Efficient detection algorithms for Multiple-Input Multiple-Output (MIMO) systems

[EN] In the last ten years, one of the most significant technological developments that will lead to the new broadband wireless generation is the communication via Multiple-Input Multiple-Output (MIMO) systems. MIMO systems are known to provide an increase of the maximum rate, reliability and coverage of current wireless communications. Maximum-Likelihood (detection over Gaussian MIMO channels is shown to get the lowest Bit Error Rate for a given scenario. However, it has a prohibitive complexity which grows exponentially with the number of transmit antennas and the size of the constellation. Motivated by this, there is a continuous search for computationally efficient optimal or suboptimal detectors. In this work, we carry out an state of the art review of detection algorithms and propose the combination of a suboptimal MIMO detector called K-Best Sphere Decoder with a channel matrix condition number estimator to obtain a versatile combined detector with predictable performance and suitable for hardware implementation. The effect of the channel matrix condition number in data detection is exploited in order to achieve a decoding complexity lower than the one of already proposed algorithms with similar performance. Some practical algorithms for finding the 2-norm condition number of a given channel matrix and for performing the threshold selection are also presented and their computational costs and accuracy are discussed[ES] Uno de los desarrollos tecnol'ogicos m'as significativos de la ' ultima d'ecada que llevar'an a la nueva generaci'on de banda ancha en movilidad es la comunicaci'on mediante sistemas de m' ultiples entradas y m' ultiples salidas (MIMO). Los sistemas MIMO proporcionan un notable incremento en la capacidad, fiabilidad y cobertura de las comunicaciones inal'ambricas actuales. Se puede demostrar que la detecci'on 'optima o dem'axima verosimilitud (ML) en canales MIMO Gaussianos proporciona la m'¿nima tasa de error de bit (BER) para un escenario dado pero tiene el inconveniente de que su complejidad crece exponencialmente con el n'umero de antenas y el tama¿no de la constelaci'on utilizada. Por este motivo, hay una cont'¿nua b' usqueda de detectores 'optimos o sub'optimos que sean m'as eficientes computacionalmente. En este trabajo, se ha llevado a cabo una revisi 'on del estado del arte de los principales algoritmos de detecci'on para sistemas MIMO y se ha propuesto la combinaci'on de un detector MIMO sub'optimo conocido como K-Best Sphere Decoder con un estimador del n'umero de condici'on de la matriz de canal, para conseguir un detector combinado basado en umbral con complejidad predecible y adecuado para implementaci'on en hardware. Se ha explotado el efecto del n'umero de condici'on en la detecci'on de datos para disminuir la complejidad de los algoritmos de detecci 'on existentes sin apenas alterar sus prestaciones. Por ' ultimo tambi'en se presentan distintos algoritmos pr'acticos para encontrar el dos n'umero de condici'on as'¿ como para realizar la selecci 'on del umbral.Roger Varea, S. (2008). Efficient detection algorithms for Multiple-Input Multiple-Output (MIMO) systems. http://hdl.handle.net/10251/12200Archivo delegad

Design and Implementation of Efficient Algorithms for Wireless MIMO Communication Systems

