Emerging from the symbiotic combination of nanotechnology and communications, the field of nanonetworking has come a long way since its inception more than fifteen years ago. Significant progress has been achieved in several key communication technologies as enablers of the paradigm, as well as in the multiple application areas that it opens. In this paper, the focus is placed on the electromagnetic nanonetworking paradigm, providing an overview of the advances made in wireless nanocommunication technology from microwave through terahertz to optical bands. The characteristics and potential of the compared technologies are then confronted with the requirements and challenges of the broad set of nanonetworking applications in the Internet of NanoThings (IoNT) and on-chip networks paradigms, including quantum computing applications for the first time. Finally, a selection of cross-cutting issues and possible directions for future work are given, aiming to guide researchers and practitioners towards the next generation of electromagnetic nanonetworks.

An RMSCA approach based on binary particle swarm optimization is proposed for programmable filterless SDM networks, aimed at minimizing core and spectrum usage. Nearoptimal resource consumption.

This paper discusses novel joint (intracell and intercell) resource allocation algorithms for self-organized interference coordination in multicarrier multiple-input multiple-output (MIMO) small cell networks. The proposed algorithms enable interference coordination autonomously, over multiple degrees of freedom, such as base station transmit powers, transmit precoders, and user scheduling weights. A generic a-fair utility maximization framework is considered to analyze performance-fairness tradeoff and to quantify the gains achievable in interference-limited networks. The proposed scheme involves limited inter-base station signaling in the form of two step (power and precoder) pricing. Based on this decentralized coordination, autonomous power and precoder update decision rules are considered, leading to algorithms with different characteristics in terms of user data rates, signaling load, and convergence speed. Simulation results in a practical setting show that the proposed pricing-based self-organization can achieve up to 100% improvement in cell-edge data rates when compared to baseline optimization strategies. Furthermore, the convergence of the proposed algorithms is also proved theoretically.

Spectrum sharing and management constitute an important tool for alleviating the problem of spectrum scarcity and underutilization. For 5G networks, a number of spectrum sharing schemes are currently under consideration, which differ significantly from the traditional approaches, namely, exclusive access and unlicensed access. The choice of spectrum sharing scheme has a significant impact on the virtualization aspects of the network. This chapter discusses spectrum sharing and its ramifications on the architecture of virtualized wireless networks. We provide a survey of important spectrum sharing schemes and discuss their pros and cons from a 5G perspective. This is followed by an up-to-date account of regulatory aspects and field trials of the important spectrum sharing schemes. The main challenges pertinent to spectrum sharing in virtualized networks are identified. Furthermore, we discuss an architecture for enabling efficient spectrum management and network virtualization in multi-operator 5G networks. 

This paper investigates the benefits of dynamic restoration with service relocation in resilient optical clouds. Results from the proposed optimization model show that service availability can be significantly improved by allowing a few service relocations.

This paper investigates the benefits of dynamic restoration with service relocation in resilient optical clouds. Results from the proposed optimization model show that service availability can be significantly improved by allowing a few service relocations.

In the telecom business, there has been a heavy competition from Internet, media and handset vendors companies. These over-the-top (OTT) players offer compiling telecom services, cause a transformation in the telecom business ecosystem, and the most challenging services posed here are media services. China, India and Indonesia, as world’s emerging markets in Asia, are predicted to take the largest share in the global mobile traffic explosion by 2015. It is critical for mobile network operators (MNOs) in this region to explore strategy for mobile media services, as mobile broadband is likely preferred compared to fixed broadband. In this paper, we analyze and compare mobile music business models used in these markets and structure the relation models between the key actors, using Actors, Relations and Business Activities (ARA) model. We present the economic models that are emerging, and an insight of why and how these multitudes actors are betting on currently. We found that the MNOs generally have a much stronger position compared to their counterparts in the developed markets, and the personalization services, like ring-back tone, are still a huge success. The actors tend to deliver the services by their own, rather than to collaborate in a horizontal business setting.

The performance prediction of user equipment (UE) metrics has many applications in the 5G era and beyond. For instance, throughput prediction can improve carrier selection, adaptive video streaming's quality of experience (QoE), and traffic latency. Many studies suggest distributed learning algorithms (e.g., federated learning (FL)) for this purpose. However, in a multi-tier design, features are measured in different tiers, e.g., UE tier, and gNodeB (gNB) tier. On one hand, neglecting the measurements in one tier results in inaccurate predictions. On the other hand, transmitting the data from one tier to another improves the prediction performance at the expense of increasing network overhead and privacy risks. In this paper, we propose cascaded FL to enhance UE throughput prediction with minimum network footprint and privacy ramifications (if any). The idea is to introduce feedback to conventional FL, in multi-tier architectures. Although we use cascaded FL for UE prediction tasks, the idea is rather general and can be used for many prediction problems in multi-tier architectures, such as cellular networks. We evaluate the performance of cascaded FL by detailed and 3GPP compliant simulations of London's city center. Our simulations show that the proposed cascaded FL can achieve up to 54% improvement over conventional FL in the normalized gain, at the cost of 1.8 MB (without quantization) and no cost with quantization.

In this paper, we propose general service-oriented end-to-end energy and delay models. They facilitate the evaluation of new schemes, by enabling flexible allocation of processing functions at multilayer radio access network. These models describe the underlaying processing of communication function (PCF)'s complexity and the induced bandwidth. We also introduce the concept of processing interference and link it to the average PCF option of the competing users at the cloud. The proposed framework shows the possibility to compensate the delay or energy degradation, e.g., due to radio environment, by reallocating processing parameters, e.g., allocation of PCF or number of virtual central processing units (VCPUs). Several conclusions are inferred from the system's evaluation. For instance, at lowmodulation index (MI), the behavior of overall energy consumption is dominated by PCF energy, not the transmission energy, whereas, at high MI, the behavior of overall energy consumption is dominated by transmission energy. Centralization of PCF reduces the impact of processing interference (of competing users) on the targeted service's delay from 11 to 2 ms, and service's energy consumption from 18 to 2 J. We further evaluate the impact of PCF on the decision of offloading the service, to be computed at the cloud.

