Complexity reduced MIMO detection with three iterative loops
01 Oct 2017-pp 213-216
TL;DR: A SIC-MMSE detection scheme for MIMO system, where three iterative loops are utilized and a single matrix inversion method is employed to reduce complexity and improve the reliability of the symbol estimation to enhance the performance.
Abstract: The full potential of multiple-input multiple-output (MIMO) wireless technology can be achieved through iterative MIMO decoding with soft information. In iterative MIMO decoder, the complexity can be major obstacle for practical implementation. The soft interference cancellation-minimum mean squared error (SIC-MMSE) approach for detection is considered as a feasible approach due to its complexity-performance tradeoff. In this paper, we show the performance of a SIC-MMSE detection scheme for MIMO system, where three iterative loops are utilized. We employ a single matrix inversion method to reduce complexity, while the third loop inside MIMO detector is employed to improve the reliability of the symbol estimation to enhance the performance. Simulation results demonstrates that the proposed method produces comparable error-rate performance as the conventional schemes with much less complexity.1
Citations
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TL;DR: In this paper, two approaches to reduce the complexity of SIC-MMSE detection were proposed, one is to find the approximation of SSE by investigating the estimation in the log-domain, which leads to a simple min-sum operation, and the second is to approximate the hyperbolic tangent with low-order piecewise polynomial functions.
Abstract: Soft symbol estimation (SSE) is the first process in soft interference cancellation minimum mean squared error (SIC-MMSE) detection for multiple-input multiple-output (MIMO) systems. SSE requires the sum of exhaustive multiplications of probability that occupies a non-negligible amount of the entire SIC-MMSE complexity. This paper proposes two approaches to reduce the complexity of SSE. The first is to find the approximation of SSE by investigating the estimation in the log-domain, which leads to a simple min-sum operation. The second is to approximate the hyperbolic tangent with low-order piecewise polynomial functions. These two approaches are then integrated. The simulation results show that the proposed methods do not degrade the performance in terms of bit error rate (BER), and that they can greatly reduce the number of multiplications, from $O(K2^{K})$ to $O(K)$ .
3 citations
01 Oct 2018
TL;DR: The proposed complexity reduced soft interference cancellation minimum mean squared error detection scheme for coded massive MIMO systems can efficiently reduce the complexity without appreciable performance degradation.
Abstract: In this paper, we propose a complexity reduced soft interference cancellation minimum mean squared error (SIC-MMSE) detection scheme for coded massive MIMO systems. The presented method works efficiently when the channel matrix becomes asymptotically orthogonal with a sufficiently large number of receive antennas at the base station. With such a characteristic, the conventional SIC-MMSE detection method can be simplified to a layer independent matrix inversion process, and further complexity reduction is achieved by introducing the Neumann series expansion (NSE) method. The simulation results investigated in this paper reveal that the proposed method can efficiently reduce the complexity without appreciable performance degradation.
2 citations
Cites background or methods from "Complexity reduced MIMO detection w..."
...To estimate the transmitted symbols at the receiver, we adopt the layer independent matrix inversion based SICMMSE detection scheme [2], then the expected value of the jth transmitted symbol with the SIC-MMSE detection process can be estimated as follows, where the superscript l is the index for the number of iterations at Loop 3, i....
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...As a conventional scheme, we employ a turbo coded MIMO system with the three-loop SIC-MMSE based JIDD using a layer independent matrix-inversion process [2]....
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...Especially, JIDD schemes with three loops were proposed to enhance performance of the soft interference cancellation-minimum mean squared error (SIC-MMSE) based detection methods [1][2]....
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...By considering that the critical computational complexity of the conventional SICMMSE based detection methods lies in the layer by layer complex matrix inversion processes as in [1], a single matrix inversion based scheme was applied in [2] in order to reduce the complexity....
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TL;DR: A computationally efficient soft detection scheme for massive multiple-input multiple-output (MIMO) systems that adopts joint iterative detection and decoding methods for their capacity limiting performances is presented.
Abstract: This paper presents a computationally efficient soft detection scheme for massive multiple-input multiple-output (MIMO) systems. The proposed scheme adopts joint iterative detection and decoding (JIDD) methods for their capacity limiting performances. In addition, the minimum mean square error parallel interference cancellation (MMSE-PIC)-based detection scheme is used for soft information exchange. We propose a number of techniques to reduce the computational complexity, while keeping almost the same performance as the conventional ones. First, a technique is proposed to approximate the Gram matrix to a constant valued diagonal matrix. This proposal can lead to elimination of complex matrix inversion process and multiple layer dependent estimations, resulting in huge complexity reduction. Second, compact equations to estimate soft-symbol values for M-ary (quadrature amplitude modulation) QAM are derived. From the investigation example of 2 8 -QAM in this paper, this proposal showed more than two orders of less computations compared to the conventional scheme. The simulation results demonstrate that the proposed method can achieve approximating performance to the conventional method with a largely reduced computational complexity.
