Base stations (BSs) are the most energy-consuming segment of mobile networks. To reduce the energy consumption of BSs, inactive components of BSs, with a certain activation/deactivation time, can sleep. In this study, we model the problem of BS energy saving utilizing multiple sleep modes as a sequential MDP and propose an online traffic-aware deep Q-learning approach to maximize the long-term energy saving. However, there is a risk that BS is not sleeping at the right time and incurs delays to the users. To tackle this issue, we propose to use a digital twin model to encapsulate the dynamics underlying the investigated system and estimate the risk of decision-making (RDM) in advance. We define a novel metric to quantify RDM and predict the performance degradation. Mobile operators can compare the RDM with a tolerable threshold to decide on deactivating SMs, re-training, or activating SMs. We trained an agent using real traffic, obtained from an operator's BS in Stockholm. The data-set contains data rate information with very coarse-grained time granularity. Thus, we propose a scheme to generate a new data-set using the real network data-set which 1) has finer-grained time granularity and 2) considers the bursty behavior of traffic data. Simulation results show that using proposed methods, considerable energy saving is obtained at cost of negligible number of delayed users. Moreover, the proposed digital twin model can predict the performance of the DQN proactively in terms of RDM hence preventing the performance degradation in the network in anomalous situations.

In cell-free massive MIMO networks, scalability is one of the fundamental problems since a significant number of access points (APs) are widely distributed throughout the network area to cater to the needs of multiple user equipments (UEs). One approach to addressing this issue is through network-centric clustering, which involves dividing the network area into isolated clusters of APs, each connected to its cloud unit (CU). To address these challenges, this paper proposes a deep reinforcement learning (DRL) algorithm that jointly optimizes the network-centric cluster boundaries and decides AP deployment in each cluster to improve long-term energy efficiency. The DRL agent also aims to minimize the average UE drop rate by considering the delay requirements of each UE's requested service. The results show that at least 16% improvement in energy efficiency is obtained compared to the heuristically developed benchmarks.

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.

Previous works on cell-free massive MIMO mostly consider physical-layer and fronthaul transport aspects. How to deploy cell-free massive MIMO functionality in a practical wireless system is an open problem. This paper proposes a new cell-free architecture that can be implemented on top of a virtualized cloud radio access network (V-CRAN). We aim to minimize the end-to-end power consumption by jointly considering the radio, optical fronthaul, virtualized cloud processing resources, and spectral efficiency requirements of the user equipments. The considered optimization problem is cast in a mixed binary second-order cone programming form and, thus, the global optimum can be found using a branch-and-bound algorithm. The optimal power-efficient solution of our proposed cell-free system is compared with conventional small-cell implemented using V-CRAN, to determine the benefits of cell-free networking. The numerical results demonstrate that cell-free massive MIMO increases the maximum rate substantially, which can be provided with almost the same energy per bit. We show that it is more power-efficient to activate cell-free massive MIMO already at low spectral efficiencies (above 1 bit/s/Hz).

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.

Network slicing is a promising technology for realizing the vision of supporting a wide range of services with diverse and heterogeneous service requirements. With network slicing, the network is partitioned into multiple individual dedicated networks tailored and customized for specific services. However, this causes extra energy consumption to reserve resources for each slice. On the other hand, minimizing the energy consumption in radio access network (RAN) may result in increasing the energy consumption in the cloud and the fronthaul due to higher required processing and data transport. Therefore, the energy should be evaluated from an end-to-end perspective. In this study, we address the problem of minimizing the end-to-end energy consumption of a network with network slicing by jointly reserving communication and computation resources among slices. First, we propose an end-to-end delay and energy model for each slice. We take into account the delays and energy consumption of the radio site, midhaul/fronthaul transport, and the cloud site in an Ethernet-based cloud RAN (C-RAN). Then, we formulate a non-convex optimization problem to minimize the total energy consumption of the network by jointly allocating transmission bandwidth and processing resources in the digital unit pool of the cloud, respectively, to each slice. The constraints of the optimization problem are the total delay requirement of each slice, the maximum allowable bandwidth at each radio unit, the maximum rate limitation of the Ethernet links, and the total processing limit of the cloud. To solve the problem optimally, we transform it into a convex quadratic programming problem. The simulation results show that end-to-end network slicing can decrease the total energy consumption of the network compared to only RAN slicing. We also investigate the impact of the 5G numerology on the allocated resources to each slice and the total energy consumption. We show that using mixed numerology depending on the slice type, we can interplay between delay and energy consumption for each slice.

Providing energy-efficient Internet of Things (IoT) connectivity has attracted significant attention in fifth-generation (5G) wireless networks and beyond. A potential solution for realizing a long-lasting network of IoT devices is to equip each IoT device with a wake-up receiver (WuR) to have always-accessible devices instead of always-on devices. WuRs typically comprise a radio frequency demodulator, sequence decoder, and digital address decoder and are provided with a unique authentication address in the network. Although the literature on efficient demodulators is mature, it lacks research on fast, low-power, and reliable address decoders. As this module continuously monitors the received ambient energy for potential paging of the device, its contribution to WuR’s power consumption is crucial. Motivated by this need, a low-power, reliable address decoder is developed in this paper. We further investigate the integration of WuR in low-power uplink/downlink communications and, using system-level energy analysis; we characterize operation regions in which WuR can contribute significantly to energy saving. The device-level energy analysis confirms the superior performance of our decoder. The results show that the proposed decoder significantly outperforms the state-of-the-art with power consumption of 60 nW, at cost of compromising negligible increase in decoding delay.

The implementation of various base station (BS) energy saving (ES) features and the widely varying network traffic demand makes it imperative to quantitatively evaluate the energy consumption (EC) of 5G BSs. An accurate evaluation is essential to understand how to adapt a BS's resources to reduce its EC. On the other hand, modeling the variation in the power consumption (PC) of a BS with its resources considering the user equipment(UE) performance is mathematically rigorous. In this work, we present a novel analytical methodology to evaluate the EC of a 5G BS under varying traffic load. We mathematically formulate the impact of massive multiple-input and multiple-output (MIMO) arrays, vast spectral resources, and the spatial multiplexing ability of these systems on the UE performance and activity of the BS. Next, we present an updated power model to capture the PC variation of two BSs types: a 4T and a 64T BS. Our proposed analytical methodology simplifies the complex network EC evaluation. Using this methodology, we show that identifying the right BS type for a given deployment area can reduce the overall network EC by up to 60%. Furthermore, by implementing deep sleep modes (SMs) facilitated by 5G, one can gain considerable energy savings (ES), especially during the off-peak hours of the day.

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.

Low-power wide-area networks (LPWANs) are emerging technologies aiming at serving sporadic IoT traffic. While reliability of LPWANs in single-technology deployment has been well investigated, there is lack of research on performance evaluation in coexisting scenarios. To tackle this problem, we start by modeling statistics of aggregated interference from asynchronous sources over shared radio resources. Then, using stochastic geometry, we derive the closed-form statistics of reliability, mean success probability, and the mean local delay, defined as the mean time (in numbers of attempts) until a packet is successfully received by the nearest access point. We further investigate the interplay between traffic-load, access points density, and quality of communications in coexistence scenarios. Then, we derive the communications parameters regions, in terms of transmission power, access point density, and time activity factor, for which, the mean local delay is finite. The numerical and simulation results also corroborate the analysis.