Cloud radio access network (CRAN) architecture is proposed to save energy, facilitate coordination between radio units, and achieve scalable solutions to improve radio network's performance. However, stringent delay and bandwidth constraints are incurred by fronthaul in CRAN [the network segment connecting RUs and digital units (DUs)]. Therefore, we propose a hybrid cloud radio access network architecture, where a DU's functionalities can be virtualized and split at several conceivable points. Each split option results in two-level deployment of the processing functions (central site level and remote site level) connected by a transport network, called midhaul. We study the interplay of energy efficiency and midhaul bandwidth consumption under optimal processing allocation. We jointly minimize the power and midhaul bandwidth consumption in H-CRAN, while satisfying network constraints, i.e., processing and midhaul bandwidth capacity. We enable power saving functionalities by shutting down different network components. The proposed model is formulated as a constraint programming problem. The proposed solution shows that 42 percentile of midhaul bandwidth savings can be achieved compared to the fully centralized CRAN; and 35 percentile of power consumption saving can be achieved compared to the case where all the network functions are distributed at the edge. 

Extremely large aperture arrays can enable unprecedented spatial multiplexing in beyond 5G systems due to their extremely narrow beamfocusing capabilities. However, acquiring the spatial correlation matrix to enable efficient channel estimation is a complex task due to the vast number of antenna dimensions. Recently, a new estimation method called the 'reduced-subspace least squares (RS-LS) estimator' has been proposed for densely packed arrays. This method relies solely on the geometry of the array to limit the estimation resources. In this paper, we address a gap in the existing literature by deriving the average spectral efficiency for a certain distribution of user equipments (UEs) and a lower bound on it when using the RS-LS estimator. This bound is determined by the channel gain and the statistics of the normalized spatial correlation matrices of potential UEs but, importantly, does not require knowledge of a specific UE's spatial correlation matrix. We establish that there exists a pilot length that maximizes this expression. Additionally, we derive an approximate expression for the optimal pilot length under low signal-to-noise ratio (SNR) conditions. Simulation results validate the tightness of the derived lower bound and the effectiveness of using the optimized pilot length.

Mobile money, a means of transferring payments via mobile devices, has become increasingly popular. The demand for convenient financial products or services is a crucial factor in why innovative developers want to incorporate mobile money into existing financial products/services. The goal is to provide convenient financial services that enable customers to quickly send and receive money between two mobile payment platforms.

The Swedish blockchain company, Centiglobe, is searching for a system whereby payments can be made conveniently between two mobile payment platforms, specifically Alipay and M PESA. This thesis sought to develop such a system by utilizing the application programming interfaces (APIs) (provided by Alipay and M PESA) coupled with Centiglobe’s blockchain to facilitate payments between an Alipay user and an M PESA user.

Solving this problem began with an initial literature study of previous work related to this topic and reading the extensive API documentation provided by Alipay and Daraja Safaricom (the developers of M PESA). Next, a flowchart was created and used as a guide throughout the development of the system. Testing the system entailed integration testing. The performance of the system was determined by measuring the execution time to make a cross system payment.

A one-way transfer system was developed, as Alipay users can make a payment to M PESA users but not the reverse. The results of the integration testing shows that the system is a feasible solution. The execution time of a payment shows that it is relatively quick (~9.1 seconds); thus the performance is adequate.The conclusion is that this system is a viable solution for incorporating Alipay and M PESA as mobile payment services. Moreover, the system partially facilitates person-to-person payments between them – subject to the limitations of the Alipay API. In addition, this system provides a foundation for other inter-platform mobile payment solutions.

The adoption of low latency persistent memory modules (PMMs) upends the long-established model of remote storage for distributed file systems. Instead, by colocating computation with PMM storage, we can provide applications with much higher IO performance, sub-second application failover, and strong consistency. To demonstrate this, we built the Assise distributed file system, based on a persistent, replicated coherence protocol that manages client-local PMM as a linearizable and crash-recoverable cache between applications and slower (and possibly remote) storage. Assise maximizes locality for all file IO by carrying out IO on process-local, socket-local, and client-local PMM whenever possible. Assise minimizes coherence overhead by maintaining consistency at IO operation granularity, rather than at fixed block sizes.

We compare Assise to Ceph/BlueStore, NFS, and Octopus on a cluster with Intel Optane DC PMMs and SSDs for common cloud applications and benchmarks, such as LevelDB, Postfix, and FileBench. We find that Assise improves write latency up to 22x, throughput up to 56x, fail-over time up to 103x, and scales up to 6x better than its counterparts, while providing stronger consistency semantics.

Innovation in interdomain routing has remained stagnant for over a decade. Recently, Internet eXchange Points (IXPs) have emerged as economically-advantageous interconnection points for reducing path latencies and exchanging ever increasing traffic volumes among, possibly, hundreds of networks. Given their far-reaching implications on interdomain routing, IXPs are the ideal place to foster network innovation and extend the benefits of software defined networking (SDN) to the interdomain level. In this paper, we present, evaluate, and demonstrate ENDEAVOUR, an SDN platform for IXPs. ENDEAVOUR can be deployed on a multi-hop IXP fabric, supports a large number of use cases, and is highly scalable, while avoiding broadcast storms. Our evaluation with real data from one of the largest IXPs, demonstrates the benefits and scalability of our solution: ENDEAVOUR requires around 70% fewer rules than alternative SDN solutions thanks to our rule partitioning mechanism. In addition, by providing an open source solution, we invite everyone from the community to experiment (and improve) our implementation as well as adapt it to new use cases.

 Mobile payments are transforming the payments industry. These services open up the opportunity for non-banking actors to enter the market. In order to embrace this challenge, banks, traditional payments market players, are forced to launch mobile payments. However, in Europe and most developed economies, a big number of launched services get terminated soon after their introduction in the market. Hence, the ability of different actors to act locally calls for additional research.

The main objective of this thesis is to broaden knowledge and understanding about the ways mobile payment service providers address the opportunities and challenges of mobile payment services. In order to investigate this problem, this research (i) explores factors stimulating and hindering the introduction of mobile payments using perspectives of different types of service providers (i.e., banks, independent providers, direct operator billing providers, retailers, and public transport companies) and (ii) seeks to explain the importance of these factors for each type of provider.

