Improving A*OMP: Theoretical and Empirical Analyses With a Novel Dynamic Cost Model

Best-first search has been recently utilized for compressed sensing (CS) by the A* orthogonal matching pursuit (A*OMP) algorithm. In this work, we concentrate on theoretical and empirical analyses of A*OMP. We present a restricted isometry property (RIP) based general condition for exact recovery of sparse signals via A*OMP. In addition, we develop online guarantees which promise improved recovery performance with the residue-based termination instead of the sparsity-based one. We demonstrate the recovery capabilities of A*OMP with extensive recovery simulations using the adaptive-multiplicative (AMul) cost model, which effectively compensates for the path length differences in the search tree. The presented results, involving phase transitions for different nonzero element distributions as well as recovery rates and average error, reveal not only the superior recovery accuracy of A*OMP, but also the improvements with the residue-based termination and the AMul cost model. Comparison of the run times indicate the speed up by the AMul cost model. We also demonstrate a hybrid of OMP and A?OMP to accelerate the search further. Finally, we run A*OMP on a sparse image to illustrate its recovery performance for more realistic coefcient distributions

Sparse Vector Codes para Comunicaciones Espaciales

(Castellano) En el ámbito de la exploración espacial, las comunicaciones inalámbricas son de vital importancia, y de ellas depende en muchos casos, el éxito de una misión. Debido a las grandes limitaciones, en cuanto a recursos, que impone el entorno espacial, las técnicas de codificación contra errores suponen una gran ayuda para la optimización de las comunicaciones. Permiten la recepción de mensajes a pesar de la poca potencia disponible y el ruido introducido, lo cual sería imposible sin hacer uso de este tipo de técnicas. Muchas de estos códigos ya están siendo utilizados para misiones espaciales, pero a pesar de que son eficientes para mensajes largos, pierden eficacia a medida que la longitud de mensaje disminuye. Por ello, es importante introducir nuevos códigos que sean óptimos para el envío de mensajes cortos, como son los SVC (Sparse Vector Coding). Los SVC son unos códigos de corrección contra errores optimizados para la transmisión de mensajes cortos. En 2018, Hyoung-ju et al. presento a los SVC como una alternativa a las actuales propuestas de codificación. Este trabajo pretende partir de TFM presentado por Iñigo Bilbao, en el cuál realiza un estudio más concluyente, validando los resultados presentados por la bibliografía, aportando más conocimiento acerca de su comportamiento general y proponiendo una arquitectura mediante una plataforma de simulación. Este TFM concentra sus esfuerzos en la etapa de decodificación del sistema, ya que es uno de los puntos clave para optimizar su rendimiento, y busca plantear alternativas para conseguir este objetivo

Iterative signal detection for large scale GSM-MIMO systems

Generalized spatial modulations (GSM) represent a novel multiple input multiple output (MIMO) scheme which can be regarded as a compromise between spatial multiplexing MIMO and conventional spatial modulations (SM), achieving both spectral efficiency (SE) and energy efficiency (EE). Due to the high computational complexity of the maximum likelihood detector (MLD) in large antenna settings and symbol constellations, in this paper we propose a lower complexity iterative suboptimal detector. The derived algorithm comprises a sequence of simple processing steps, namely an unconstrained Euclidean distance minimization problem, an element wise projection over the signal constellation and a projection over the set of valid active antenna combinations. To deal with scenarios where the number of possible active antenna combinations is large, an alternative version of the algorithm which adopts a simpler cardinality projection is also presented. Simulation results show that, compared with other existing approaches, both versions of the proposed algorithm are effective in challenging underdetermined scenarios where the number of receiver antennas is lower than the number of transmitter antennas.info:eu-repo/semantics/acceptedVersio

Efficient recovery algorithm for discrete valued sparse signals using an ADMM approach

Motivated by applications in wireless communications, in this paper we propose a reconstruction algorithm for sparse signals whose values are taken from a discrete set, using a limited number of noisy observations. Unlike conventional compressed sensing algorithms, the proposed approach incorporates knowledge of the discrete valued nature of the signal in the detection process. This is accomplished through the alternating direction method of the multipliers which is applied as a heuristic to decompose the associated maximum likelihood detection problem in order to find candidate solutions with a low computational complexity order. Numerical results in different scenarios show that the proposed algorithm is capable of achieving very competitive recovery error rates when compared with other existing suboptimal approaches.info:eu-repo/semantics/publishedVersio