Beamforming technique based on adaptive diagonal loading in wireless access networks

Gao, Junqi; Zhen, Jiaqi; Lv, Yingnan; Guo, Baoyu

doi:10.1016/j.adhoc.2020.102249

Cited by 12 publications

(2 citation statements)

References 29 publications

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“…Several representative robust adaptive beamforming (RAB) methods have been widely studied, including sample matrix inversion (SMI) beamforming [5], diagonal loading (DL) [6], and eigenspace-based beamforming [7]. For example, several algorithms were developed based on the SMI algorithm [8] and are widely used because of their good real-time performance, fast convergence, and low computational complexity [9,10]. Although the SMI algorithm performs well for small-scale arrays since the number of observations usually greatly exceeds that of sensors and the RMB rule is always satisfied [11], the estimation error of the sample covariance matrix can significantly impair the performance of modular or massive-scale arrays due to finite training data, leading to a rise in the sidelobe level, the shift of the mainlobe, the degradation of the output signal-to-interference-plus-noise ratio (SINR), etc.…”

Section: Introductionmentioning

confidence: 99%

Robust Low-Sidelobe Transmit Beamforming under Peak-to-Average-Power Ratio Constraint

Cai

Chu

Ding

et al. 2023

Sensors

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Transmit beamforming (TBF) provides the capability of focusing illuminating power in the desired directions while reducing the emitting power in undesired directions. It is significantly important in low-altitude and slow-speed small (LSS) radar, which usually suffers from heavy clutter and rapidly changing interference on the near-ground side. Due to nonideal factors such as an inaccurate target direction and array gain-phase error, the robustness of TBF is also necessary to consider in practical applications. In this paper, we provide a robust TBF method that enables sidelobe control in preset regions and possesses high transmit efficiency in virtue of the peak-to-average-power ratio (PAPR) constraint on transmit weights. To achieve robustness, a norm upper bound is introduced to limit the fluctuation of transmit weights, and the steering vector mismatch is also considered by using a spherical uncertainty set surrounding the nominal steering vector. As the proposed robust TBF is nonconvex because of the nonconvexity of both the objective function and constraints, we translate it into a series of convex subproblems via several kinds of convex relaxation schemes. In particular, based on the special structure of the objective function and constraints, the translation of the nonconvex problem into a tractable SOCP problem is realized by using the combination of the triangle inequality and Cauchy–Schwartz inequality. Numerical results demonstrate the improvement in the efficiency and robustness of the proposed TBF method in comparison with traditional TBF methods.

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Section: Introductionmentioning

confidence: 99%

Robust Low-Sidelobe Transmit Beamforming under Peak-to-Average-Power Ratio Constraint

Cai

Chu

Ding

et al. 2023

Sensors

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show abstract

“…This method of beamforming exploits the properties of the smart antenna (SA) and makes use of the property of a thinned antenna array, which reduces power consumption by keeping some of the antennas in the array off. An SA is an antenna array of any type of antenna that by using a digital signal processor, produces a highly secure radiation beam towards the user and a null towards the interferer [18][19][20]. The smart antenna identifies the direction of arrival (DOA) of the signal from the mobile and produces a retro-directive beam towards the user.…”

Section: Introductionmentioning

confidence: 99%

Thinned Smart Antenna of a Semi-circular Dipole Array for Massive MIMO Systems

Khan,

Roy

2023

AEM

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Massive MIMO (multiple-input multiple-output) is a multi-user MIMO technology that can provide high-speed multimedia services in 5G wireless networks using sub-6 GHz and millimeter wave bands. The massive MIMO (MMIMO) installs array antennas in the base stations, using hundreds of transceivers with other RF modules. One of the drawbacks of the MMIMO system is its huge power consumption, and the beamforming network with RF modules for a large number of antennas is the main contributor to the power consumption. In this paper, a novel beamforming method is proposed for the low power consumption of an MMIMO system. The proposed thinned smart antenna (TSA) of a semi-circular array produces a secure beam toward the user’s terminal with reduced interference. By thinning the antenna array, some of the antenna elements are kept off, resulting in less power consumption, while the array pattern remains the same as a fully populated array with a reduced side lobe level (SLL). The sub-6 GHz band of 5 GHz is used for the design of thinned array antennas. The genetic algorithm (GA) is used to determine the array sequence in thinning, and the adaptive signal processing algorithms least mean square (LMS), recursive least square (RLS), and sample matrix inversion (SMI) are used for the beamforming of the TSA, and the corresponding algorithms are GA-LMS, GA-RLS, and GA-SMI. The power saving of 40% to 55% is achieved using TSA. The maximum SLL reductions of 13 dB, 12 dB, and 14 dB are achieved for TSA using GA-LMS, GA-RLS, and GA-SMI algorithms, respectively.

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Robust adaptive null broadening beamforming based on subspace projection

Yang

et al. 2022

International Journal of Electronics

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Beamforming technique based on adaptive diagonal loading in wireless access networks

Cited by 12 publications

References 29 publications

Robust Low-Sidelobe Transmit Beamforming under Peak-to-Average-Power Ratio Constraint

Robust Low-Sidelobe Transmit Beamforming under Peak-to-Average-Power Ratio Constraint

Thinned Smart Antenna of a Semi-circular Dipole Array for Massive MIMO Systems

Robust adaptive null broadening beamforming based on subspace projection

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