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This article presents a new notch steering scheme for hybrid beamforming transmitters (TXs) aimed at suppressing spatial interference, thereby enhancing the signal-to-interference-plus-noise ratio (SINR) to support spatial multiplexing. Built upon existing phased arrays, this scheme integrates an auxiliary-path vector modulator (VM) into each antenna element, which in turn, forms an interference-canceling beam. By spatially combining the array factors (AFs) of the main beam and the interference-canceling beam, a deep spatial notch is created while ensuring minimal main-beam power degradation. Unlike the conventional zero-forcing method that requires matrix inversion in digital for spatial notch creation, our scheme enables the computation of antenna weights in analog, significantly reducing the computational cost and latency. Leveraging this new notch steering scheme, we develop a 28-GHz four-element fully connected (FC) hybrid beamforming TX array using the GlobalFoundries 45-nm CMOS Silicon-on-Insulator (SOI) process. It is capable of simultaneously transmitting two independent, wideband data streams (DSs) in the same polarization toward two directions. In probing-based measurements, each TX channel delivers 19.7-dBm OP1 dB, 20.4-dBm PSAT , and 30.6% peak power-added efficiency (PAE) at 29 GHz, demonstrating state-of-the-art TX linearity and efficiency. In over-the-air (OTA) measurements, the packaged TX array achieves 29.8-dBm EIRP1 dB and is able to steer a spatial notch outside the −10-dB beamwidth of the main beam, with a notch depth of >35 dB and a main-beam power degradation of < 0.8 dB. Moreover, in spatial multiplexing demonstrations, the TX array is capable of transmitting a 400-MHz 64-quadrature amplitude modulation (QAM) signal to the intended receiver (RX) in the first DS, while suppressing the co-channel continuous-wave or wideband modulated interference created by the second DS with a high SINR. 

Cross-view geo-localization aims to estimate the location of a query ground image by matching it to a reference geo-tagged aerial images database. As an extremely challenging task, its difficulties root in the drastic view changes and different capturing time between two views. Despite these difficulties, recent works achieve outstanding progress on cross-view geo-localization benchmarks. However, existing methods still suffer from poor performance on the cross-area benchmarks, in which the training and testing data are captured from two different regions. We attribute this deficiency to the lack of ability to extract the spatial configuration of visual feature layouts and models' overfitting on low-level details from the training set. In this paper, we propose GeoDTR which explicitly disentangles geometric information from raw features and learns the spatial correlations among visual features from aerial and ground pairs with a novel geometric layout extractor module. This module generates a set of geometric layout descriptors, modulating the raw features and producing high-quality latent representations. In addition, we elaborate on two categories of data augmentations, (i) Layout simulation, which varies the spatial configuration while keeping the low-level details intact. (ii) Semantic augmentation, which alters the low-level details and encourages the model to capture spatial configurations. These augmentations help to improve the performance of the cross-view geo-localization models, especially on the cross-area benchmarks. Moreover, we propose a counterfactual-based learning process to benefit the geometric layout extractor in exploring spatial information. Extensive experiments show that GeoDTR not only achieves state-of-the-art results but also significantly boosts the performance on same-area and cross-area benchmarks. Our code can be found at https://gitlab.com/vail-uvm/geodtr. 

Spatial multiplexing, or multi-user MIMO, can improve the communication throughput by simultaneously supporting multiple spatially non-collocated data streams. Most multi-user MIMO TRXs at GHz are based on digital beamforming. However, as the data rate of each user approaches multi-Gb/s at mmWave, performing dynamic beamforming weights calculation in digital and high-speed digital-to-analog conversion faces a significant energy efficiency bottleneck for large-scale mmWave antenna arrays. Alternatively, hybrid beamforming can support a handful of concurrent data streams by combining analog beamforming with digital precoding. Although hybrid beamforming loses degrees-of-freedom compared to all-digital processing, it reduces digital computation complexity and the number of digital-to-analog conversion chains, resulting in greatly enhanced energy efficiency.