Iterative MIMO detector using a group-wise approach

Koshy, John C.; Liberti, J.C.; Hoerning, T.R.

doi:10.1109/sarnof.2007.4567359

Cited by 3 publications

(6 citation statements)

References 7 publications

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“…In this section, we provide a formal description of the principal decoding task used in the rest of this paper (see e.g. [2], [11], [12], [13], [14]). Suppose that n > 0 and m > 0 are integers, that…”

Section: Principal Decoding Taskmentioning

confidence: 99%

“…This topic (generally referred to as multiple-input and multiple-output, or MIMO) is now at the frontier of the research in modern communications (see e.g. [2], [5], [7], [11], [12], [13], [14], [17], [19]).…”

Section: Introductionmentioning

confidence: 99%

“…Then, x best will coincide with x true in about half of all cases; yet, in more than 70% of these cases x best will not be among even as many as 500 nearest neighbors of x in X. Some other decoding schemes and approaches can be found, for example, in [2], [11], [12], [13], [14] (see also references therein).…”

Section: Introductionmentioning

confidence: 99%

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On decoding of digital data sent over a noisy MIMO channel

Osipov¹

2015

Preprint

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The transmission of digital data is one of the principal tasks in modern wireless communication. Classically, the communication channel consists of one transmitter and one receiver; however, due to the constantly increasing demand in higher transmission rates, the popularity of using several receivers and transmitters has been rapidly growing.In this paper, we combine a number of fairly standard techniques from numerical linear algebra and probability to develop several (apparently novel) randomized schemes for the decoding of digital messages sent over a noisy multivariate Gaussian channel.We use a popular mathematical model for such channels to illustrate the performance of our schemes via numerical experiments.

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Section: Principal Decoding Taskmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On decoding of digital data sent over a noisy MIMO channel

Osipov¹

2015

Preprint

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“…K-best algorithm has the nature to generate the candidate list for soft output, however, it needs large K to guarantee performance in bit error rate (BER) when high order modulation is considered, which will introduce high computational complexity [4]. Due to the property of Gaussian output, a low complexity detector based on MMSE algorithm is proposed for coded systems [5], [6], where the symbols from different transmit antennas are first decoupled and then the corresponding soft information associated with its symbol is calculated to be fed to the soft decoder. For the determined system, MMSE-based detector can reach ML's performance in limited iterations, but when the underdetermined system is considered, group-wise MMSE-based detector should be utilized to get tradeoffs between performance and complexity [6].…”

Section: Introductionmentioning

confidence: 99%

“…Due to the property of Gaussian output, a low complexity detector based on MMSE algorithm is proposed for coded systems [5], [6], where the symbols from different transmit antennas are first decoupled and then the corresponding soft information associated with its symbol is calculated to be fed to the soft decoder. For the determined system, MMSE-based detector can reach ML's performance in limited iterations, but when the underdetermined system is considered, group-wise MMSE-based detector should be utilized to get tradeoffs between performance and complexity [6]. The computational complexity of MMSE-based detector is linear with the number of transmit antennas and exponential with the modulation order.…”

Section: Introductionmentioning

confidence: 99%

A low complexity MIMO detector based on combination of MRC and MMSE algorithm

Zhao

Wang

et al. 2010

2010 2nd International Conference on Future Computer and Communication

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In the absence of exhaustive search and matrix inversion, this paper proposes a low complexity multi-input multi-output (MIMO) detector for coded systems based on the combination of maximal ratio combining (MRC) and minimum mean square error (MMSE) algorithm. The proposed scheme takes advantage of MMSE for the first iteration to guarantee reliable soft information derived from channel decoder. With these soft information and interference cancellation (IC), the MIMO system is converted to a single-input multi-output (SIMO) system. Hence, MRC, which is a simple space signal processing technology only for SIMO systems, is used in rest iterations to avoid matrix inverse associated with MMSE-based detector. Because only linear operations are required in rest iterations, the proposed method remarkably reduces the computational complexity of MIMO detector. The simulation results show that the proposed method does not suffer the performance degradation in bit error rate nor convergence compared with MMSE-based detector. Meanwhile, the computational complexity of the former one is at least reduced from O(n 2.376 ) to O(n) except the first iteration compared to the latter. Consequently, the proposed method is a very attractive MIMO detection scheme for practical applications.

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Low complexity iterative MIMO receiver based on successive soft interference-cancellation and MMSE spatial-filtering

Koshy¹

2010

2010 IEEE Sarnoff Symposium

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Iterative MIMO detector using a group-wise approach

Cited by 3 publications

References 7 publications

On decoding of digital data sent over a noisy MIMO channel

On decoding of digital data sent over a noisy MIMO channel

A low complexity MIMO detector based on combination of MRC and MMSE algorithm

Low complexity iterative MIMO receiver based on successive soft interference-cancellation and MMSE spatial-filtering

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