En la última década, uno de los avances tecnológicos más importantes que han hecho culminar la nueva generación de banda ancha inalámbrica es la comunicación mediante sistemas de múltiples entradas y múltiples salidas (MIMO). Las tecnologías MIMO han sido adoptadas por muchos estándares inalámbricos tales como LTE, WiMAS y WLAN. Esto se debe principalmente a su capacidad de aumentar la máxima velocidad de transmisión , junto con la fiabilidad alcanzada y la cobertura de las comunicaciones inalámbricas actuales sin la necesidad de ancho de banda extra ni de potencia de transmisión adicional. Sin embargo, las ventajas proporcionadas por los sistemas MIMO se producen a expensas de un aumento sustancial del coste de implementación de múltiples antenas y de la complejidad del receptor, la cual tiene un gran impacto sobre el consumo de energía. Por esta razón, el diseño de receptores de baja complejidad es un tema importante que se abordará a lo largo de esta tesis. En primer lugar, se investiga el uso de técnicas de preprocesado de la matriz de canal MIMO bien para disminuir el coste computacional de decodificadores óptimos o bien para mejorar las prestaciones de detectores subóptimos lineales, SIC o de búsqueda en árbol. Se presenta una descripción detallada de dos técnicas de preprocesado ampliamente utilizadas: el método de Lenstra, Lenstra, Lovasz (LLL) para lattice reduction (LR) y el algorimo VBLAST ZF-DFE. Tanto la complejidad como las prestaciones de ambos métodos se han evaluado y comparado entre sí. Además, se propone una implementación de bajo coste del algoritmo VBLAST ZF-DFE, la cual se incluye en la evaluación. En segundo lugar, se ha desarrollado un detector MIMO basado en búsqueda en árbol de baja complejidad, denominado detector K-Best de amplitud variable (VB K-Best). La idea principal de este método es aprovechar el impacto del número de condición de la matriz de canal sobre la detección de datos con el fin de disminuir la complejidad de los sistemasRoger Varea, S. (2012). Design and Implementation of Efficient Algorithms for Wireless MIMO Communication Systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/16562Palanci

Multicarrier Waveform Harmonization and Complexity Analysis for an Efficient 5G Air Interface Implementation

[EN] The coexistence of multiple air interface variants in the upcoming fifth generation (5G) wireless technology remains a matter of ongoing discussion. This paper focuses on the physical layer of the 5G air interface and provides a harmonization solution for the joint implementation of several multicarrier waveform candidates. Waveforms based either on cyclic prefix-orthogonal frequency division multiplexing (CP-OFDM) or on filter bank multicarrier (FBMC) are first presented through a harmonized system model. Complexity comparisons among five different waveforms are provided. Then, the complexity of a proposed configurable hardware implementation setup for waveform transmission and reception is evaluated. As a result, the harmonized transmitter and receiver exhibit 25¿40% and 15¿25% less complexity in floating-point operations, respectively, in comparison to two standalone implementations of the most complex waveform instances of the CP-OFDM and FBMC families. This highlights the similarities between both families and illustrates the component reuse advantages associated with the proposed harmonized solution.This work was performed in the framework of the H2020 Project METIS-II with reference 671680, which is partly funded by the European Union. The authors would like to acknowledge the contributions of their colleagues in METIS-II. This work was also supported in part by the Ministerio de Economia y Competitividad, under Grant TEC2014-60258-C2-1-R.Garcia-Roger, D.; Roger Varea, S.; Flores De Valgas, J.; Monserrat, JF. (2017). Multicarrier Waveform Harmonization and Complexity Analysis for an Efficient 5G Air Interface Implementation. Wireless Communications and Mobile Computing. 2017:1-11. https://doi.org/10.1155/2017/9765614S111201

Fast channel estimation in the transformed spatial domain for analog millimeter wave systems

Fast channel estimation in millimeter-wave (mmWave) systems is a fundamental enabler of high-gain beamforming, which boosts coverage and capacity. The channel estimation stage typically involves an initial beam training process where a subset of the possible beam directions at the transmitter and receiver is scanned along a predefined codebook. Unfortunately, the high number of transmit and receive antennas deployed in mmWave systems increase the complexity of the beam selection and channel estimation tasks. In this work, we tackle the channel estimation problem in analog systems from a different perspective than used by previous works. In particular, we propose to move the channel estimation problem from the angular domain into the transformed spatial domain, in which estimating the angles of arrivals and departures corresponds to estimating the angular frequencies of paths constituting the mmWave channel. The proposed approach, referred to as transformed spatial domain channel estimation (TSDCE) algorithm, exhibits robustness to additive white Gaussian noise by combining low-rank approximations and sample autocorrelation functions for each path in the transformed spatial domain. Numerical results evaluate the mean square error of the channel estimation and the direction of arrival estimation capability. TSDCE significantly reduces the first, while exhibiting a remarkably low computational complexity compared with well-known benchmarking schemes

