Implementation of a High Throughput Soft MIMO Detector on GPU

Multiple-input multiple-output (MIMO) significantly increases the throughput of a communication system by employing multiple antennas at the transmitter and the receiver. To extract maximum performance from a MIMO system, a computationally intensive search based detector is needed. To meet the challenge of MIMO detection, typical suboptimal MIMO detectors are ASIC or FPGA designs. We aim to show that a MIMO detector on Graphic processor unit (GPU), a low-cost parallel programmable co-processor, can achieve high throughput and can serve as an alternative to ASIC/FPGA designs. However, careful architecture aware software design is needed to leverage the performance offered by GPU. We propose a novel soft MIMO detection algorithm, multi-pass trellis traversal (MTT), and show that we can achieve ASIC/FPGA-like performance and handle different configurations in software on GPU. The proposed design can be used to accelerate wireless physical layer simulations and to offload MIMO detection processing in wireless testbed platforms.NokiaNokia Siemens Networks (NSN)Texas InstrumentsXilinxNational Science Foundatio

Soft MIMO Detection on Graphics Processing Units and Performance Study of Iterative MIMO Decoding

In this thesis we have presented an implementation of soft Multi Input Multi Output (MIMO) detection, single tree search algorithm on Graphics Processing Units (GPUs). We have compared its performance on different GPUs and a Central Processing Unit (CPU). We have also done a performance study of iterative decoding algorithms. We have shown that by increasing the number of outer iterations error rate performance can be further improved. GPUs are specialized devices specially designed to accelerate graphics processing. They are massively parallel devices which can run thousands of threads simultaneously. Because of their tremendous processing power there is an increasing interest in using them for scientific and general purpose computations. Hence companies like Nvidia, Advanced Micro Devices (AMD) etc. have started their support for General Purpose GPU (GPGPU) applications. Nvidia came up with Compute Unified Device Architecture (CUDA) to program its GPUs. Efforts are made to come up with a standard language for parallel computing that can be used across platforms. OpenCL is the first such language which is supported by all major GPU and CPU vendors. MIMO detector has a high computational complexity. We have implemented a soft MIMO detector on GPUs and studied its throughput and latency performance. We have shown that a GPU can give throughput of up to 4Mbps for a soft detection algorithm which is more than sufficient for most general purpose tasks like voice communication etc. Compare to CPU a throughput increase of ~7x is achieved. We also compared the performances of two GPUs one with low computational power and one with high computational power. These comparisons show effect of thread serialization on algorithms with the lower end GPU's execution time curve shows a slope of 1/2. To further improve error rate performance iterative decoding techniques are employed where a feedback path is employed between detector and decoder. With an eye towards GPU implementation we have explored these algorithms. Better error rate performance however, comes at a price of higher power dissipation and more latency. By simulations we have shown that one can predict based on the Signal to Noise Ratio (SNR) values how many iterations need to be done before getting an acceptable Bit Error Rate (BER) and Frame Error Rate (FER) performance. Iterative decoding technique shows that a SNR gain of ~1:5dB is achieved when number of outer iterations is increased from zero. To reduce the complexity one can adjust number of possible candidates the algorithm can generate. We showed that where a candidate list of 128 is not sufficient for acceptable error rate performance for a 4x4 MIMO system using 16-QAM modulation scheme, performances are comparable with the list size of 512 and 1024 respectively

Flexible N-Way MIMO Detector on GPU

This paper proposes a flexible Multiple-Input Multiple-Output (MIMO) detector on graphics processing units (GPU). MIMO detection is a key technology in broadband wireless system such as LTE,WiMAX, and 802.11n. Existing detectors either use costly sorting for better performance or sacrifice sorting for higher throughput. To achieve good performance with high thoughput, our detector runs multiple search passes in parallel, where each search pass detects the transmit stream with a different permuted detection order. We show that this flexible detector, including QR decomposition preprocessing, outperforms existing GPU MIMO detectors while maintaining good bit error rate (BER) performance. In addition, this detector can achieve different tradeoffs between throughput and accuracy by changing the number of parallel search passes.National Science Foundation (NSF

