Element-based Lattice Reduction aided K-Best detector for large-scale MIMO systems

Toma, Ogeen H.; El‐Hajjar, Mohammed

doi:10.1109/spawc.2016.7536870

Cited by 5 publications

(7 citation statements)

References 15 publications

(32 reference statements)

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“…Hence, in order to reduce this complexity, LR-aided detectors were proposed in [9,10], where the channel matrix H in transformed into its equivalent channel matrix H, which is more orthogonal and better conditioned than H [25,26]. The LR-aided detector uses the new orthogonal channel matrix H, which gives more reliable estimation for the received signal than that of the detector that uses the original channel matrix H [19].…”

Section: Lr-aided Mimo Detector Designmentioning

confidence: 99%

“…Recently, Element-based Lattice Reduction (ELR) algorithm [13] has been proposed to perform lattice reduction for large-scale MIMO systems with an improved performance compared to the LLL algorithm, while also requiring lower complexity [13]. Furthermore, we have proposed in [19] an ELR-aided K-Best detector that is capable of outperforming the LLL-aided detectors when used for large-scale MIMO at a reduced complexity.…”

Section: Elr-aided Detectorsmentioning

confidence: 99%

“…In [19], we proposed to further improve the performance of ELR-aided detectors by employing the ELR algorithm before the K-Best detection process for large-scale MIMO. Our proposed ELR-aided K-Best detector can achieve an improved BER performance compared to the LLL-aided detectors, while requiring significantly lower complexity.…”

Section: Elr-aided Detectorsmentioning

confidence: 99%

“…Therefore, motivated by the results of [18], where the performance of the K-Best detector was improved by performing LR of the channel matrix before the detection, in [19] we proposed to improve the large-scale MIMO detection performance by applying the ELR algorithm with the K-Best detector, which we refer to as ELR-aided K-Best detector. We have shown in [19] that the ELR-aided K-Best detector is capable of achieving an improved BER performance compared to the LLL-aided K-Best detector, while requiring a significantly lower complexity.…”

Section: Introductionmentioning

confidence: 99%

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Hardware Efficient Architecture for Element-Based Lattice Reduction Aided K-Best Detector for MIMO Systems

Halak

El‐Hajjar

Hassanein

2018

JSAN

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Multiple-Input Multiple-Output (MIMO) systems are characterised by increased capacity and improved performance compared to the single-input single-output (SISO) systems. One of the main challenge in the design of MIMO systems is the detection of the transmitted signals due to the interference caused by the multiple simultaneously transmitted symbols from the multiple transmit antennas. Several detection techniques have been proposed in the literature in order to reduce the detection complexity, while maintaining the required quality of service. Among these low-complexity techniques is the Lattice Reduction (LR), which can provide good performance and significantly lower complexity compared to Maximum Likelihood (ML) detector. In this paper we propose to use the so-called Element-based Lattice Reduction (ELR) combined with K-Best detector for the sake of attaining a better Bit Error Ratio (BER) performance and lower complexity than the conventional Lenstra, Lanstra, and Lovasz (LLL) LR-aided detection. Additionally, we propose a hardware implementation for the ELR-aided K-Best detector for a MIMO system equipped with four transmit and four receive antennas. The ELR-aided K-Best detector requires an extra 18% increase in power consumption and an extra 20% in area overhead compared to a regular K-Best detector dispensing with ELR, where this increase in the hardware requirements is needed in order to achieve a 2 dB performance improvement at a bit error rate of 10 −5 .

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Section: Lr-aided Mimo Detector Designmentioning

confidence: 99%

Section: Elr-aided Detectorsmentioning

confidence: 99%

Section: Elr-aided Detectorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hardware Efficient Architecture for Element-Based Lattice Reduction Aided K-Best Detector for MIMO Systems

Halak

El‐Hajjar

Hassanein

2018

JSAN

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“…Against this background, in this paper we investigate the performance of hybrid beamforming system in the face of mutual coupling relying on a practical codebook using discrete Fourier transform (DFT) and mutually unbiased bases (MUB) precoders of [8]. Furthermore, we employ an Element-based Lattice Reduction (ELR)-aided decoder at the receiver [18], [19], which performs close to the maximum-likelihood (ML) detector at a significantly lower complexity [20]. Although there are other state-of-the-art lattice reduction techniques, such as the Lenstra, Lanstra, and Lovasz (LLL) algorithm [21], and the Korkine-Zolotareff (KZ) algorithm [22], ELR assisted detection significantly reduces the complexity when large-scale MIMOs are employed.…”

