Complexity Reduced Soft-In Soft-Out Sphere Detection Based on Search Tuples

Mennenga, Bjorn; Borany, A. von; Fettweis, Gerhard

doi:10.1109/icc.2009.5198839

Cited by 18 publications

(25 citation statements)

References 17 publications

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“…On the contrary, tuple search can be combined with separated bit-specific storage of information for the L-value calculation, leading generally to much smaller T . As described in [8], this separated candidate processing enables the application of MMSE detection without BER performance degradation (in contrast to LSD). The resulting TSD achieves much better BER performance than LSD at a significantly reduced n compared to STS detection.…”

Section: Tuple Search and Bit-specific Candidate Determinationmentioning

confidence: 97%

“…Throughout this work a tuple search detector (TSD) with matched candidate determination is employed [8]. The TSD approach reduces the search complexity significantly, outperforming state-of-the-art detection strategies like single tree search (STS) [9] [10] or list sphere detection (LSD) [4] [11] while providing close to full max-log-APP BER performance.…”

Section: Introductionmentioning

confidence: 99%

“…[4], [8], [13] has been used for our simulations. These have been carried out for a rate 1/2 PCCC with (7 R , 5) convolutional codes, an information block size of 9216 bits (including tail bits), gray mapping, and spatial and temporal fading.…”

Section: Introductionmentioning

confidence: 99%

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Survey on an efficient, low-complex tuple search based sphere detector

Adeva

Mennenga

Fettweis

2011

34th IEEE Sarnoff Symposium

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Nowadays, high detection complexity is known to be one of the major challenges in MIMO communications based on spatial multiplexing. Tuple search (TS) sphere detection was recently introduced, demonstrating to represent a promising approach in this context. It provides significant complexity reduction in comparison to conventional algorithms, providing in addition close to full max-log-APP BER performance. Due to the increasing multiplicity of communication standards as well as variety of mobile applications demanded by users, tackling the lack of flexibility of common receiver realizations has become an additional key challenge in MIMO detection. Aim of this work is to demonstrate that the benefit provided by the tuple search strategy is still present in a wide range of possible transmission schemes. For this purpose, a novel efficiency indicator is introduced, based on which an exhaustive analysis is performed. The existing tuple search detector has been adapted to deal with different constellation orders and transmit/receive antenna configurations. In addition, the applied MMSE strategy has been modified to support undetermined systems. The obtained results show the superiority of the proposed sphere detector under different transmission conditions, thus demonstrating its efficiency and flexibility.Keywords-MIMO detection, sphere decoder, tuple search, efficiency, overhead, asymmetric MIMO. I. INTRODUCTIONFuture mobile communication systems will make use of multiple-input multiple-output (MIMO) techniques in combination with high constellation orders to enhance spectral efficiency. Transmission of spatially multiplexed data streams allows increasing data rates as well as diversity. However, the inherent high detection complexity and lack of flexibility of common detector realizations still represent limiting factors towards efficient implementations. Tree search strategies have been shown to represent a promising approach to overcome the complexity problem. High order systems allow high data rates, but also imply great detection search spaces. As a consequence, detection algorithms exploring all or most of the possible sent symbols become impractical due to the resulting high computational complexity. This is the case of e.g. full max-log-APP detection. To solve this problem, several complexity-reduced detection algorithms presenting close to full max-log-APP detection accuracy have been widely studied. Sphere detector [1], M-algorithm [2], LISS-algorithm [3], or variations of them [4]-[7] are some examples, still subjected to numerous BER performance-complexity trade-offs.

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Section: Tuple Search and Bit-specific Candidate Determinationmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

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Survey on an efficient, low-complex tuple search based sphere detector

Adeva

Mennenga

Fettweis

2011

34th IEEE Sarnoff Symposium

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“…The list size and the sphere radius must be very carefully chosen. A more sophisticated version of this method can be found in [32].…”

Section: B Today's State-of-the-art Mimo Detectorsmentioning

confidence: 99%

SUMIS: Near-Optimal Soft-In Soft-Out MIMO Detection With Low and Fixed Complexity

2014

IEEE Trans. Signal Process.

