After years of experimentation, online teaching has gone through a phase transition with the appearance of massive open online courses (MOOCs), following the model pioneered by Salman Khan with short videos and the use of tablets. Dozens of university-level courses are now available, and followed by hundreds of thousands of online students. We report on early experiences with teaching “Fundamentals of Electrical Engineering” and “Digital Signal Processing” on an open online platform (Coursera). We address in particular: (i) suitability of online platforms for signal processing oriented topics; (ii) structuring of material for the online format; (iii) quizzes and exercises for large classes; (iv) grading methods and possible certification issues; (v) learning from teaching, forums, and data mining of students feedback.

The ability to exchange secret information is critical to many commercial, governmental, and military networks. Information-theoretic security-widely accepted as the strictest notion of security-relies on channel coding techniques that exploit the inherent randomness of the propagation channels to strengthen the security of digital communications systems. Motivated by recent developments in the field, we aim to characterize the fundamental secrecy limits of wireless networks. The paper is comprised of two separate parts. In Part I, we define the intrinsically secure communications graph (iS-graph), a random graph which describes the connections that can be securely established over a large-scale network. We provide conclusive results for the local connectivity of the Poisson iS-graph, in terms of node degrees and isolation probabilities. We show how the secure connectivity of the network varies with the wireless propagation effects, the secrecy rate threshold of each link, and the noise powers of legitimate nodes and eavesdroppers. We then propose sectorized transmission and eavesdropper neutralization as viable strategies for improving the secure connectivity. Our results help clarify how the spatial density of eavesdroppers can compromise the intrinsic security of wireless networks. In Part II of the paper, we study the achievable secrecy rates and the effect of eavesdropper collusion.

In Part I of this paper, we introduced the intrinsically secure communications graph (iS-graph)-a random graph which describes the connections that can be established with strong secrecy over a large-scale network, in the presence of eavesdroppers. We focused on the local connectivity of the iS-graph, and proposed techniques to improve it. In this second part, we characterize the maximum secrecy rate (MSR) that can be achieved between a node and its neighbors. We then consider the scenario where the eavesdroppers are allowed to collude, i.e., exchange and combine information. We quantify exactly how eavesdropper collusion degrades the secrecy properties of the network, in comparison to a noncolluding scenario. Our analysis helps clarify how the presence of eavesdroppers can jeopardize the success of wireless physical-layer security.

The ability to exchange secret information is critical to many commercial, governmental, and military networks. The intrinsically secure communications graph (iS-graph) is a random graph which describes the connections that can be securely established over a large-scale network, by exploiting the physical properties of the wireless medium. This paper aims to characterize the global properties of the iS-graph in terms of (1) percolation on the infinite plane, and (2) full connectivity on a finite region. First, for the Poisson iS-graph defined on the infinite plane, the existence of a phase transition is proven, whereby an unbounded component of connected nodes suddenly arises as the density of legitimate nodes is increased. This shows that long-range secure communication is still possible in the presence of eavesdroppers. Second, full connectivity on a finite region of the Poisson iS-graph is considered. The exact asymptotic behavior of full connectivity in the limit of a large density of legitimate nodes is characterized. Then, simple, explicit expressions are derived in order to closely approximate the probability of full connectivity for a finite density of legitimate nodes. These results help clarify how the presence of eavesdroppers can compromise long-range secure communication.

Signal interference and packet collisions are typically viewed as negative factors that hinder wireless communication networks. When security is the primary concern, signal interference may actually be very helpful. Starting with a stochastic network model, we are able to show that packet collisions caused by jamming nodes can indeed be used effectively to attain new levels of secrecy in multiterminal wireless environments. To this effect, we propose a practical jamming protocol that uses the well-known request-to-send/clear-to-send (RTS/CTS) handshake of the IEEE 802.11 standard as a signaling scheme. Various jammer selection strategies are investigated depending on the position of source, destination, and jamming nodes. The goal is to cause as much interference as possible to eavesdroppers that are located in unknown positions, while limiting the interference observed by the legitimate receiver. To evaluate the performance of each strategy, we introduce and compute a measure for the secure throughput. Our results show that jamming can increase the levels of secrecy significantly albeit at a substantial cost in terms of energy efficiency.

Information-theoretic security--widely accepted as the strictest notion of security--relies on channel coding techniques that exploit the inherent randomness of propagation channels to strengthen the security of communications systems. Within this paradigm, we explore strategies to improve secure connectivity in a wireless network. We first consider the intrinsically secure communications graph (iS-graph), a convenient representation of the links that can be established with information-theoretic security on a large-scale network. We then propose and characterize two techniques--sectorized transmission and eavesdropper neutralization--which are shown to dramatically enhance the connectivity of the iS-graph.

