QC 20101206

With the introduction of the Internet of Things (IoT) and 5G cellular networks, edge computing will substantially alleviate the quality of service shortcomings of cloud computing. With the advancements in edge computing, machine learning (ML) has performed a significant role in analyzing the data produced by IoT devices. Such advancements have mainly enabled ML proliferation in distributed optimization algorithms. These algorithms aim to improve training and testing performance for prediction and inference tasks, such as image classification. However, state-of-the-art ML algorithms demand massive communication and computation resources that are not readily available on wireless devices. Accordingly, a significant need is to extend ML algorithms to wireless communication scenarios to cope with the resource limitations of the devices and the networks.  Federated learning (FL) is one of the most prominent algorithms with data distributed across devices. FL reduces communication overhead by avoiding data exchange between wireless devices and the server. Instead, each wireless device executes some local computations and communicates the local parameters to the server using wireless communications. Accordingly, every communication iteration of FL experiences costs such as computation, latency, communication resource utilization, bandwidth, and energy. Since the devices' communication and computation resources are limited, it may hinder completing the training of the FL due to the resource shortage. The main goal of this thesis is to develop cost-efficient approaches to alleviate the resource constraints of devices in FL training. In the first chapter of the thesis, we overview ML and discuss the relevant communication and computation efficient works for training FL models. Next, a comprehensive literature review of cost efficient FL methods is conducted, and the limitations of existing literature in this area are identified. We then present the central focus of our research, which is a causal approach that eliminates the need for future FL information in the design of communication and computation efficient FL. Finally, we summarize the key contributions of each paper within the thesis. In the second chapter, the thesis presents the articles on which it is based in their original format of publication or submission. A multi-objective optimization problem, incorporating FL loss and iteration cost functions, is proposed where communication between devices and the server is regulated by the slotted-ALOHA wireless protocol. The effect of contention level in the CSMA/CA on the causal solution of the proposed optimization is also investigated. Furthermore, the multi-objective optimization problem is extended to cover general scenarios in wireless communication, including convex and non-convex loss functions. Novel results are compared with well-known communication-efficient methods, such as the lazily aggregated quantized gradients (LAQ), to further improve the communication efficiency in FL over wireless networks. / Med introduktionen av Internet of Things~(IoT) och 5G~cellulära nätverk, kommer edge computing avsevärt att lindra bristerna på tjänstekvaliteten hos molnberäkningar. Med framstegen inom edge computing har maskininlärning~(ML) spelat en betydande roll i att analysera data som produceras av IoT-enheter. Sådana framsteg har huvudsakligen möjliggjort ML-proliferation i distribuerade optimeringsalgoritmer. Dessa algoritmer syftar till att förbättra tränings- och testprestanda för förutsägelse- och slutledningsuppgifter, såsom bildklassificering. Men de senaste ML-algoritmerna kräver enorma kommunikations- och beräkningsresurser som inte är lätt tillgängliga på trådlösa enheter. Följaktligen är ett betydande behov att utöka ML-algoritmer till scenarier för trådlös kommunikation för att klara av resursbegränsningarna hos enheterna och nätverken. Federated learning~(FL) är en av de mest framträdande algoritmerna med data fördelade över enheter. FL minskar kommunikationskostnader genom att undvika datautbyte mellan trådlösa enheter och servern. Istället utför varje trådlös enhet några lokala beräkningar och kommunicerar de lokala parametrarna till servern med hjälp av trådlös kommunikation. Följaktligen upplever varje kommunikationsiteration av FL kostnader som beräkning, latens, kommunikationsresursanvändning, bandbredd och energi. Eftersom enheternas kommunikations- och beräkningsresurser är begränsade kan det på grund av resursbristen hindra att fullfölja utbildningen av FL. Huvudmålet med denna avhandling är att utveckla kostnadseffektiva metoder för att lindra resursbegränsningarna för enheter i FL-träning. I det första kapitlet av avhandlingen överblickar vi ML och diskuterar relevanta kommunikations- och beräkningseffektiva arbeten för att träna FL-modeller. Därefter