The advances made in wireless communication technology have led to efforts to improve the quality of reception, prevent poor connections and avoid disconnections between wireless and cellular devices. One of the most important steps toward preventing communication failures is to correctly estimate the received signal strength indicator (RSSI) of a wireless device. RSSI prediction is important for addressing various challenges such as localization, power control, link quality estimation, terminal connectivity estimation, and handover decisions. In this study, we compare different machine learning (ML) techniques that can be used to predict the received signal strength values of a device, given the received signal strength values of other devices in the region. We consider various ML methods, such as multi-layer ANN, K nearest neighbors, decision trees, random forest, and the K-means based method, for the prediction challenge. We checked the accuracy level of the learning process using a real dataset provided by a major national cellular operator. Our results show that the weighted K nearest neighbors algorithm, for K = 3 neighbors, achieved, on average, the most accurate RSSI predictions. We conclude that in environments where the size of data is relatively small, and data of close geographical points is available, a method that predicts the coverage of a point using the coverage near geographical points can be more successful and more accurate compared with other ML methods.

In this work, we considered power allocation and request scheduling in mobile ad hoc networks (MANET) clusters, which are generally modeled as optimization problems with constraints. Optimization algorithms often incur significant time complexity, which creates significant discrepancies between theoretical results and real-time processing. We aim to provide a novel machine-learning-based perspective to address this challenge. We use a deep neural network (DNN) to approximate the nonlinear mapping between the inputs and outputs of an optimization algorithm. If a nonlinear mapping can be learned accurately by a DNN, then optimization tasks can be performed more efficiently. We propose SPCDNet as a scheduling and power control deep network mapping method. A key challenge in training a DNN for resource allocation problems is a lack of ground-truth data, meaning the optimal power allocation between the time slots of each transmitter is unknown. To address this issue, we designed an optimal solver based on linear programming optimization methods and used its solutions to train SPCDNet. Simulation results demonstrate that SPCDNet can serve as a computationally inexpensive replacement for traditionally expensive optimization algorithms in real-time tasks and provide very good approximation solutions, where the average run time of SPCDNet for each network size is very low compared to the hundreds of seconds used by an optimal solver, whose time complexity increases exponentially with the input size.

The Internet of Things world is in urgent need of practical solutions for its security. In this paper, we propose a new approach to extend existing authentication protocols, using Physical Layer Radio Frequency Fingerprinting to provide efficient hybrid authentication mechanisms. Our approach uses a hybrid protocol, that can save energy and computation time on the IoT devices side, proportionally to the accuracy of the Radio Frequency Fingerprinting used, having a measurable gain while keeping an acceptable security level.

In network management architectures of 5G and IoT networks, standardization groups often consider the network resource virtualization layer between the physical network and the SDN controller, as a means to allow deployment and placement of network services with their virtual network functions. However, the following question arises: is this layer enough to react to real-time changes originating from customers or the network without interrupting the service? We consider that a dynamic architecture should allow different and evolving assemblies to be provisioned during a session, in order to meet modification requests without requiring total redesign of the network service. Therefore, in this study, we propose an enhanced architecture. This novel architecture adds a network virtualization layer above the SDN controller with its associated orchestrator. Then, efficiently distributing orchestration among the different layers ensures network autonomy. In this context, we show how real-time service modifications and network failures are handled without losing the existing services and how network management gains additional dynamicity and flexibility.

Software Defined Networking decouples the control and data planes. The response time and quality of service of the controllers is a key aspect in implementing the Software Defined Networking paradigm. A wireless Software Defined Networking control plane is even more challenging. Many radio communication problems arise in modeling the wireless east west bound and southbound interfaces. Wireless networks feature many unique components and metrics that often do not exist in wired networks: separate control transport may intensify on the latency within the wireless data plain with additional interference. Obviously, Wi-Fi based control plane has its implications, e.g., higher packet loss and hidden or exposed terminals. Moreover, wireless links can be operated in a number of different wireless characteristics, e.g., transmission rates and power settings. In this paper we define and solve the known Controllers Placement Problem, but for a Wi-Fi based control plane: the Wireless Control Placement Problem. We define the metrics for an effective wireless controllers placement and propose a multi-objective optimization for the Wireless Controller Placement Problem. Then we evaluate the influence of the variant metrics on the number of controllers and their locations.

Accurate assessment of the wireless coverage of a station is a critical step toward deploying more base stations in Ultra Dense Networks, and it is considered as one of the key features of the 5G networks. Quickly and efficiently determining the reception coverage of transmitters becomes a complicated problem when interfering transmitters are introduced to the scenario. It becomes increasingly more complicated when the transmission powers of those transmitters are not uniform. Artificial Neural Networks are the most suitable learning algorithms for recognizing and predicting non-linear patterns. In particular, a Radial Basis Network is a type of Artificial Neural Network which typically uses a Gaussian kernel as an activator as opposed to a sigmoid function. In this paper, we suggest using Radial Basis networks in order to predict coverage maps. We show how it is possible to train the Radial Basis Network to generate coverage maps based on samples and we check the accuracy level of the learning process on a test set. Using Radial Basis Network can improve the cellular coverage prediction and therefore it can enable a more efficient spectrum allocation.