Section: Introductionmentioning

confidence: 99%

Effects of Mutual Coupling on Lattice Reduction-Aided Millimeter Wave Hybrid Beamforming

Prisiceanu

Satyanarayana

El‐Hajjar

et al. 2018

2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

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Millimeter wave (mmWave) communications has gained considerable attention due to the availability of large bandwidths, which can be harnessed to meet the ever-increasing data rate demands. Directional beamforming combined with baseband precoding should be used owing to the high propagation losses encountered at mmWave frequencies. This is typically referred to as hybrid beamforming. In hybrid beamforming arrangements, the adjacent antenna elements are closely spaced, typically at halfwavelength spacing in order to compensate for the propagation losses. In this antenna array configuration, the mutual coupling between the adjacent antenna elements becomes significant and may limit the performance of the system. Therefore, in this paper, we propose a reduced-complexity near-optimal detection scheme, namely the so-called Element-based Lattice Reduction algorithm, for hybrid beamforming in mmWave communications and we investigate its performance in the presence of mutual coupling. We demonstrate that the mutual coupling affects the spatial correlation of the channels between the different antennas depending on the distance between the antenna elements, which has a direct effect on the achievable rate as well as bit error ratio (BER) performance of the system.

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Low Complexity Linear Detectors for Massive MIMO: A Comparative Study

et al. 2021

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Massive multiple-input multiple-output (M-MIMO) is a significant pillar in fifth generation (5G) networks where a large number of antennas is deployed. It provides massive advantages to modern communication systems in data rate, spectral efficiency, number of users serviced simultaneously, energy efficiency, and quality of service (QoS). However, it requires advanced signal processing for data detection. The growing MIMO size leads to complicated scenarios, which makes the detector design a knotty problem. The problem is also becoming more complicated when high-order modulation schemes are exploited and more users are multiplexed. Therefore, it is not practical to employ the maximum likelihood (ML) detector despite the excellent performance. Linear detectors are alternative solutions and relatively simple. Unfortunately, they still need an exact matrix inversion computation, which bears to a significant high complexity. Therefore, several iterative methods are utilized to approximate or evade the matrix inversion rather than computing it. This paper studies the pros and cons of iterative matrix inversion methods where the number of computations and bit-error-rate (BER) are considered to compare between the methods. The comparison is conducted in several scenarios such as different ratio between the number of base station (BS) antennas and user terminal (UT) antennas (β), the number of iterations (n), and the relaxation parameter (ω). This paper also studies the impact of ω in the performance of Richardson (RI) and the successive over-relaxation (SOR) methods. Numerical results show that the conjugate gradient (CG) and optimized coordinate descent (OCD) methods exhibit the lowest complexity with an acceptable performance. In addition, the Gauss-Seidel (GS) method outperforms all other detectors with a trivial complexity increment. It is also noticed that the performance is not improved with every iteration. It is also shown that ω has a great impact and a significant role in achieving a satisfactory performance in both RI and SOR based detectors. From implementation point of view, detectors based on RI, OCD, and CG methods have achieved the highest hardware efficiency (HE) while Jacobi (JA) based detector has obtained the lowest HE. Recent research advances of detection methods are also presented in the open research direction with a potential impact of linear detection methods in initialization and pre-processing.

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Element-based Lattice Reduction aided K-Best detector for large-scale MIMO systems

Cited by 5 publications

References 15 publications

Hardware Efficient Architecture for Element-Based Lattice Reduction Aided K-Best Detector for MIMO Systems

Hardware Efficient Architecture for Element-Based Lattice Reduction Aided K-Best Detector for MIMO Systems

Effects of Mutual Coupling on Lattice Reduction-Aided Millimeter Wave Hybrid Beamforming

Low Complexity Linear Detectors for Massive MIMO: A Comparative Study

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