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Background: Blood pressure (BP) is an essential indicator for human health and is known to be greatly influenced by lifestyle factors, like activity and sleep factors. However, the degree of impact of each lifestyle factor on BP is unknown and may vary between individuals. Our goal is to investigate the relationships between BP and lifestyle factors and provide personalized and precise recommendations to improve BP, as opposed to the current practice of general lifestyle recommendations. Method: Our proposed system consists of automated data collection using home BP monitors and wearable activity trackers and feature engineering techniques to address time-series data and enhance interpretability. We propose Random Forest with Shapley-Value-based Feature Selection to offer personalized BP modeling and top lifestyle factor identification, and subsequent generation of precise recommendations based on the top factors. Result: In collaboration with UC San Diego Health and Altman Clinical and Translational Research Institute, we performed a clinical study, applying our system to 25 patients with elevated BP or stage I hypertension for three consecutive months. Our study results validate our system’s ability to provide accurate personalized BP models and identify the top features which can vary greatly between individuals. We also validate the effectiveness of personalized recommendations in a randomized controlled experiment. After receiving recommendations, the subjects in the experimental group decreased their BPs by 3.8 and 2.3 for systolic and diastolic BP, compared to the decrease of 0.3 and 0.9 for the subjects without recommendations. Conclusion: The study demonstrates the potential of using wearables and machine learning to develop personalized models and precise lifestyle recommendations to improve BP.

Magnetic nanoparticles have garnered sustained research interest for their promise in biomedical applications including diagnostic imaging, triggered drug release, cancer hyperthermia, and neural stimulation. Many of these applications make use of heat dissipation by ferrite nanoparticles under alternating magnetic fields, with these fields acting as an externally administered stimulus that is either present or absent, toggling heat dissipation on and off. Here, we motivate and demonstrate an extension of this concept, magnetothermal multiplexing, in which exposure to alternating magnetic fields of differing amplitude and frequency can result in selective and independent heating of magnetic nanoparticle ensembles. The differing magnetic coercivity of these particles, empirically characterized by a custom high amplitude alternating current magnetometer, informs the systematic selection of a multiplexed material system. This work culminates in a demonstration of magnetothermal multiplexing for selective remote control of cellular signaling in vitro.

To sustainably provide low-latency communication and edge computing for connected vehicles, a promising solution is using Solar-powered Roadside Units (SRSUs), which consist of small cell base stations and Mobile Edge Computing servers. However, due to the intermittent nature of solar power, SRSUs may suffer from a high risk of power deficiency, which will lead to severe disruption of vehicular edge computing applications. In this paper, we aim to address this challenge of Quality of Service (QoS) loss (i.e., edge computing service outage for vehicle users (VUs)). We formulate a QoS optimization problem for VUs and solve it in two phases: an offline solar energy scheduling phase, and an online user association and SRSU resource allocation phase. We simulate our proposed technique in a dense SRSU network environment with real-world urban vehicular traffic data and solar generation profile. The simulation results show that our proposed approach can significantly reduce QoS loss of vehicular edge computing applications using SRSUs, compared to existing techniques. Further, the results are beneficial to service providers and city planners to identify adequate SRSU configurations for expected solar energy generation and edge computing service demands.

Various physical stimulation methods are developed to minimize the invasiveness of deep brain stimulation (DBS)1–3. Among them, only magnetic field can penetrate into the biological tissues without scattering or absorption4, which makes it ideal for untethered DBS. Recently developed magnetogenetics have shown the potential of developing treatments for neurological disorders5. However, magnetogenetic approaches have potential side effects from overexpression of exogenous ion channels and gene delivery with viral vectors6,7. Here, we demonstrated that the iron oxide magnetic nanodiscs (~270 nm) can be used as transducers to trigger calcium responses in the wild-type cultured neurons during the application of slow varying weak magnetic fields (50 mT at 10 Hz). Moreover, we identified that the intrinsic mechanosensitive ion channel transient receptor potential canonical (TRPC), which were widely expressed in the brain8, plays the main roles in this magnetomechanical stimulation. Finally, when we applied magnetic fields to the awake mice with magnetic nanodiscs injecting into subthalamic nucleus, the magnetomechanical stimulation triggered neuronal activities in the targeted region and the downstream region. Overall, this research demonstrated a magnetomechanical approach that can be used for wireless neuronal stimulation in vitro and untethered DBS in awake mice in vivo without implants or genetic manipulation.

