Efficient Near-Optimum Detectors for Large MIMO Systems Under Correlated Channels

Kobayashi, Ricardo Tadashi; Ciriaco, Fernando; Abrão, Taufik

doi:10.1007/s11277-015-2450-y

Cited by 20 publications

(12 citation statements)

References 24 publications

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“…Next, we apply some classical detectors to (21) to estimate the transmitted vector in LR domain, c k . It is worth noting that the entries of c k must be selected from a set of consecutive integer numbers.…”

Section: Proposed Zf-pgd-slb and Qrd-pgd-slb Detectorsmentioning

confidence: 99%

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Signal Detection Based on Parallel Group Detection Algorithm for High-Load Massive MIMO Systems

Nguyen

Le²,

Ngo

2019

Wireless Communications and Mobile Computing

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In this paper, a parallel group detection (PGD) algorithm is proposed in order to address the degradation in the bit error rate (BER) performance of linear detectors when they are used in high-load massive MIMO systems. e algorithm is constructed by converting the equivalent extended massive MIMO system into two subsystems, which can be simultaneously detected by the classical detection procedures. en, using the PGD and the classical ZF as well as the QR-decomposition-(QRD-) based detectors, we proposed two new detectors, called ZF-based PGD (ZF-PGD) and QRD-based PGD (QRD-PGD). e PGD is further combined with the sorted longest basis (SLB) algorithm to make the signal recovery more accurate, thereby resulting in two new detectors, namely, the ZF-PGD-SLB and the QRD-PGD-SLB. Various complexity evaluations and simulations prove that the proposed detectors can signi cantly improve the BER performance compared to their classical linear and QRD counterparts with the practical complexity levels. Hence, our proposed detectors can be used as e cient means of estimating the transmitted signals in high-load massive MIMO systems.

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Section: Proposed Zf-pgd-slb and Qrd-pgd-slb Detectorsmentioning

confidence: 99%

“…Furthermore, we would like to note that the complexity of a detector can be computed using either the complex or the equivalent real system. However, as shown in [21], the real system requires more flops than the complex one.…”

Section: Complexity Analysismentioning

confidence: 99%

Signal Detection Based on Parallel Group Detection Algorithm for High-Load Massive MIMO Systems

Nguyen

Le²,

Ngo

2019

Wireless Communications and Mobile Computing

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“…From the analysis above, we learn that, instead of using the fixed RA parameter, the unstrained algorithms can achieve the same performance as the constrained algorithms with a set of properly selected parameters. Therefore, a rough search for parameter over the integer set [4,18] for SNR ranges from 20 to 36 dB is conducted, and the corresponding optimal set for both SA-ILR and CLLL-ILR is [9,9,9,9,9,9,9,9,10,10,10,11,12,12,12,12,12]. They have the same optimal set because, on the one hand, this is the optimal set based on the rough search.…”

Section: Uncorrelated Scenario With Optimal Ra Constantmentioning

confidence: 99%

“…Observing this, the lattice reduction (LR) technique is introduced to improve their performance. [8][9][10][11] It has been proven that the LR-aided linear 12 and successive interference cancellation (SIC) 13 detectors are able to achieve the same diversity order as the ML detector, as the LR provides a near orthogonal channel matrix to reform the system model. 14 Moreover, a lot of research has been done to directly reduce the complexity of LR algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Seysen's algorithm–based incremental lattice reduction

Schmeink

2019

Trans Emerging Tel Tech

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Lattice reduction (LR)–aided detections have attracted great attention in the symbol detection of multiple‐input–multiple‐output communication, especially for systems with larger scale antennas. Recently, the Lenstra‐Lenstra‐Lovász (LLL)–based incremental LR (ILR) algorithms have been proposed to employ an early termination to jointly conduct the LR and retrieve the symbol based on a partial successive interference cancellation (SIC) detection, which induces a significant performance improvement. In this paper, we propose to apply the Seysen's algorithm (SA) to the ILR. After providing the feasibility analysis, two novel SA involved algorithms are introduced and studied. In particular, a new sorted QR decomposition‐based SA algorithm is proposed and tested as it plays a key role in the SIC detection. Subsequently, a novel SA‐based ILR scheme is further developed and investigated. By simulations, we show that the proposed approach outperforms conventional LLL‐based ILR in various aspects, while only a marginal increment in the complexity, restricted to medium signal‐to‐noise‐ratio regions, is observed.

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“…The system model is fully defined by complex variables; however, as we are focus on the optimization procedures, for simplicity and computational convenience, the complex variables are split into a double realvalue structure. So, rewriting the received MIMO signal in (1) with imaginary and real part separately [1], [12]:…”

Section: Problem Statementmentioning

confidence: 99%

Semidefinite Relaxation for Large Scale MIMO Detection

Negrão¹,

Mussi²,

Abrão³

2016

Anais De XXXIV Simpósio Brasileiro De Telecomunicações

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The semi-definite relaxation (SDR) is a high performance efficient approach to MIMO detection especially for low modulation orders. We focus on developing a computationally efficient approximation of the maximum likelihood detector (ML) algorithm based on semi-definite programming (SDP) for M-QAM constellations. The detector is based on a convex relaxation of the ML problem. A comparative analysis including the performance-complexity trade-off of the SDR and the lattice reduction (LR) aided linear MIMO detectors considering high number of antennas is carried out aiming to demonstrate the effectiveness of the SDR-based conventional and large scale MIMO detector. SDR-MIMO detector can provide a close, and under high order antennas cases, a better performance than the LR-aided linear MIMO detectors.

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Efficient Near-Optimum Detectors for Large MIMO Systems Under Correlated Channels

Cited by 20 publications

References 24 publications

Signal Detection Based on Parallel Group Detection Algorithm for High-Load Massive MIMO Systems

Signal Detection Based on Parallel Group Detection Algorithm for High-Load Massive MIMO Systems

A Seysen's algorithm–based incremental lattice reduction

Semidefinite Relaxation for Large Scale MIMO Detection

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