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In this paper, we considered four different interference suppression algorithms in a single-input multiple-output receiver, where channel diversity is intentionally introduced to improve interference tolerance. Matched filter (MF), zero forcing (ZF), blind interference estimation and suppression (BIES) which we had previously proposed, and minimum variance distortionless response (MVDR) are considered. Each algorithm is introduced, and the recombining weight vectors are derived. A loss function is defined to compare the performance of the algorithms, showing superior performance of MVDR, and confirming that the proposed BIES algorithm achieves a comparable performance to MVDR. The four algorithms are then applied on measured data from a chip that was designed and fabricated in \qty{45}{\nm} RF SOI process for the frequency range of 1.2-2.4GHz. Measurement results are compared for the four algorithms, confirming significant improvement by using MVDR, BIES, and ZF compared to MF for large interference, as predicted by the derived equations, and showing adaptability of MVDR and BIES to small levels of interference as opposed to ZF. 

This paper presents a novel system architecture to suppress in-band artifacts (IBAs) generated from out-of-band (OOB) interferers, including reciprocal mixing by the local oscillator's (LO) spurs and phase noise (PN), third-order intermodulation (IM3) artifacts, and harmonic down-conversion (HDC) artifacts. Theory and design procedure are explained, and measurement results from a prototype taped out in 45nm RF SOI process are presented. The receiver was designed for the frequency range of 1.2-2.4GHz and achieved a noise figure (NF) of 3.1-6.2dB, blocker -1dB compression point (B1dB) of -10.3Bm, and OOB third-order input-referred intercept point (IIP3) of 9.3dBm on average, before artifact suppression. Measurements were performed on 16-quadrature amplitude modulated (16QAM) signals with modulated and unmodulated OOB interferers to show artifact suppression for various kinds of IBA. For each IBA, artifact suppression performance was assessed across frequency and interferer power. Interference tolerance improvement of up to 38dB was achieved. Additionally, reconstruction of the artifacts for the cases of spur and HDC was demonstrated, showing simultaneous recovery of two signals, providing a form of carrier aggregation. 

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. 

This paper presents a novel technique for suppression of in-band artifacts from out-of-band (OOB) interference in widely tunable RF receivers. The technique employs a multi-tap inductor-capacitor network (LCN) to generate diversity in gain and phase between taps across the targeted frequency range. Using this network to feed a bank of identical receivers sharing a single local oscillator (LO) allows multiple kinds of interferer artifact to be suppressed. Here we considered spur-induced and phase noise-induced artifacts. In each case, the resulting artifacts are linearly separable from signal when the outputs of the sub-receivers are recombined. AC and transient simulations were first performed to show feasibility of the proposed approach. A prototype was implemented in 45nm CMOS which confirmed the validity of the synthetic diversity (SD) approach for suppressing interferer artifacts, showing a maximum lowering in EVM and BER of 38% and 60% respectively. 

Microscopic robots controlled by onboard integrated circuits that walk when powered by light are realized. 

Abstract In digital agriculture, large-scale data acquisition and analysis can improve farm management by allowing growers to constantly monitor the state of a field. Deploying large autonomous robot teams to navigate and monitor cluttered environments, however, is difficult and costly. Here, we present methods that would allow us to leverage managed colonies of honey bees equipped with miniature flight recorders to monitor orchard pollination activity. Tracking honey bee flights can inform estimates of crop pollination, allowing growers to improve yield and resource allocation. Honey bees are adept at maneuvering complex environments and collectively pool information about nectar and pollen sources through thousands of daily flights. Additionally, colonies are present in orchards before and during bloom for many crops, as growers often rent hives to ensure successful pollination. We characterize existing Angle-Sensitive Pixels (ASPs) for use in flight recorders and calculate memory and resolution trade-offs. We further integrate ASP data into a colony foraging simulator and show how large numbers of flights refine system accuracy, using methods from robotic mapping literature. Our results indicate promising potential for such agricultural monitoring, where we leverage the superiority of social insects to sense the physical world, while providing data acquisition on par with explicitly engineered systems. 

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×.