Connecting neural circuit output to behaviour can be facilitated by the precise chemical manipulation of specific cell populations1,2. Engineered receptors exclusively activated by designer small molecules enable manipulation of specific neural pathways3,4. However, their application to studies of behaviour has thus far been hampered by a trade-off between the low temporal resolution of systemic injection versus the invasiveness of implanted cannulae or infusion pumps2. Here, we developed a remotely controlled chemomagnetic modulation—a nanomaterials-based technique that permits the pharmacological interrogation of targeted neural populations in freely moving subjects. The heat dissipated by magnetic nanoparticles (MNPs) in the presence of alternating magnetic fields (AMFs) triggers small-molecule release from thermally sensitive lipid vesicles with a 20 s latency. Coupled with the chemogenetic activation of engineered receptors, this technique permits the control of specific neurons with temporal and spatial precision. The delivery of chemomagnetic particles to the ventral tegmental area (VTA) allows the remote modulation of motivated behaviour in mice. Furthermore, this chemomagnetic approach activates endogenous circuits by enabling the regulated release of receptor ligands. Applied to an endogenous dopamine receptor D1 (DRD1) agonist in the nucleus accumbens (NAc), a brain area involved in mediating social interactions, chemomagnetic modulation increases sociability in mice. By offering a temporally precise control of specified ligand–receptor interactions in neurons, this approach may facilitate molecular neuroscience studies in behaving organisms. Controlled delivery of neuromodulators in the brain might improve the understanding of the molecular basis of behaviour. In this letter, magnetic liposomes injected in deep brain regions release small molecules under remote magnetic stimulation, activating specific neuronal circuits in freely moving mice.

Deep brain stimulation (DBS) has long been used to alleviate symptoms in patients suffering from psychiatric and neurological disorders through stereotactically implanted electrodes that deliver current to subcortical structures via wired pacemakers. The application of DBS to modulate neural circuits is, however, hampered by its mechanical invasiveness and the use of chronically implanted leads, which poses a risk for hardware failure, hemorrhage, and infection. Here, we demonstrate that a wireless magnetothermal approach to DBS (mDBS) can provide similar therapeutic benefits in two mouse models of Parkinson’s disease, the bilateral 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) and in the unilateral 6-hydroxydopamine (6-OHDA) model. We show magnetothermal neuromodulation in untethered moving mice through the activation of the heat-sensitive capsaicin receptor (transient receptor potential cation channel subfamily V member 1, TRPV1) by synthetic magnetic nanoparticles. When exposed to an alternating magnetic field, the nanoparticles dissipate heat, which triggers reversible firing of TRPV1-expressing neurons. We found that mDBS in the subthalamic nucleus (STN) enables remote modulation of motor behavior in healthy mice. Moreover, mDBS of the STN reversed the motor deficits in a mild and severe parkinsonian model. Consequently, this approach is able to activate deep-brain circuits without the need for permanently implanted hardware and connectors., Nature Communications, 12 (1), ISSN:2041-1723

