Performance analysis of wireless networks based on time-scale separation: A new iterative method

The complexity of modern communication networks makes the solution of the Markov chains that model their traffic dynamics, and therefore, the determination of their performance parameters, computationally costly. However, a common characteristic of these networks is that they manage multiple types of traffic flows operating at different time-scales. This time-scale separation can be exploited to substantially reduce the computational cost. Following this approach, we propose a novel solution method named Absorbing Markov Chains Approximation (AMCA) based on the transient regime analysis. Briefly, we model the time the system spends in a series of subsets of states by a phase-type distribution and, for each of them, determine the probabilities of finding the system in each state of this subset until absorption. We compare the AMCA performance to that obtained by classical methods and by a recently proposed approach that aims at generalizing the conventional quasi-stationary approximation. We find that AMCA has a more predictable behavior, is applicable to a wider range of time-scale separations, and achieves higher accuracy for a given computational cost.This research has been supported in part by the Ministry of Economy and Competitiveness of Spain under Grants TIN2013-47272-C2-1-R and TEC2015-71932-REDT. The research of L. Tello-Oquendo was supported in part by Programa de Ayudas de Investigacion y Desarrollo (PAID) of the Universitat Politecnica de Valencia.Tello Oquendo, LP.; Pla, V.; Martínez Bauset, J.; Casares Giner, V. (2016). Performance analysis of wireless networks based on time-scale separation: A new iterative method. Computer Communications. 86:40-48. https://doi.org/10.1016/j.comcom.2016.04.004S40488

Joint Channel Estimation and Pilot Allocation in Underlay Cognitive MISO Networks

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 primary and cognitive base stations, each equipped with multiple antennas and serving multiple users. Primary networks often suffer from the cognitive interference, which can be mitigated by deploying beamforming at the cognitive systems to spatially direct the transmissions away from the primary receivers. The accuracy of the estimated channel state information (CSI) plays an important role in designing accurate beamformers that can regulate the amount of interference. However, channel estimate is affected by interference. Therefore, we propose different channel estimation and pilot allocation techniques to deal with the channel estimation at the cognitive systems, and to reduce the impact of contamination at the primary and cognitive systems. In an effort to tackle the contamination problem in primary and cognitive systems, we exploit the information embedded in the covariance matrices to successfully separate the channel estimate from other users' channels in correlated cognitive single input multiple input (SIMO) channels. A minimum mean square error (MMSE) framework is proposed by utilizing the second order statistics to separate the overlapping spatial paths that create the interference. We validate our algorithms by simulation and compare them to the state of the art techniques.Comment: 6 pages, 2 figures, invited paper to IWCMC 201

Communication Subsystems for Emerging Wireless Technologies

The paper describes a multi-disciplinary design of modern communication systems. The design starts with the analysis of a system in order to define requirements on its individual components. The design exploits proper models of communication channels to adapt the systems to expected transmission conditions. Input filtering of signals both in the frequency domain and in the spatial domain is ensured by a properly designed antenna. Further signal processing (amplification and further filtering) is done by electronics circuits. Finally, signal processing techniques are applied to yield information about current properties of frequency spectrum and to distribute the transmission over free subcarrier channels

Semi-Supervised Radio Signal Identification

Radio emitter recognition in dense multi-user environments is an important tool for optimizing spectrum utilization, identifying and minimizing interference, and enforcing spectrum policy. Radio data is readily available and easy to obtain from an antenna, but labeled and curated data is often scarce making supervised learning strategies difficult and time consuming in practice. We demonstrate that semi-supervised learning techniques can be used to scale learning beyond supervised datasets, allowing for discerning and recalling new radio signals by using sparse signal representations based on both unsupervised and supervised methods for nonlinear feature learning and clustering methods

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig