High Precision Low Complexity Matrix Inversion Based on Newton Iteration for Data Detection in the Massive MIMO

Tang, Chuan; Liu, Cang; Ling, Yuan; Zhang, Xing

doi:10.1109/lcomm.2015.2514281

Cited by 116 publications

(87 citation statements)

References 10 publications

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“…The convergence rates of the GSBMIA approach, the joint algorithm, the Neumann series [7] and Newton iteration method [8] are compared by their bit error-rate (BER) performances in this section. Moreover, ZF pre-coding with exact matrix inversion of W is also included as the benchmark.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Using the analysis method similar to [8], we simulate I K À WZ 1 ð Þ under different N=K ratios, which is shown in Fig. 1.…”

Section: Convergence Analysismentioning

confidence: 99%

“…Compared with other approximation approaches, the joint algorithm has a faster convergence rate and it is still convergent when the user number becomes close to the BS antenna number. Simulation results demonstrate that the joint algorithm and the GSBMIA approach converge faster than the Neumann series [7] and Newton iteration method [8] which approximate matrix inversion as well.…”

Section: Introductionmentioning

confidence: 98%

“…Thus the matrix inversion must be separated from the iteration result of the three approaches, which brings about complicated calculations. Unlike the three approaches, the Neumann series [7] and Newton iteration method [8] both approximate matrix inversion and their results can be more easily applied in comparison. However, the Neumann series does not converge fast enough and Newton iteration method usually requires a complex calculation to get an appropriate initial input.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Approaches of approximating matrix inversion for zero-forcing pre-coding in downlink massive MIMO systems

Shao

2017

Wireless Netw

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Several approximation approaches including the Gauss-Seidel (GS) method have been proposed to reduce the complexity of matrix inversion for zero-forcing pre-coding in massive multiple-input-multiple-output systems. However, extra computation is required to obtain the matrix inversion from the iteration result of the GS method. In this paper, we propose a new GS-based matrix inversion approximation (GSBMIA) approach. Unlike the traditional GS method, the GSBMIA approach approximates the matrix inversion, which will simplify further calculations. Furthermore, in order to speed up convergence, we propose a joint algorithm based on the GSBMIA and Newton iteration method where the GSBMIA approach is employed to provide an efficient searching direction for the following Newton iterations. Compared with other approximation methods, the joint algorithm can accommodate more single antenna users for the same base station antenna number. Simulation results demonstrate that the joint algorithm and the GSBMIA approach converge faster than the Neumann series and Newton iteration method.

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Section: Simulation Resultsmentioning

confidence: 99%

“…Using the analysis method similar to [8], we simulate I K À WZ 1 ð Þ under different N=K ratios, which is shown in Fig. 1.…”

Section: Convergence Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Approaches of approximating matrix inversion for zero-forcing pre-coding in downlink massive MIMO systems

Shao

2017

Wireless Netw

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“…It should be noted that the pseudo inverse computational complexity is of a polynomial order , while the SD search imposes an exponential computational complexity. It should also be noted that any SD algorithm must compute the QR decomposition of the channel matrix H .…”

Section: Performance Analysismentioning

confidence: 99%

Zero forcing based sphere decoder for generalized spatial modulation systems

2019

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To reduce the number of radio frequency (RF) chains in multiple input multiple output (MIMO) systems, generalized spatial modulation (GSM) techniques have been proposed in the literature. In this paper, we propose a zero‐forcing (ZF)‐based detector, which performs an initial pruning of the search tree that will be considered as the initial condition in a sphere decoding (SD) algorithm. The proposed method significantly reduces the computational complexity of GSM systems while achieving a near maximum likelihood (ML) performance. We analyze the performance of the proposed method and provide an analytic performance difference between the proposed method and the ML detector. Simulation results show that the performance of the proposed method is very close to that of the ML detector, while achieving a significant computational complexity reduction in comparison with the conventional SD method, in terms of the number of visited nodes. We also present some simulations to assess the accuracy of our theoretical results.

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Reliability feedback–aided low‐complexity detection in uplink massive MIMO systems

Datta

Mandloi

Bhatia

2019

Int J Communication

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Summary Massive multiple‐input multiple‐output (MIMO) plays a crucial role in realizing the demand for higher data rates and improved quality of service for 5G and beyond communication systems. Reliable detection of transmitted information bits from all the users is one of the challenging tasks for practical implementation of massive‐MIMO systems. The conventional linear detectors such as zero forcing (ZF) and minimum mean square error (MMSE) achieve near‐optimal bit error rate (BER) performance. However, ZF and MMSE require large dimensional matrix inversion which induces high computational complexity for symbol detection in such systems. This motivates for devising alternate low‐complexity near‐optimal detection algorithms for uplink massive‐MIMO systems. In this work, we propose an ordered sequential detection algorithm that exploits the concept of reliability feedback for achieving near‐optimal performance in uplink massive‐MIMO systems. In the proposed algorithm, symbol corresponding to each user is detected in an ordered sequence by canceling the interference from all the other users, followed by reliability feedback‐based decision. Incorporation of the sequence ordering and the reliability feedback‐based decision enhances the interference cancellation, which reduces the error propagation in sequential detection, and thus, improves the BER performance. Simulation results show that the proposed algorithm significantly outperforms recently reported massive‐MIMO detection techniques in terms of BER performance. In addition, the computational complexity of the proposed algorithm is substantially lower than that of the existing algorithms for the same BER. This indicates that the proposed algorithm exhibits a desirable trade‐off between the complexity and the performance for massive‐MIMO systems.

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High Precision Low Complexity Matrix Inversion Based on Newton Iteration for Data Detection in the Massive MIMO

Cited by 116 publications

References 10 publications

Approaches of approximating matrix inversion for zero-forcing pre-coding in downlink massive MIMO systems

Approaches of approximating matrix inversion for zero-forcing pre-coding in downlink massive MIMO systems

Zero forcing based sphere decoder for generalized spatial modulation systems

Reliability feedback–aided low‐complexity detection in uplink massive MIMO systems

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