Analysis on parallel implementations of fixed-complexity sphere decoder

Wu, Bin; Masera, Guido

doi:10.1007/s11432-011-4441-2

Cited by 3 publications

(1 citation statement)

References 19 publications

(24 reference statements)

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“…These methods have been implemented over different hardware technologies such as FPGA and GPUs [40], particularly FSD algorithm because its structure is well suited for parallel computing [41][42] [43].…”

Section: Fixed-complexity Detectorsmentioning

confidence: 99%

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: Fixed-complexity Detectorsmentioning

confidence: 99%

MIMOPack: A High Performance Computing Library for MIMO Communication Systems

SÁNCHEZ¹

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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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Algorithm and VLSI Implementation of Sphere Decoding

Bello

Halak

2022

Algorithms and VLSI Implementations of MIMO Detection

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Analysis on parallel implementations of fixed-complexity sphere decoder

Cited by 3 publications

References 19 publications

MIMOPack: A High Performance Computing Library for MIMO Communication Systems

MIMOPack: A High Performance Computing Library for MIMO Communication Systems

Massively Parallel and Flexible Processing forMIMOSystems

Algorithm and VLSI Implementation of Sphere Decoding

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