To reversibly manipulate neural circuits with increased spatial and temporal control, photoswitchable ligands can add an optical switch to a target receptor or signaling cascade. This approach, termed photopharmacology, has been enabling to molecular neuroscience, however, its application to behavioral experiments has been impeded by a lack of integrated hardware capable of delivering both light and compounds to deep brain regions in moving subjects. Here, we devise a hybrid photochemical genetic approach to target neurons using a photoswitchable agonist of capsaicin receptor (TRPV1), red-AzCA-4. Using the thermal drawing process we created multifunctional fibers that can deliver viruses, photoswitchable ligands, and light to deep brain regions in awake, freely moving mice. We implanted our fibers into the ventral tegmental area (VTA), a midbrain hub of the mesolimbic pathway, and used them to deliver a transgene coding for TRPV1. This sensitized excitatory VTA neurons to red-AzCA-4, and allowed us to optically control conditioned place preference using a mammalian ion-channel, thus extending applications of photopharmacology to behavioral experiments. Applied to endogenous receptors, our approach may accelerate studies of molecular mechanisms underlying animal behavior.

Magnetic nanomaterials in magnetic fields can serve as versatile transducers for remote interrogation of cell functions. In this study, we leveraged the transition from vortex to in-plane magnetization in iron oxide nanodiscs to modulate the activity of mechanosensory cells. When a vortex configuration of spins is present in magnetic nanomaterials, it enables rapid control over their magnetization direction and magnitude. The vortex configuration manifests in near zero net magnetic moment in the absence of a magnetic field, affording greater colloidal stability of magnetic nanomaterials in suspensions. Together, these properties invite the application of magnetic vortex particles as transducers of externally applied minimally invasive magnetic stimuli in biological systems. Using magnetic modeling and electron holography, we predict and experimentally demonstrate magnetic vortex states in an array of colloidally synthesized magnetite nanodiscs 98–226 nm in diameter. The magnetic nanodiscs applied as transducers of torque for remote control of mechanosensory neurons demonstrated the ability to trigger Ca(2+) influx in weak (≤28 mT), slowly varying (≤5 Hz) magnetic fields. The extent of cellular response was determined by the magnetic nanodisc volume and magnetic field conditions. Magnetomechanical activation of a mechanosensitive cation channel TRPV4 (transient receptor potential vanilloid family member 4) exogenously expressed in the non-mechanosensitive HEK293 cells corroborated that the stimulation is mediated by mechanosensitive ion channels. With their large magnetic torques and colloidal stability, magnetic vortex particles may facilitate basic studies of mechanoreception and its applications to control electroactive cells with remote magnetic stimuli.

Understanding and modulating proton-mediated biochemical processes in living organisms have been impeded by the lack of tools to control local pH. Here, we design nanotransducers capable of converting non-invasive alternating magnetic fields (AMFs) into protons in physiological environments by combining magnetic nanoparticles (MNPs) with polymeric scaffolds. When exposed to AMFs, the heat dissipated by MNPs triggered a hydrolytic degradation of surrounding polyanhydride or polyester, releasing protons into the extracellular space. pH changes induced by these nanotransducers can be tuned by changing the polymer chemistry or AMF stimulation parameters. Remote magnetic control of local protons was shown to trigger acid-sensing ion channels and evoke intracellular calcium influx in neurons. By offering a wireless modulation of local pH, our approach can accelerate the mechanistic investigation of the role of protons in biochemical signalling in the nervous system.

Renewable energy (RE) is a promising solution to reduce grid energy consumption and carbon dioxide emissions of cellular networks. However, the benefit of utilizing RE is limited by its highly intermittent and unreliable nature, resulting in low savings in grid energy. To minimize the grid energy cost, we propose to utilize data buffer of user equipments as well as energy storage at the base station (BS) to better adapt the BS resource allocation and hence its energy consumption to the dynamic nature of RE. We consider the scenario of downlink orthogonal frequency division multiple access networks with non-ideal hybrid energy supply. To jointly optimize the energy consumption and the quality of service (QoS) of users, we adopt the weighted sum of users’ utility of data rates and grid energy consumption as our performance metric. We propose a low-complexity online control scheme based on Lyapunov optimization framework. The proposed technique can provide asymptotically optimal performance bound without requiring the stochastic distribution information of RE arrival and channel state condition. The experimental results demonstrate the ability of the proposed approach to significantly improve the performance in terms of grid power consumption and user QoS compared with the existing schemes.

