Commutative association schemes

Association schemes were originally introduced by Bose and his co-workers in the design of statistical experiments. Since that point of inception, the concept has proved useful in the study of group actions, in algebraic graph theory, in algebraic coding theory, and in areas as far afield as knot theory and numerical integration. This branch of the theory, viewed in this collection of surveys as the "commutative case," has seen significant activity in the last few decades. The goal of the present survey is to discuss the most important new developments in several directions, including Gelfand pairs, cometric association schemes, Delsarte Theory, spin models and the semidefinite programming technique. The narrative follows a thread through this list of topics, this being the contrast between combinatorial symmetry and group-theoretic symmetry, culminating in Schrijver's SDP bound for binary codes (based on group actions) and its connection to the Terwilliger algebra (based on combinatorial symmetry). We propose this new role of the Terwilliger algebra in Delsarte Theory as a central topic for future work.Comment: 36 page

Optimal Precoders for Tracking the AoD and AoA of a mm-Wave Path

In millimeter-wave channels, most of the received energy is carried by a few paths. Traditional precoders sweep the angle-of-departure (AoD) and angle-of-arrival (AoA) space with directional precoders to identify directions with largest power. Such precoders are heuristic and lead to sub-optimal AoD/AoA estimation. We derive optimal precoders, minimizing the Cram\'{e}r-Rao bound (CRB) of the AoD/AoA, assuming a fully digital architecture at the transmitter and spatial filtering of a single path. The precoders are found by solving a suitable convex optimization problem. We demonstrate that the accuracy can be improved by at least a factor of two over traditional precoders, and show that there is an optimal number of distinct precoders beyond which the CRB does not improve.Comment: Resubmission to IEEE Trans. on Signal Processing. 12 pages and 9 figure

On the Stability of Distribution Topologies in Peer-to-Peer Live Streaming Systems

Peer-to-Peer Live-Streaming-Systeme sind ständigen Störungen ausgesetzt.Insbesondere ermöglichen unzuverlässige Teilnehmer Ausfälle und Angriffe, welche überraschend Peers aus dem System entfernen. Die Folgen solcher Vorfälle werden großteils von der Verteilungstopologie bestimmt, d.h. der Kommunikationsstruktur zwischen den Peers.In dieser Arbeit analysieren wir Optimierungsprobleme welche bei der Betrachtung von Stabilitätsbegriffen für solche Verteilungstopologien auftreten. Dabei werden sowohl Angriffe als auch unkoordinierte Ausfälle berücksichtigt.Zunächst untersuchen wir die Berechnungskomplexität und Approximierbarkeit des Problems resourcen-effiziente Angriffe zu bestimmen. Dies demonstriert Beschränkungen in den Planungsmöglichkeiten von Angreifern und zeigt inwieweit die Topologieparameter die Schwierigkeit solcher Angriffsrobleme beeinflussen. Anschließend studieren wir Topologieformationsprobleme. Dabei sind Topologieparameter vorgegeben und es muss eine passende Verteilungstopologie gefunden werden. Ziel ist es Topologien zu erzeugen, welche den durch Angriffe mit beliebigen Parametern erzeugbaren maximalen Schaden minimieren.Wir identifizieren notwendige und hinreichende Eigenschaften solcher Verteilungstopologien. Dies führt zu mathematisch fundierten Zielstellungen für das Topologie-Management von Peer-to-Peer Live-Streaming-Systemen.Wir zeigen zwei große Klassen effizient konstruierbarer Verteilungstopologien, welche den maximal möglichen, durch Angriffe verursachten Paketverlust minimieren. Zusätzlich beweisen wir, dass die Bestimmung dieser Eigenschaft für beliebige Topologien coNP-vollständig ist.Soll die maximale Anzahl von Peers minimiert werden, bei denen ein Angriff zu ungenügender Stream-Qualität führt, ändern sich die Anforderungen an Verteilungstopologien. Wir zeigen, dass dieses Topologieformationsproblem eng mit offenen Problemen aus Design- und Kodierungstheorie verwandt ist.Schließlich analysieren wir Verteilungstopologien die den durch unkoordinierte Ausfälle zu erwartetenden Paketverlust minimieren. Wir zeigen Eigenschaften und Existenzbedingungen. Außerdem bestimmen wir die Berechnungskomplexität des Auffindens solcher Topologien. Unsere Ergebnisse liefern Richtlinien für das Topologie-Management von Peer-to-Peer Live-Streaming-Systemen und zeigen auf, welche Stabilitätsziele effizient erreicht werden können.The stability of peer-to-peer live streaming systems is constantly challenged. Especially, the unreliability and vulnerability of their participants allows for failures and attacks suddenly disabling certain sets of peers. The consequences of such events are largely determined by the distribution topology, i.e., the pattern of communication between the peers.In this thesis, we analyze a broad range of optimization problems concerning the stability of distribution topologies. For this, we discuss notions of stability against both attacks and failures.At first, we investigate the computational complexity and approximability of finding resource-efficient attacks. This allows to point out limitations of an attacker's planning capabilities and demonstrates the influence of the chosen system parameters on the hardness of such attack problems.Then, we turn to study topology formation problems. Here, a set of topology parameters is given and the task consists in finding an eligible distribution topology. In particular, it has to minimize the maximum damage achievable by attacks with arbitrary attack parameters.We identify necessary and sufficient conditions on attack-stable distribution topologies. Thereby, we give mathematically sound guidelines for the topology management of peer-to-peer live streaming systems.We find large classes of efficiently-constructable topologies minimizing the system-wide packet loss under attacks. Additionally, we show that determining this feature for arbitrary topologies is coNP-complete.Considering topologies minimizing the maximum number of peers for which an attack leads to a heavy decrease in perceived streaming quality, the requirements change. Here, we show that the corresponding topology formation problem is closely related to long-standing open problems of Design and Coding Theory.Finally, we study topologies minimizing the expected packet loss due to uncoordinated peer failures. We investigate properties and existence conditions of such topologies. Furthermore, we determine the computational complexity of constructing them.Our results provide guidelines for the topology management of peer-to-peer live streaming systems and mathematically determine which goals can be achieved efficiently