The primary data collected using interview method. More than 40 industry representatives in six countries were contacted. The Service, Technology, Organisation, and Finance (STOF) model was used as a theoretical research framework. This is a business modelling framework that specifies a set of critical design issues that have to  be considered within each model’s domain.

Research findings highlight that the organisation domain is the key domain, which  affects all other domains and has an impact on the general viability of the business model. A comparison of the approaches used by different service providers to address each of the critical design issues helped to identify the factors that are most important within each domain. These factors stimulate or hinder development of a viable business model within each category of service providers.

This research contributes to a better understanding of challenges and success factors associated with the design of business models for new mobile services and uses the lens of the STOF model. The contributions to the academic research on mobile payments are: (i) collection and analysis of a rich empirical data set on mobile payment services implemented in six Northern European countries, (ii) discussion of a bigger picture by connecting research findings to the context of the existing payment system, (iii) extension  of knowledge on business models for mobile ticketing, and (iv) extension of knowledge on the value of mobile ticketing services in the business-to-business (B2B) context.

Mobile payments are new services enabled by evolution of information and communication technologies. These services can be provided by different types of actors both banks and non-banks. The understanding of capabilities and limitations of different service providers to act in local markets requires further understanding. Implemented research seeks to extend knowledge in this area. I have implemented research focused on approaches used by different types of service providers including banks, independent providers, operator billing providers, retailers, and public transport companies in six Northern European countries.

Exploratory part of the research aims to address the following research question: What factors stimulate and hinder the introduction of mobile payments? The main objective of this report is to present primary data collected during the research through interviews with contacted companies.

The collected primary data is classified and organised using the STOF (Service, Technology, Organisation, and Finance) model. Evidence is presented in tables. This primary data is further used for explanatory study. At the same time, this data can be used by other researchers studying the same area. The collected data is reach in facts and presents the overview of different strategies. 

A recent review on mobile payment research by Dahlberg et al. (2015) concludes that there is a need to synthesise this research area by studying contexts in which innovation is done as well as to integrate different aspects of research. This article aims to provide a proposal for how to achieve such integration and context-orientation by building on previous studies as well as an additional review. Our systematic literature review of mobile payments research is focused on papers published during 2006–2016. The main objective is to examine how mobile payments research has been conducted from the methodological and theoretical perspectives. Our findings show that research on mobile payments is a multidisciplinary research. Three main themes, which are in line with previous studies, in research are: customer adoption, technological aspects, and business aspects. Moreover, research is mainly analytical based on deductive approach. To meet the challenge formulated in the previous research, we propose and apply a socio-technical system framework to achieve synthesis and context-specific consideration in future research on mobile payments.

A recent review of mobile payment research by Dahlberg et al. (2015) concludes that there is a need to synthesise this research area by studying contexts in which innovation is carried out and to integrate different aspects of research. This article aims to provide a proposal for how to achieve such integration and context orientation by building on previous studies and offering an additional review. Our systematic literature review of mobile payments research is focused on papers published during 2006–2016. The main objective is to examine how mobile payments research has been conducted from the methodological and theoretical perspectives. Our findings show that research on mobile payments is a multidisciplinary research. Three main themes in the research (in line with previous studies) are customer adoption, technological aspects, and business aspects. Moreover, research is mainly analytical based on a deductive approach. To meet the challenge formulated in the previous research, we propose and apply a socio-technical system framework to achieve synthesis and context-specific consideration in future research on mobile payments.

Started as SMS ticketing, today, mobile ticketing services become a platform for combined mobility solutions that integrate mobile ticketing, bike renting, car sharing, and parking payment services. Mobile ticketing services provide a number of benefits for public transport companies and passengers, however, in some Northern European countries, the share of mobile ticket sales does not exceed 10 percent. In this paper, we analyse business models of three types of mobile ticketing services (SMS, in-app, and "Be-InBe-Out" (BIBO)) in order to identify factors that negatively affect wider penetration of mobile ticketing. In order to do so, we use STOF (Service, Technology, Organisation, and Finance) model. The research reveals that there are a number of challenges related to service, technology, and finance domains of mobile ticketing. Due to this, with the help of price policy, many public transport companies push choices of their customers towards use of plastic travel card.

Started as SMS ticketing, today, mobile ticketing services become a platform for combined mobility solutions that integrate mobile ticketing, bike renting, car sharing, and parking payment services. Mobile ticketing services provide a number of benefits for public transport companies and passengers, however, in some Northern European countries, the share of mobile ticket sales does not exceed 10 percent. In this paper, we analyse business models of three types of mobile ticketing services (SMS, in-app, and “Be-In Be-Out” (BIBO)) in order to identify factors that negatively affect wider penetration of mobile ticketing. In order to do so, we use STOF (Service, Technology, Organisation, and Finance) model. The research reveals that there are a number of challenges related to service, technology, and finance domains of mobile ticketing. Due to this, with the help of price policy, many public transport companies push choices of their customers towards use of plastic travel card.

Within the field of mobile payments, the market segment for mobile ticketing services is expected to grow, driven by the public transport sector. Although a large number of studies have explored the value of mobile payments to customers (ie service end users), there is a lack of research exploring the value of mobile payment and mobile ticketing for business customers (ie transport companies and retailers). This paper aims to address this gap and to explore the kinds of value that mobile ticketing services create for public transport companies. The research considers mobile ticketing services in six Northern European countries. The research findings suggest that within the business-to-business context, mobile ticketing services create multiple dimensions of value, namely enhanced technical functionality; financial value; better working environment; operational value; and reputational and customer relationship value. For passengers, meanwhile, value takes the form of convenience and service enhancement. These benefits are factors driving adoption of mobile ticketing and mobile payment services by public transport companies.

The dynamic provisioning of Service Function Chain (SFC) using Virtual Network Functions (VNFs) is a challenging problem, especially for availability-constrained services. The provisioning of backup resources is often used to ensure that availability requirements are fulfilled. However, the assignment of backup resources should be carefully designed to avoid resource inefficiencies as much as possible. This paper proposes the Optional Backup with Shared Path and Shared Function (OBSPSF) strategy, which aims at improving resource efficiency while fulfilling the availability requirements of SFC requests. The strategy uses optional backup provisioning to ensure that backup resources are assigned only when strictly needed (i.e., when the SFC alone does not meet the availability constraint). Moreover, OBSPSF encourages backup sharing (among both connectivity and backup VNFs) to reduce the backup resource overhead. Results show that the strategy can accommodate orders-of-magnitude more services than benchmark heuristics from the literature.

