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The purpose of this thesis is to explore the concept of resource scheduling and pricing and its relation to carrier aggregation. The first main topic is a modified Frank Kelly algorithm that allows for the use of utility functions that are piecewise concave, but not a member of a strictly \'diminishing return\' model. This adjustment to the Frank Kelly algorithm allows resource allocation to take into account devices with multiple applications. The second topic introduces the idea of scheduling resources in a carrier aggregation scenario assuming the carriers are scheduled sequentially. / Master of Science

New technologies are expected to play a major role for wireless cellular systems beyond the existing 4G paradigm. The need for several orders of magnitude increase in system capacity has led to the proliferation of low-powered cellular layers overlaid on the existing macrocell layer. This type of network consisting of different cellular layers, each with their unique characteristics including transmission power and frequency of operation among others is termed as a heterogeneous network (HetNet). The emergence of HetNets leads to several research challenges and calls for a profound rethinking of several existing approaches for mobility management and interference management among other issues.

Mobile penetration rates have already exceeded 100% in many countries. Nowadays, mobile phones are part of our daily lives not only for voice or short text messages but for a plethora of multimedia services they provide via their internet connection. Thus, mobile broadband has become the main driver for the evolution of mobile networks and it is estimated that until 2018 the mobile broadband traffic will exceed the level of 15 exabytes. This estimation is a threat to the current mobile networks which have to significantly improve their capacity performance. Furthermore, another important aspect is the fact that 80% of the mobile broadband demand comes from indoor environments which add to the signal propagation the burden of building penetration loss. Keeping these facts in mind, there are many potential solutions that can solve the problem of the increasing indoor mobile broadband demand. In general, there are two approaches; improve the existing macro-cellular networks by for example enhancing them with carrier aggregation or enter the buildings and deploy small cell solutions such as femtocells or WiFi APs. Both the academia and the industry have already shown interest in these two approaches demonstrating the importance of the problem. Various research papers and reports have been produced describing the technologies and presenting their capability to satisfy the perpetual increase in mobile broadband demand. However, according to the best of our knowledge, no research has been done so far that compares the two different approaches with a techno-economic perspective. Hence, the contribution of this thesis project is a holistic techno-economic study of the two approaches (macrocells with carrier aggregation and small cells) to encounter the tremendous growth of indoor mobile broadband demand. In order to achieve this goal, an indoor deployment scenario is presented and the different indoor and outdoor solutions are applied and studied in terms of radio performance (capacity) as well as total cost of ownership. The final result of the project is a comparison of the two approaches as well as a proposed strategy to deal with the problem. The result will help network operators to plan the evolution of their networks, vendors to focus their production as well as regulatory bodies to set the rules and supervise operators’ deployments. The simulations have shown that all the examined technologies are capable of supporting the mobile broadband demand of the studied scenario but macrocells enhanced with carrier aggregation is the most cost effective solution. However, if the requirements for guaranteed data rate and thus the QoS provided increase then eventually MNOs will have to abandon outdoor solutions and extend their infrastructure inside the buildings where the mobile data traffic is mainly generated. / Graden av mobilpenetration har redan nått över 100% i många länder. Nuförtiden är mobiltelefoner en del av vårt dagliga liv inte bara för röstsamtal eller korta textmeddelanden men också för en lång rad multimediatjänster som de tillhandahåller via sin Internetuppkoppling. På grund av detta har mobilt bredband blivit den huvudsakliga drivkraften för utvecklingen av mobila nätverk och det uppskattas att fram till 2018 kommer den mobila bredbandstrafiken överstiga 15 exabyte. Denna uppskattning är ett hot mot de nuvarande mobila nätverken som måste öka sin kapacitet avsevärt. Utöver det är en annan viktig aspekt det faktum att 80% av efterfrågan på mobilt bredband kommer från inomhusmiljöer vilket ger spridningen av radiosignaler problem med minskad penetrationsförmåga orsakad av byggnaden. Med hänsyn till dessa faktorer finns det många potentiella lösningar som kan åtgärda problemet med den ökande efterfrågan på bredband i inomhusmiljöer. Generellt sett finns två tillvägagångssätt; förbättra de existerande makrocells-baserade nätverken exempelvis genom att förstärka dem med carrier aggregation alternativt att i byggnader installera lösningar baserade på små celler som femtoceller eller WiFi-accesspunkter. Både den akademiska världen och industrin har redan visat intresse för dessa två tillvägagångssätt vilket visar att detta är ett viktigt problem. Ett antal forskningsartiklar och rapporter har producerats vilka beskriver teknologierna och deras förmåga att tillfredställa den ständigt ökande efterfrågan på mobilt bredband. Med det sagt, enligt vår kunskap i dagsläget har dock ingen forskning hittills utförts som jämför dessa två tillvägagångssätt från ett teknoekonomiskt perspektiv. Följaktligen är det bidrag denna uppsats ger en holistisk teknoekonomisk studie av de två metoderna (makroceller med carrier aggregation och lösningar baserade på små celler) för att möta den mycket stora ökningen av efterfrågan på mobilt bredband inomhus. För att uppnå detta mål presenteras ett scenario med installation i inomhusmiljö och de olika lösningarna för inomhus- och utomhusmiljöer appliceras och studeras med hänsyn till radioprestanda (kapacitet) och även total ägandekostnad. Projektets slutgiltiga resultat är en jämförelse mellan de två metoderna och ett förslag på en strategi som kommer hjälpa nätverksoperatörer att planera utvecklingen av sina nätverk, leverantörer att fokusera sin tillverkning och tillsynsmyndigheter att upprätta regler samt övervaka att dessa efterföljs. Simuleringarna har visat att alla de granskade teknikerna klarar av att uppfylla efterfrågan på mobilt bredband i de studerade scenariona men att makroceller med carrier aggregation är den mest kostnadseffektiva lösningen. Men om kraven på garanterad datahastighet, och med det den QoS som tillhandahålls, ökar så kommer MNOs till slut att behöva överge utomhuslösningar och utöka deras infrastruktur inuti byggnader där den mobila datatrafiken i huvudsak genereras.

