Grant-free Radio Access IoT Networks: Scalability Analysis in Coexistence Scenarios

IoT networks with grant-free radio access, like SigFox and LoRa, offer low-cost durable communications over unlicensed band. These networks are becoming more and more popular due to the ever-increasing need for ultra durable, in terms of battery lifetime, IoT networks. Most studies evaluate the system performance assuming single radio access technology deployment. In this paper, we study the impact of coexisting competing radio access technologies on the system performance. Considering \mathpzc K technologies, defined by time and frequency activity factors, bandwidth, and power, which share a set of radio resources, we derive closed-form expressions for the successful transmission probability, expected battery lifetime, and experienced delay as a function of distance to the serving access point. Our analytical model, which is validated by simulation results, provides a tool to evaluate the coexistence scenarios and analyze how introduction of a new coexisting technology may degrade the system performance in terms of success probability and battery lifetime. We further investigate solutions in which this destructive effect could be compensated, e.g., by densifying the network to a certain extent and utilizing joint reception

Amplitude-Based Sequential Optimization of Energy Harvesting with Reconfigurable Intelligent Surfaces

Reconfigurable Intelligent Surfaces (RISs) have gained immense popularity in recent years because of their ability to improve wireless coverage and their flexibility to adapt to the changes in a wireless environment. These advantages are due to RISs' ability to control and manipulate radio frequency (RF) wave propagation. RISs may be deployed in inaccessible locations where it is difficult or expensive to connect to the power grid. Energy harvesting can enable the RIS to self-sustain its operations without relying on external power sources. In this paper, we consider the problem of energy harvesting for RISs in the absence of coordination with the ambient RF source. We consider both direct and indirect energy harvesting scenarios and show that the same mathematical model applies to them. We propose a sequential phase-alignment algorithm that maximizes the received power based on only power measurements. We prove the convergence of the proposed algorithm to the optimal value under specific circumstances. Our simulation results show that the proposed algorithm converges to the optimal solution in a few iterations and outperforms the random phase update method in terms of the number of required measurements.Comment: 6 pages, 6 figures, Accepted at an IEEE Asilomar Conference on Signals, Systems and Computers conferenc

Cell-Free Massive MIMO in O-RAN: Energy-Aware Joint Orchestration of Cloud, Fronthaul, and Radio Resources

For the energy-efficient deployment of cell-free massive MIMO functionality in a practical wireless network, the end-to-end (from radio site to the cloud) energy-aware operation is essential. In line with the cloudification and virtualization in the open radio access networks (O-RAN), it is indisputable to envision prospective cell-free infrastructure on top of the O-RAN architecture. In this paper, we explore the performance and power consumption of cell-free massive MIMO technology in comparison with traditional small-cell systems, in the virtualized O-RAN architecture. We compare two different functional split options and different resource orchestration mechanisms. In the end-to-end orchestration scheme, we aim to minimize the end-to-end power consumption by jointly allocating the radio, optical fronthaul, and virtualized cloud processing resources. We compare end-to-end orchestration with two other schemes: i) "radio-only" where radio resources are optimized independently from the cloud and ii) "local cloud coordination" where orchestration is only allowed among a local cluster of radio units. We develop several algorithms to solve the end-to-end power minimization and sum spectral efficiency maximization problems. The numerical results demonstrate that end-to-end resource allocation with fully virtualized fronthaul and cloud resources provides a substantial additional power saving than the other resource orchestration schemes.Comment: 17 pages, 8 figures, 3 tables, published in IEEE Journal on Selected Areas in Communications, vol. 42, no. 2, pp. 356-372, Feb. 2024, doi: 10.1109/JSAC.2023.3336187. arXiv admin note: text overlap with arXiv:2202.0925