In this work, we demonstrate the application of anisotropic magnetite nanodiscs (MNDs) as transducers of torque to mechanosensory cells under weak, slowly varying magnetic fields (MFs). These MNDs possess a ground state vortex configuration of magnetic spins which affords greater colloidal stability due to eliminated dipole-dipole interactions characteristic of isotropic magnetic particles of similar size. We first predict vortex magnetization using micromagnetic stimulations in sub-micron anisotropic magnetite particles and then use electron holography to experimentally investigate the magnetization of MNDs 98–226 nm in diameter. When MNDs are coupled to MFs, they transition between vortex and in-plane magnetization allowing for the exertion of the torque on the pN scale, which is sufficient to activate mechanosensitive ion channels in cell membranes.

In this work, we demonstrate the application of anisotropic magnetite nanodiscs (MNDs) as transducers of torque to mechanosensory cells under weak, slowly varying magnetic fields (MFs). These MNDs possess a ground state vortex configuration of magnetic spins which affords greater colloidal stability due to eliminated dipole-dipole interactions characteristic of isotropic magnetic particles of similar size. We first predict vortex magnetization using micromagnetic stimulations in sub-micron anisotropic magnetite particles and then use electron holography to experimentally investigate the magnetization of MNDs 98–226 nm in diameter. When MNDs are coupled to MFs, they transition between vortex and in-plane magnetization allowing for the exertion of the torque on the pN scale, which is sufficient to activate mechanosensitive ion channels in cell membranes.

Blood pressure (BP) is the most important indicator of cardiovascular diseases. Traditional cuff-based methods for measuring BP require manual intervention and time. These methods may lead to inaccurate measurements and are not practical for continuous BP monitoring, which is crucial for detecting abnormal BP fluctuations. In this study, we propose a personalized machine learning model to estimate BP using his/her previous BPs and the photoplethysmogram (PPG) signal, the simplest and most popular tool for non-invasive diagnosis. To best utilize the information contained in the PPG signal, we propose to apply wavelet decomposition to extract features from the PPG signal. The arterial blood pressure (ABP) time series is processed with an exponentially weighted moving average (EWMA) and a peak detection technique to derive the SBP, DBP, and their corresponding trends. Finally, a random forest model is used to construct a predictive model based on these features. The MIMIC dataset is used for analysis and comparison with other BP estimation methods. Our experimental results demonstrate the proposed approach has smaller estimation error than existing methods, with mean average errors (MAE) for SBP and DBP equal to 3.43 and 1.73, respectively.

Remote magnetic modulation of adrenal cell calcium in the presence of nanoparticles enables on-demand hormone release., The field of bioelectronic medicines seeks to modulate electrical signaling within peripheral organs, providing temporally precise control of physiological functions. This is usually accomplished with implantable devices, which are often unsuitable for interfacing with soft and highly vascularized organs. Here, we demonstrate an alternative strategy for modulating peripheral organ function, which relies on the endogenous expression of a heat-sensitive cation channel, transient receptor potential vanilloid family member 1 (TRPV1), and heat dissipation by magnetic nanoparticles (MNPs) in remotely applied alternating magnetic fields. We use this approach to wirelessly control adrenal hormone secretion in genetically intact rats. TRPV1-dependent calcium influx into the cells of adrenal cortex and medulla is sufficient to drive rapid release of corticosterone and (nor)epinephrine. As altered levels of these hormones have been correlated with mental conditions such as posttraumatic stress disorder and major depression, our approach may facilitate the investigation of physiological and psychological impacts of stress.