As wireless communications is becoming an increasingly important part of ourevery day lives, the amount of transmitted data is constantly growing, creating ademand for ever-increasing data rates. One of the technologies used for boostingdata rates is carrier aggregation, which allows for wireless units to combine multipleconnections to the cellular network. However, there is a limited number ofpossible combinations defined, meaning that there is a need to search for the bestcombination in any given setup. This thesis introduces software capable of organizingthe defined combinations into tree structures, simplifying the search foroptimal combinations as well as allowing for visualizations of the connectionspossible. In the thesis, a proposed method of creating these trees is presented,together with suggestions on how to visualize important combination characteristics.Studies has also been made on different tree traversal algorithms, showingthat there is little need for searching through all possible combinations, but thata greedy approach has a high performance while substantially limiting the searchcomplexity. / I samband med att trådlösa kommunikationssystem blir en allt större del av våraliv och mängden data som skickas fortsätter att stiga, skapas en efterfrågan förökade datatakter. En av teknologierna som används för att skapa högre datatakterär bäraraggregering (carrier aggregation), som möjliggör för trådlösa enheteratt kombinera flertalet uppkopplingar mot det mobila nätverket. Det finns dockbara ett begränsat antal kombinationer definierade, vilket skapar ett behov av attsöka upp den bästa kombinationen i varje givet tillfälle. Detta arbete introducerarmjukvara som organiserar dessa kombinationer i trädstrukturer, vilket förenklarsökning efter optimala kombinationer tillsammans med möjligheten att visualiserade potentiella uppkopplingarna. I arbetet presenteras en föreslagen metodför att skapa dessa träd, tillsammans med uppslag på hur viktiga egenskaperhos kombinationerna kan visualiseras. Olika trädsökningsalgoritmer har ocksåundersökts, och det visas att det inte är nödvändigt att söka igenom hela träd.Istället visar sig giriga algoritmer ha hög prestanda, samtidigt som sökstorlekenkan hållas kraftigt begränsad.