Actors vs Shared Memory: two models at work on Big Data application frameworks

This work aims at analyzing how two different concurrency models, namely the shared memory model and the actor model, can influence the development of applications that manage huge masses of data, distinctive of Big Data applications. The paper compares the two models by analyzing a couple of concrete projects based on the MapReduce and Bulk Synchronous Parallel algorithmic schemes. Both projects are doubly implemented on two concrete platforms: Akka Cluster and Managed X10. The result is both a conceptual comparison of models in the Big Data Analytics scenario, and an experimental analysis based on concrete executions on a cluster platform

5G transport network requirements for the next generation fronthaul interface

To meet the requirements of 5G mobile networks, several radio access technologies, such as millimeter wave communications and massive MIMO, are being proposed. In addition, cloud radio access network (C-RAN) architectures are considered instrumental to fully exploit the capabilities of future 5G RANs. However, RAN centralization imposes stringent requirements on the transport network, which today are addressed with purpose-specific and expensive fronthaul links. As the demands on future access networks rise, so will the challenges in the fronthaul and backhaul segments. It is hence of fundamental importance to consider the design of transport networks alongside the definition of future access technologies to avoid the transport becoming a bottleneck. Therefore, we analyze in this work the impact that future RAN technologies will have on the transport network and on the design of the next generation fronthaul interface. To understand the especially important impact of varying user traffic, we utilize measurements from a real-world 4G network and, taking target 5G performance figures into account, extrapolate its statistics to a 5G scenario. With this, we derive both per-cell and aggregated data rate requirements for 5G transport networks. In addition, we show that the effect of statistical multiplexing is an important factor to reduce transport network capacity requirements and costs. Based on our investigations, we provide guidelines for the development of the 5G transport network architecture.Peer ReviewedPostprint (published version

Generalizations of All-or-Nothing Transforms and their Application in Secure Distributed Storage

An all-or-nothing transform is an invertible function that maps s inputs to s outputs such that, in the calculation of the inverse, the absence of only one output makes it impossible for an adversary to obtain any information about any single input. In this thesis, we generalize this structure in several ways motivated by different applications, and for each generalization, we provide some constructions. For a particular generalization, where we consider the security of t input blocks in the absence of t output blocks, namely, t-all-or-nothing transforms, we provide two applications. We also define a closeness measure and study structures that are close to t-all-or-nothing transforms. Other generalizations consider the situations where: i) t covers a range of values and the structure maintains its t-all-or-nothingness property for all values of t in that range; ii) the transform provides security for a smaller, yet fixed, number of inputs than the number of absent outputs; iii) the missing output blocks are only from a fixed subset of the output blocks; and iv) the transform generates n outputs so that it can still reconstruct the inputs as long as s outputs are available. In the last case, the absence of n-s+t outputs can protect the security of any t inputs. For each of these transforms, various existence and non-existence results, as well as bounds and equivalence results are presented. We finish with proposing an application of generalization (iv) in secure distributed storage

Non-linear Machine Learning with Active Sampling for MOX Drift Compensation

Abstract—Metal oxide (MOX) gas detectors based on SnO2 provide low-cost solutions for real-time sensing of complex gas mixtures for indoor ambient monitoring. With high sensitivity under ideal conditions, MOX detectors may have poor longterm response accuracy due to environmental factors (humidity and temperature) along with sensor aging, leading to calibration drifts. Finding a simple and efficient solution to correct such calibration drifts has been the subject of numerous studies but remains an open problem. In this work, we present an efficient approach to MOX calibration using active and transfer sampling techniques coupled with non-linear machine learning algorithms, namely neural networks, extreme gradient boosting (XGBoost) and radial kernel support vector machines (SVM). Applied on the UCI’s HT detectors dataset, the study evaluates methods for active sampling, makes an assessment of suitable neural networks architectures and compares the performance of neural networks, XGBoost and radial kernel SVM to classify gas mixtures (banana and wine odours, clean air) in the presence of humidity and temperature changes. The results show high classification accuracy levels (above 90%) and confirm that active sampling can provide a suitable solution. Index Terms—Neural Networks, Extreme Gradient Boosting, XGBoost, Support Vector Machines, Non-Linear Learning Methods, Machine Learnin