Network Function Virtualization (NFV) replaces the hardware that supports packet processing in network operation from specific-by general-purpose ones, reducing costs and bringing more flexibility and agility to the network operation. However, this shift can cause performance losses due to the non-optimal packet processing capabilities of the general-purpose hardware. Moreover, supporting the line rate of optical network channels with Virtualized Network Functions (VNFs) is a challenging task. This work analyzes the benefits of using Graphics Processing Units (GPUs) to support the execution of a Deep Packet Inspection (DPI) VNF towards supporting the line rate of an optical channel. The use of GPUs in VNFs has a great potential to increase throughput, but the delay incurred might be an issue for some functions. Our simulation was performed using an Intrusion Detection Systems (IDS) which performs DPI deployed as a VNF under real-world traffic scaled to high bit rates. Results show that the packet processing speedup achieved by using GPUs can reach up to 19 times, at the expense of a higher packet delay.

As the use of mobile payment applications (apps) and electronic wallets (e-wallets)increases, so does the demand for a improved user experience when interactingwith these apps. The field of Human-Computer interaction (HCI) focuses onthe design, evaluation, and implementation of interactive computing systems forhuman use. One aspect of HCI is usability, i.e., the quality of the interactions witha product or system.

This thesis investigates how an e-wallet can be designed to provide a high level ofusability by conforming to best HCI practices and by formative evaluation using aset of usability evaluation methods.The research process consisted of an initial literature study and developmentof a prototype, which was evaluated iteratively through Thinking-aloud-protocol(TAP) and a combination of performance measurements and questionnaire by achosen test group.

By each iteration, the results of the performance measurements, as well as theverbal data improved. The most complex or difficult task, for the test subjectsto perform, was, according to the results, Pay via manual input. All goals wereachieved for all tasks except for the performance goal of a percentage of errorsbelow 5%.

To conclude, it was clear that the test subjects had more trouble understandingthe concept of the e-wallet rather than navigating and completing tasks. Thedifficulties lay in understanding how currencies were stored and how transactionshappened. When developing this e-wallet we noticed that the most importantissue was to make new functions and concepts familiar to the user through relatingit to recognizable ideas.

Bitcoin is considered as the most valuable currency in the world. Besides being highly valuable, its value has also experienced a steep increase, from around 1 dollar in 2010 to around 18000 in 2017. Then, in recent years, it has attracted considerable attention in a diverse set of fields, including economics and computer science. The former mainly focuses on studying how it affects the market, determining reasons behinds its price fluctuations, and predicting its future prices. The latter mainly focuses on its vulnerabilities, scalability, and other techno-cryptoeconomic issues. Here, we aim at revealing the usefulness of traditional autoregressive integrative moving average (ARIMA)model in predicting the future value of bitcoin by analyzing the price time series in a 3-years-long time period. On the one hand, our empirical studies reveal that this simple scheme is efficient in sub-periods in which the behavior of the time-series is almost unchanged, especially when it is used for short-term prediction,e.g. 1-day. On the other hand, when we try to train the Arima model to a 3-years-long period, during which the bitcoin price has experienced different behaviors, or when we try to use it for a long-term prediction, we observe that it introduces large prediction errors. Especially, the ARIMA model is unable to capture the sharp fluctuations in the price, e.g. the volatility at the end of 2017. Then, it calls for more features to be extracted and used along with the price for a more accurate prediction of the price. We have further investigated the bitcoin price prediction using an ARIMA model trained over the whole dataset, as well as a limited part of the history of the bitcoin price, with length of w, as inputs. Our study sheds lights on the interaction of the prediction accuracy, choice of (p; q; d), and window size w.

Interference channel, in which multiple user pairs communicate over shared resources, is a building block of communications networks. Here, the K-user interference channel (IC) aided by J instantaneous relays (IRs), i.e. relays without delay, is considered. For KICJR networks, where K>2 and J>1, the DoF performance and achievable schemes have not been investigated in literature. Here, we devise a novel achievable scheme, called restricted interference alignment (RIA), which restricts the received interference from each source at each destination in a limited sub-space, and then, aligns the restricted interferences. Furthermore, we develop an analytical tool for finding a tight upper bound on DoF of KICJR networks by analyzing the properness of the interference alignment equations. Moreover, we develop linear beamforming design for sources and relays based on the mean square error (MSE) minimization, as an achievable scheme. The performance evaluation results show that the achievable sum DoF by using the proposed RIA scheme and the MSE-based beamforming design match well with the derived upper bounds. Furthermore, the results confirm that the achieved sum DoF using the RIA scheme outperforms the existing achievable schemes. Motivated by these promising results, we further investigate impact of IRs in providing physical layer security, i.e. achieving secure DoF in interference networks. Specifically, we derive a lower bound on the achievable secure DoF by devising an achievable scheme, called transmission in the null space (TNS). This scheme makes the cross channels rank deficient and enables sources to select their transmit filters in the constructed null spaces. Performance evaluation shows that by leveraging IRs, the secure DoF performance of interference networks could be increased significantly. The proposed analytical framework in this work for rank deficiency-powered DoF analysis is expected to also attract attention from other research areas, e.g. beamforming design for millimeter wave communications.

