REAL-TIME IMPLEMENTATION OF A SPHERE DECODER-BASED MIMO WIRELESS SYSTEM (original) (raw)
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System Architecture and Implementation of MIMO Sphere Decoders on FPGA
IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2008
Multiple-input-multiple-output (MIMO) systems use multiple antennas in both transmitter and receiver ends for higher spectrum efficiency. The hardware implementation of MIMO detection becomes a challenging task as the computational complexity increases. This paper presents the architectures and implementations of two typical sphere decoding algorithms, including the Viterbo-Boutros (VB) algorithm and the Schnorr-Euchner (SE) algorithm. Hardware/software codesign technique is applied to partition the decoding algorithm on a single field-programmable gate array (FPGA) device. Three levels of parallelism are explored to improve the decoding rate: the concurrent execution of the channel matrix preprocessing on an embedded processor and the decoding functions on customized hardware modules, the parallel decoding of real/imaginary parts for complex constellation, and the concurrent execution of multiple steps during the closest lattice point search. The decoders for a 4 4 MIMO system with 16-QAM modulation are prototyped on a Xilinx XC2VP30 FPGA device with a MicroBlaze soft core processor. The hardware prototypes of the SE and VB algorithms show that they support up to 81.5 and 36.1 Mb/s data rates at 20 dB signal-to-noise ratio, which are about 22 and 97 times faster than their respective implementations in a digital signal processor.
VLSI implementation of MIMO detection using the sphere decoding algorithm
IEEE Journal of Solid-State Circuits, 2000
Multiple-input multiple-output (MIMO) techniques are a key enabling technology for high-rate wireless communications. This paper discusses two ASIC implementations of MIMO sphere decoders. The first ASIC attains maximum-likelihood performance with an average throughput of 73 Mbps at a signalto-noise ratio (SNR) of 20 dB; the second ASIC shows only a negligible bit error rate degradation and achieves a throughput of 170 Mbps at the same SNR. The three key contributing factors to high throughput and low complexity are: Depth-first tree traversal with radius reduction, implemented in a one-node-percycle architecture, the use of the ∞ -instead of 2 -norm, and finally the efficient implementation of the enumeration approach recently proposed in [1]. The resulting ASICs currently rank among the fastest reported MIMO detector implementations.
An efficient GPU implementation of fixed-complexity sphere decoders for MIMO wireless systems
Integrated Computer-Aided Engineering, 2012
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 costeffectiveness 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.
Complexity assessment of sphere decoding methods for MIMO detection
2009 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), 2009
Sphere Decoding (SD) algorithms have been shown to provide maximum likelihood (ML) detection over Gaussian multiple input-multiple output (MIMO) channels with lower complexity than the exhaustive search. These methods are based on a closest lattice point search over a limited search space (hypersphere). There exist several implementations of these algorithms pursuiting different search strategies and working either within a set of real numbers, thus called Real Sphere Decoders (RSD), or performing the search directly within a set of complex numbers, commonly known as Complex Sphere Decoders (CSD). In this paper, a performance comparison between the real and the complex version of the Schnorr-Euchner (SE) sphere decoder has been carried out in order to find out which algorithm is the most suitable depending on the application. Furthermore a recently appeared fixed-complexity version of the SE decoder (FSD) has been evaluated both in terms of complexity and performance and the results have been compared with the original version. In contrast to yet existing complexity analyses, not only the number of visited nodes has been investigated but also the total number of operations.
IRJET-A Sphere Decoding Algorithm for MIMO
Modern wireless communication system demanding high data rate operating in bandwidth deficient world is using Multiple Input Multiple Output (MIMO) antenna arrangement. MIMO transmission has become a popular technique to increase spectral efficiency. MIMO supports greater data rate and higher reliability in wireless communication. The receivers employing linear receivers or decision feedback detectors in detection use less hardware are of suboptimal. Optimal detectors are realized by Maximum Likelihood detector can achieve superb performance, yet the computational complexity is enormously high. Therefore suboptimal detectors such as Viterbi Decoding, Sphere Decoding, Genetic Algorithm based Decoder are reach the performance of ML detectors, and potentially a great deal of computational cost can be saved. In this paper, a practical sphere-Decoding algorithm is proposed. It utilizes a simple and effective way to set the initial radius which plays a decisive role in determining the computational complexity. The complexity and SER rate of the sphere decoder is good when compare with other decoders used in MIMO receiver design. The performance of sphere decoder and maximum likelihood decoder is same but the complexity is reduced in sphere decoder.
