Fixing the Complexity of the Sphere Decoder for MIMO Detection

Barbero, L.G.; Thompson, John

doi:10.1109/twc.2008.060378

Cited by 442 publications

(394 citation statements)

References 25 publications

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“…This implementation is cumbersome for practical systems; however, its complexity can be substantially reduced by means of tree-search detection methods such as the fixed-complexity sphere decoder (FSD) [8] or many other sphere decoding (SD) methods [9]. If a QR factorization of the channel matrix (H = QR) is employed, the problem (2) can be transformed into an equivalent one that can be solved using a tree structure [10].…”

Section: System Model and Mimo Detectionmentioning

confidence: 99%

“…In [8] the authors proposed a MIMO detection strategy intended to overcome the two main problems of other tree-search methods from an implementation point of view: their variable complexity and their sequential nature. This algorithm was called fixed-complexity SD (FSD) and combines a preprocessing stage followed by a predetermined tree-search composed of two di↵erent stages: a full expansion of the tree (FE) in the first (highest) T levels and a single-path expansion (SE) in the remaining tree-levels n T T [8].…”

Section: Fixed-complexity Sphere Decodermentioning

confidence: 99%

“…This algorithm was called fixed-complexity SD (FSD) and combines a preprocessing stage followed by a predetermined tree-search composed of two di↵erent stages: a full expansion of the tree (FE) in the first (highest) T levels and a single-path expansion (SE) in the remaining tree-levels n T T [8].…”

Section: Fixed-complexity Sphere Decodermentioning

confidence: 99%

“…The symbols are detected following a specific ordering also proposed by the authors in [8] which is based on the following reasoning: if all the possibilities are searched in one level, the robustness of the signal at such level is not relevant to the final performance. Therefore, the signals that su↵er the largest noise amplification are placed at the levels where a FE is performed.…”

Section: Fixed-complexity Sphere Decodermentioning

confidence: 99%

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Multicore implementation of a fixed-complexity tree-search detector for MIMO communications

et al. 2012

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Multi-core systems allow the e cient implementation of signal processing algorithms for communication systems due to their high parallel processing capabilities. In this paper, we present a high-throughput multi-core implementation of a fixed-complexity tree-search-based detector interesting for MIMO wireless communication systems. Experimental results confirm that this implementation allows to accelerate the data detection stage for di↵erent constellation sizes and number of subcarriers.

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Section: System Model and Mimo Detectionmentioning

confidence: 99%

Section: Fixed-complexity Sphere Decodermentioning

confidence: 99%

Section: Fixed-complexity Sphere Decodermentioning

confidence: 99%

Section: Fixed-complexity Sphere Decodermentioning

confidence: 99%

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Multicore implementation of a fixed-complexity tree-search detector for MIMO communications

et al. 2012

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“…첫 번째는 추가 검출 할 간섭신호의 스트림의 수를 제 한하는 방법이고 [7] , 두 번째는 전체 간섭신호 검출을 위한 수신기를 quasi-ML 성능을 갖는 저 복잡도 알고 리즘을 사용하는 방법이다. 본 논문에서는 qausi-ML 성능을 갖는 fixed-complexity SD(FSD) [9,10] …”

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An Extendable Fixed-Complexity Sphere Decoder for Downlink Multi-User MIMO Communication System

Koo¹,

Kim²,

Kim³

2014

The Journal of Korea Information and Communications Society

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In this paper, a extension of a fixed-complexity sphere decoder (FSD) to perform interference signal detection and cancelling is proposed for downlink multiuser multiple input-multiple output (MIMO) communication system. It is based on the application of channel matrix expansion on generalized sphere decoder (GSD), and modification of the channel matrix ordering scheme to a FSD algorithm for interference detection. A MonteCarlo simulation shows that the proposed algorithm improves the receiver performance by 3 dB as compared to maximum likelihood detection without interference cancelling at 10% packet error rate in configuration of 702Mbit/s datarate for four users respectively on IEEE802.11ac.

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Adaptive switching detection algorithm for iterative‐MIMO systems to enable power savings

2014

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Key Points:• Adaptivity achieves optimal error rate performance with minimal power • Receiver adapts to conditions according to the mutual information thresholds • DVFS technique reduces the circuit power demands of the adaptive algorithm Input Multiple Output (MIMO) detection systems, which is to achieve optimal error rate performance with minimal power consumption. This is realized by proposing a new algorithm design that comprises multiple thresholds within the detector that, in real time, specify the receiver behavior according to the current channel in both slow and fast fading conditions, giving it adaptivity. This adaptivity enables energy savings within the system since the receiver chooses whether to accept or to reject the transmission, according to the success rate of detecting thresholds. The thresholds are calculated using the mutual information of the instantaneous channel conditions between the transmitting and receiving antennas of iterative-MIMO systems. In addition, the power saving technique, Dynamic Voltage and Frequency Scaling, helps to reduce the circuit power demands of the adaptive algorithm. This adaptivity has the potential to save up to 30% of the total energy when it is implemented on Xilinx®Virtex-5 simulation hardware. Results indicate the benefits of having this "intelligence" in the adaptive algorithm due to the promising performance-complexity tradeoff parameters in both software and hardware codesign simulation.

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Fixing the Complexity of the Sphere Decoder for MIMO Detection

Cited by 442 publications

References 25 publications

Multicore implementation of a fixed-complexity tree-search detector for MIMO communications

Multicore implementation of a fixed-complexity tree-search detector for MIMO communications

An Extendable Fixed-Complexity Sphere Decoder for Downlink Multi-User MIMO Communication System

Adaptive switching detection algorithm for iterative‐MIMO systems to enable power savings

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