Reconfigurable Real-time MIMO Detector on GPU

In a high performance multiple-input multiple-output (MIMO) system, a soft output MIMO detector combined with a channel decoder is often used at the receiver to maximize performance gain. Graphic processor unit (GPU) is a low-cost parallel programmable co-processor that can deliver extremely high computation throughput and is well suited for signal processing applications. We propose and implement a novel soft MIMO detection algorithm and show we meet real-time performance while maintaining flexibility using GPU.NokiaNokia Siemens Networks (NSN)Texas InstrumentsXilinxNational Science Foundatio

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

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

FlexCore: Massively Parallel and Flexible Processing for Large MIMO Access Points

Large MIMO base stations remain among wireless network designers’ best tools for increasing wireless throughput while serving many clients, but current system designs, sacrifice throughput with simple linear MIMO detection algorithms. Higher-performance detection techniques are known, but remain off the table because these systems parallelize their computation at the level of a whole OFDM subcarrier, sufficing only for the less demanding linear detection approaches they opt for. This paper presents FlexCore, the first computational architecture capable of parallelizing the detection of large numbers of mutually-interfering information streams at a granularity below individual OFDM subcarriers, in a nearly-embarrassingly parallel manner while utilizing any number of available processing elements. For 12 clients sending 64-QAM symbols to a 12-antenna base station, our WARP testbed evaluation shows similar network throughput to the state-of-the-art while using an order of magnitude fewer processing elements. For the same scenario, our combined WARP-GPU testbed evaluation demonstrates a 19x computational speedup, with 97% increased energy efficiency when compared with the state of the art. Finally, for the same scenario, an FPGA-based comparison between FlexCore and the state of the art shows that FlexCore can achieve up to 96% better energy efficiency, and can offer up to 32x the processing throughput