The intrinsically secure communications graph (iS-graph) is a random graph which captures the connections that can be securely established over a large-scale network, in the presence of eavesdroppers. It is based on principles of information-theoretic security, widely accepted as the strictest notion of security. In this paper, we are interested in characterizing the global properties of the iS-graph in terms of percolation on the infinite plane. We prove the existence of a phase transition in the Poisson iS-graph, whereby an unbounded component of securely connected nodes suddenly arises as we increase the density of legitimate nodes. Our work shows that long-range communication in a wireless network is still possible when a secrecy constraint is present.

We present a mathematical model for communication subject to both network interference and noise. We introduce a framework where the interferers are scattered according to a spatial Poisson process, and are operating asynchronously in a wireless environment subject to path loss, shadowing, and multipath fading. We consider both cases of slow and fast-varying interferer positions. The paper is comprised of two separate parts. In Part I, we determine the distribution of the aggregate network interference at the output of a linear receiver. We characterize the error performance of the link, in terms of average and outage probabilities. The proposed model is valid for any linear modulation scheme (e.g., M-ary phase shift keying or M-ary quadrature amplitude modulation), and captures all the essential physical parameters that affect network interference. Our work generalizes the conventional analysis of communication in the presence of additive white Gaussian noise and fast fading, allowing such results to account for the effect of network interference. In Part II of the paper, we derive the capacity of the link when subject to network interference and noise, and characterize the spectrum of the aggregate interference.

In Part I of this paper, we presented a mathematical model for communication subject to both network interference and noise, where the interferers are scattered according to a spatial Poisson process, and are operating asynchronously in a wireless environment subject to path loss, shadowing, and multipath fading. We determined the distribution of the aggregate interference and the error performance of the link. In this second part, we characterize the capacity of the link subject to both network interference and noise. Then, we put forth the concept of spectral outage probability (SOP), a new characterization of the aggregate radio-frequency emission generated by communicating nodes in a wireless network. We present some applications of the SOP, namely the establishment of spectral regulations and the design of covert military networks. The proposed framework captures all the essential physical parameters that affect the aggregate network emission, yet is simple enough to provide insights that may be of value in the design and deployment of wireless networks.

There has been an emerging interest in non-coherent ultra-wide bandwidth (UWB) communications, particularly for low-data rate applications because of its low-complexity and low-power consumption. However, the presence of narrowband (NB) interference severely degrades the communication performance since the energy of the interfering signals is also collected by the receiver. In this paper, we compare the performance of two non-coherent UWB receiver structures - the autocorrelation receiver (AcR) and the energy detection receiver (EDR) - in terms of the bit error probability (BEP). The AcR is based on the transmitted reference signaling with binary pulse amplitude modulation, while the EDR is based on the binary pulse position modulation. We analyze the BEPs for these two non-coherent systems in a multipath fading channel, both in the absence and presence of NB interference. We consider two cases: a) single NB interferer, where the interfering node is located at a fixed distance from the receiver, and b) multiple NB interferers, where the interfering nodes with the same carrier frequency are scattered according to a spatial Poisson process. Our framework is simple enough to enable a tractable analysis and provide insights that are of value in the design of practical UWB systems subject to interference.

With the increased competition for the electromagnetic spectrum, it is important to characterize the impact of interference in the performance of a wireless packet network, which is traditionally measured by its throughput. This paper presents a unifying framework for characterizing the throughput in wireless packet networks. We analyze the throughput from an outage perspective, in which a packet is successfully received if the signal-to-interference-plus-noise ratio (SINR) exceeds some threshold, considering the aggregate interference generated by all emitting nodes in the network. Our work generalizes and unifies various throughput results scattered throughout the literature. Furthermore, the proposed framework encompasses all types of wireless propagation effects (e.g, Nakagami-m fading, Rician fading, and log-normal shadowing), as well as traffic patterns (e.g., slotted-synchronous, slotted-asynchronous, and exponential-interarrivals traffic).

We consider the fundamental security limits of stochastic wireless networks in the presence of colluding eavesdroppers. By establishing a direct connection with the single-input multiple-output (SIMO) Gaussian wiretap channel, we are able to provide a complete characterization of the secrecy capacity for the case in which the eavesdroppers are scattered according to a spatial Poisson process. Our analysis, which includes the probabilities of existence and outage of secrecy capacity, helps clarify how the spatial density of eavesdroppers can jeopardize the success of wireless physical-layer security based on information-theoretic principles.

