A Discrete-Time Mixing Receiver Architecture with Wideband Harmonic Rejection

A discrete-time mixing architecture for software-defined radio receivers exploits 8 RF voltage oversampling followed by charge-domain weighting to achieve 40dB 3rd and 5th harmonic rejection without channel bandwidth limitations. Noise folding is also reduced by 3dB. A zero-IF downconverter chip in 65nm CMOS can receive RF signals up to 900MHz, with NFmin=12dB, IIP3=11dBm at <20mW power consumption including multi-phase clock generation

A Software-Defined Radio Receiver Architecture Robust to Out-of-Band Interference

In a software-defined radio (SDR) receiver it is desirable to minimize RF band-filtering for flexibility, size and cost reasons, but this leads to increased out-of-band interference (OBI). Besides harmonic and intermodulation distortion (HD/IMD), OBI can also lead to blocking and harmonic mixing. A wideband LNA [1, 2] amplifies signal and interference with equal gain. Even a low gain of 6dB can clip 0dBm OBI to a 1.2V supply, blocking the receiver. Hard-switching mixers not only translate the wanted signal to baseband but also the interference around LO harmonics. Harmonic rejection (HR) mixers have been used [3, 1, 4], but are sensitive to phase and gain mismatch. Indeed the HR in [4] shows a large spread, whereas other work only shows results from one chip [3, 1]. This paper describes techniques to relax blocking and HD/IMD, and make HR robust to mismatch

A 0.2-to-2.0GHz 65nm CMOS Receiver without LNA achieving >11dBm IIP3 and <6.5 dB NF

Spurious-free dynamic range (SFDR) is a key specification of radio receivers and spectrum analyzers, characterizing the maximum distance between signal and noise+distortion. SFDR is limited by the linearity (intercept point IIP3 mostly, sometimes IIP2) and the noise floor. As receivers already have low noise figure (NF) there is more room for improving the SFDR by increasing the linearity. As there is a strong relation between distortion and voltage swing, it is challenging to maintain or even improve linearity intercept points in future CMOS processes with lower supply voltages. Circuits can be linearized with feedback but loop gain at RF is limited [1]. Moreover, after LNA gain, mixer linearity becomes even tougher. If the amplification is postponed to IF, much more loop gain is available to linearize the amplifier. This paper proposes such an LNA-less mixer-first receiver. By careful analysis and optimization of a passive mixer core [2,3] for low conversion loss and low noise folding it is shown that it is possible to realize IIP3≫11dBm and NF≪6.5dB, i.e. a remarkably high SFDR≫79dB in 1MHz bandwidth over a decade of RF frequencies

Nuclear matter and neutron matter for improved quark mass density- dependent model with ρ\rho mesons

A new improved quark mass density-dependent model including u, d quarks, $\sigma$ mesons, $\omega$ mesons and $\rho$ mesons is presented. Employing this model, the properties of nuclear matter, neutron matter and neutron star are studied. We find that it can describe above properties successfully. The results given by the new improved quark mass density- dependent model and by the quark meson coupling model are compared.Comment: 18 pages, 7 figure

Nuclear symmetry potential in the relativistic impulse approximation

Using the relativistic impulse approximation with the Love-Franey \textsl{NN} scattering amplitude developed by Murdock and Horowitz, we investigate the low-energy (100 MeV$\leq E_{\mathrm{kin}}\leq 400$ MeV) behavior of the nucleon Dirac optical potential, the Schr\"{o}dinger-equivalent potential, and the nuclear symmetry potential in isospin asymmetric nuclear matter. We find that the nuclear symmetry potential at fixed baryon density decreases with increasing nucleon energy. In particular, the nuclear symmetry potential at saturation density changes from positive to negative values at nucleon kinetic energy of about 200 MeV. Furthermore,the obtained energy and density dependence of the nuclear symmetry potential is consistent with those of the isospin- and momentum-dependent MDI interaction with $x=0$, which has been found to describe reasonably both the isospin diffusion data from heavy-ion collisions and the empirical neutron-skin thickness of $^{208}$Pb.Comment: 8 pages, 5 figures, revised version to appear in PR

Transition Density and Pressure at the Inner Edge of Neutron Star Crusts

Using the nuclear symmetry energy that has been recently constrained by the isospin diffusion data in intermediate-energy heavy ion collisions, we have studied the transition density and pressure at the inner edge of neutron star crusts, and they are found to be 0.040 fm$^{-3}$ $\leq \rho_{t}\leq 0.065$ fm$^{-3}$ and 0.01 MeV/fm$^{3}$ $\leq P_{t}\leq 0.26$ MeV/fm$^{3}$, respectively, in both the dynamical and thermodynamical approaches. We have also found that the widely used parabolic approximation to the equation of state of asymmetric nuclear matter gives significantly higher values of core-crust transition density and pressure, especially for stiff symmetry energies. With these newly determined transition density and pressure, we have obtained an improved relation between the mass and radius of neutron stars.Comment: 7 pages, 3 figures, proceeding of "The International Workshop on Nuclear Dynamics in Heavy-Ion Reactions and the Symmetry Energy (IWND2009)

Interpretable neural architecture search via Bayesian optimisation with Weisfeiler-Lehman kernels

Current neural architecture search (NAS) strategies focus only on finding a single, good, architecture. They offer little insight into why a specific network is performing well, or how we should modify the architecture if we want further improvements. We propose a Bayesian optimisation (BO) approach for NAS that combines the Weisfeiler-Lehman graph kernel with a Gaussian process surrogate. Our method optimises the architecture in a highly data-efficient manner: it is capable of capturing the topological structures of the architectures and is scalable to large graphs, thus making the high-dimensional and graph-like search spaces amenable to BO. More importantly, our method affords interpretability by discovering useful network features and their corresponding impact on the network performance. Indeed, we demonstrate empirically that our surrogate model is capable of identifying useful motifs which can guide the generation of new architectures. We finally show that our method outperforms existing NAS approaches to achieve the state of the art on both closed- and open-domain search spaces