genomförs en omfattande litteraturgenomgång av kostnadseffektiva FL-metoder, och begränsningarna för befintlig litteratur inom detta område identifieras. Vi presenterar sedan det centrala fokuset i vår forskning, vilket är ett kausalt synsätt som eliminerar behovet av framtida FL-information vid utformning av kommunikations- och beräkningseffektiv FL. Slutligen sammanfattar vi de viktigaste bidragen från varje artikel i avhandlingen. I det andra kapitlet presenterar avhandlingen de artiklar som den bygger på i deras ursprungliga publicerings- eller inlämningsformat. Ett multi-objektiv optimeringsproblem, som inkluderar FL-förlust- och iterationskostnadsfunktioner, föreslås där det trådlösa ALOHA-protokollet med slitsar reglerar kommunikationen mellan enheter och servern. Effekten av konfliktnivån i CSMA/CA på den kausala lösningen av den föreslagna optimeringen undersöks också. Dessutom utökas problemet med optimering av flera mål till att täcka allmänna scenarier inom trådlös kommunikation, inklusive konvexa och icke-konvexa förlustfunktioner. Nya resultat jämförs med välkända kommunikationseffektiva metoder som LAQ för att ytterligare förbättra kommunikationseffektiviteten i FL över trådlösa nätverk. Med introduktionen av Internet of Things~(IoT) och 5G~cellulära nätverk, kommer edge computing avsevärt att lindra bristerna på tjänstekvaliteten hos molnberäkningar. Med framstegen inom edge computing har maskininlärning~(ML) spelat en betydande roll i att analysera data som produceras av IoT-enheter. Sådana framsteg har huvudsakligen möjliggjort ML-proliferation i distribuerade optimeringsalgoritmer. Dessa algoritmer syftar till att förbättra tränings- och testprestanda för förutsägelse- och slutledningsuppgifter, såsom bildklassificering. Men de senaste ML-algoritmerna kräver enorma kommunikations- och beräkningsresurser som inte är lätt tillgängliga på trådlösa enheter. Följaktligen är ett betydande behov att utöka ML-algoritmer till scenarier för trådlös kommunikation för att klara av resursbegränsningarna hos enheterna och nätverken. Federated learning~(FL) är en av de mest framträdande algoritmerna med data fördelade över enheter. FL minskar kommunikationskostnader genom att undvika datautbyte mellan trådlösa enheter och servern. Istället utför varje trådlös enhet några lokala beräkningar och kommunicerar de lokala parametrarna till servern med hjälp av trådlös kommunikation. Följaktligen upplever varje kommunikationsiteration av FL kostnader som beräkning, latens, kommunikationsresursanvändning, bandbredd och energi. Eftersom enheternas kommunikations- och beräkningsresurser är begränsade kan det på grund av resursbristen hindra att fullfölja utbildningen av FL. Huvudmålet med denna avhandling är att utveckla kostnadseffektiva metoder för att lindra resursbegränsningarna för enheter i FL-träning. I det första kapitlet av avhandlingen överblickar vi ML och diskuterar relevanta kommunikations- och beräkningseffektiva arbeten för att träna FL-modeller. Därefter genomförs en omfattande litteraturgenomgång av kostnadseffektiva FL-metoder, och begränsningarna för befintlig litteratur inom detta område identifieras. Vi presenterar sedan det centrala fokuset i vår forskning, vilket är ett kausalt synsätt som eliminerar behovet av framtida FL-information vid utformning av kommunikations- och beräkningseffektiv FL. Slutligen sammanfattar vi de viktigaste bidragen från varje artikel i avhandlingen. I det andra kapitlet presenterar avhandlingen de artiklar som den bygger på i deras ursprungliga publicerings- eller inlämningsformat. Ett multi-objektiv optimeringsproblem, som inkluderar FL-förlust- och iterationskostnadsfunktioner, föreslås där det trådlösa ALOHA-protokollet med slitsar reglerar kommunikationen mellan enheter och servern. Effekten av konfliktnivån i CSMA/CA på den kausala lösningen av den föreslagna optimeringen undersöks också. Dessutom utökas problemet med optimering av flera mål till att täcka allmänna scenarier inom trådlös kommunikation, inklusive konvexa och icke-konvexa förlustfunktioner. Nya resultat jämförs med välkända kommunikationseffektiva metoder som LAQ för att ytterligare förbättra kommunikationseffektiviteten i FL över trådlösa nätverk. / <p>QC 20230310</p>

