rates, and low latency. A central vulnerability or security threat is that it may allow spying on users. Nevertheless, this is a system architecture and technology or regulatory issue but not a biological effect or health safety matter.

5G Cellular Mobile Technology

5G cellular mobile technology is a telecommunication technology multilayered in frequency assignment and varied in operational scope and performance. It includes an extremely wide range of multiple RF bands. Its frequency coverage may be roughly separated into two ranges: sub-6-GHz bands and 24–60-GHz frequencies that reach well into the mm-wave region. The frequency ranges have often been further divided into low-, mid, and high-band 5G. Low-band 5G starts at roughly 400 MHz and uses existing or previous 3G or 4G frequencies or newly opened frequencies to operate; the latter, for example, may overlap with the existing 4G band. The 5G rollout began with midband, which includes popular frequencies between 3 and 4 GHz. However, primary 5G technological advances are associated with high-band 5G, which promises performance bandwidth as high as 20 GHz, and multiple-input, multiple-output strategies using 64–256 antennas at short distances and offering performances up to 10 times better than the current 4G networks

Introduction

One of the most noteworthy arcs of the 21st century has been the explosive growth seen in the usage and deployment of technology. More specifically, the advancement of information and communications technology (ICT) has been the foundation for many of the innovations seen over this century thus far. The wide-spanning adoption of the Internet for social media, e-commerce, news consumption, and more has redefined much of our everyday lives and industries across the world. Due to the advancements of ICT, there are many exciting opportunities for what is yet to come. One longstanding opportunity for the future is the vision of the smart city.

There are countless different ideas and definitions for what exactly qualifies as a smart city. A more general definition, provided by Bakıcı et al. in [1], defines smart cities as being high-tech cities that serve to connect people, information, and city-wide infrastructure and resources using advances in technology to improve sustainability, innovation, and overall quality-of-life. The extent to which how pervasive (or intrusive) technologies are incorporated to accomplish such goals is often a point of contention in the literature [2]. However, the many different visions for what define smart cities all generally involve heavy utilization of technology to improve the maintenance of cities to further improve and/or optimize city-specific operations for different stakeholders (e.g., citizens, the environment).

Realizing the ambitious vision of smart cities requires seamlessly making decisions for a host of different applications and domains with minimal sacrifice with respect to performance and resource costs. Some applications of interest for smart cities include, but are not limited to, smart traffic light control, smart information spread (or advertisement), smart energy distribution, smart waste management, etc. Ideally, data generated in urban environments can be collected in smart cities using CT-powered infrastructure to train and inform models designed for these applications to make realtime decisions. For instance, insistent collection of traffic congestion data from roadside units(RSU) and other sources can be sent to ICT infrastructure to train machine learning models how to make optimal decisions related to traffic lights to reduce congestion. This approach is becoming more within reach given the advances in vehicular ad hoc networks (VANET) [3, 4, 5] and other Internetof-Vehicles (IoV) systems [6, 7, 8].

Making smart decisions in a timely fashion will require a powerful pipeline ready to support intense amounts of data collection, analytics, and adaptive decision-making. For instance, video feeds at road intersections — to empower models for smart traffic light control — are large in size (in terms of bytes) and will require automated processes to make meaningful decisions in real-time. However, video cameras that provide such video feeds will be unable, on their own, to perform such analytics and decision-making. Such tasks would require more computational resources than would be available at video cameras and other similar sensors that can be found in urban environments. Thus, for such applications, smart cities will need to be able to provide heavy computational power on-demand, even for devices that do not have such resources locally. Applications (e.g., big data analytics, video classification) all require large amounts of compute resources in order to adequately process. Because Internet-of-Things (IoT) devices like smartphones are often ill-equipped to meet the needs of such processes [9, 10], a standard approach for providing these services on these devices is to rely on remote resources.