Polarization diversity is generally achieved by using two orthogonal polarizations on the same link, which enables to double the available bandwidth. In this paper, we study the possibility to connect the nodes of a line-of- sight ring topology network with one single channel for all the links, with the condition that the polarization of any link is orthogonal to the polarization of the two adjacent links. The solution proposed in this paper can improve spectrum efficiency by up to 50% in comparison with the widespread polarization multiplexing solution.

Over the last decade or so, significant research has focused on defining Quality of Experience (QoE) of Multimedia Systems and identifying the key factors that collectively determine it. Some consensus thus exists as to the role of System Factors, Human Factors and Context Factors. In this paper, the notion of context is broadened to include information gleaned from simultaneous out-of-band channels, such as social network trend analytics, that can be used if interpreted in a timely manner, to help further optimise QoE. A case study involving simulation of HTTP adaptive streaming (HAS) and load balancing in a content distribution network (CDN) in a flash crowd scenario is presented with encouraging results.

The unprecedented cellular traffic growth caused by the ubiquitous mobile Internet access has motivated the search for solutions to cope with this explosive demand of connectivity. Mobile traffic offloading through Wi-Fi networks has emerged as an effective way of supplementing cellular services. This approach has been proven very effective to reduce the load of delay-tolerant traffic in cellular networks. Recent developments suggest that real-time traffic like VoIP could be also offloaded if Wi-Fi networks supported a reasonable number of concurrent calls. In this way, ubiquitous IEEE 802.11 networks operating in the 2.4 GHz ISM band could complement the cellular systems in indoor and rural environments. Wi-Fi networks are allocated 14 channels in this frequency band, thereof only 3 non-overlapped channels are allowed for Wi-Fi extended service sets in order to avoid interference. In this paper we investigate the use of assignment configurations with 4 partially-overlapped channels for VoIP transmissions, which may increase the network capacity in terms of concurrent users supported by Wi-Fi networks. Through computer simulations we demonstrate that 4-channel configurations are feasible at the expense of a tolerable degradation in quality of service for voice traffic with pedestrian mobility.

The celebrated Perron–Frobenius (PF) theorem is stated for irreducible nonnegative square matrices, and provides a simple characterization of their eigenvectors and eigenvalues. The importance of this theorem stems from the fact that eigenvalue problems on such matrices arise in many fields of science and engineering, including dynamical systems theory, economics, statistics and optimization. However, many real-life scenarios give rise to nonsquare matrices. Despite the extensive development of spectral theories for nonnegative matrices, the applicability of such theories to non-convex optimization problems is not clear. In particular, a natural question is whether the PF Theorem (along with its applications) can be generalized to a nonsquare setting. Our paper provides a generalization of the PF Theorem to nonsquare multiple choice matrices. The extension can be interpreted as representing systems with additional degrees of freedom, where each client entity may choose between multiple servers that can cooperate in serving it (while potentially interfering with other clients). This formulation is motivated by applications to power control in wireless networks, economics and others, all of which extend known examples for the use of the original PF Theorem. We show that the option of cooperation does not improve the situation, in the sense that in the optimum solution, no cooperation is needed, and only one server per client entity needs to work. Hence, the additional power of having several potential servers per client translates into choosing the “best” single server and not into sharing the load between the servers in some way, as one might have expected. The two main contributions of the paper are (i) a generalized PF Theorem that characterizes the optimal solution for a non-convex problem, and (ii) an algorithm for finding the optimal solution in polynomial time. In addition, we extend the definitions of irreducibility and largest eigenvalue of square matrices to nonsquare ones in a novel and non-trivial way, which turns out to be necessary and sufficient for our generalized theorem to hold. To characterize the optimal solution, we use techniques from a wide range of areas. In particular, the analysis exploits combinatorial properties of polytopes, graph-theoretic techniques and analytic tools such as spectral properties of nonnegative matrices and root characterization of integer polynomials.

This paper studies the reception zones of a wireless network in the SINR model with receivers that employ interference cancellation (IC). IC is a recently developed technique that allows a receiver to decode interfering signals, and cancel them from the received signal in order to decode its intended message. We first derive the important topological properties of the reception zones and their relation to high-order Voronoi diagrams and other geometric objects. We then discuss the computational issues that arise when seeking an efficient description of the zones. Our main fundamental result states that although potentially there are exponentially many possible cancellation orderings, and as a result, reception zones, in fact there are much fewer nonempty such zones. We prove a linear bound (hence tight) on the number of zones and provide a polynomial time algorithm to describe the diagram. Moreover, we introduce a novel parameter, the Compactness Parameter, which influences the tightness of our bounds. We then utilize these properties to devise a logarithmic time algorithm to answer point-location queries for networks with IC.