MIMOPack: A High Performance Computing Library for MIMO Communication Systems

[EN] Nowadays, several communication standards are emerging and evolving, searching higher transmission rates, reliability and coverage. This expansion is primarily driven by the continued increase in consumption of mobile multimedia services due to the emergence of new handheld devices such as smartphones and tablets. One of the most significant techniques employed to meet these demands is the use of multiple transmit and receive antennas, known as MIMO systems. The use of this technology allows to increase the transmission rate and the quality of the transmission through the use of multiple antennas at the transmitter and receiver sides. MIMO technologies have become an essential key in several wireless standards such as WLAN, WiMAX and LTE. These technologies will be incorporated also in future standards, therefore is expected in the coming years a great deal of research in this field. Clearly, the study of MIMO systems is critical in the current investigation, however the problems that arise from this technology are very complex. High Performance Computing (HPC) systems, and specifically, modern hardware architectures as multi-core and many-cores (e.g Graphics Processing Units (GPU)) are playing a key role in the development of efficient and low-complexity algorithms for MIMO transmissions. Proof of this is that the number of scientific contributions and research projects related to its use has increased in the last years. Also, some high performance libraries have been implemented as tools for researchers involved in the development of future communication standards. Two of the most popular libraries are: IT++ that is a library based on the use of some optimized libraries for multi-core processors and the Communications System Toolbox designed for use with MATLAB, which uses GPU computing. However, there is not a library able to run on a heterogeneous platform using all the available resources. In view of the high computational requirements in MIMO application research and the shortage of tools able to satisfy them, we have made a special effort to develop a library to ease the development of adaptable parallel applications in accordance with the different architectures of the executing platform. The library, called MIMOPack, aims to implement efficiently using parallel computing, a set of functions to perform some of the critical stages of MIMO communication systems simulation. The main contribution of the thesis is the implementation of efficient Hard and Soft output detectors, since the detection stage is considered the most complex part of the communication process. These detectors are highly configurable and many of them include preprocessing techniques that reduce the computational cost and increase the performance. The proposed library shows three important features: portability, efficiency and easy of use. Current realease allows GPUs and multi-core computation, or even simultaneously, since it is designed to use on heterogeneous machines. The interface of the functions are common to all environments in order to simplify the use of the library. Moreover, some of the functions are callable from MATLAB increasing the portability of developed codes between different computing environments. According to the library design and the performance assessment, we consider that MIMOPack may facilitate industrial and academic researchers the implementation of scientific codes without having to know different programming languages and machine architectures. This will allow to include more complex algorithms in their simulations and obtain their results faster. This is particularly important in the industry, since the manufacturers work to analyze and to propose their own technologies with the aim that it will be approved as a standard. Thus allowing to enforce their intellectual property rights over their competitors, who should obtain the corresponding licenses to include these technologies into their products.[ES] En la actualidad varios estándares de comunicación están surgiendo buscando velocidades de transmisión más altas y mayor fiabilidad. Esta expansión está impulsada por el aumento en el consumo de servicios multimedia debido a la aparición de nuevos dispositivos como los smartphones y las tabletas. Una de las técnicas empleadas más importantes es el uso de múltiples antenas de transmisión y recepción, conocida como sistemas MIMO, que permite aumentar la velocidad y la calidad de la transmisión. Las tecnologías MIMO se han convertido en una parte esencial en diferentes estándares tales como WLAN, WiMAX y LTE. Estas tecnologías se incorporarán también en futuros estándares, por lo tanto, se espera en los próximos años una gran cantidad de investigación en este campo. Está claro que el estudio de los sistemas MIMO es crítico en la investigación actual, sin embargo los problemas que surgen de esta tecnología son muy complejos. La sistemas de computación de alto rendimiento, y en concreto, las arquitecturas hardware actuales como multi-core y many-core (p. ej. GPUs) están jugando un papel clave en el desarrollo de algoritmos eficientes y de baja complejidad en las transmisiones MIMO. Prueba de ello es que el número de contribuciones científicas y proyectos de investigación relacionados con su uso se han incrementado en el últimos años. Algunas librerías de alto rendimiento se están utilizando como herramientas por investigadores en el desarrollo de futuros estándares. Dos de las librerías más destacadas son: IT++ que se basa en el uso de distintas librerías optimizadas para procesadores multi-core y el paquete Communications System Toolbox diseñada para su uso con MATLAB, que utiliza computación con GPU. Sin embargo, no hay una biblioteca capaz de ejecutarse en una plataforma heterogénea. En vista de los altos requisitos computacionales en la investigación MIMO y la escasez de herramientas capaces de satisfacerlos, hemos implementado una librería que