Interference channel, in which multiple user pairs communicate over shared resources, is a building block of communications networks. Here, the K-user interference channel (IC) aided by J instantaneous relays (IRs), i.e. relays without delay, is considered. For KICJR networks, where K > 2 and J > 1, the DoF performance and achievable schemes have not been investigated in literature. Here, we devise a novel achievable scheme, called restricted interference alignment (RIA), which restricts the received interference from each source at each destination in a limited sub-space, and then, aligns the restricted interferences. Furthermore, we develop an analytical tool for finding a tight upper bound on DoF of KICJR networks by analyzing the properness of the interference alignment equations. Moreover, we develop linear beamforming design for sources and relays based on the mean square error (MSE) minimization, as an achievable scheme. The performance evaluation results show that the achievable sum DoF by using the proposed RIA scheme and the MSE-based beamforming design match well with the derived upper bounds. Furthermore, the results confirm that the achieved sum DoF using the RIA scheme outperforms the existing achievable schemes. Motivated by these promising results, we further investigate impact of IRs in providing physical layer security, i.e. achieving secure DoF in interference networks. Specifically, we derive a lower bound on the achievable secure DoF by devising an achievable scheme, called transmission in the null space (TNS). This scheme makes the cross channels rank deficient and enables sources to select their transmit filters in the constructed null spaces. Performance evaluation shows that by leveraging IRs, the secure DoF performance of interference networks could be increased significantly. The proposed analytical framework in this work for rank deficiency-powered DoF analysis is expected to also attract attention from other research areas, e.g. beamforming design for millimeter wave communications.

Internet of Things (IoT) communications refer to the interconnections of smart devices, with reduced human intervention, which enable them to participate more actively in everyday life. It is expected that introduction of a scalable, energy efficient, and reliable IoT connectivity solution can bring enormous benefits to the society, especially in healthcare, wellbeing, and smart homes and industries. In the last two decades, there have been efforts in academia and industry to enable IoT connectivity over the legacy communications infrastructure. In recent years, it is becoming more and more clear that the characteristics and requirements of the IoT traffic are way different from the legacy traffic originating from existing communications services like voice and web surfing, and hence, IoT-specific communications systems and protocols have received profound attention. Until now, several revolutionary solutions, including cellular narrowband-IoT, SigFox, and LoRaWAN, have been proposed/implemented. As each of these solutions focuses on a subset of performance indicators at the cost of sacrificing the others, there is still lack of a dominant player in the market capable of delivering scalable, energy efficient, and reliable IoT connectivity. The present work is devoted to characterizing state-of-the-art technologies for enabling large-scale IoT connectivity, their limitations, and our contributions in performance assessment and enhancement for them. Especially, we focus on grant-free radio access and investigate its applications in supporting massive and critical IoT communications. The main contributions presented in this work include (a) developing an analytical framework for energy/latency/reliability assessment of IoT communications over grant-based and grant-free systems; (b) developing advanced RRM techniques for energy and spectrum efficient serving of massive and critical IoT communications, respectively; and (c) developing advanced data transmission/reception protocols for grant-free IoT networks. The performance evaluation results indicate that supporting IoT devices with stringent energy/delay constraints over limited radio resources calls for aggressive technologies breaking the barrier of the legacy interference-free orthogonal communications.

The disclosure relates to communication systems and, more particularly, to a system and method for providing communication rules based on a status associated with a battery of a device.

Leveraging grant-free radio access for enabling low-power wide-area (LPWA) Internet of Things (IoT) connectivity has attracted lots of attention in recent years. Regarding lack of research on LPWA IoT networks, this work is devoted to reliability modeling, battery-lifetime analysis, and operation-control of such networks. We derive the interplay amongst density of the access points, communication bandwidth, volume of traffic from heterogeneous sources, and quality of service (QoS) in communications. The presented analytical framework comprises modeling of interference from heterogeneous sources with correlated deployment locations and time-frequency asynchronous radio-resource usage patterns. The derived expressions represent the operation regions and rates in which, energy and cost resources of devices and the access network, respectively, could be traded to achieve a given level of QoS in communications. For example, our expressions indicate the expected increase in QoS by increasing number of transmitted replicas, transmit power, density of the access points, and communication bandwidth. Our results further shed light on scalability of such networks and figure out the bounds up to which, scaling resources can compensate the increase in traffic volume and QoS demand. Finally, we present an energy-optimized operation control policy for IoT devices. The simulation results confirm tightness of the derived analytical expressions, and indicate usefulness of them in planning and operation control of IoT networks.

Enabling large-scale energy-efficient Internet-ofthings (IoT) connectivity is an essential step towards realization of networked society. While legacy wide-area wireless systems are highly dependent on network-side coordination, the level of consumed energy in signaling, as well as the expected increase in the number of IoT devices, makes such centralized approaches infeasible in future. Here, we address this problem by self-coordination for IoT networks through learning from past communications. To this end, we first study low-complexity distributed learning approaches applicable in IoT communications. Then, we present a learning solution to adapt communication parameters of devices to the environment for maximizing energy efficiency and reliability in data transmissions. Furthermore, leveraging tools from stochastic geometry, we evaluate the performance of proposed distributed learning solution against the centralized coordination. Finally, we analyze the interplay amongst energy efficiency, reliability of communications against noise and interference over data channel, and reliability against adversarial interference over data and feedback channels. The simulation results indicate that compared to the state of the art approaches, both energy efficiency and reliability in IoT communications could be significantly improved using the proposed learning approach. These promising results, which are achieved using lightweight learning, make our solution favorable in many low-cost low-power IoT applications.

Supporting ultra-reliable low-latency communications (URLLC) is a major challenge of 5G wireless networks. Whilst enabling URLLC is essential for realizing many promising 5G applications, the design of communications' solutions for serving such unseen type of traffic with stringent delay and reliability requirements is in its infancy. In prior studies, physical and MAC layer solutions for assuring the end-to-end delay requirement of scheduled URLLC traffic have been investigated. However, there is lack of study on enabling non-scheduled transmission of urgent URLLC traffic, especially in coexistence with the scheduled URLLC traffic. This study at first sheds light into the coexistence design challenges, especially the radio resource management (RRM) problem. It also leverages recent advances in machine learning (ML) to exploit spatial/temporal correlation in user behaviors and use of radio  resources, and proposes a distributed risk-aware ML solution for RRM. The proposed solution benefits from hybrid orthogonal/non-orthogonal radio resource slicing, and proactively regulates the spectrum needed for satisfying delay/reliability requirement of each traffic type. A case study is introduced to investigate the potential of the proposed RRM in serving coexisting URLLC traffic types. The results further provide insights on the interplay between the reliabilities of coexisting traffic, uncertainties in users' demands and channel conditions, and amount of required radio resources.