1 citations
01 Oct 2019
TL;DR: This paper proposes a complexity reduced soft MMSE-PIC detection scheme by approximating the Gram matrix with a derived diagonal matrix and applies an efficient soft symbol estimation method based on a symbol mapping technique to further reduce the computational complexity.
Abstract: Joint iterative detection and decoding (JIDD) methods with minimum mean-squared-error parallel interference cancellation (MMSE-PIC) are usually used for signal detection in multi-input multi-output (MIMO) systems. However, the computational complexity caused by computation of Gram matrix and matrix inversion is unbearable for massive MIMO systems. In addition, MMSE-PIC also suffers from high complexity in calculating soft bit information (SBI) and post-equalization signal-to-interference-plus-noise ratio (PE-SINR). In this paper, we propose a complexity reduced soft MMSE-PIC detection scheme by approximating the Gram matrix with a derived diagonal matrix. Besides, we also apply an efficient soft symbol estimation method based on a symbol mapping technique to further reduce the computational complexity. The simulation results demonstrate that the proposed method can achieve approximating performance to the conventional method with linear-order computational complexity.
Cites background or methods from "Complexity reduced MIMO detection w..."
...Recently, researchers proposed a number of approaches to reduce the computational complexity of the conventional JIDD scheme [3]- [6]....
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...In [3], a method was proposed to reduce the complexity by reducing the number of matrix inversions, leaving the estimation of Gram matrix and PE-SINR as they were....
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TL;DR: Simulation results show that the proposed method can reduce the complexity of the STS method by limiting the candidates of calculating soft information to those bits whose a priori information provided by turbo decoder is less reliable.
Abstract: In MIMO systems, soft iterative detection and decoding can produce the near capacity performance. One of the promising detection techniques known as sphere decoder can play an important role in order to meet the requirements of achieving near optimal performance. The single tree search (STS) is based on the sphere decoding which can produce near optimal performance in iterative detection and decoding. The main hindering in STS method is that it is computationally complex. The complexity increases as we increase the iterations. In this paper, we propose to reduce complexity of the STS method by limiting the candidates of calculating soft information to those bits whose a priori information provided by turbo decoder is less reliable. Simulation results show that the proposed method can reduce the complexity with negligible performance degradation compared to the conventional full search and STS methods.
References
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TL;DR: A new family of convolutional codes, nicknamed turbo-codes, built from a particular concatenation of two recursive systematic codes, linked together by nonuniform interleaving appears to be close to the theoretical limit predicted by Shannon.
Abstract: This paper presents a new family of convolutional codes, nicknamed turbo-codes, built from a particular concatenation of two recursive systematic codes, linked together by nonuniform interleaving. Decoding calls on iterative processing in which each component decoder takes advantage of the work of the other at the previous step, with the aid of the original concept of extrinsic information. For sufficiently large interleaving sizes, the correcting performance of turbo-codes, investigated by simulation, appears to be close to the theoretical limit predicted by Shannon.
3,003 citations
TL;DR: This work provides a simple method to iteratively detect and decode any linear space-time mapping combined with any channel code that can be decoded using so-called "soft" inputs and outputs and shows that excellent performance at very high data rates can be attained with either.
Abstract: Recent advancements in iterative processing of channel codes and the development of turbo codes have allowed the communications industry to achieve near-capacity on a single-antenna Gaussian or fading channel with low complexity. We show how these iterative techniques can also be used to achieve near-capacity on a multiple-antenna system where the receiver knows the channel. Combining iterative processing with multiple-antenna channels is particularly challenging because the channel capacities can be a factor of ten or more higher than their single-antenna counterparts. Using a "list" version of the sphere decoder, we provide a simple method to iteratively detect and decode any linear space-time mapping combined with any channel code that can be decoded using so-called "soft" inputs and outputs. We exemplify our technique by directly transmitting symbols that are coded with a channel code; we show that iterative processing with even this simple scheme can achieve near-capacity. We consider both simple convolutional and powerful turbo channel codes and show that excellent performance at very high data rates can be attained with either. We compare our simulation results with Shannon capacity limits for ergodic multiple-antenna channel.
2,291 citations
"Complexity reduced MIMO detection w..." refers methods in this paper
...The MIMO systems uses multiple antennas at both transmitter and receiver side, in combination with forward error correction (FEC) coding scheme is considered to be the key to target a capacity limiting results [1]....
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...The detection method which can produce near optimal bit error rate (BER) performance follows the sphere-decoding (SD) method [1]-[2]....
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TL;DR: Simulation results demonstrate that the proposed low complexity iterative receivers structure for interference suppression and decoding offers significant performance gain over the traditional noniterative receiver structure.
