Related papers: Interference Alignment-Aided Base Station Clusteri…
A new approach to clustering, based on the physical properties of inhomogeneous coupled chaotic maps, is presented. A chaotic map is assigned to each data-point and short range couplings are introduced. The stationary regime of the system…
In this paper, the problem of self-organizing, correlation-aware clustering is studied for a dense network of machine-type devices (MTDs) deployed over a cellular network. In dense machine-to-machine networks, MTDs are typically located…
We consider the problem of decentralized clustering and estimation over multi-task networks, where agents infer and track different models of interest. The agents do not know beforehand which model is generating their own data. They also do…
This work addresses the problem associated with coordinating scheduling decisions among multiple base stations in an LTE-Advanced downlink network in order to manage inter-cell interference with a centralized controller. To solve the…
Clustering is a technique for the analysis of datasets obtained by empirical studies in several disciplines with a major application for biomedical research. Essentially, clustering algorithms are executed by machines aiming at finding…
To enable densely deployed base stations (BSs) or access points (APs) to serve an increasing number of users and provide diverse mobile services, we need to improve spectrum utilization in wireless communication networks. Although spectral…
We consider the problem of collaborative inference in a sensor network with heterogeneous and statistically dependent sensor observations. Each sensor aims to maximize its inference performance by forming a coalition with other sensors and…
Clustering is one of the most common unsupervised learning tasks in machine learning and data mining. Clustering algorithms have been used in a plethora of applications across several scientific fields. However, there has been limited…
Emerging large-scale engineering systems rely on distributed fusion for situational awareness, where agents combine noisy local sensor measurements with exchanged information to obtain fused estimates. However, at the sheer scale of these…
Cooperative spectrum sensing has been shown to yield a significant performance improvement in cognitive radio networks. In this paper, we consider distributed cooperative sensing (DCS) in which secondary users (SUs) exchange data with one…
Motivated by the fact that distances between data points in many real-world clustering instances are often based on heuristic measures, Bilu and Linial~\cite{BL} proposed analyzing objective based clustering problems under the assumption…
Partitioning large networks into stable clusters of synchronized nodes is a challenging task. Recent approaches based on spectral analysis can provide exact results on specific dynamics but remain unfeasible for very large networks.…
This work considers clustering nodes of a largely incomplete graph. Under the problem setting, only a small amount of queries about the edges can be made, but the entire graph is not observable. This problem finds applications in…
Cognitive radios have been proposed as agile technologies to boost the spectrum utilization. This paper tackles the problem of channel estimation and its impact on downlink transmissions in an underlay cognitive radio scenario. We consider…
This paper considers a Massive multiple-input multiple-output (MIMO) network, where the base station (BS) with a large number of antennas communicates with a smaller number of users. The signals are transmitted using frequency division…
In spectrum sharing networks, a base station (BS) needs to mitigate the interference to users associated with other coexisting network in the same band. The BS can achieve this by transmitting its downlink signal in the null space of…
In today's wireless communication systems, the exponentially growing needs of mobile users require the combination of new and existing techniques to meet the demands for reliable and high-quality service provision. This may not always be…
The paper tackles the problem of clustering multiple networks, directed or not, that do not share the same set of vertices, into groups of networks with similar topology. A statistical model-based approach based on a finite mixture of…
In this study, we explore the integration of satellites with ground-based communication networks. Specifically, we analyze downlink data transmission from a constellation of satellites to terrestrial users and address the issue of delayed…
We present a structural clustering algorithm for large-scale datasets of small labeled graphs, utilizing a frequent subgraph sampling strategy. A set of representatives provides an intuitive description of each cluster, supports the…