In this paper, we focus on a prediction-based novelty estimation strategy upon the deep reinforcement learning (DRL) framework, and present a flow-based intrinsic curiosity module (FICM) to exploit the prediction errors from optical flow estimation as exploration bonuses. We propose the concept of leveraging motion features captured between consecutive observations to evaluate the novelty of observations in an environment. FICM encourages a DRL agent to explore observations with unfamiliar motion features, and requires only two consecutive frames to obtain sufficient information when estimating the novelty. We evaluate our method and compare it with a number of existing methods on multiple benchmark environments, including Atari games, Super Mario Bros., and ViZDoom. We demonstrate that FICM is favorable to tasks or environments featuring moving objects, which allow FICM to utilize the motion features between consecutive observations. We further ablatively analyze the encoding efficiency of FICM, and discuss its applicable domains comprehensively., The SOLE copyright holder is IJCAI (International Joint Conferences on Artificial Intelligence), all rights reserved. The link is provided as follows: https://www.ijcai.org/Proceedings/2020/286

Blood pressure (BP) is one of the most important indicator of human health. In this paper, we investigate the relationship between BP and health behavior (e.g. sleep and exercise). Using the data collected from off-the-shelf wearable devices and wireless home BP monitors, we propose a data driven personalized model to predict daily BP level and provide actionable insight into health behavior and daily BP. In the proposed machine learning model using Random Forest (RF), trend and periodicity features of BP time-series are extracted to improve prediction. To further enhance the performance of the prediction model, we propose RF with Feature Selection (RFFS), which performs RF-based feature selection to filter out unnecessary features. Our experimental results demonstrate that the proposed approach is robust to different individuals and has smaller prediction error than existing methods. We also validate the effectiveness of personalized recommendation of health behavior generated by RFFS model.

The use of solar photovoltaic (PV) power is a promising solution to reduce grid power consumption and carbon dioxide emissions. However, the benefit of utilizing solar PV power is limited by its highly intermittent and unreliable nature. The non-stationary and non-linear characteristic of solar irradiance makes solar PV difficult to predict by traditional time series and artificial intelligence (AI) approaches. To address the above challenges, we propose a novel technique integrating stationary wavelet transform (SWT) and random forest models. Instead of conventional decompose-and-reconstruct process in SWT, we only apply the wavelet decomposition to extract the information from raw data with better time-frequency resolutions. We also propose a bias-compensation technique to minimize the prediction error. Our experimental results using sensors data from the on-campus microgrid demonstrate the proposed approach is robust to different forecast time horizons and has smaller prediction error.

Microchannel scaffolds accelerate nerve repair by guiding growing neuronal processes across injury sites. Although geometry, materials chemistry, stiffness, and porosity have been shown to influence nerve growth within nerve guidance scaffolds, independent tuning of these properties in a high-throughput manner remains a challenge. Here we combine fiber drawing with salt leaching to produce microchannels with tunable cross-sections and porosity. Our technique is applicable to an array of biochemically inert polymers and delivers hundreds of meters of porous microchannel fibers. Employing these fibers as filaments during three-dimensional printing enables the production of microchannel scaffolds with geometries matching those of biological nerves, including branched topographies. Applied to sensory neurons, fiber-based porous microchannels enhance growth as compared to non-porous channels with matching materials and geometries. The combinatorial scaffold fabrication approach may advance the studies of neural regeneration and accelerate the development of nerve repair devices.

The Atacama Large Millimeter/submillimeter Array(ALMA) Band 1 receiver covers the 35-50 GHz frequency band. Development of prototype receivers, including the key components and subsystems has been completed and two sets of prototype receivers were fully tested. We will provide an overview of the ALMA Band 1 science goals, and its requirements and design for use on the ALMA. The receiver development status will also be discussed and the infrastructure, integration, evaluation of fully-assembled band 1 receiver system will be covered. Finally, a discussion of the technical and management challenges encountered will be presented.