The Internet and power distribution grids are examples of ubiquitous systems that are composed of subsystems that cooperate using a communication network. We loosely define such systems as networked systems. These systems are usually designed by using trial and error. With this thesis, we aim to fill some of the many gaps in the diverse theory of networked systems. Therefore, we cast resource allocation in networked systems as optimization problems, and we investigate a versatile class of optimization problems. We then use decomposition methods to devise decentralized algorithms that solve these optimization problems. The thesis consists of four main contributions: First, we review decomposition methods that can be used to devise decentralized algorithms for solving the posed optimization problems. Second, we consider cross-layer optimization of communication networks. Network performance can be increased if the traditionally separated network layers are jointly optimized. We investigate the interplay between the data sending rates and the allocation of resources for the communication links. The communication networks we consider have links where the data transferring capacity can be controlled. Decomposition methods are applied to the design of fully distributed protocols for two wireless network technologies: networks with orthogonal channels and network-wide resource constraints, as well as wireless networks using spatial-reuse time division multiple access. Third, we consider the problem of designing a distributed control strategy such that a linear combination of the states of a number of vehicles coincide at a given time. The vehicles are described by linear difference equations and are subject to convex input constraints. It is demonstrated how primal decomposition techniques and incremental subgradient methods allow us to find a solution in which each vehicle performs individual planning of its trajectory and exchanges critical information with neighbors only. We explore various communication, computation, and control structures. Fourth, we investigate the resource allocation problem for large-scale server clusters with quality-of-service objectives, in which key functions are decentralized. Specifically, the problem of selecting which services the servers should provide is posed as a discrete utility maximization problem. We develop an efficient centralized algorithm that solves this problem, and we propose three suboptimal schemes that operate with local information. / QC 20101117

This thesis addresses the problem of low-cost multicast delivery of multimedia content in future mobile networks. The trend towards reuse of existing infrastructure for cellular and broadcasting for building new systems is challenged, with respect to the opportunities for low cost service provision and scalable deployment of networks. The studies outline significant potential of hybrid cellular-broadcasting infrastructure to deliver lower-cost mobile multimedia, compared to conventional telecom or broadcasting systems. Even with simple interworking techniques the achievable cost savings can be significant, at least under some specific settings. The work starts with a foresight study shaped around four scenarios of the future, and continues with the introduction of a high-level framework for radio resource management in Ambient Networks. Two approaches on the hybrid system architecture are considered. The first one assumes different degrees of interworking between conventional cellular and broadcasting systems, in single and multi-operator environments. Second, is a broadcast only system where cellular sites are used for synchronized, complementary transmitters for the broadcasting site. In the first approach, the key issue is the multi-radio resource management, which is strongly affected by the degree of integration between the two networks. Two case studies deal with the problem of delivering, for lowest cost, a data item to a certain number of recipient users. A flexible broadcasting air interface, which offers several transmission data rates that can be dynamically changed, is demonstrated to significantly increase cost efficiency under certain conditions. An interesting result is that real-time monitoring of the user reception conditions is not needed, at least when multicast group is large. This indicates a high degree of integration between cellular and broadcasting networks may not by generally justified by visible cost savings. Scalability of the hybrid infrastructure deployment is the main topic in the second approach. For a DVB-H type of network, the numerical evaluations show that achievement of economies of scale while increasing network capacity and coverage, by employing higher modulation and coding rate or installing new transmission sites, is difficult. Therefore, a technique based on application-layer forward error correction with Raptor codingA is suggested for enabling a flexible trading between system capacity, perceived coverage and delay, in the case of mobile users. / QC 20101110