Cellular connectivity for drones comes with a wide set of challenges as well as opportunities. Communication of cellular-connected drones is influenced by 3-dimensional mobility and line-of-sight channel characteristics which results in higher number of handovers with increasing altitude. Our cell planning simulations in coexistence of aerial and terrestrial users indicate that the severe interference from drones to base stations is a major challenge for uplink communications of terrestrial users. Here, we first present the major challenges in co-existence of terrestrial and drone communications by considering real geographical network data for Stockholm. Then, we derive analytical models for the key performance indicators (KPIs), including communications delay and interference over cellular networks, and formulate the handover and radio resource management (H-RRM) optimization problem. Afterwards, we transform this problem into a machine learning problem, and propose a deep reinforcement learning solution to solve HRRM problem. Finally, using simulation results, we present how the speed and altitude of drones, and the tolerable level of interference, shape the optimal H-RRM policy in the network. Especially, the heat-maps of handover decisions for different altitudes/speeds of drones have been presented, which promote a revision of the legacy handover schemes and boundaries of cells in the sky.

The main building block of Internet of Things (IoT) ecosystem is providing low-cost scalable connectivity for the radio/compute-constrained devices. This connectivity could be realized over the licensed spectrum like Narrowband-IoT (NBIoT) networks, or over the unlicensed spectrum like NBIoT-Unlicensed, SigFox and LoRa networks. In this paper, performance of IoT communications utilizing the unlicensed band, e.g. the 863–870 MHz in the Europe, in indoor use-cases like smart home, is investigated. More specifically, we focus on two scenarios for channel access management: (i) coordinated access, where the activity patterns of gateways and sensors are coordinated with neighbors, and (ii) uncoordinated access, in which each gateway and its associated nodes work independently from the neighbor ones. We further investigate a distributed coordination scheme in which, devices learn to coordinate their activity patterns leveraging tools from reinforcement learning. Closed-form expressions for capacity of the system, in terms of the number of sustained connections per gateway fulfilling a minimum quality of service (QoS) constraint are derived, and are further evaluated using simulations. Furthermore, delay-reliability and inter network interference-intra network collision performance tradeoffs offered by coordination are figured out. The simulation results highlight the impact of system and traffic parameters on the performance tradeoffs and characterize performance regions in which coordinated scheme outperforms the uncoordinated one, and vice versa. For example, for a packet loss requirement of 1%, the number of connected devices could be doubled by coordination.

Grant-free radio access is a promising solution for reducing energy consumption and access delay in low power wide-area (LPWA) Internet of Things (IoT) networks. This work is devoted to reliability modeling, battery-lifetime analysis, resource provisioning, and operation control for grantfreeIoT networks. Our modeling captures correlation in devices’locations, benefits from 3D (time/frequency/code) interference analysis, and enables coexistence analysis of multi-type IoT technologies. We derive the interplay amongst density of the access points, communication bandwidth, traffic volume, and quality of service (QoS) of communications. Deriving the interplay enables scalability analysis, i.e. it figures out the required increase in device's energy consumption (or access network’s resources) for compensating the increase in traffic volume or QoS demand. Our major contribution consists in deriving traffic loads and respective exchange rates in which, energy and cost resources of devices and the access network, respectively, could be traded to achieve a given level of QoS. We further indicate operation regions in which scaling a parameter turns from being a friend into a foe. Finally, we present energy- and cost-optimized operation control and resource provisioning strategies, respectively. The simulation results confirm tightness of the analytical expressions, and indicate the usefulness of them in planning and operation control of IoT networks.

Currently, there is a plethora of low-power wide-area IoT networking solutions available, each targeting a specific niche of use-cases and deployment scenarios. Existing studies on reliability evaluations of IoT solutions rely on the assumption that a single technology is deployed in the service area, or different IoT technologies operate over dedicated spectrum bands. Here, we investigate the reliability performance of IoT communications in coexisting scenarios, where multiple competing radio-access technologies share spectrum resources. Our focus is on solutions exploiting grant-free communications, which are gaining traction due to their potential to lower the energy consumption, and have been adopted in recent IoT technologies like SigFox and LoRa. We first derive an analytical model of the interference, comprising both inter- and intra-technology interference sources. We then leverage the Poisson Cluster Process for modeling distribution of devices in the service area, and derive expressions for the communication reliability, energy consumption, and battery lifetime of IoT devices. Exploiting these expressions, we study the energy-reliability trade-offs and investigate strategies to maintain or improve communication reliability, while minimizing energy consumption in coexisting scenarios by proper adjustment of communications parameters at the device side and provisioning resources at the network side. We verify the analytical results via numerical evaluations, confirming their accuracy and performing optimization in some example networking setups.

Narrowband Internet of Things (NB-IoT) is the latest IoT connectivity solution presented by the 3rd generation partnership project (3GPP). NB-IoT introduces coverage classes and offers a significant link budget improvement by allowing repeated transmissions by nodes that experience high path loss. However, those repetitions necessarily increase the energy consumption and the latency in the whole NB-IoT system. The extent to which the whole system is affected depends on the scheduling of the uplink and downlink channels. We address this question, not treated previously, by developing a tractable model of NB-IoT access protocol operation, comprising message exchanges in random-access, control, and data channels, both in the uplink and downlink The model is then used to analyze the impact of channel scheduling as well as the interaction of coexisting coverage classes, through derivation of the expected latency and battery lifetime for each coverage class. These results are subsequently employed in investigation of latency-energy tradeoff in NB-IoT channel scheduling as well as determining the optimized operation points. Simulations results show validity of the analysis and confirm that channel scheduling and coexistence of coverage classes significantly affect latency and battery lifetime performance of NB-IoT devices.