On the use of composite indicators for mobile communications network management in smart sustainable cities

Beyond 5G networks will be fundamental towards enabling sustainable mobile communication networks. One of the most challenging scenarios will be met in ultra-dense networks that are deployed in densely populated areas. In this particular case, mobile network operators should benefit from new assessment metrics and data science tools to ensure an effective management of their networks. In fact, incorporating architectures allowing a cognitive network management framework could simplify processes and enhance the network's performance. In this paper, we propose the use of composite indicators based on key performance indicators both as a tool for a cognitive management of mobile communications networks, as well as a metric which could successfully integrate more advanced user-centric measurements. Composite indicators can successfully synthesize and integrate large amounts of data, incorporating in a single index different metrics selected as triggers for autonomous decisions. The paper motivates and describes the use of this methodology, which is applied successfully in other areas with the aim of ranking metrics to simplify complex realities. A use case that is based on a universal mobile telecommunications system network is analyzed, due to technology simplicity and scalability, as well as the availability of key performance indicators. The use case focuses on analyzing the fairness of a network over different coverage areas as a fundamental metric in the operation and management of the networks. To this end, several ranking and visualization strategies are presented, providing examples of how to extract insights from the proposed composite indicator

Application of Radio environment map reconstruction techniques to platoon-based cellular V2X communications

Vehicle platoons involve groups of vehicles travelling together at a constant inter-vehicle distance, with different common benefits such as increasing road efficiency and fuel saving. Vehicle platooning requires highly reliable wireless communications to keep the group structure and carry out coordinated maneuvers in a safe manner. Focusing on infrastructure-assisted cellular vehicle to anything (V2X) communications, the amount of control information to be exchanged between each platoon vehicle and the base station is a critical factor affecting the communication latency. This paper exploits the particular structure and characteristics of platooning to decrease the control information exchange necessary for the channel acquisition stage. More precisely, a scheme based on radio environment map (REM) reconstruction is proposed, where geo-localized received power values are available at only a subset of platoon vehicles, while large-scale channel parameters estimates for the rest of platoon members are provided through the application of spatial Ordinary Kriging (OK) interpolation. Distinctive features of the vehicle platooning use case are explored, such as the optimal patterns of vehicles within the platoon with available REM values for improving the quality of the reconstruction, the need for an accurate semivariogram modeling in OK, or the communication cost when establishing a centralized or a distributed architecture for achieving REM reconstruction. The evaluation results show that OK is able to reconstruct the REM in the platoon with acceptable mean squared estimation error, while reducing the control information for REM acquisition in up to 64% in the best-case scenario

Multi-user non-coherent detection for downlink MIMO communication

© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] Current cellular technologies are based on the concept of coherent communication, in which the channel matrix used for demodulation is estimated via reference or pilot signals. Coherent systems, however, involve a significant increase of the signalling overhead, especially when the number of transmission points is increased or when the mobile channel changes rapidly, which motivates the use of non-coherent techniques. This letter extends the use of non-coherent communications to a multi-user (MU) multiple-input multiple-output (MIMO) framework by combining superposition coding with a reduced-complexity detection method. Numerical results confirm that our scheme achieves higher user rates than non-coherent MU transmission based on time multiplexing. In addition to the well-known sum-rate gain of MU systems, an extra performance gain given by downlink non-coherent MU communication is shown and qualitatively justified.This work was performed in the framework of the FP7 project ICT-317669 METIS, supported in part by the European Union. The authors would like to acknowledge the contributions of their colleagues in METIS, although the views expressed are those of the authors and do not necessarily represent the project. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Chandra Ramabhadra Murthy.Roger Varea, S.; Calabuig Soler, D.; Cabrejas Peñuelas, J.; Monserrat Del Río, JF. (2014). Multi-user non-coherent detection for downlink MIMO communication. IEEE Signal Processing Letters. 21(10):1225-1229. https://doi.org/10.1109/LSP.2014.2330854S12251229211