The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe

Energy and Delay-aware Communication and Computation in Wireless Networks

Power conservation has become a severe issue in devices since battery capability advancement is not keeping pace with the swift development of other technologies such as processing technologies. This issue becomes critical when both the number of resource-intensive applications and the number of connected devices are rapidly growing. The former results in an increase in power consumption per device, and the latter causes an increase in the total power consumption of devices. Mobile edge computing (MEC) and low power wide area networks (LPWANs) are raised as two important research areas in wireless networks, which can assist devices to save power. On the one hand, devices are being considered as a platform to run resource-intensive applications while they have limited resources such as battery and processing capabilities. On the other hand, LPWANs raised as an important enabler for massive IoT (Internet of Things) to provide long-range and reliable connectivity for low power devices. The scope of this thesis spans over these two main research areas: (1) MEC, where devices can use radio resources to offload their processing tasks to the cloud to save energy. (2) LPWAN, with grant-free radio access where devices from different technology transmit their packets without any handshaking process. In particular, we consider a MEC network, where the processing resources are distributed in the proximity of the users. Hence, devices can save energy by transmitting the data to be processed to the edge cloud provided that the delay requirement is met and transmission power consumption is less than the local processing power consumption. This thesis addresses the question of whether to offload or not to minimize the uplink power consumption in a multi-cell multi-user MEC network. We consider the maximum acceptable delay as the QoS metric to be satisfied in our system. We formulate the problem as a mixed-integer nonlinear problem, which is converted into a convex form using D.C. approximation. To solve the converted optimization problem, we have proposed centralized and distributed algorithms for joint power allocation and channel assignment together with decision-making on job offloading. Our results show that there exists a region in which offloading can save power at mobile devices and increases the battery lifetime. Another focus of this thesis is on LPWANs, which are becoming more and more popular, due to the limited battery capacity and the ever-increasing need for durable battery lifetime for IoT networks. Most studies evaluate the system performance assuming single radio access technology deployment. In this thesis, we study the impact of coexisting competing radio access technologies on the system performance. We consider K technologies, defined by time and frequency activity factors, bandwidth, and power, which share a set of radio resources. Leveraging tools from stochastic geometry, we derive closed-form expressions for the successful transmission probability, expected battery lifetime, experienced delay, and expected number of retransmissions. Our analytical model, which is validated by simulation results, provides a tool to evaluate the coexistence scenarios and analyze how the introduction of a new coexisting technology may degrade the system performance in terms of success probability, delay, and battery lifetime. We further investigate the interplay between traffic load, the density of access points, and reliability/delay of communications, and examine the bounds beyond which, mean delay becomes infinite.Antalet anslutna enheter till nätverk ökar. Det finns olika trender som mobil edgecomputing (MEC) och low power wide area-nätverk (LPWAN) som har blivit intressantai trådlösa nätverk. Därför står trådlösa nätverk inför nya utmaningar som ökadenergiförbrukning. I den här avhandlingen beaktar vi dessa två mobila nätverk. I MECavlastar mobila enheter sina bearbetningsuppgifter till centraliserad beräkningsresurser (”molnet”). I avhandlingensvarar vi på följande fråga: När det är energieffektivt att avlasta dessa beräkningsuppgifter till molnet?Vi föreslår två algoritmer för att bestämma den rätta tiden för överflyttning av beräkningsuppgifter till molnet.I LPWANs, antar vi att det finns ett mycket stort antal enheter av olika art som kommunicerar mednätverket. De använder s.k. ”Grant-free”-åtkomst för att ansluta till nätverket, där basstationerna inte ger explicita sändningstillstånd till enheterna. Denanalytiska modell som föreslås i avhandlingen utgör ett verktyg för att utvärdera sådana samexistensscenarier.Med verktygen kan vi analysera olika systems prestanda när det gäller framgångssannolikhet, fördröjning och batteriershållbarhetstid.QC 20200228SOOGree