Radio resource management (RRM) is a major factor affecting the delay, reliability, and energy consumption of Internet of Things (IoT) communications. This article is focused on grant-free access, a class of techniques suited to support massive IoT connectivity. Within the proposed scheme, the IoT devices transmit multiple replicas of the same packet. In addition to that, the receiver makes use of the random timing and frequency offsets in order to carry out Successive Interference Cancellation (SIC). The system performance is investigated by using a model based on stochastic geometry, leading to closed-form expressions for the key performance indicators, such as reliability and battery lifetime. The framework allows optimization of the number of replicas per device. This results in overall improvement of the energy consumption, delay and reliability, at the expense of more complex processing at the Base Station. The evaluation results indicate that the proposed data transmission and reception schemes can significantly prolong battery lifetime of IoT devices by removing the need for connection establishment and reducing the number of retransmissions. The obtained results also indicate existence of traffic-load regions, where grant-free radio access outperforms the grant-based one, which is used in LTE and NB-IoT systems. These results pave the way for enabling intelligent grant-based/free operation mode switching in 5G networks.

There is a lack of research on the analysis of peruser traffic in cellular networks, for deriving and following traffic-aware network management. In fact, the legacy design approach, in which resource provisioning and operation control are performed based on the cell-aggregated traffic scenarios, are not so energy- and cost-efficient and need to be substituted with user-centric predictive analysis of mobile network traffic and proactive network resource management. Here, we shed light on this problem by designing traffic prediction tools that utilize standard machine learning (ML) tools, including long shortterm memory (LSTM) and autoregressive integrated moving average (ARIMA) on top of per-user data. We present an expansive empirical evaluation of the designed solutions over a real network traffic dataset. Within this analysis, the impact of different parameters, such as the time granularity, the length of future predictions, and feature selection are investigated. As a potential application of these solutions, we present an ML-powered Discontinuous reception (DRX) scheme for energy saving. Towards this end, we leverage the derived ML models for dynamic DRX parameter adaptation to user traffic. The performance evaluation results demonstrate the superiority of LSTM over ARIMA in general, especially when the length of the training time series is high enough, and it is augmented by a wisely-selected set of features. Furthermore, the results show that adaptation of DRX parameters by online prediction of future traffic provides much more energy-saving at low latency cost in comparison with the legacy cell-wide DRX parameter adaptation.

Narrowband Internet-of-Things (NB-IoT) offers a significant link budget improvement in comparison with the legacy networks by introducing different coverage classes, allowing repeated transmissions, and tuning the repetition order based on the path-loss in communications. However, those repetitions necessarily increase energy consumption and latency in the whole NB-IoT system. The extent to which the whole system is affected depends on the scheduling of the uplink and downlink channels. We address this question, not treated previously, by developing a tractable model of NB-IoT connectivity, comprising message exchanges in random-access, control, and data channels. The model is then used to analyze the impact of channel scheduling and interaction of coverage classes on the performance of IoT devices through the derivation of the expected latency and battery lifetime. These results are subsequently employed in determining the optimized operation points, i.e., (i) scheduling of data and control channels for a given set of users and respective coverage classes, or (ii) determining the optimal set of coverage classes and served users per coverage class for a given scheduling strategy. Simulations results show the validity of the analysis and confirm that channel scheduling and coexistence of coverage classes significantly affect latency and battery lifetime performance of NB-IoT devices.

One of the major challenges for Internet-of-Things applications is that the existing cellular technologies do not support the uplink IoT traffic in an energy-efficient manner. There are two principal ways for serving the uplink IoT traffic: grant-based (i.e., scheduled) and grant-free (i.e., random access). Grant-based access provides fine-grained control of reliability and latency at the cost of energy consumption required for signaling. Grant-free access removes the signaling overhead at the cost of looser control of performance in terms of reliability and latency. However, a precise analysis of reliability, latency and energy performance of grant-free access (GFA) is largely missing. This article focuses on a GFA-type protocol, in which a device transmits several packet replicas, asynchronously with respect to the other devices. Using stochastic geometry, we derive closed-form expressions for reliability, delay, and energy consumption, which can be used to identify the tradeoffs among these performance parameters. In order to improve the performance of the protocol, we develop a receiver that leverages the random timing and frequency offsets among the devices in order to facilitate resolution of collisions. This is complemented by a per-device adaptive scheme that controls the number of transmitted replicas. The evaluation confirms the validity of the analysis and the potential of the proposed solution, identifying operating regions in which GFA outperforms the grant-based access.

In this paper, we present an achievable security scheme for an interference channel with arbitrary number of users. In this model, each receiver should be able to decode its intended message while it should remain ignorant regarding messages intended for other receivers. Our scheme relies ontransmitters to collectively ensure the confidentiality of the transmitted messages using a cooperative jamming technique and lattice alignment. The Asymmetric compute-and-forward framework is used to perform the decoding operation. The proposed scheme is the first asymptotically optimal achievable scheme for this security scenario which scales to arbitrary number of users and works for any finite-valued SNR. Also, our scheme achieves the upper bound sum secure degrees of freedom of 1 without using external helpers and thus the achievable rates lie within constant gap from sum secure capacity.

The forthcoming 6G and beyond wireless networks are anticipated to introduce new groundbreaking applications, such as Integrated Sensing and Communications (ISAC), potentially leveraging much wider bandwidths at higher frequencies and using significantly larger antenna arrays at base stations. This puts the system operation in the radiative near-field regime of the BS antenna array, characterized by spherical rather than flat wavefronts. In this letter, we refer to such a system as near-field ISAC. Unlike the far-field regime, the near-field regime allows for precise focusing of transmission beams on specific areas, making it possible to simultaneously determine a target's direction and range from a single base station and resolve targets located in the same direction. This work designs the transmit symbol vector in near-field ISAC to maximize a weighted combination of sensing and communication performances subject to a total power constraint using symbol-level precoding (SLP). The formulated optimization problem is convex, and the solution is used to estimate the angle and range of the considered targets using the 2D MUSIC algorithm. The simulation results suggest that the SLP-based design outperforms the block-level-based counterpart. Moreover, the 2D MUSIC algorithm accurately estimates the targets' parameters.

Modern multiple-input multiple-output (MIMO) communication systems are almost exclusively designed assuming locally plane wavefronts over antenna arrays. This is known as the far-field approximation and is soundly justified at sub-6-GHz frequencies at most relevant transmission ranges. However, when higher frequencies and shorter transmission ranges are used, the wave curvature over the array is no longer negligible, and arrays operate in the so-called radiative near-field region. This letter aims to show that the classical far-field approximation may significantly underestimate the achievable spectral efficiency of multi-user MIMO communications operating in the 30-GHz bands and above, even at ranges beyond the Fraunhofer distance. For planar arrays with typical sizes, we show that computing combining schemes based on the far-field model significantly reduces channel gain and spatial multiplexing capability. When the radiative near-field model is used, interference rejection schemes, such as the optimal minimum mean-square-error combiner, appear to be very promising, when combined with electrically large arrays, to meet the requirements of next-generation networks.

