Modelling Load Balancing and Carrier Aggregation in Mobile Networks

In this paper, we study the performance of multicarrier mobile networks. Specifically, we analyze the flow-level performance of two inter-carrier load balancing schemes and the gain engendered by Carrier Aggregation (CA). CA is one of the most important features of HSPA+ and LTE-A networks; it allows devices to be served simultaneously by several carriers. We propose two load balancing schemes, namely Join the Fastest Queue (JFQ) and Volume Balancing (VB), that allow the traffic of CA and non-CA users to be distributed over the aggregated carriers. We then evaluate the performance of these schemes by means of analytical modeling. We show that the proposed schemes achieve quasi-ideal load balancing. We also investigate the impact of mixing traffic of CA and non-CA users in the same cell and show that performance is practically insensitive to the traffic mix.Comment: 8 pages, 6 figures, submitted to WiOpt201

Spectral Segmentation with Multiscale Graph Decomposition

We present a multiscale spectral image segmentation algorithm. In contrast to most multiscale image processing, this algorithm works on multiple scales of the image in parallel, without iteration, to capture both coarse and fine level details. The algorithm is computationally efficient, allowing to segment large images. We use the Normalized Cut graph partitioning framework of image segmentation. We construct a graph encoding pairwise pixel affinity, and partition the graph for image segmentation.We demonstrate that large image graphs can be compressed into multiple scales capturing image structure at increasingly large neighborhood. We show that the decomposition of the image segmentation graph into different scales can be determined by ecological statistics on the image grouping cues. Our segmentation algorithm works simultaneously across the graph scales, with an inter-scale constraint to ensure communication and consistency between the segmentations at each scale. As the results show, we incorporate long-range connections with linear-time complexity, providing high-quality segmentations efficiently. Images that previously could not be processed because of their size have been accurately segmented thanks to this method

The Distributed Multiple Voting Problem

A networked set of agents holding binary opinions does not seem to be able to compute its majority opinion by means of local binary interactions only. However, the majority problem can be solved using two or more bits, instead of one [1]. Pairs of agents asynchronously exchange their states and update them according to a voting automaton. This paper presents binary voting automata as well as solutions to the multiple voting problem, where agents can vote for one candidate among |C| >= 2 candidates and need to determine the majority vote. The voting automata are derived from the pairwise gossip algorithm, which computes averages. In the binary case (|C| = 2), we focus on averages in dimension 1, but in the multiple case (|C| >= 2) we quantize gossip in dimension |C | - 1, which is larger than or equal to 1. We show in particular that a consensus on majority can be reached using 15 possible states (4 bits) for the ternary voting problem, and using 100 possible states (7 bits) for the quaternary voting problem

Weighted Gossip: Distributed Averaging Using Non-Doubly Stochastic Matrices

This paper presents a general class of gossip-based averaging algorithms, which are inspired from Uniform Gossip [1]. While Uniform Gossip works synchronously on complete graphs, weighted gossip algorithms allow asynchronous rounds and converge on any connected, directed or undirected graph. Unlike most previous gossip algorithms [2]–[6], Weighted Gossip admits stochastic update matrices which need not be doubly stochastic. Double-stochasticity being very restrictive in a distributed setting [7], this novel degree of freedom is essential and it opens the perspective of designing a large number of new gossip-based algorithms. To give an example, we present one of these algorithms, which we call One-Way Averaging. It is based on random geographic routing, just like Path Averaging [5], except that routes are one way instead of round trip. Hence in this example, getting rid of double stochasticity allows us to add robustness to Path Averaging

Intra- and inter-hemispheric structural connectome in agenesis of the corpus callosum

Agenesis of the corpus callosum (AgCC) is a congenital brain malformation characterized by the complete or partial failure to develop the corpus callosum. Despite missing the largest white matter bundle connecting the left and right hemispheres of the brain, studies have shown preserved inter-hemispheric communication in individuals with AgCC. It is likely that plasticity provides mechanisms for the brain to adjust in the context of AgCC, as the malformation disrupts programmed developmental brain processes very early on. A proposed candidate for neuroplastic response in individuals with AgCC is strengthening of intra-hemispheric structural connections. In the present study, we explore this hypothesis using a graph-based approach of the structural connectome, which enables intra- and inter-hemispheric analyses at multiple resolutions and quantification of structural characteristics through graph metrics. Structural graph metrics of 19 children with AgCC (13 with complete, 6 with partial AgCC) were compared to those of 29 typically developing controls (TDC). Associations between structural graph metrics and a wide range of neurobehavioral outcomes were examined using a multivariate data-driven approach (Partial Least Squares Correlation, PLSC). Our results provide new evidence suggesting structural strengthening of intra-hemispheric pathways as a neuroplastic response in the acallosal brain, and highlight regional variability in structural connectivity in children with AgCC compared to TDC. There was little evidence that structural graph properties in children with AgCC were associated with neurobehavioral outcomes. To our knowledge, this is the first report leveraging graph theory tools to explicitly characterize whole-brain intra- and inter-hemispheric structural connectivity in AgCC, opening avenues for future research on neuroplastic responses in AgCC

Distributed average consensus for wireless sensor networks

Wireless sensor networks have emerged a few years ago, enabling large scale sensing at low cost. There are many interesting problems related to this new sensing tool: designing robust and small hardware, defining adapted routing protocols, minimizing the energy consumption of each component, synchronizing the sensors, etc. In this thesis, we focus on the processing of the sensed data within the network itself. We study a specific network signal processing problem, called distributed average consensus. In this problem, the sensors, which are connected in a wireless network, need to know the average of all the measurements in the network. Instead of gathering the data at a central node, which would compute the average and broadcast it to the network, average consensus algorithms offer a distributed solution to the averaging problem. By local message passing and iterative local computations only, nodes can learn the average of the measurements. More precisely, in average consensus algorithms, nodes iteratively compute local weighted averages that conserve the global average of the estimates in the network. The estimates at each node contract until they all converge to the average. Many distributed average consensus algorithms were designed and the literature is vast. This thesis starts by classifying the existing algorithms. Then it describes a small number of useful techniques, which can handle the analysis of all the algorithms. Preexisting algorithms as well as algorithms that we designed are revisited with these unifying and simple techniques. In addition, the performance of the algorithms depends on the topology of the network, and a variety of networks are explored: simple graphs as circles or trees, lattices, random geometric graphs, complete graphs etc. Finally, an extension of average consensus to voting consensus is derived. In particular, we show that with two bits of memory at each node, a network can reach consensus in finite time on majority, when the initial measurements are binary. Distributed algorithms to compute majority with finite memory for ternary and quaternary signals are also given