Data Driven AI Assisted Green Network Design and Management

The energy consumption of mobile networks is increasing due to an increase in traffic demands and the number of connected users to the network. To assure the sustainability of mobile networks, energy efficiency must be a key design pillar of the next generations of mobile networks. In this thesis, we deal with improving the energy efficiency of 5G and beyond networks from two perspectives, i.e., minimizing the energy consumption of the network, and energy-efficient network architecture design.  In the first part of this thesis, we focus on energy-saving methods at the base stations (BSs) which are the most energy-consuming component of mobile networks. We obtain a data set from a mobile network operator which contains network load information. It is a challenge to use mobile network traffic data to train ML algorithms for sleep mode management decisions due to the coarse time granularity of data. We propose a method to regenerate mobile network traffic data taking into account the burstiness of arrivals. We propose ML-based algorithms to decide when and how deep to put BSs into sleep. The current literature on using ML in network management lacks guaranteeing any quality of service. To handle this issue, we combine analytical model-based approaches with ML where the former is used for risk analyses in the network. We define a novel metric to quantify the risk of decision-making. We design a digital twin that can mimic the behavior of a real BS with advanced sleep modes to continuously assess the risk and monitor the performance of ML algorithms. Simulation results show that using proposed methods considerable energy saving is obtained compared to the baselines at cost of a negligible number of delayed users.  In the second part of the thesis, we study and model end-to-end energy consumption and delay of a cloud-native network architecture based on virtualized cloud RAN forming foundations of open RAN. Today large telco players achieved a consensus on an open RAN architecture based on hybrid C-RAN which is studied in this thesis.  Migrating from conventional distributed RAN architectures to network architectures based on hybrid C-RAN is challenging in terms of energy consumption and costs. We model the migration cost, in terms of both OPEX and CAPEX, with economic viability analyses of a virtualized cloud-native architecture considering the future traffic forecasts. It is not clear under what conditions C-RAN-based architectures are more cost-efficient than D-RAN considering the infrastructure cost of fronthaul and fiber links.  We formulate an integer linear programming (ILP) optimization problem to optimally design the fronthaul minimizing the migration costs. We solve the problem optimally using commercial solvers and propose AI-based heuristic algorithm to deal with the scalability issue of the problem for large problem sizes. Dealing with the trade-off between network energy consumption and delay is a challenging issue in network design and management. In a multi-layer hybrid C-RAN architecture, we formulate an ILP problem to optimize the delay by storing the popular contents in the edge closer to the users and to minimize the network energy consumption. Moreover, we investigate the trade-off between the overall energy consumption and occupied bandwidth in the network. We demonstrate that intelligent content placement reduces not only delay but also saves energy by finding a compromise between performance metrics. With a similar objective of minimizing network energy consumption, we propose a method for end-to-end network slicing, where logical networks are tailored and customized for a specific service. As per literature, end-to-end network slicing is optimized for the first time considering energy consumption. Most network slicing studies consider only radio access network resources. Intuitively, energy consumption goes down if more bandwidth resources are allocated to users when the RAN segment of the network is considered. However, with the end-to-end energy consumption model, presented in this thesis, it is demonstrated that increasing bandwidth allocation also increases processing energy consumption in the cloud and the fronthaul segment of the network. To deal with this issue, we formulate a non-convex optimization problem to allocate end-to-end resources to minimize the energy consumption of the network while guaranteeing the slices’ QoS. We transform the problem into a second-order cone programming problem and solve the problem optimally. We show that end-to-end network slicing can decrease the total energy consumption of the network compared to radio access network slicing.Energiförbrukningen i mobilnäten ökar ständigt i takt med ökade trafikvolymeroch det växande antalet användare. För att mobilnäten skall kunna vara hållbara, måsteenergieffektiviteten vara en viktig designparameter. I den här avhandlingen föreslår vihur energieffektiviteten hos 5G-nät och framtida generationers nät kan förbättras urtvå perspektiv, dels genom att minimera de befintliga nätens energiförbrukning ochdels genom en energieffektiv design av framtida nätverksarkitekturer.I avhandlingen presenteras först en översikt av olika energibesparande funktioneri mobila nät. Därefter fokuserar vi på metoder för att minska energiförbrukningen ibasstationerna, där den största mängden energi förbrukas. Det finns flera metoder beskrivna i litteraturen där man försätter basstationen i olika energisparlägen. Vi föreslåren trafikberoende metod för att maximera energibesparingen i basstationen. I dennametod försätts olika delar av basstationen i olika energisparlägen av olika varaktighet när basstationen inte är aktiv. Vi definierar ett mått för att beräkna ”risken” för attbasstationen momentant befinner sig i ett energisparläge så att den inte kan betjänaen användare som vill få access. Denna risk definieras som den fördröjning som uppstår för användarna. Metodens prestanda utvärderas genom simuleringar baserade pånätverksdata från verklig trafik från Tele2. Resultaten visar att betydande energibesparingar kan uppnås i nätet, trots en låg risk.5G-näten kommer att stödja ett brett utbud av tjänster med olika krav. Den nuvarande nätverksarkitekturen är dock inte bra på att tillhandahålla heterogena tjänster tillanvändarna med goda prestanda. För att förbättra dessa möjligheter behöver vi migrerafrån konventionella nät till nya arkitekturer. Den nya, flexiblare, arkitekturen bör utformas effektivt både vad gäller kostnader och energi. Avhandlingens andra del ägnas åtdessa frågor. Vi undersöker kostnaderna för att gå från konventionella nätarkitekturertill molnbaserade arkitekturer, s.k C-RAN arkitekturer. Vi föreslår prestandamått förend-to-end fördröjning och en energiförbrukningsmodell för C-RAN-arkitekturen. Viföreslår även s.k. ”edge caching” och nätverksdelning för att förbättra tjänstekvaliteten(QoS) i näten samtidigt som vi minimerar energiförbrukningen. I avhandlingen visarvi att metoderna kan spara energi samtidigt som de tillfredsställer användarnas krav påtjänstekvalitet.QC 20220117AI4Gree