Efficient MIMO Preprocessor With Sorting-Relaxed QR Decomposition and Modified Greedy LLL Algorithm

Chen, Lirui; Zhang, Xing; Li, Yongzhong; Qiu, Shikai

doi:10.1109/access.2020.2980922

Cited by 10 publications

(4 citation statements)

References 28 publications

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“…In this study, we demonstrated the effectiveness of ZF and MMSE algorithms when they were combined with the LLR and KZ algorithms and proved they are better than IF, as presented in [34]. In [39], the authors put emphasis more on the LLL algorithm-based receiver for large scale MIMO systems and proposed sophisticated QR decomposition-based LLL algorithms. In this paper, the authors demonstrated the performance of the proposed LLL algorithms concerning BER performances.…”

Section: Related Workmentioning

confidence: 71%

Capacity Analysis of Lattice Reduction Aided Equalizers for Massive MIMO Systems

et al. 2020

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Massive multi-input-multi-output (MIMO) systems are the future of the communication system. The proper design of the MIMO system needs an appropriate choice of detection algorithms. At the same time, Lattice reduction (LR)-aided equalizers have been well investigated for MIMO systems. Many studies have been carried out over the Korkine–Zolotareff (KZ) and Lenstra–Lenstra–Lovász (LLL) algorithms. This paper presents an analysis of the channel capacity of the massive MIMO system. The mathematical calculations included in this paper correspond to the channel correlation effect on the channel capacity. Besides, the achievable gain over the linear receiver is also highlighted. In this study, all the calculations were further verified through the simulated results. The simulated results show the performance comparison between zero forcing (ZF), minimum mean squared error (MMSE), integer forcing (IF) receivers with log-likelihood ratio (LLR)-ZF, LLR-MMSE, KZ-ZF, and KZ-MMSE. The main objective of this work is to show that, when a lattice reduction algorithm is combined with the convention linear MIMO receiver, it improves the capacity tremendously. The same is proven here, as the KZ-MMSE receiver outperforms its counterparts in a significant margin.

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Section: Related Workmentioning

confidence: 71%

Capacity Analysis of Lattice Reduction Aided Equalizers for Massive MIMO Systems

et al. 2020

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“…The LR-aided MIMO receiver first finds the set of small, nearly orthogonal matrices for the given channel matrix and decodes the symbols using this matrix rather than the original channel matrix. Different LR algorithms (such as LLL [18] or Seysen [19] ) can be used to generate near orthogonal matrices of a given lattice. Compared with the hard discrimination method, the soft discrimination method has higher system performance.…”

Section: Of 11mentioning

confidence: 99%

Modifed SIC Algorithm based on Lattice Reduction Aided for MIMO OFDM Detection

Liu¹,

Fan²,

Cheng³

et al. 2022

Preprint

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The lattice reduction aided(LRA) algorithm has attracted widely attention of researchers in MIMO systems because of its low complexity to achieve full diversity. However, most lattice reduction auxiliary algorithms do not directly improve system performance. In this paper, a modified lattice reduction successive interference cancellation (SIC) algorithm is proposed for MIMO OFDM system mild changing the complexity of LRA algorithm. At present, the MMSE SQRD algorithm has the problem of regarding too much useful signals as the residual interference which cannot be cancelled by successive interference cancellation. Since the diagonal elements of useful signals are non-zero, the components of the symbols to be detected still present in the interference term, treating all the signals as interference will be resulted in performance loss. To solve this problem, the modified matrix is proposed.by moving this symbol component into the signal term, the proposed algorithm can provide the better performance than MMSE SQRD algorithm. The analysis shows that the LRA-MMSE-SIC algorithm of proposed in this paper significantly improves the system performance with mild increasing the complexity. The simulation results show that the performance of the proposed algorithm is improved by 1dB and 8dB compared with the comparison algorithm when the bit error rate is 10−3.

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“…There are so many variations of LLL algorithm. For example, the modified greedy LLL algorithm paralleled the greedy LLL algorithm [22], [23]. Furthermore, the LLL algorithm is used in Lattice-based cryptography and in many other fields.…”

Section: Introductionmentioning

confidence: 99%

A Study on the Average Number of LLL-based Using Statistical Learning

Song,

Seo

2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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Lattice-based Cryptography is known as one of the key technologies in modern cryptography. This encryption scheme has the basis vectors from the lattice as the public key and a short-length vector in the lattice consisting of an integer combination of the basis vectors as the secret key. To break this encryption, we need to solve the Shortest Vector Problem (SVP), known as NP-hard. Therefore, instead of finding the shortest vector, LLL algorithm is often used to find a vector of sufficiently short length to break the encryption. The LLL algorithm is a well-known method for breaking this encryption, but there is still no clear answer to the question of how many times the LLL algorithm needs to be used to obtain the desired level of secret key, the average number of the (, )-LLL bases in dimension n is a tool to measure the probability that the LLL algorithm solves the SVP. We can expect that this number indicates how many times the appropriate algorithm should run. There is a formula for this, but it contains some functions that take a long time to compute. We apply linear regression to the formula of the average number of the (, )-LLL bases in dimension n, and therefore we obtain some formulas to approximate the average number of the (, )-LLL is based on dimension n, which contains simple functions. When the dimensions are high, our model is much better regarding the computation time.

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Efficient MIMO Preprocessor With Sorting-Relaxed QR Decomposition and Modified Greedy LLL Algorithm

Cited by 10 publications

References 28 publications

Capacity Analysis of Lattice Reduction Aided Equalizers for Massive MIMO Systems

Capacity Analysis of Lattice Reduction Aided Equalizers for Massive MIMO Systems

Modifed SIC Algorithm based on Lattice Reduction Aided for MIMO OFDM Detection

A Study on the Average Number of LLL-based Using Statistical Learning

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