Platooning has the potential to be a breakthrough in increasing road capacity and reducing fuel consumption, as it allows a chain of vehicles to closely follow each other on the road. When the number of vehicles increases, platoons will follow one another in what is referred to as multiplatooning. Many Cooperative Intelligent Transportation Systems (C-ITS) applications rely on periodically exchanged beacons among vehicles to improve traffic safety. However, as the number of connected vehicles increases, the network may become congested due to periodically exchanged beacons. Therefore, without some congestion control method, safety critical messages such as Cooperative Awareness Messages (CAMs) may not be delivered on time in high vehicle density scenarios. Both the European Telecommunications Standards Institute (ETSI) and the Institute of Electrical and Electronics Engineers (IEEE) have been working on different standards to support vehicular communication. ETSI dened the Decentralized Congestion Control (DCC) mechanism which adapts transmission parameters (message rate, transmit data rate, and transmit power, etc.) to keep channel load under control. ETSI DCC utilizes a three-state machine with RELAXED, ACTIVE, and RESTRICTIVE states. In this thesis, we implemented this three-state machine by adapting the message rate based on the channel busy ratio (CBR). We name this message-rate based three-state machine DCC-3. DCC-3 has the ability to control channel load; however, it has unfairness and instability problems due to the dramatic parameter changes between states. Therefore, we divided the ACTIVE state of DCC-3 into ve sub-states, and refer to this as DCC-7. We benchmarked DCC-3 against static beaconing (STB), dynamic beaconing (DynB), LInear MEssage Rate Integrated Control (LIMERIC), and DCC-7 using different evaluation metrics with different numbers of platoons. Our results from the Plexe simulator demonstrate that DCC-7 has the best performance when considering all evaluation metrics, including CBR, Inter-reception time (IRT), collisions, safe time ratio, and fairness. Furthermore, we found using transmit power control could greatly improve the performance of CBR and collision rates.

Industry 4.0 imposes strict requirements on the fifth generation of wireless systems (5G), such as high reliability, high availability, and low latency. Guaranteeing such requirements implies that system failures should occur with an extremely low probability. However, some applications (e.g., training a reinforcement learning algorithm to operate in highly reliable systems or rare event simulations) require access to a broad range of observed failures and extreme values, preferably in a short time. In this paper, we propose IL-GAN, an alternative training framework for generative adversarial networks (GANs), which leverages incremental learning (IL) to enable the generation to learn the tail behavior of the distribution using only a few samples. We validate the proposed IL-GAN with data from 5G simulations on a factory automation scenario and real measurements gathered from various video streaming platforms. Our evaluations show that, compared to the state-of-the-art, our solution can significantly improve the learning and generation performance, not only for the tail distribution but also for the rest of the distribution.

Electrification turned over a new leaf in aviation by introducing new types of aerial vehicles along with new means of transportation. Addressing a plethora of use cases, drones are gaining attention in the industry and increasingly appear in the sky. Emerging concepts of flying taxi enable passengers to be transported over several tens of kilometers. Therefore, unmanned traffic management systems are under development to cope with the complexity of future airspace, thereby resulting in unprecedented communication needs. Moreover, the long-term increase in the number of commercial airplanes pushes the limits of voice-oriented communications, and future options such as single-pilot operations demand robust connectivity. In this survey, we provide a comprehensive review and vision for enabling the connectivity applications of aerial vehicles utilizing current and future communication technologies. We begin by categorizing the connectivity use cases per aerial vehicle and analyzing their connectivity requirements. By reviewing more than 500 related studies, we aim for a comprehensive approach to cover wireless communication technologies, and provide an overview of recent findings from the literature toward the possibilities and challenges of employing the wireless communication standards. After analyzing the proposed network architectures, we list the open-source testbed platforms to facilitate future investigations by researchers. This study helped us observe that while numerous works focused on cellular technologies to enable connectivity for aerial platforms, a single wireless technology is not sufficient to meet the stringent connectivity demands of the aerial use cases, especially for the piloting operations. We identified the need of further investigations on multi-technology heterogeneous network architectures to enable robust and real-time connectivity in the sky. Future works should consider suitable technology combinations to develop unified aerial networks that can meet the diverse quality of service demands of the aerial use cases.

Today, datacenter operators deploy Load-balancers (LBs) to efficiently utilize server resources, but must over-provision server resources (by up to 30%) because of load imbalances and the desire to bound tail service latency. We posit one of the reasons for these imbalances is the lack of per-core load statistics in existing LBs. As a first step, we designed CrossRSS, a CPU core-aware LB that dynamically assigns incoming connections to the least loaded cores in the server pool. CrossRSS leverages knowledge of the dispatching by each server's Network Interface Card (NIC) to specific cores to reduce imbalances by more than an order of magnitude compared to existing LBs in a proof-of-concept datacenter environment, processing 12% more packets with the same number of cores.

While the current literature typically focuses on load-balancing among multiple servers, in this paper, we demonstrate the importance of load-balancing within a single machine (potentially with hundreds of CPU cores). In this context, we propose a new load-balancing technique (RSS++) that dynamically modifies the receive side scaling (RSS) indirection table to spread the load across the CPU cores in a more optimal way. RSS++ incurs up to 14x lower 95th percentile tail latency and orders of magnitude fewer packet drops compared to RSS under high CPU utilization. RSS++ allows higher CPU utilization and dynamic scaling of the number of allocated CPU cores to accommodate the input load, while avoiding the typical 25% over-provisioning. RSS++ has been implemented for both (i) DPDK and (ii) the Linux kernel. Additionally, we implement a new state migration technique, which facilitates sharding and reduces contention between CPU cores accessing per-flow data. RSS++ keeps the flow-state by groups that can be migrated at once, leading to a 20% higher efficiency than a state of the art shared flow table.