As networked systems grow in size and dynamicity, management systems must become adaptive to changing networking conditions. The focus of the work presented in this thesis is on developing engineering principles for adaptive management systems. We investigate three problems in the context of adaptive management for networked systems. First, we address the control of the performance of an SMS system. We present a design for policy-based performance management of such systems. The design takes as input the operator's performance goals, which are expressed as policies that can be adjusted at run-time. The system attempts to achieve the given goals by periodically solving an optimization problem that takes as input the policies and traffic statistics and computes a new configuration. We have evaluated the design through extensive simulations in various scenarios and compared it with an ideal system. A prototype has been developed on a commercial SMS platform, which proves the validity of our design. Second, we address the problem of decentralized continuous monitoring of network state variables with configurable accuracy. Network state variables are computed from device counters using aggregation functions, such as SUM, AVERAGE and MAX. We present A-GAP, a protocol that aims at minimizing the management overhead for a configurable average error of the estimation of the global aggregate. The protocol follows the push approach to monitoring and uses the concept of incremental aggregation on a self-stabilizing spanning tree. A-GAP is decentralized and asynchronous to achieve robustness and scalability. We evaluate the protocol through simulation in several scenarios. The results show that we can effectively control the fundamental trade-off in monitoring between accuracy and overhead. Third, we aim at improving the performance of the policy distribution task: the mechanism that provides the right policies at the right locations in the network when they are needed. Policy distribution is a key aspect for developing policy-based systems that scale, which is a must for dynamic scenarios. We present a scalable framework for policy distribution. The framework is based on aggregating the addresses of the policies and applying multipoint communication techniques. We show the validity of the framework in a case study. / QC 20101115

In this work, a method to achieve reduced beam-squint in fully metallic leaky-wave antennas is proposed and its applicability for high frequency, high gain, base station antennas in future communication networks is indicated. The antenna is built in gap waveguide technology, where the fundamental mode is allowed to leak by removal of one of the waveguide walls. The leakage is varied along the structure for reduced side lobes and a stable radiation pattern is obtained by coupling the leaked energy from the waveguide through a dispersive prism-lens. The gap waveguide is formed as a groove, sided by three rows of a glide-symmetric holey EBG-structure on one side, suppressing propagation of waves in that direction, and one row of vertical square pins, with tailored heights for optimal leakage, on the other. Beyond the single row of tailored pins, a prism-lens is placed. The prism lens is made of multiple rows of equally spaced and dimensioned, vertical square pins. The dispersive nature of the TE10-mode inside the waveguide is canceled by the oppositely dispersive prism-lens and a stable radiation beam (&lt;1 degree beam-squint) is achieved over a 20% bandwidth. Two antennas are realized, both operating with a center frequency of roughly 60 GHz. The first design is optimized for single-beam operation such that the achieved efficiency is close to 90% across the band and the side lobe levels are below -20 dB. The second design is optimized for dual-beam operation such that two highly directive beams can be obtained, simultaneously or separately. The second design is placed in a 1D-array configuration for electrical beam-steering in one plane, and beam-switching in the orthogonal plane. The two antennas are simulated using CST Microwave Studio. / I det här arbetet föreslås en metod för att reducera skanningen av strålnings-riktningen i helt metalliska läckande-vågantenner. Dess applicerbarhet till hög-frekventa, direktiva, basstationsantenner i framtida kommunikationsnätverk är indikerad. Antennen består av en "gap"-vågledare, där den fundamentala moden tillåts läcka genom att den ena sidoväggen tagits bort. Läckaget varieras längs strukturen för att minska sidlobs-nivån och ett konstant strålningsfält uppnås genom att låta den läckta energin brytas genom en dispersiv prism-lins. Vågledaren formas som en fåra, brevid vilken, på ena sidan, tre rader av glidsymmetriska håliga EBG-strukturer placeras för att undertrycka propagering av vågor i den riktningen, och på andra sidan, en enstaka rad av vertikala, fyrkantiga, pinnar placeras, vilkas höjd är skräddarsydd för optimalt läckage. Bakom den enstaka raden med pinnar placeras prism-linsen. Linsen består av flera rader av lika stora, jämnt fördelade, vertikala fyrkantiga pinnar. Frekvensberoendet hos den fundamentala TE10-moden i vågledaren motverkas av det omvända frekvensberoendet i linsen och ett stabilt strålningsfällt (&lt;1 grad skanning) uppnås med 20% bandbredd. Två antenner realiseras, båda med centerfrekvensen 60 GHz. Den första designen är optimerad för en enkel stråle, så att nästan 90% effektivitet och mindre än -20 dB sidlober uppnås över hela bandet. Den andra designen är optimerad för att tillåta två direktiva strålar, samtidigt eller enskilt. Den andra designen staplas också för att forma en 1-dimensionell gruppantenn, vilket tillåter elektrisk utstyrning i ett plan, och strålningsväxling i det ortogonala planet. De två antennerna simuleras med hjälp av CST Microwave Studio.