Fifth-Generation (5G) and sixth-Generation (6G) are new global wireless standards providing everyone and everything, machines, objects, and devices, with massive network capacity. The technological advances in wireless communication enable 5G and 6G networks to support resource and computation-hungry services such as smart agriculture and smart city applications. Among these advances are two state-of-the-art technologies: Carrier Aggregation (CA) and Multi Access Edge Computing (MEC). CA unlocks new sources of spectrum in both the mid-band and high-band radio frequencies. It provides the unique capability of aggregating several frequency bands for higher peak rates, and increases cell coverage. The latter is obtained by activating the Component Carriers (CC) in low-band and mid-band frequency (below 7 GHz) while 5G high-band (above 24GHz) delivers unprecedented peak rates with poorer Uplink (UL) coverage. MEC provides computing and storage resources with sufficient connectivity close to end users. These execution resources are typically within/at the boundary of access networks providing support for application use cases such as Augmented Reality (AR)/Virtual Reality (VR). The key technology in MEC is task offloading, which enables a user to offload a resource-hungry application to the MEC hosts to reduce the cost (in terms of energy and latency) of processing the application. This thesis focuses on using CA and task offloading in 5G and 6G wireless networks. These advanced infrastructures are an enabler for many broader use cases, e.g., autonomous driving and Internet of Things (IoT) applications. However, the pertinent problems are the high dimensional ones with combinatorial characteristics. Furthermore, the time-varying features of the 5G/6G wireless networks, such as the stochastic nature of the wireless channel, should be concurrently met. The above challenges can be tackled by using data-driven techniques and Machine Learning (ML) algorithms to derive intelligent and autonomous resource management techniques in the 5G/6G wireless networks. The resource management problems in these networks are sequential decision-making problems, additionally with conflicting objectives. Therefore, among the ML algorithms, the ones based on the Reinforcement Learning (RL), constitute a promising tool to make a trade-off between the conflicting objectives of the resource management problems in the 5G/6G wireless networks, are used. This research considers the objective of maximizing the achievable rate and minimizing the users’ transmit power levels in the MEC-enabled network. Additionally, we try to simultaneously maximize the network capacity and improve the network coverage by activating/deactivating the CCs. Compared with the derived schemes in the literature, our contributions are two folded: deriving distributed resource management schemes in 5G/6G wireless networks to efficiently manage the limited spectrum resources and meet the diverse requirements of some resource-hungry applications, and developing intelligent and energy-aware algorithms to improve the performance in terms of energy consumption, delay, and achievable rate.

LTE heterogeneous wireless networks promise significant increase in data rates and improved coverage through (i) the deployment of relays and cell densification, (ii) carrier aggregation to enhance bandwidth usage and (iii) by enabling nodes to have dual connectivity. These emerging cellular networks are complex and large systems which are difficult to optimize with centralized control and where mobiles need to balance spectral efficiency, power consumption and fairness constraints. In this dissertation we focus on how decentralized and autonomous mobiles in multihop cellular systems can optimize their own local objectives by taking into account end-to-end or network-wide conditions. We propose several link-adaptive schemes where nodes can adjust their transmit power, aggregate carriers and select points of access to the network (relays and/or macrocell base stations) autonomously, based on both local and global conditions. Under our approach, this is achieved by disseminating the dynamic congestion level in the backhaul links of the points of access. As nodes adapt locally, the congestion levels in the backhaul links can change, which can in turn induce them to also change their adaptation objectives. We show that under our schemes, even with this dynamic congestion feedback, nodes can distributedly converge to a stable selection of transmit power levels and points of access. We also analytically derive the transmit power levels at the equilibrium points for certain cases. Moreover, through numerical results we show that the corresponding system throughput is significantly higher than when nodes adapt greedily following traditional link layer optimization objectives. Given the growing data rate demand, increasing system complexity and the difficulty of implementing centralized cross-layer optimization frameworks, our work simplifies resource allocation in heterogeneous cellular systems. Our work can be extended to any multihop wireless system where the backhaul link capacity is limited and feedback on the dynamic congestion levels at the access points is available. / Ph. D.