A Novel Approach for Sphere Decoder MIMO System
In sphere decoding techniques, it was seen that the generalized sphere decoder algorithms have been applied to decode the MIMO systems. The transmitted vector is determined by decoding a sequence of determined sub problems.In this paper, the proposed sphere decoder has promised considerable performance as compared to the generalized sphere decoder. The proposed sphere decoding algorithm follows an adaptive radius selection approach for reducing the computational complexity as compared to the generalized sphere decoding algorithm and with the ideal ML decoder which applies on the QPSK signaling. Also it has proven that the proposed sphere decoder has given the performance near optimal like the ML decoder. The performance comparison is done under the Flat Rayleigh fading environment. We substantiate our new proposed reduced complexity near optimal sphere decoding algorithm with simulations.
Design and architecture of spatial multiplexing MIMO decoders for FPGAs
2008 42nd Asilomar Conference on Signals, Systems and Computers, 2008
Spatial multiplexing multiple-input-multiple-output (MIMO) communication systems have recently drawn significant attention as a means to achieve tremendous gains in wireless system capacity and link reliability. The optimal hard decision detection for MIMO wireless systems is the maximum likelihood (ML) detector. ML detection is attractive due to its superior performance (in terms of BER). However, direct implementation grows exponentially with the number of antennas and the modulation scheme, making its ASIC or FPGA implementation infeasible for all but low-density modulation schemes using a small number of antennas. Sphere decoding (SD) solves the ML detection problem in a computationally efficient manner. However, even with this complexity reduction, real-time implementation on a DSP processor is generally not feasible and high-performance parallel computing platforms such as FPGAs are increasingly being employed for this class of applications. The sphere detection problem affords many opportunities for algorithm and micro-architecture optimizations and tradeoffs. This paper provides an overview of techniques to simplify and minimize FPGA resource utilization of sphere detectors for highperformance low-latency systems.
Performance of Sphere Decoder for MIMO System Using LLL Algorithm
Lecture Notes in Electrical Engineering, 2013
Maximum likelihood (ML) decoding is an optimal detector with high-performance for multiple-input-multiple-output (MIMO) communication systems. While it is attractive due to its superior performance in terms of BER, its complexity using an exhaustive search which grows exponentially with the number of antennas and order of the modulation. It becomes infeasible to apply to practical systems as it searches through all lattice points in the constellation. Sphere decoding (SD) is a promising method to reduce the average decoding complexity without compromising performance. It provides optimal performance with reduced complexity as it searches the points within the specified radius of sphere. The complexity of the sphere decoder depends on the initial radius selection of the sphere, to begin search process. Attention is drawn to initial radius selection strategy, since an inappropriate initial radius can result in either a large number of lattice points to be searched, or a large number of restart actions. The simulations are performed for constellation size of 4-QAM, 8-QAM and 16-QAM for antenna size of 2X2 MIMO. It is observed that the performance of Probabilistic Tree Pruning (PTP)-SD converges with ML by taking less time and maintaining the same performance. It is proposed that a Look Up Table (LUT) for initial radius Using Radius Choice Algorithm is generated. The complexity reduces by 13% as the number of FLOPS required reduces.
Improved Channel Matrix in Sphere Decoder for MIMO Wireless Systems
The recent demand for mobile communication systems with high data rates has increased in the recent years. New methods are essential in order to satisfy this huge communication demand, exploiting the limited resources like as the power and bandwidth as efficient as possible. Multiple Input and multiple Output (MIMO) systems with multiple antennas at the transmitter side and multiple antenna as at the receiver side provides a better solution for the demand of high data rates by exploiting the spatial domain under the constraints of limited bandwidth and transmit power. Sphere decoding approach is one of the effective approach in MIMO system to identify the problem in multi user detection and ML detection problem. There are different approaches to improve the Sphere Decoding approach so that more accurate and optimized results will be obtained. One of such approach is suggested in this paper. In this work, instead of processing on complete state space, a K-best sphere decoder approach is suggested in this work. The presented paper is about to define an improve approach for the selection of K best neighbors.
Soft iterative decoding techniques have shown to a great extent result in terms of Bit Error Rate performance in wireless communication. By using MIMO system we attain high multiplexing gain. However, this is only possible if an expedient detection technique is used. ML detection technique allows soft decisions for each received bit along with good error performance. In this paper we propose new technique for decoding Multiple Input -Multiple Output (MIMO) system, which combine Sphere Decoding (SD) with Zero Forcing (ZF) ,MMSE and V-BLAST techniques to make accessible near optimal low complexity and high performance modified sphere decoding algorithm. Simulation results on a QPSK Modulation with 4 transmit and 4 receive antennas show that the propose sphere decoder can achieve the near-optimal performance.