ALMA covering 35-950 GHz is the largest existing telescope array in the world. Among the 10 receiver bands, Band-1, which covers 35-50 GHz, is the lowest. Due to its small dimension and its time-variant frequency-dependent gain characteristics, current solar filter located above the cryostat cannot be applied to Band-1 for solar observation. Here we thus adopt new strategies to fulfill the goals. Thanks to the flexible dc biasing scheme of the HEMT-based amplifier in Band-1 front-end, bias adjustment of the cryogenic low noise amplifier is investigated to accomplish solar observation without using solar filter. Large power handling range can be achieved by the de-tuning bias technique with little degradation in system performance.

GABAergic signaling in hippocampal pyramidal neurons undergoes a switch from depolarizing to hyperpolarizing during early neuronal development. Whether such a transformation of GABAergic action occurs in dentate granule cells (DGCs), located at the first stage of the hippocampal trisynaptic circuit, is unclear. Here, we use noninvasive extracellular recording to monitor the effect of synaptically released GABA on the DGC population. We find that GABAergic responses in adolescent and adult rat DGCs are still depolarizing from rest. Using a morphologically realistic DGC model, we show that GABAergic action, depending on its precise timing and location, can have either an excitatory or inhibitory role in signal processing in the dentate gyrus.

Cholinergic transmission through muscarinic acetylcholine receptors (mAChRs) plays a key role in cortical oscillations. Although fast-spiking (FS), parvalbumin-expressing basket cells (BCs) are proposed to be the cellular substrates of gamma oscillations, previous studies reported that FS nonpyramidal cells in neocortical areas are unresponsive to cholinergic modulation. Dentate gyrus (DG) is an independent gamma oscillator in the hippocampal formation. However, in contrast to other cortical regions, the direct impact of mAChR activation on FS BC excitability in this area has not been investigated. Here, we show that bath-applied muscarine or carbachol, two mAChR agonists, depolarize DG BCs in the acute brain slices, leading to action potential firing in the theta-gamma bands in the presence of blockers of ionotropic glutamate and gamma-aminobutyric acid type A receptors at physiological temperatures. The depolarizing action persists in the presence of tetrodotoxin, a voltage-gated Na(+) channel blocker. In voltage-clamp recordings, muscarine markedly reduces background K(+) currents. These effects are mimicked by oxotremorine methiodide, an mAChR-specific agonist, and largely reversed by atropine, a non-selective mAChR antagonist, or pirenzepine, an M(1) receptor antagonist, but not by gallamine, an M(2/4) receptor antagonist. Interestingly, in contrast to M(1)-receptor-mediated depolarization, M(2) receptor activation by the specific agonist arecaidine but-2-ynyl ester tosylate down-regulates GABA release at BC axons-the effect is occluded by gallamine, an M(2) receptor antagonist. Overall, muscarinic activation results in a net increase in phasic inhibitory output to the target cells. Thus, cholinergic activation through M(1)-like receptor enhances BC activity and promotes the generation of nested theta and gamma rhythms, thereby enhancing hippocampal function and associated performance.

A full-scale field study was carried out at a 795 MWe coal-fired power plant equipped with selective catalytic reduction (SCR), an electrostatic precipitator (ESP), and wet flue gas desulfurization (FGD) systems to investigate the distribution of selected trace elements (i.e., mercury, arsenic, selenium, boron, and chloride) from coal, FGD reagent slurry, makeup water to flue gas, solid byproduct, and wastewater streams. Flue gases were collected from the SCR outlet, ESP inlet, FGD inlet, and stack. Concurrent with flue gas sampling, coal, bottom ash, economizer ash, and samples from the FGD process were also collected for elemental analysis. By combining plant operation parameters, the overall material balances of selected elements were established. The removal efficiencies of As, Se, Hg, and B by the ESP unit were 88, 56, 17, and 8%, respectively. Only about 2.5% of Cl was condensed and removed from flue gas by fly ash. The FGD process removed over 90% of Cl, 77% of B, 76% of Hg, 30% of Se, and 5% of As...