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Abstract-The fundamental problem of our interest here is soft-input soft-output multiple-input multiple-output (MIMO) detection. We propose a method, referred to as subspace marginalization with interference suppression (SUMIS), that yields unprecedented performance at low and fixed (deterministic) complexity. Our method provides a well-defined tradeoff between computational complexity and performance. Apart from an initial sorting step consisting of selecting channel-matrix columns, the algorithm involves no searching nor algorithmic branching; hence the algorithm has a completely predictable run-time and allows for a highly parallel implementation. We numerically assess the performance of SUMIS in different practical settings: full/partial channel state information, sequential/iterative decoding, and low/high rate outer codes. We also comment on how the SUMIS method performs in systems with a large number of transmit antennas.

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“…Therefore, any hard detector of choice can produce soft values via this approximation. In particular, there are variations of the SD algorithm that utilize this in order to produce soft values [27,[58][59][60]. In [58], a list of a fixed number of solution candidates is stored during the SD search through the tree in Fig.…”

Section: Max-log Detectionmentioning

confidence: 99%

Efficient MIMO Detection Metho

Čirkić¹

2014

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Sve za moju ljubav i moju familiju."Što ne boli-to nije život; što ne prolazi-to nije sreća" Ivo Andrić AbstractFor the past decades, the demand in transferring large amounts of data rapidly and reliably has been increasing drastically. One of the more promising techniques that can provide the desired performance is multiple-input multipleoutput (MIMO) technology where multiple antennas are placed at both the transmitting and receiving side of the communication link. This performance potential is extremely high when the dimensions of the MIMO system are increased to an extreme (in the number of hundreds or thousands of antennas). One major implementation difficulty of the MIMO technology is the signal separation (detection) problem at the receiving side of the MIMO link, which holds for medium-size MIMO systems and even more so for large-size systems. This is due to the fact that the transmitted signals interfere with each other and that separating them can be very difficult if the MIMO channel conditions are not beneficial, i.e., the channel is not well-conditioned.The main problem of interest is to develop algorithms for practically feasible MIMO implementations without sacrificing the promising performance potential that such systems bring. These methods involve inevitably different levels of approximation. There are computationally cheap methods that come with low accuracy and there are computationally expensive methods that come with high accuracy. Some methods are more applicable in medium-size MIMO than in large-size MIMO and vice versa. Some simple methods for instance, which are typically inaccurate for medium-sized settings, can achieve optimal accuracy for certain large-sized settings that offer close-to-orthogonal spatial signatures. However, when the dimensions are overly increased, then even these (previously) simple methods become computationally burdensome. In different MIMO setups, the difficulty in detection shifts since methods with optimal accuracy are not the same. Therefore, devising one single algorithm which is well-suited for feasible MIMO implementations in all settings is not easy.i This thesis addresses the general MIMO detection problem in two ways. One part treats a development of new and more efficient detection techniques for the different MIMO settings. The techniques that are proposed in this thesis demonstrate unprecedented performance in many relevant cases. The other part revolves around utilizing already proposed detection algorithms and their advantages versus disadvantages in an adaptive manner. For well-conditioned channels, low-complexity detection methods are often sufficiently accurate. In such cases, performing computationally very expensive optimal detection would be a waste of computational power. This said, for MIMO detection in a coded system, there is always a trade-off between performance and complexity. Intuitively, computational resources should be utilized more efficiently by performing optimal detection only when it is needed, and something simpler when it i...

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Complexity Reduced Soft-In Soft-Out Sphere Detection Based on Search Tuples

Cited by 18 publications

References 17 publications

Survey on an efficient, low-complex tuple search based sphere detector

Survey on an efficient, low-complex tuple search based sphere detector

SUMIS: Near-Optimal Soft-In Soft-Out MIMO Detection With Low and Fixed Complexity

Efficient MIMO Detection Metho

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