Novel Reception and Transmission Calibration Technique for Active Antenna Array Based on Phase Center Estimation

Natera, Miguel Alejandro Salas; Rodríguez-Osorio, Ramón Martínez; Ariet, Leandro de Haro; Sierra‐Pérez, M.

doi:10.1109/tap.2017.2738067

Cited by 8 publications

(19 citation statements)

References 29 publications

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“…where ∆ ∈ 𝐶 × denotes the BS antenna array manifold, and, which considers the mutual coupling matrix. The array manifold matrix ∆(𝜃, 𝜑) is given [25] by: ∆(𝜃, 𝜑) = 𝐂 ∆𝑮 ∆𝑮(𝜃, 𝜑)∆𝑨(𝜃, 𝜑) .…”

Section: System Modelmentioning

confidence: 99%

“…where 𝑮 , ∆𝑮 , ∆𝑨(𝜃, 𝜑) and ∆𝑮(𝜃, 𝜑) are 𝑀 × 𝑀 diagonal matrices with complex elements representing each (𝜃, 𝜑), is the gain of the RF circuit, and the effect of the gain and phase uncertainty sources owing to the active antenna array components and antenna elements, respectively [25], [26]. In addition, 𝒈(𝜃, 𝜑) ∈ 𝐶 × denotes the amplitude and phase of the 𝑚-th element, and 𝒂(𝜃, 𝜑) ∈ 𝐶 × denotes the ideal steering vector of the array containing the information for each (𝜃, 𝜑) [25]. The mutual coupling matrix 𝐂 ∈ 𝐶 × consists of the amplitude and phase coupling coefficients 𝑪 , , 𝑘 is the 𝑘-th neighbor of the antenna element 𝑚.…”

Section: System Modelmentioning

confidence: 99%

“…One of the main focuses of this study was to evaluate and analyze the proposed algorithm under realistic conditions; thus, a new correlated channel model was considered. Referring to the channel model, a mutual coupling channel model [3] was used in this study, including array manifolds, which were modeled as in, [25] and extended to mMIMO. Array manifolds include manufacturing tolerances, active radio frequency (RF) element gain, phase variations, and mutual coupling, which are modeled using the k-nearest neighbor approach, as explained in detail in [25].…”

Section: Introductionmentioning

confidence: 99%

“…Referring to the channel model, a mutual coupling channel model [3] was used in this study, including array manifolds, which were modeled as in, [25] and extended to mMIMO. Array manifolds include manufacturing tolerances, active radio frequency (RF) element gain, phase variations, and mutual coupling, which are modeled using the k-nearest neighbor approach, as explained in detail in [25]. By contrast, the Rayleigh fading channel model was used for a simpler evaluation of the performance of the proposed algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Improved Iterative Inverse Matrix Approximation Algorithm for Zero Forcing Precoding in Large Antenna Arrays

Zorkun

Natera²,

Rodríguez-Osorio³

2022

IEEE Access

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Precoding algorithms are used in massive multiple-input multiple-output (mMIMO) communication systems to ensure effective signal transmission. The zero-forcing (ZF) is one of the most common linear precoding algorithms used in MIMO and mMIMO systems. ZF precoding is complex to implement because it requires direct matrix inversion of the gram matrix. Iterative algorithms have been proposed for approximating matrix inversions. However, iterative algorithms require initial conditions and pre-computations to converge to the optimal transmitted signal vector. This paper proposes a new improved iterative algorithm that guarantees convergence under any circumstances without dependency on any optimized initial parameter or condition. The proposed algorithm is based on a three-step iterative and iterative generalized inverse matrix approximation algorithm. The proposed algorithm was verified using a new correlated channel model that included mutual coupling effects and gain and phase variances caused by radio frequency elements at a base station (BS). The computational complexity of the proposed algorithm was then computed. This study analyzes and compares the bit error rate (BER) performance of the proposed algorithm with that of prominent existing algorithms. Moreover, the sum-rate performance of the proposed algorithm was analyzed. Simulations were performed under both correlated and uncorrelated channel conditions, for comparison and analysis. The simulation results demonstrate that the proposed algorithm outperforms the compared algorithms in terms of the convergence and convergence rates.INDEX TERMS Adaptive antenna system, approximate matrix inversion, iterative algorithms, massive MIMO, ZF precoding.

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Section: System Modelmentioning

confidence: 99%

Section: System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improved Iterative Inverse Matrix Approximation Algorithm for Zero Forcing Precoding in Large Antenna Arrays

Zorkun

Natera²,

Rodríguez-Osorio³

2022

IEEE Access

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“…To tackle this problem, many scholars make a detailed study. Traditional alignment methods measure phase center or use optical tools with mechanical fixtures [2]- [4]. These methods can achieve accurate alignment.…”

Section: Introductionmentioning

confidence: 99%

An Improved Method for Antenna and Probe Alignment in Auto-calibration of Millimeter-Wave Digital Monopulse Radar

Zheng

Chen

Zhao

2022

J. Phys.: Conf. Ser.

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Calibration is an effective method for improving digital monopulse radar tracking performance since it can eliminate the amplitude-phase imbalance between channels. Fast and accurate alignment between the antenna under test and the probe is an important part of the calibration. however, the narrow beam and high gain of a millimetre-wave band antenna pose a challenge to the alignment. To tackle this problem, we propose an improved alignment method based on the gradient feature of the radar radiation pattern. The method incorporates adaptive moment estimation to resist noise in the iteration process of the alignment. The high-accuracy alignment of the proposed method is verified by simulation and experiment. The root mean squared error of the alignment of the proposed method is 0.0046 degree in the absence of noise. At the SNR of 30 dB, the root mean squared error of the alignment is 0.008 degree. The proposed method improves the calibration efficiency and thus has a broad application prospect.

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Computer‐aided experimental studies for adaptive beamforming algorithms and array processing in engineering education

Salas‐Natera,

Zorkun,

Rodríguez‐Osorio

2024

Comp Applic In Engineering

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Smart antenna and digital antenna systems are considered as a crucial technology for current and future communication systems supporting 5G/6G and nonterrestrial‐networks communications. Based on the information of the received signal/s, smart antenna systems toward the radiation pattern to the desired direction/s. This technique is known as adaptive beamforming. The theories and the applications of adaptive beamforming are taught in engineering educational courses. This paper proposes a set of real‐time interactive experiments for adaptive beamforming algorithms in antenna array processing subject. The proposed experiments can be performed with the computer‐aided experimental studies tool prepared in the MATLAB® environment. The users can easily make comparisons and can perform trade‐offs between different types of adaptive beamforming algorithms, scenarios, and antenna architecture. This is used as a teaching tool for a graduate course on adaptive beamforming algorithms.

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Novel Reception and Transmission Calibration Technique for Active Antenna Array Based on Phase Center Estimation

Cited by 8 publications

References 29 publications

Improved Iterative Inverse Matrix Approximation Algorithm for Zero Forcing Precoding in Large Antenna Arrays

Improved Iterative Inverse Matrix Approximation Algorithm for Zero Forcing Precoding in Large Antenna Arrays

An Improved Method for Antenna and Probe Alignment in Auto-calibration of Millimeter-Wave Digital Monopulse Radar

Computer‐aided experimental studies for adaptive beamforming algorithms and array processing in engineering education

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