With the increasing proliferation of different communication devices sharing the same spectrum, it is critical to understand the impact of interference in heterogeneous wireless networks. In this paper, we put forth a mathematical model for coexistence in networks composed of both narrowband (NB) and ultrawideband (UWB) wireless nodes, based on fundamental tools from stochastic geometry. Our model considers that the interferers are spatially scattered according to a Poisson field, and are operating asynchronously in a wireless environment. We first determine the statistical distribution of the aggregate interference for both cases of NB and UWB emitters. We then provide error probability expressions for two dual configurations: 1) a NB victim link subject to the aggregate UWB interference, and 2) a UWB victim link subject to the aggregate NB interference. The results show that while the impact of a single interferer on a link is often negligible due to restrictions on the transmitted power, the aggregate effect of multiple interferers may cause significant degradation. Therefore, aggregate interference must be considered to ensure coexistence in heterogeneous networks. The proposed analytical framework shows good agreement with physical-level simulations of the system.

In this paper, we introduce a mathematical framework for the characterization of network interference in wireless systems. We consider a network in which the interferers are scattered according to a spatial Poisson process and are operating asynchronously in a wireless environment subject to path loss, shadowing, and multipath fading. We start by determining the statistical distribution of the aggregate network interference. We then investigate four applications of the proposed model: 1) interference in cognitive radio networks; 2) interference in wireless packet networks; 3) spectrum of the aggregate radio-frequency emission of wireless networks; and 4) coexistence between ultrawideband and narrowband systems. Our framework accounts for all the essential physical parameters that affect network interference, such as the wireless propagation effects, the transmission technology, the spatial density of interferers, and the transmitted power of the interferers.

Bayesian networks (BNs) are knowledge representation tools capable of representing dependence or independence relationships among random variables. Learning the structure of BNs from datasets has received increasing attention in the last two decades, due to the BNs' capacity of providing good inference models and discovering the structure of complex domains. One approach for BNs' structure learning from data is to define a scoring metric that evaluates the quality of the candidate networks, given a dataset, and then apply an optimization procedure to explore the set of candidate networks. Among the most frequently used optimization methods for BN score-based learning is greedy hill climbing (GHC) search. This paper proposes a new local discovery ant colony optimization (ACO) algorithm and a hybrid algorithm max-min ant colony optimization (MMACO), based on the local discovery algorithm max-min parents and children (MMPC) and ACO to learn the structure of a BN. In MMACO, MMPC is used to construct the skeleton of the BN and ACO is used to orientate the skeleton edges, thus returning the final structure. The algorithms are applied to several sets of benchmark networks and are shown to outperform the GHC and simulated annealing algorithms.

Bayesian networks (BNs) are knowledge representation tools capable of representing dependence or independence relationships among random variables that compose a problem domain. Bayesian networks learned from data sets are receiving increasing attention within the community of researchers of uncertainty in artificial intelligence, due to their capacity to provide good inference models and to discover the structure of complex domains. One approach to learning BNs from data is to use a scoring metric to evaluate the fitness of any given candidate network for the database, and apply an optimization procedure to explore the set of candidate networks. Among the most frequently used optimization methods for this purpose is greedy search, either deterministic or stochastic. This article proposes a hybrid Bayesian network learning algorithm MMACO, based on the local discovery algorithm max-min parents and children (MMPC) and ant colony optimization (ACO). MMPC is used to construct the skeleton of the Bayesian network and then ACO is used to orientate its edges, thus returning the final structure. We apply MMACO (max-min ACO) to several sets of benchmark networks and show that it outperforms greedy search (GS) and simulated annealing (SA) algorithms.

This paper puts forth the new concept of spectral outage probability (SOP), which can be used to quantify and limit the impact of the aggregate network interference on any given receiver operating in the same frequency band. We consider a scenario with asynchronous, slowly-moving interferers which are scattered according to a spatial Poisson process, and are operating in a wireless environment subject to path loss, shadowing, and multipath fading. We determine the power spectral density (PSD) of the aggregate interference at any location in the plane, and then define and provide expressions for the corresponding SOP. Lastly, we quantify the PSD and SOP as a function of various parameters, such as the path loss exponent, and the interferers' power and spatial density. Our analysis is valid for any linear modulation scheme, and provides insights that may be of value to the network designer.