The interest in wireless communications has grown constantly for the past decades, leading to an enormous number of applications and services embraced by billions of users. In order to meet the increasing demand for mobile Internet access, several high data-rate radio networking technologies have been proposed to offer wide area high-speed wireless communications, eventually replacing fixed (wired) networks for many applications. This thesis considers cross-layer optimization of multi-hop radio networks where the system performance can be improved if the traditionally separated network layers are jointly optimized. The networks we consider have links with variable transmission rates, influenced by the allocation of transmission opportunities and channels, modulation and coding schemes and transmit powers. First, we formulate the optimal network operation as the solution to a network utility maximization problem and review decomposition methods from mathematical programming that allow translating a centralized network optimization problem into distributed mechanisms and protocols. Second, particular focus is given to networks employing spatial-reuse TDMA, where we develop detailed distributed solutions for joint end-to-end communication rate selection, multiple time-slot transmission scheduling and power allocation which achieve the optimal network utility. In the process, we introduce a novel decomposition method for convex optimization, establish its convergence and demonstrate how it suggests a distributed solution based on flow control optimization and incremental updates of the transmission schedule. We develop a two-step procedure for distributed maximization of computing the schedule updates (maximizing congestion-weighted throughput) and suggest two schemes for distributed channel reservation and power control under realistic interference models. Third, investigate the advantages of employing multi-user detectors within a CDMA/TDMA framework. We demonstrate how column generation techniques can be combined with resource allocation schemes for the multi-access channel into a very efficient computational method. Fourth, we investigate the benefits and challenges of using the emerging OFDMA modulation scheme within our framework. Specifically, we consider the problem of assigning sub-carriers to wireless links in multi-hop mesh networks. Since the underlying mathematical programming problem is computationally hard, we develop a specialized algorithm that computes optimal near-optimal solutions in a reasonable time and suggest a heuristic for improving computation at the price of relatively modest performance losses. / <p>QC 20101117</p>

To deliver a reliable communication service it is essential for the network operator to manage how traffic flows in the network. The paths taken by the traffic is controlled by the routing function. Traditional ways of tuning routing in IP networks are designed to be simple to manage and are not designed to adapt to the traffic situation in the network. This can lead to congestion in parts of the network while other parts of the network are far from fully utilized. In this thesis we explore issues related to optimization of the routing function to balance load in the network. We investigate methods for efficient derivation of the traffic situation using link count measurements. The advantage of using link counts is that they are easily obtained and yield a very limited amount of data. We evaluate and show that estimation based on link counts give the operator a fast and accurate description of the traffic demands. For the evaluation we have access to a unique data set of complete traffic demands from an operational IP backbone. Furthermore, we evaluate performance of search heuristics to set weights in link-state routing protocols. For the evaluation we have access to complete traffic data from a Tier-1 IP network. Our findings confirm previous studies that use partial traffic data or synthetic traffic data. We find that optimization using estimated traffic demands has little significance to the performance of the load balancing. Finally, we device an algorithm that finds a routing setting that is robust to shifts in traffic patterns due to changes in the interdomain routing. A set of worst case scenarios caused by the interdomain routing changes is identified and used to solve a robust routing problem. The evaluation indicates that performance of the robust routing is close to optimal for a wide variety of traffic scenarios. The main contribution of this thesis is that we demonstrate that it is possible to estimate the traffic matrix with good accuracy and to develop methods that optimize the routing settings to give strong and robust network performance. Only minor changes might be necessary in order to implement our algorithms in existing networks. / QC 20101105