facilita el desarrollo de aplicaciones paralelas adaptables de acuerdo con las diferentes arquitecturas de la plataforma de ejecución. La librería, llamada MIMOPack, implementa de manera eficiente un conjunto de funciones para llevar a cabo algunas de las etapas críticas en la simulación de un sistema de comunicación MIMO. La principal aportación de la tesis es la implementación de detectores eficientes de salida Hard y Soft, ya que la etapa de detección es considerada la parte más compleja en el proceso de comunicación. Estos detectores son altamente configurables y muchos de ellos incluyen técnicas de preprocesamiento que reducen el coste computacional y aumentan el rendimiento. La librería propuesta tiene tres características importantes: la portabilidad, la eficiencia y facilidad de uso. La versión actual permite computación en GPU y multi-core, incluso simultáneamente, ya que está diseñada para ser utilizada sobre plataformas heterogéneas que explotan toda la capacidad computacional. Para facilitar el uso de la biblioteca, las interfaces de las funciones son comunes para todas las arquitecturas. Algunas de las funciones se pueden llamar desde MATLAB aumentando la portabilidad de códigos desarrollados entre los diferentes entornos. De acuerdo con el diseño de la biblioteca y la evaluación del rendimiento, consideramos que MIMOPack puede facilitar la implementación de códigos sin tener que saber programar con diferentes lenguajes y arquitecturas. MIMOPack permitirá incluir algoritmos más complejos en las simulaciones y obtener los resultados más rápidamente. Esto es particularmente importante en la industria, ya que los fabricantes trabajan para proponer sus propias tecnologías lo antes posible con el objetivo de que sean aprobadas como un estándar. De este modo, los fabricantes pueden hacer valer sus derechos de propiedad intelectual frente a sus competidores, quienes luego deben obtener las correspon[CA] En l'actualitat diversos estàndards de comunicació estan sorgint i evolucionant cercant velocitats de transmissió més altes i major fiabilitat. Aquesta expansió, està impulsada pel continu augment en el consum de serveis multimèdia a causa de l'aparició de nous dispositius portàtils com els smartphones i les tablets. Una de les tècniques més importants és l'ús de múltiples antenes de transmissió i recepció (MIMO) que permet augmentar la velocitat de transmissió i la qualitat de transmissió. Les tecnologies MIMO s'han convertit en una part essencial en diferents estàndards inalàmbrics, tals com WLAN, WiMAX i LTE. Aquestes tecnologies s'incorporaran també en futurs estàndards, per tant, s'espera en els pròxims anys una gran quantitat d'investigació en aquest camp. L'estudi dels sistemes MIMO és crític en la recerca actual, no obstant açó, els problemes que sorgeixen d'aquesta tecnologia són molt complexos. Els sistemes de computació d'alt rendiment com els multi-core i many-core (p. ej. GPUs)), estan jugant un paper clau en el desenvolupament d'algoritmes eficients i de baixa complexitat en les transmissions MIMO. Prova d'açò és que el nombre de contribucions científiques i projectes d'investigació relacionats amb el seu ús s'han incrementat en els últims anys. Algunes llibreries d'alt rendiment estan utilitzant-se com a eines per investigadors involucrats en el desenvolupament de futurs estàndards. Dos de les llibreries més destacades són: IT++ que és una llibreria basada en lús de diferents llibreries optimitzades per a processadors multi-core i el paquet Communications System Toolbox dissenyat per al seu ús amb MATLAB, que utilitza computació amb GPU. No obstant açò, no hi ha una biblioteca capaç d'executar-se en una plataforma heterogènia. Degut als alts requisits computacionals en la investigació MIMO i l'escacès d'eines capaces de satisfer-los, hem implementat una llibreria que facilita el desenvolupament d'aplicacions paral·leles adaptables d'acord amb les diferentes arquitectures de la plataforma d'ejecució. La llibreria, anomenada MIMOPack, implementa de manera eficient, un conjunt de funcions per dur a terme algunes de les etapes crítiques en la simulació d'un sistema de comunicació MIMO. La principal aportació de la tesi és la implementació de detectors eficients d'exida Hard i Soft, ja que l'etapa de detecció és considerada la part més complexa en el procés de comunicació. Estos detectors són altament configurables i molts d'ells inclouen tècniques de preprocessament que redueixen el cost computacional i augmenten el rendiment. La llibreria proposta té tres característiques importants: la portabilitat, l'eficiència i la facilitat d'ús. La versió actual permet computació en GPU i multi-core, fins i tot simultàniament, ja que està dissenyada per a ser utilitzada sobre plataformes heterogènies que exploten tota la capacitat computacional. Amb el fi de simplificar l'ús de la biblioteca, les interfaces de les funcions són comunes per a totes les arquitectures. Algunes de les funcions poden ser utilitzades des de MATLAB augmentant la portabilitat de còdics desenvolupats entre els diferentes entorns. D'acord amb el disseny de la biblioteca i l'evaluació del rendiment, considerem que MIMOPack pot facilitar la implementació de còdics a investigadors sense haver de saber programar amb diferents llenguatges i arquitectures. MIMOPack permetrà incloure algoritmes més complexos en les seues simulacions i obtindre els seus resultats més ràpid. Açò és particularment important en la industria, ja que els fabricants treballen per a proposar les seues pròpies tecnologies el més prompte possible amb l'objectiu que siguen aprovades com un estàndard. D'aquesta menera, els fabricants podran fer valdre els seus drets de propietat intel·lectual enfront dels seus competidors, els qui després han d'obtenir les corresponents llicències si voleRamiro Sánchez, C. (2015). MIMOPack: A High Performance Computing Library for MIMO Communication Systems [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/53930TESISPremios Extraordinarios de tesis doctorale