The use of renewable energy (RE) sources is a promising solution to reduce grid power consumption and carbon dioxide emissions (CO2e) of cellular networks. However, the benefit of utilizing RE is limited by its highly intermittent and unreliable nature leading to mismatch between generation and base station (BS) needs, resulting in low savings in grid power. To address the above challenges, we propose a novel base station time resource allocation technique using data storage at user equipments (UEs) to optimally utilize renewable energy to reduce grid power consumption. The proposed approach transforms the surplus RE (in excess of the BS power requirements) to excess data delivered to the users and stored in UE data storages, to draw from in deficit periods (when RE generation is lesser than BS requirements) to reduce grid power. Though the proposed approach is applicable to any application that utilizes UE data buffer, we formulate the problem for mobile video and propose a algorithm for RE aware BS resource allocation during mobile video download, as the latter will dominate wireless traffic and hence BS power consumption. Our experimental results using sample solar and BS utilization traces demonstrate the ability of the proposed approach to reduce grid power consumption by increasing solar power utilization while satisfying user QoS requirements.

Genetic variants in the human ortholog of acid-sensing ion channel-1a subunit (ASIC1a) gene are associated with panic disorder and amygdala dysfunction. Both fear learning and activity-induced long-term potentiation (LTP) of cortico-basolateral amygdala (BLA) synapses are impaired in ASIC1a-null mice, suggesting a critical role of ASICs in fear memory formation. In this study, we found that ASICs were differentially expressed within the amygdala neuronal population and the extent of LTP at various glutamatergic synapses correlated with the level of ASIC expression in postsynaptic neurons. Importantly, selective deletion of ASIC1a in GABAergic cells, including amygdala output neurons, eliminated LTP in these cells and reduced fear learning to the same extent as that found when ASIC1a was selectively abolished in BLA glutamatergic neurons. Thus, fear learning requires ASIC-dependent LTP at multiple amygdala synapses, including both cortico-BLA input synapses and intra-amygdala synapses on output neurons.

A 206 ∼ 220 GHz varactor-free voltage-controlled oscillator (VCO) is proposed to improve the output power and tuning range. The topology of the VCO is push-push crosscoupled pair. By using body-bias technique, the parasitic capacitor of the cross-coupled pair can be adjusted, and the varactors in the conventional VCO design can be removed. The proposed VCO was designed and implemented in TSMC 65-nm CMOS technology with a chip size of 0.25 × 0.25 mm2. The tuning range is 6.6% (from 206.2 to 220 GHz). The measured phase noise is −66 dBc/Hz at 1 MHz offset frequency. The output power is 1.3 dBm at 216 GHz with 54-mW dc power consumption and its maximum dc-to-RF efficiency is 2.1%. To the best of our knowledge, this varactor-free VCO achieves the highest dc-to-RF efficiency among published CMOS VCOs around 200 GHz.

A heterogeneous network (HetNet), which consists of macro cells and small cells, is an effective tool to improve the system spectral efficiency by exploiting the benefit of spatial reuse. However, the benefit of HetNet is limited by the asymmetry of transmit power between macro base stations (BSs) and pico BSs. Traditional solutions lead to a tradeoff between received signal strength (RSS) and load balancing. To tackle this problem, we propose a novel mechanism with joint macro BS transmit power control and load-aware user association. As such, we can realize traffic offloading from the macro cell to pico cells in a two-tier HetNet and mitigate inter-cell interference (ICI) in the network. We formulate this problem as a nonlinear integer programming problem, and prove that it is NP-hard. We then propose a polynomial-time heuristic. The simulation results show that our algorithm is indeed an efficient mechanism with better network performance in terms of average throughput and fairness.

A 53-to-84 GHz wideband power amplifier (PA) is implemented in 65-nm GP CMOS technology. This PA exhibits a measured small signal gain of 29.1 ± 1.5 dB from 53 to 84 GHz and a P sat of 10.8 dBm, P 1dB of 7.2 dBm, power added efficiency (PAE) of 9.7% at 80 GHz at drain voltage of 1.2 V. The DC consumption is 123 mW.