An efficient GPU implementation of fixed-complexity sphere decoders for MIMO wireless systems

The use of many-core processors such as general purpose Graphic Processing Units (GPUs) has recently become attractive for the efficient implementation of signal processing algorithms for communication systems. This is due to the cost-effectiveness of GPUs together with their potential capability of parallel processing. This paper presents an implementation of the widely employed fixed-complexity sphere decoder on GPUs, which allows to considerably decrease the computational time required for the data detection stage in multiple-input multiple-output systems. Both, the hard-and soft-output versions of the method have been implemented. Speedup results show the proposed GPU implementation boosts the runtime of the parallel execution of the methods in a high performance multi-core CPU. In addition, the throughput of the algorithm is evaluated and is shown to outperform other recent implementations and to fulfill the real-time requirements of several LTE configurations. ©2012-IOS Press and the authors. All rights reserved.This work was partially funded by the TEC2009-13741 project of the Spanish Ministry of Science and by the PROMETEO/2009/013 project of the Generalitat Valenciana.Roger Varea, S.; Ramiro Sánchez, C.; González Salvador, A.; Almenar Terré, V.; Vidal Maciá, AM. (2012). An efficient GPU implementation of fixed-complexity sphere decoders for MIMO wireless systems. Integrated Computer-Aided Engineering. 19(4):341-350. https://doi.org/10.3233/ICA-2012-0410S34135019

Distributed Hybrid Precoding for Indoor Deployments Using Millimeter Wave Band

[EN] DistributedAntenna Systems (DAS) are an alternative of network deployment that allows reducing the distance between transmitter and receiver by distributing the antennas throughout the coverage area. Moreover, the performance of the millimeter wave (mmWave) band can be significantly high within short transmitter-receiver distances. In this paper, the potential benefits of DAS deployments in the mmWave band are studied. To this aim, a distributed hybrid precoding (DHP) solution with remote antenna unit (RAU) selection capabilities is proposed and analyzed in an indoor DAS working in mmWaves and compared to two other indoor deployment strategies: a conventional cellular system with colocated antenna arrays and a small cell deployment. Theresults show that, using DHP, DAS not only brings huge gains to cell-edge users rate but also increases system capacity, becoming the best overall deployment. Further simulations including practical limitations have revealed that DAS using DHP is quite robust to combiner losses, although its performance is significantly degraded by outdated channel reports.This work has been supported by Ministerio de Economia y Competitividad, Spain (BES-2012-055975 and TEC2014-60258-C2-1-R), and by the European FEDER funds.Giménez Colás, S.; Calabuig Soler, D.; Roger Varea, S.; Monserrat Del Río, JF.; Cardona Marcet, N. (2017). Distributed Hybrid Precoding for Indoor Deployments Using Millimeter Wave Band. Mobile Information Systems. 2017:1-12. https://doi.org/10.1155/2017/5751809S112201

Hybrid CPU-GPU implementation of the transformed spatial domain channel estimation algorithm for mmWave MIMO systems

Hybrid platforms combining multicore central processing units (CPU) with manycore hardware accelerators such as graphic processing units (GPU) can be smartly exploited to provide efcient parallel implementations of wireless communication algorithms for Fifth Generation (5G) and beyond systems. Massive multiple-input multiple-output (MIMO) systems are a key element of the 5G standard, involving several tens or hundreds of antenna elements for communication. Such a high number of antennas has a direct impact on the computational complexity of some MIMO signal processing algorithms. In this work, we focus on the channel estimation stage. In particular, we develop a parallel implementation of a recently proposed MIMO channel estimation algorithm. Its performance in terms of execution time is evaluated both in a multicore CPU and in a GPU. The results show that some computation blocks of the algorithm are more suitable for multicore implementation, whereas other parts are more efciently implemented in the GPU, indicating that a hybrid CPU-GPU implementation would achieve the best performance in practical applications based on the tested platform