Background: 6G mobile communication is one of the fastest-growing fields of technology. The present 5G mobile networks will not be adequate to meet society’s wireless connectivity demand in the near to mid-term future. A new generation of wireless mobile networks has to be developed to address this demand. With the current spectrum already congested in 5G networks, the future 6G networks will have to be operated in the high mmWave and sub-THz frequency bands. Along with this, the parallel advancement in wireless communication and sensing made the researchers understand that these two fields have a lot of things in common in terms of signal processing algorithms, devices and system architecture. This has motivated research on integrating communication and sensing into the same spectrum and system which is a major focus in 6G. Thus, the integration of mobile sensing and mobile communication known as Joint Communication and Sensing (JCAS) will be a key feature of 6G as it enhances spectral efficiency. The usage of higher frequency bands offers a wider bandwidth for the increasing data rate demands. This also enables transceivers to employ massive antenna arrays coupled with wider bandwidth to aid in high resolution sensing of the target devices.                    Objectives: The present research focuses on JCAS that allows a common transmission signal to be used jointly for both communication and sensing. The need for simultaneous transmission and reception on the same frequency and channel for sensing creates a full duplex problem. The foundation of current communication systems is either based on time division duplexing (TDD) or frequency division duplexing (FDD), which avoids simultaneous transmission and reception at the same frequency due to the significant self-interference that would need to be managed. A basic challenge involved while building a full duplex system is the self-interference reduction. The research addresses self-interference mitigation. Methods: Simulation is done in MATLAB to verify the objectives. Results: The suitable candidate spectrum bands for the potential applications ofJCAS along with their sensing profile were identified. A path loss model suitablefor JCAS applications was developed. NR waveform can be used for sensing andcommunication. The digital self- interference mitigation technique was able to handle the self- interference cancellation budget much greater than the self- interference cancellation budget that was allocated for it and establish full duplex. Conclusions: Thus, the thesis explored the candidate frequency bands suitable forJCAS applications, sensing profile of these frequency bands in terms of use cases that can be addressed. The thesis also developed a path loss model suitable for JCAS applications. NR signal performance was evaluated for sensing capability as well. A mono static sensing environment is modeled to study full duplex problem and a technique to handle self-interference mitigation is developed and evaluated

Yes / We are delighted to bring to you this special issue on satellite communications, which we have prepared as part of the spreading of excellence remit of the satellite communications network of excellence (SatNEx). The SatNEx project, which began in 2004, is funded for five years under the European Union¿s Sixth Framework Programme (FP6) Information Society Technologies (IST) Thematic Area. Led by the German Aerospace Center, SatNEx brings together a network of 24 partners, distributed throughout Europe, with membership drawn from ten countries. The philosophy underlying the SatNEx approach revolves around the selection of focused actions under Joint Programmes of Activities, which are carried out collectively by the partners and include research, integration, and dissemination activities. Training represents an important part of the SatNEx remit and is supported through a number of initiatives including the hosting of internship projects and an annual summer school. The call for papers resulted in a high number of submissions, from which we have been able to select 12 excellent papers dealing with the different aspects of satellite communications and navigation. / European Union