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  1. We present a novel architecture for the design of single-photon detecting arrays that captures relative intensity or timing information from a scene, rather than absolute. The proposed method for capturing relative information between pixels or groups of pixels requires very little circuitry, and thus allows for a significantly higher pixel packing factor than is possible with per-pixel TDC approaches. The inherently compressive nature of the differential measurements also reduces data throughput and lends itself to physical implementations of compressed sensing, such as Haar wavelets. We demonstrate this technique for HDR imaging and LiDAR, and describe possible future applications.

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    Free, publicly-accessible full text available December 1, 2024
  2. All-digital basestation (BS) architectures for millimeter-wave (mmWave) massive multi-user multiple-input multiple-output (MU-MIMO), which equip each radio-frequency chain with dedicated data converters, have advantages in spectral efficiency, flexibility, and baseband-processing simplicity over hybrid analog-digital solutions. For all-digital architectures to be competitive with hybrid solutions in terms of power consumption, novel signal-processing methods and baseband architectures are necessary. In this paper, we demonstrate that adapting the resolution of the analog-to-digital converters (ADCs) and spatial equalizer of an all-digital system to the communication scenario (e.g., the number of users, modulation scheme, and propagation conditions) enables orders-of-magnitude power savings for realistic mmWave channels. For example, for a 256-BS-antenna 16-user system supporting 1 GHz bandwidth, a traditional baseline architecture designed for a 64-user worst-case scenario would consume 23 W in 28 nm CMOS for the ADC array and the spatial equalizer, whereas a resolution-adaptive architecture is able to reduce the power consumption by 6.7×. 
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