Modulation Classification for MIMO-OFDM Signals via Approximate Bayesian Inference

The problem of modulation classification for a multiple-antenna (MIMO) system employing orthogonal frequency division multiplexing (OFDM) is investigated under the assumption of unknown frequency-selective fading channels and signal-to-noise ratio (SNR). The classification problem is formulated as a Bayesian inference task, and solutions are proposed based on Gibbs sampling and mean field variational inference. The proposed methods rely on a selection of the prior distributions that adopts a latent Dirichlet model for the modulation type and on the Bayesian network formalism. The Gibbs sampling method converges to the optimal Bayesian solution and, using numerical results, its accuracy is seen to improve for small sample sizes when switching to the mean field variational inference technique after a number of iterations. The speed of convergence is shown to improve via annealing and random restarts. While most of the literature on modulation classification assume that the channels are flat fading, that the number of receive antennas is no less than that of transmit antennas, and that a large number of observed data symbols are available, the proposed methods perform well under more general conditions. Finally, the proposed Bayesian methods are demonstrated to improve over existing non-Bayesian approaches based on independent component analysis and on prior Bayesian methods based on the `superconstellation' method.Comment: To be appear in IEEE Trans. Veh. Technolog

Gibbs Sampling Based Distributed OFDMA Resource Allocation

International audienceIn this article, we present a distributed resource and power allocation scheme for multiple-resource wireless cellular networks. The global optimization of multi-cell multi-link resource allocation problem is known to be NP-hard in the general case. We use Gibbs sampling based algorithms to perform a distributed optimization that would lead to the global optimum of the problem. The objective of this article is to show how to use the Gibbs sampling (GS) algorithm and its variant the Metropolis-Hastings (MH) algorithm. We also propose an enhanced method of the MH algorithm, based on a priori known target state distribution, which improves the convergence speed without increasing the complexity. Also, we study different temperature cooling strategies and investigate their impact on the network optimization and convergence speed. Simulation results have also shown the effectiveness of the proposed methods

D5.2 - Evaluation of Selected Measurement-based Techniques

Deliverable D5.2 del projecte FARAMIRPreprin

Design of a DVB-T2 simulation platform and network optimization with Simulated Annealing

The implementation of the Digital Terrestrial Television is becoming a reality in the Spanish territory. In this context, with the satellite and cable systems, this technology is one of the possible mediums for the television signal transmission. Its development is becoming crucial for the digital transition in those countries which mainly depend on the terrestrial networks for the reception of multimedia contents. However, due to the maturity of the current standard, and also to the higher requirements of the customer needing (HDTV, new contents, etc.), a revision of the current standard becomes necessary. The DVB organisation in collaboration with other entities and organisms has developed a new standard version capable to satisfy those requirements. The main objective of the project is the design and implementation of a physical layer simulation platform for the DVB-T2 standard. This simulator allows the theoretical evaluation of the new enhanced proposals, making easier a later field measurement stage and the future network deployment. The document describes the implementation of the simulation platform as well as its subsequent validation stage, including large graphical results that allow the evaluation and quantification of the improvements introduced over the current standard version (DVB-T). On the other hand, and as future investigation lines, a solution for the future DVB-T2 network deployment is performed, enhancing the coverage capacity of the current network by the use of iterative meta-heuristic techniques. Finally it has to be mentioned that this work has been performed within the context of a project called FURIA, which is a strategic research project funded by the Spanish Ministry of Industry, Tourism and Commerce

Design of a DVB-T2 simulation platform and network optimization with Simulated Annealing

The implementation of the Digital Terrestrial Television is becoming a reality in the Spanish territory. In this context, with the satellite and cable systems, this technology is one of the possible mediums for the television signal transmission. Its development is becoming crucial for the digital transition in those countries which mainly depend on the terrestrial networks for the reception of multimedia contents. However, due to the maturity of the current standard, and also to the higher requirements of the customer needing (HDTV, new contents, etc.), a revision of the current standard becomes necessary. The DVB organisation in collaboration with other entities and organisms has developed a new standard version capable to satisfy those requirements. The main objective of the project is the design and implementation of a physical layer simulation platform for the DVB-T2 standard. This simulator allows the theoretical evaluation of the new enhanced proposals, making easier a later field measurement stage and the future network deployment. The document describes the implementation of the simulation platform as well as its subsequent validation stage, including large graphical results that allow the evaluation and quantification of the improvements introduced over the current standard version (DVB-T). On the other hand, and as future investigation lines, a solution for the future DVB-T2 network deployment is performed, enhancing the coverage capacity of the current network by the use of iterative meta-heuristic techniques. Finally it has to be mentioned that this work has been performed within the context of a project called FURIA, which is a strategic research project funded by the Spanish Ministry of Industry, Tourism and Commerce

A Planning and Optimization Framework for Hybrid Ultra-Dense Network Topologies

The deployment of small cells has been a critical upgrade in Fourth Generation (4G) mobile networks as they provide macrocell traffic offloading gains, improved spectrum reuse and reduce coverage holes. The need for small cells will be even more critical in Fifth Generation (5G) networks due to the introduction of higher spectrum bands, which necessitate denser network deployments to support larger traffic volumes per unit area. A network densification scenario envisioned for evolved fourth and fifth generation networks is the deployment of Ultra-Dense Networks (UDNs) with small cell site densities exceeding 90 sites/km2 (or inter-site distances of less than 112 m). The careful planning and optimization of ultra-dense networks topologies have been known to significantly improve the achievable performance compared to completely random (unplanned) ultra-dense network deployments by various third-part stakeholders (e.g. home owners). However, these well-planned and optimized ultra-dense network deployments are difficult to realize in practice due to various constraints, such as limited or no access to preferred optimum small cell site locations in a given service area. The hybrid ultra-dense network topologies provide an interesting trade-off, whereby, an ultra-dense network may constitute a combination of operator optimized small cell deployments that are complemented by random small cell deployments by third-parties. In this study, an ultra-dense network multiobjective optimization framework and post-deployment power optimization approach are developed for realization and performance comparison of random, optimized and hybrid ultra-dense network topologies in a realistic urban case study area. The results of the case study demonstrate how simple transmit power optimization enable hybrid ultra-dense network topologies to achieve performance almost comparable to optimized topologies whilst also providing the convenience benefits of random small cell deployments