Parallel SUMIS Soft Detector for Large MIMO Systems on Multicore and GPU

[EN] The number of transmit and receiver antennas is an important factor that affects the performance and complexity of a MIMO system. A MIMO system with very large number of antennas is a promising candidate technology for next generations of wireless systems. However, the vast majority of the methods proposed for conventional MIMO system are not suitable for large dimensions. In this context, the use of high-performance computing systems, such us multicore CPUs and graphics processing units has become attractive for efficient implementation of parallel signal processing algorithms with high computational requirements. In the present work, two practical parallel approaches of the Subspace Marginalization with Interference Suppression detector for large MIMO systems have been proposed. Both approaches have been evaluated and compared in terms of performance and complexity with other detectors for different system parameters.This work has been partially supported by the Spanish MINECO Grant RACHEL TEC2013-47141-C4-4-R, the PROMETEO FASE II 2014/003 Project and FPU AP-2012/71274Ramiro Sánchez, C.; Simarro, MA.; Gonzalez, A.; Vidal Maciá, AM. (2019). Parallel SUMIS Soft Detector for Large MIMO Systems on Multicore and GPU. The Journal of Supercomputing. 75(3):1256-1267. https://doi.org/10.1007/s11227-018-2403-9S12561267753Rusek F, Persson D, Lau BK, Larsson EG, Marzetta TL, Edfors O, Tufvesson F (2013) Scaling up MIMO: opportunities and challenges with very large arrays. IEEE Signal Proc Mag 30(1):40–60Studer C, Burg A, Bölcskei H (2008) Soft-output sphere decoding: algorithms and VLSI implementation. IEEE J Sel Areas Commun 26(2):290–300Wang R, Giannakis GB (2004) Approaching MIMO channel capacity with reduced-complexity soft sphere decoding. In: Wireless Communications and Networking Conference, 2004. WCNC. 2004 IEEE vol 3, pp 1620–1625Persson D, Larsson EG (2011) Partial marginalization soft MIMO detection with higher order constellations. IEEE Trans Signal Procces 59(1):453–458Cîrkić M, Larsson EG (2014) SUMIS: near-optimal soft-in soft-out MIMO detection with low and fixed complexity. IEEE Trans Signal Process 62(12):3084–3097Alberto Gonzalez C, Ramiro, M, Ángeles Simarro, Antonio M Vidal (2017) Parallel SUMIS soft detector for MIMO systems on multicore. In: Proceedings of the 17th International Conference on Computational and Mathematical Methods in Science and Engineering, pp 1729–1736Hochwald BM, ten Brink S (2003) Achieving near-capacity on a multiple-antenna channel. IEEE Trans Commun 51:389–399Kaipeng L, Bei Y, Michael W, Joseph RC, Christoph S (2015) Accelerating massive MIMO uplink detection on GPU for SDR systems. In: 2015 IEEE dallas circuits and systems conference (DCAS), pp 1–4Di W, Eilert J, Liu D (2011) Implementation of a high-speed MIMO soft-output symbol detector for software defined radio. J Signal Process Syst 63(1):27–37Anderson E, Bai Z, Bischof C, Blackford LS, Demmel J, Dongarra J, Du Croz J, Greenbaum A, Hammarling S, McKenney A, Sorensen D (1999) LAPACK users’ guide. SIAM, LondonIntel MKL Reference Manual (2015) https://software.intel.com/en-us/articles/mkl-reference-manualcuBLAS Documentation (2015) http://docs.nvidia.com/cuda/cublasDagum L, Enon R (1998) OpenMP: an industry standard API for shared-memory programming. IEEE Comput Sci Eng 5(1):46–55CUDA Toolkit Documentation, Version 7.5 (2015) https://developer.nvidia.com/cuda-toolkitRoger S, Ramiro C, Gonzalez A, Almenar V, Vidal AM (2012) Fully parallel GPU implementation of a fixed-complexity soft-output MIMO detector. IEEE Trans Veh Technol 61(8):3796–3800Senst M, Ascheid G, Lüders H (2010) Performance evaluation of the markov chain monte carlo MIMO detector based on mutual information. 2010 IEEE International Conference on Communications (ICC), pp 1–

Implementation of a 3GPP LTE Turbo Decoder Accelerator on GPU

This paper presents a 3GPP LTE compliant turbo decoder accelerator on GPU. The challenge of implementing a turbo decoder is finding an efficient mapping of the decoder algorithm on GPU, e.g. finding a good way to parallelize workload across cores and allocate and use fast on-die memory to improve throughput. In our implementation, we increase throughput through 1) distributing the decoding workload for a codeword across multiple cores, 2) decoding multiple codewords simultaneously to increase concurrency and 3) employing memory optimization techniques to reduce memory bandwidth requirements. In addition, we analyze how different MAP algorithm approximations affect both throughput and bit error rate (BER) performance of this decoder