DOA Estimation for Hybrid Massive MIMO Systems using Mixed-ADCs: Performance Loss and Energy Efficiency

Due to the power consumption and high circuit cost in antenna arrays, the practical application of massive multipleinput multiple-output (MIMO) in the sixth generation (6G) and future wireless networks is still challenging. Employing lowresolution analog-to-digital converters (ADCs) and hybrid analog and digital (HAD) structure is two low-cost choice with acceptable performance loss. In this paper, the combination of the mixedADC architecture and HAD structure employed at receiver is proposed for direction of arrival (DOA) estimation, which will be applied to the beamforming tracking and alignment in 6G. By adopting the additive quantization noise model, the exact closedform expression of the Cramer-Rao lower bound (CRLB) for the HAD architecture with mixed-ADCs is derived. Moreover, the closed-form expression of the performance loss factor is derived as a benchmark. In addition, to take power consumption into account, energy efficiency is also investigated in our paper. The numerical results reveal that the HAD structure with mixedADCs can significantly reduce the power consumption and hardware cost. Furthermore, that architecture is able to achieve a better trade-off between the performance loss and the power consumption. Finally, adopting 2-4 bits of resolution may be a good choice in practical massive MIMO systems.Comment: 11 pages, 7 figure

Two Rapid Power Iterative DOA Estimators for UAV Emitter Using Massive/Ultra-massive Receive Array

To provide rapid direction finding (DF) for unmanned aerial vehicle (UAV) emitter in future wireless networks, a low-complexity direction of arrival (DOA) estimation architecture for massive multiple input multiple output (MIMO) receiver arrays is constructed. In this paper, we propose two strategies to address the extremely high complexity caused by eigenvalue decomposition of the received signal covariance matrix. Firstly, a rapid power-iterative rotational invariance (RPI-RI) method is proposed, which adopts the signal subspace generated by power iteration to gets the final direction estimation through rotational invariance between subarrays. RPI-RI makes a significant complexity reduction at the cost of a substantial performance loss. In order to further reduce the complexity and provide a good directional measurement result, a rapid power-iterative Polynomial rooting (RPI-PR) method is proposed, which utilizes the noise subspace combined with polynomial solution method to get the optimal direction estimation. In addition, the influence of initial vector selection on convergence in the power iteration is analyzed, especially when the initial vector is orthogonal to the incident wave. Simulation results show that the two proposed methods outperform the conventional DOA estimation methods in terms of computational complexity. In particular, the RPIPR method achieves more than two orders of magnitude lower complexity than conventional methods and achieves performance close to CRLB. Moreover, it is verified that the initial vector and the relative error have a significant impact on the performance of the computational complexity

Two Rapid Power Iterative DOA Estimators for UAV Emitter Using Massive/Ultra-Massive Receive Array

To provide rapid direction finding (DF) for unmanned aerial vehicle (UAV) emitters in future wireless networks, a low-complexity direction of arrival (DOA) estimation architecture for massive multiple-input multiple-output (MIMO) receiver arrays is constructed. In this paper, we propose two strategies to address the extremely high complexity caused by eigenvalue decomposition of the received signal covariance matrix. Firstly, a rapid power iterative rotational invariance (RPI-RI) method is proposed, which adopts the signal subspace generated by power iteration to obtain the final direction estimation through rotational invariance between subarrays. RPI-RI causes a significant complexity reduction at the cost of a substantial performance loss. In order to further reduce the complexity and provide good directional measurement results, the rapid power iterative polynomial rooting (RPI-PR) method is proposed, which utilizes the noise subspace combined with the polynomial solution method to obtain the optimal direction estimation. In addition, the influence of initial vector selection on convergence in the power iteration is analyzed, especially when the initial vector is orthogonal to the incident wave. Simulation results show that the two proposed methods outperform the conventional DOA estimation methods in terms of computational complexity. In particular, the RPI-PR method achieves more than two orders of magnitude lower complexity than conventional methods and achieves performance close to the Cramér–Rao Lower Bound (CRLB). Moreover, it is verified that the initial vector and the relative error have a significant impact on the performance with respect to the computational complexity

Green manure incorporation enhanced soil labile phosphorus and fruit tree growth

IntroductionThe incorporation of green manures substantially enhances the conversion of external phosphorus (P) fertilizers and soil-reserved P into forms readily available to plants. The study aims to evaluate the influence of green manure additions on soil phosphorus dynamics and citrus growth, considering different green manure species and initial soil phosphorus levels. Additionally, the research seeks to elucidate the microbiological mechanisms underlying the observed effects.MethodsA citrus pot experiment was conducted under both P-surplus (1.50 g·P·kg-1) and P-deficient (0.17 g·P·kg-1) soils with incorporating legume (Leg), non-legume (Non-Leg) or no green manure residues (CK), and 18O-P labeled KH2PO4 (0.5 g, containing 80‰ δ18Op) was additionally introduced to trace the turnover characteristics of chemical P fertilizer mediated by soil microorganisms.Results and discussionIn P-surplus soil, compared with the CK treatment, the Leg treatment significantly increased soil H2O-Pi (13.6%), NaHCO3-Po (8.9%), NaOH-Pi (9.5%) and NaOH-Po (30.0%) content. It also promoted rapid turnover of P sources into H2O-Pi and NaHCO3-Pi pools by enhancing the phoC (576.6%) gene abundance. In contrast, the Non-Leg treatment significantly augmented soil H2O-Pi (9.2%) and NaHCO3-Po (8.5%) content, facilitating the turnover of P sources into NaHCO3-Pi pools. Under P-deficient soil conditions, compared with the CK treatment, the Leg treatment notably raised soil H2O-Pi (150.0%), NaHCO3-Pi (66.3%), NaHCO3-Po (34.8%) and NaOH-Pi (59.0%) content, contributing to the transfer of P sources into NaHCO3-Pi and NaOH-Pi pools. This effect was achieved through elevated ALP (33.8%) and ACP (12.9%) activities and increased pqqC (48.1%), phoC (42.9%), phoD (21.7%), and bpp (27.4%) gene abundances. The Non-Leg treatment, on the other hand, led to significant increases in soil NaHCO3-Pi (299.0%) and NaHCO3-Po (132.6%) content, thereby facilitating the turnover of P sources into NaHCO3-Pi and NaOH-Pi pools, except for the phoC gene abundance. Both Leg and Non-Leg treatments significantly improved citrus growth (7.3-20.0%) and P uptake (15.4-42.1%) in P-deficient soil but yielded no substantial effects in P-surplus soil. In summary, introducing green manure crops, particularly legume green manure, emerges as a valuable approach to enhance soil P availability and foster fruit tree growth in orchard production