Carrier aggregation is a technology in wireless communications which allows a user to use multiple cells simultaneously for communication. In order to select cells, it is crucial to estimate their potential throughput for a given user. As a part of this estimate, we investigate how many MIMO layers a given channel can expect to use in the future, and whether machine learning can be used to predict the number of layers. Simulated user traces are used to generate training data, and special attention is directed at the construction of features based on user history. Random forests and multi-layer perceptrons are trained on the generated data, and we show that the random forests achieve better performance than baseline models, while the MLP models fail to learn and do not reach the expected performance. The importance of the used features is analysed, and we find that the history-based features are especially useful for predicting future channel ranks and thus are promising for use in a cell set selection system for carrier aggregation.

Application Specific Integrated Circuits (ASICs) are critical to delivering on 5G’s promises of high speed, low latency, and expanded capacity. Digital Front-End (DFE) ASICs are particularly important components because they enhance crucial signal processing activities. It handles duties including carrier mixing, up-sampling, and modulation-demodulation, allowing for efficient data transmission and reception inthe complicated 5G environment. The main aim of this work is to develop a power estimation tool for DFE radio ASICs and to understand the different use cases. It also studies the spread of power consumption, taking into account process and metal variations. The thesis provides a detailed case study of the DFE ASIC, including its Intellectual Property (IP) blocks, configurations, and protocols. It investigates the power consumption of DFE ASICs under various conditions, including active processing, power-saving mode, and no clock. In this thesis we build a power model that describes how the factors affect the ASIC’s power consumption. It also performs spread analysis to evaluate the impact of all factors using MATLAB tool. Based on this we then generate three distributionmodels to study the combined likelihood of the variations. It also uses Monte Carlo simulation to understand total power usage. Through this work we can conclude that the power consumption of DFE ASICs is affected by a variety of factors. The power model and spread analysis can be usedto forecast and optimize power usage in 5G systems.

Recently, there has been a massive growth in the number of mobile users and their traffic. The data traffic volume almost doubles every year. Mobile users are currently running multiple applications that require higher bandwidth which makes users so limited to the service providers' resources. Increasing the utilization of the existing spectrum can significantly improve network capacity, data rates and user experience. Spectrum sharing enables wireless systems to harvest under-utilized swathes of spectrum, which would vastly increase the efficiency of spectrum usage. Making more spectrum available can provide significant gain in mobile broadband capacity only if those resources can be aggregated efficiently with the existing commercial mobile system resources. Carrier aggregation (CA) is one of the most distinct features of 4G systems including Long Term Evolution Advanced (LTE-Advanced). In this dissertation, a resource allocation with carrier aggregation framework is proposed to allocate multiple carriers resources optimally among users with elastic and inelastic traffic in cellular networks. We use utility proportional fairness allocation policy, where the fairness among users is in utility percentage of the application running on the user equipment (UE). A resource allocation (RA) with CA is proposed to allocate single or multiple carriers resources optimally among users subscribing for mobile services. Each user is guaranteed a minimum quality of service (QoS) that varies based on the user's application type. In addition, a resource allocation with user discrimination framework is proposed to allocate single or multiple carriers resources among users running multiple applications. Furthermore, an application-aware resource block (RB) scheduling with CA is proposed to assign RBs of multiple component carriers to users' applications based on a utility proportional fairness scheduling policy. We believe that secure spectrum auctions can revolutionize the spectrum utilization of cellular networks and satisfy the ever increasing demand for resources. Therefore, a framework for multi-tier dynamic spectrum sharing system is proposed to provide an efficient sharing of spectrum with commercial wireless system providers (WSPs) with an emphasis on federal spectrum sharing. The proposed spectrum sharing system (SSS) provides an efficient usage of spectrum resources, manages intra-WSP and inter-WSP interference and provides essential level of security, privacy, and obfuscation to enable the most efficient and reliable usage of the shared spectrum. It features an intermediate spectrum auctioneer responsible for allocating resources to commercial WSPs' base stations (BS)s by running secure spectrum auctions. In order to insure truthfulness in the proposed spectrum auction, an optimal bidding mechanism is proposed to enable BSs (bidders) to determine their true bidding values. We also present a resource allocation based on CA approach to determine the BS's optimal aggregated rate allocated to each UE from both the BS's permanent resources and winning auctioned spectrum resources. / Ph. D.