Highlights

Abstract

JEL classification

Keywords

1. Introduction

2. Background

3. Methodology

4. Insights from interviews

5. Investigation of 100 consortia

6. A taxonomy of industry consortia

7. Conclusions and implications

Declaration of Competing Interest

Acknowledgments

Annex A. List of consortia and their cluster allocation

Appendix B. Supplementary data

References

Abstract

In mobile telecommunications, numerous formal and quasi-formal standard setting organizations (SSOs) exist that are dedicated to technology standardization. Yet, hundreds of more or less informal industry consortia have been established that also deal with technical standards. Some of these aim to develop standards themselves – and thus compete with existing, formal standard setting organizations – while others complement existing organizations or relate to them in other ways. Based on a novel dataset covering 100 consortia in the field of mobile telecommunications as well as interviews, we aim to explore and understand the role and diversity of standards-related consortia. We investigate their purpose, functioning, their members’ motives, the position they occupy in the standardization landscape, and the way they interact with formal standard setting. On the basis of our analysis, we propose a taxonomy of standards-related consortia consisting of six groups: large industry and technology influencers, high-level concept developers, established standards developers, young technology specialists, small industry and technology influencers, and SSO-hosted industry drivers. We believe the resulting taxonomy to be relevant for researchers, practitioners and policymakers alike. It sheds light on the intricate landscape of industry consortia, and offers transparency on the peculiarities of these organizations. Hence, it can build the basis for an evaluation of potential memberships as well as policies and regulations.

1. Introduction

Since the 1980s, the mobile telecommunications industry has evolved rapidly, from various first generation analog standards, to second-generation GSM, third-generation UMTS/WCDMA, and fourth-generation LTE standards.1 This evolution has not only changed the performance of telecommunication networks, such as data rates and latency, but in many respects also the underlying technologies. Standards impact potential uses as well as organizations and individuals, as we see with 3G, which was the result of a convergence between traditional telecommunications and the IT industry (Leiponen, 2008). For 5G standards in the making, this convergence is happening on an even larger scale, involving the Internet of Things (IoT), Industry 4.0, various vertical sectors, and more.

Abstract

Although mobile technologies keep evolving through years, Fault management and cyber-security management in mobile networks are still treated as separated notions with different blocks and different approaches whereas in practice, they are highly correlated. In this paper, we propose a framework that takes into account the correlation between these two management systems. The framework is based on several prediction agents where each agent is composed of a security predictor, a fault predictor and a generic anomaly detection model. A re-enforcement process allows to enhance the reliability of the machine learning training and prediction phases of the different predictors. Besides, each agent can collaborate with its neighborhood for a more resilient network. An application of this framework to 5G architecture is proposed by mapping the components of our framework with network slices. Finally, an experimentation is held over a testbed that we set up on openstack in order to forecast future anomalies related to proxy overload, latency violation in call session network functions and to excessive usage of memory. The training is achieved with ARIMA and deep learning models with promising results.

1 Introduction

As mobile network technologies evolve, new services are offered and more sophisticated networks are needed. The increasing number of Internet users leads to a redesign of network architecture, forcing designers to take into account new parameters such as the need of global coverage combined with low latency, as well as a high reliability and security level. Additionally, new networking experiences are added, such as Internetof-Things (IoT), which promise to offer new services and facilities to people’s daily lives. In this demanding environment, 5G technology is emerging, playing a decisive role in the implementation of new visions and promising to deliver solutions. A major innovation introduced by 5G technology [1] is the scalability. 5G architectures take into account the possible need of extending the capabilities of the network, both at the level of user traffic growth and at the level of new services input from providers. Slicing could be the ideal solution for such networks, offering scalability as well as flexibility in managing a giant network. Network Slicing is set to be a prominent feature of 5G to allow connectivity and data processing tailored to specific customers requirements. Mobile communications provided by smart networks will enhance the efficiency and productivity of business processes and will open up opportunities for network operators to address the Business-to-Business segment more effectively.