Abstract: The presence of both multiple-access interference (MAI) and intersymbol interference (ISI) constitutes a major impediment to reliable communications in multipath code-division multiple-access (CDMA) channels. In this paper, an iterative receiver structure is proposed for decoding multiuser information data in a convolutionally coded asynchronous multipath DS-CDMA system. The receiver performs two successive soft-output decisions, achieved by a soft-input soft-output (SISO) multiuser detector and a bank of single-user SISO channel decoders, through an iterative process. At each iteration, extrinsic information is extracted from detection and decoding stages and is then used as a priori information in the next iteration, just as in turbo decoding. Given the multipath CDMA channel model, a direct implementation of a sliding-window SISO multiuser detector has a prohibitive computational complexity. A low-complexity SISO multiuser detector is developed based on a novel nonlinear interference suppression technique, which makes use of both soft interference cancellation and instantaneous linear minimum mean-square error filtering. The properties of such a nonlinear interference suppressor are examined, and an efficient recursive implementation is derived. Simulation results demonstrate that the proposed low complexity iterative receiver structure for interference suppression and decoding offers significant performance gain over the traditional noniterative receiver structure. Moreover, at high signal-to-noise ratio, the detrimental effects of MAI and ISI in the channel can almost be completely overcome by iterative processing, and single-user performance can be approached.
2,098 citations
"Complexity reduced MIMO detection w..." refers background in this paper
...The variance can illustrate the reliability of each calculated soft symbol [9]....
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TL;DR: This work explores a number of low-complexity soft-input/soft-output (SISO) equalization algorithms based on the minimum mean square error (MMSE) criterion and shows that for the turbo equalization application, the MMSE-based SISO equalizers perform well compared with a MAP equalizer while providing a tremendous complexity reduction.
Abstract: A number of important advances have been made in the area of joint equalization and decoding of data transmitted over intersymbol interference (ISI) channels. Turbo equalization is an iterative approach to this problem, in which a maximum a posteriori probability (MAP) equalizer and a MAP decoder exchange soft information in the form of prior probabilities over the transmitted symbols. A number of reduced-complexity methods for turbo equalization have been introduced in which MAP equalization is replaced with suboptimal, low-complexity approaches. We explore a number of low-complexity soft-input/soft-output (SISO) equalization algorithms based on the minimum mean square error (MMSE) criterion. This includes the extension of existing approaches to general signal constellations and the derivation of a novel approach requiring less complexity than the MMSE-optimal solution. All approaches are qualitatively analyzed by observing the mean-square error averaged over a sequence of equalized data. We show that for the turbo equalization application, the MMSE-based SISO equalizers perform well compared with a MAP equalizer while providing a tremendous complexity reduction.
985 citations
"Complexity reduced MIMO detection w..." refers background or methods in this paper
...For performance comparison, we use three conventional schemes in [3], [4], and [7]....
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...The conventional JIDD schemes in [3] and [4] did not utilize Loop 3, while the one in [7] utilized three loops including Loop 3....
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...The conventional scheme in [4] utilized a single matrix inversion approach, and it showed minor performance degradation compared to the conventional scheme in [3]....
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...The soft interference cancellation-minimum mean squared error (SIC-MMSE) scheme has much lower computational complexity than the SD algorithms, and one of the most strong advantages is the fixed complexity regardless of the channel condition [3]....
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...Compared with the conventional SIC-MMSE approaches using (11) [3][6][7], the proposed method using (9) requires only one inverse operation in (10) regardless of the layers....
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TL;DR: This paper proposes a low-complexity minimum mean-squared error (MMSE) based parallel interference cancellation algorithm, develops a suitable VLSI architecture, and presents a corresponding four-stream 1.5 mm2 detector chip in 90 nm CMOS technology, which is the first ASIC implementation of a SISO detector for iterative MIMO decoding.
Abstract: Multiple-input multiple-output (MIMO) technology is the key to meet the demands for data rate and link reliability of modern wireless communication systems, such as IEEE 802.11n or 3GPP-LTE. The full potential of MIMO systems can, however, only be achieved by means iterative MIMO decoding relying on soft-input soft-output (SISO) data detection. In this paper, we describe the first ASIC implementation of a SISO detector for iterative MIMO decoding. To this end, we propose a low-complexity minimum mean-squared error (MMSE) based parallel interference cancellation algorithm, develop a suitable VLSI architecture, and present a corresponding four-stream 1.5 mm2 detector chip in 90 nm CMOS technology. The fabricated ASIC includes all necessary preprocessing circuitry and exceeds the 600 Mb/s peak data-rate of IEEE 802.11n. A comparison with state-of-the-art MIMO-detector implementations demonstrates the performance benefits of our ASIC prototype in practical system-scenarios.
297 citations
"Complexity reduced MIMO detection w..." refers methods in this paper
...For performance comparison, we use three conventional schemes in [3], [4], and [7]....
[...]
...The conventional JIDD schemes in [3] and [4] did not utilize Loop 3, while the one in [7] utilized three loops including Loop 3....
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...The conventional scheme in [4] utilized a single matrix inversion approach, and it showed minor performance degradation compared to the conventional scheme in [3]....
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...Since the SIC-MMSE method suffers from low error-rate performance, joint iterative detection and decoding (JIDD) approach was attempted to increase the performance [4]-[6]....
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...It was demonstrated that the complexity of soft information extraction process using SIC-MMSE detection method can be reduced by using a single matrix inversion for all layers [4]....
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