Fully Parallel GPU Implementation of a Fixed-Complexity Soft-Output MIMO Detector

Roger, Sandra; Ramiro, Carla; González, Alberto; Almenar, Vicenç; Vidal, Antonio M.

doi:10.1109/tvt.2012.2210576

Cited by 38 publications

(30 citation statements)

References 15 publications

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“…The SFSD was used for the implementation of a iterative receiver for energy saving MIMO ID-BICM systems in [81]. The paper containing the design and GPU implementation using shared memory and different GPU features of the FPFSD is reported in [75] and [82].…”

Section: Discussionmentioning

confidence: 99%

“…These architectures show increasingly better computation capabilities and new features that usually offer better performance. Throughout the development of this thesis, efficient and highly coupled detectors to a given GPU architecture such as [75][76] have been developed. However, this type of specific programming requires a hugh effort and many years of development.…”

Section: Generic Implementationsmentioning

confidence: 99%

“…For example, Nvidia Tesla and Fermi GPUs were used for the evaluation of different detectors in [75] and [76] respectively. For the assessment of the library, a machine with multiple Kepler GPUs and multiple multi-core processors has been employed in order to evaluate more platform configurations (i.e multi-core, single-GPU, multi-GPU and multi-core+multi-GPU).…”

Section: Computer System For Simulation Testingmentioning

confidence: 99%

“…For this reason, we proposed a soft-output demodulator in [75] that performs a fully parallel list extension: the fully parallel FSD (FPFSD). The proposed approach is purely based on the hard-output FSD scheme.…”

Section: Fully Parallel Soft Fixed Sphere Demodulationmentioning

confidence: 99%

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MIMOPack: A High Performance Computing Library for MIMO Communication Systems

SÁNCHEZ¹

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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 (Multiple Input Multiple Output) 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 and broadband standards such as Wireless Local Area Network (WLAN), Worldwide interoperability for Microwave Acces (WiMAX), Long Term Evolution (LTE) and Next Generation Handheld (DVB-NGH), for the reception of Digital Terrestrial Television (DTT) in handheld devices. 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 or companies 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 and Simulink, 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 vi Abstract 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 perf...

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Section: Discussionmentioning

confidence: 99%

Section: Generic Implementationsmentioning

confidence: 99%

Section: Computer System For Simulation Testingmentioning

confidence: 99%

Section: Fully Parallel Soft Fixed Sphere Demodulationmentioning

confidence: 99%

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MIMOPack: A High Performance Computing Library for MIMO Communication Systems

SÁNCHEZ¹

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“…On the other hand, SFSD for moderate number of antennas achieves almost max-log behavior at reasonable computing cost, for this reason it has been chosen for comparison. An efficient version called Fully Parallel Fixed Sphere Decoder (FPFSD) [92] has been chosen. A linear detector with reduced complexity such as Soft MMSE has also been chosen.…”

Section: Mimo Detection In Massive Mimomentioning

confidence: 99%

Effi cient algorithms for iterative detection and decoding in Multiple-Input and Multiple-Output Communication Systems.

Haro¹,

Angeles²

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Massively Parallel and Flexible Processing forMIMOSystems

Nikitopoulos

2019

Wiley 5G Ref

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5G mobile systems and beyond are envisaged to provide very high throughput, to meet very strict requirements in terms of latency, and to support a very large number of devices in a very small area. Multiple‐input, multiple‐output (MIMO) systems can contribute to meeting these targets by enabling the concurrent transmission of a large number of information streams. In such MIMO systems, the choice of the corresponding required beamforming/precoding (in the downlink) and detection (in the uplink) algorithms can determine how much of the overall MIMO channel capacity and how much of MIMO's inherent capability to support multiple users we can exploit in practice. Linear beamforming and detection approaches are practical, but they highly underutilize the MIMO channel. Nonlinear methods can provide substantial gains, both in terms of throughput and connectivity, but their complexity increases exponentially with the number of concurrently transmitted information streams. This complexity, together with the soon‐to‐be‐reached plateau in the speed of processors, urges a paradigm shift toward massively parallel signal processing for wireless communications systems. Ideal parallelization methods should be scalable to any number of available processing elements, generic, adjustable to the transmission conditions, and transparent to the choice of the implementation platform. Promising parallelization algorithms in this direction enable up to 70% throughput gains without changing the number of base‐station antennas and RF chains, and can substantially increase the number of connected devices, with processing latency requirements similar to those of linear approaches.

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Fully Parallel GPU Implementation of a Fixed-Complexity Soft-Output MIMO Detector

Cited by 38 publications

References 15 publications

MIMOPack: A High Performance Computing Library for MIMO Communication Systems

MIMOPack: A High Performance Computing Library for MIMO Communication Systems

Effi cient algorithms for iterative detection and decoding in Multiple-Input and Multiple-Output Communication Systems.

Massively Parallel and Flexible Processing forMIMOSystems

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