Hans Hallen

Long-range prediction of fading signals

It was previously proposed to adapt several transmission methods, including modulation, power control, channel coding, and antenna diversity to rapidly time variant fading channel conditions. Prediction of the channel coefficients several tens-to-hundreds of symbols ahead is essential to realize these methods in practice. We describe a novel adaptive long-range fading channel prediction algorithm (LRP) and its utilization with adaptive transmission methods. The LRP is validated for standard stationary fading models and tested with measured data and with data produced by our novel realistic physical channel model. Both numerical and simulation results show that long-range prediction makes adaptive transmission techniques feasible for mobile radio channels.

Scanning Hall probe microscopy

We describe the implementation of a scanning Hall probe microscope of outstanding magnetic field sensitivity (∼0.1 G) and unprecedented spatial resolution (∼0.35 μm) to detect surface magnetic fields at close proximity to a sample. Our microscope combines the advantages of a submicron Hall probe fabricated on a GaAs/Al0.3Ga0.7As heterostructure chip and the scanning tunneling microscopy technique for precise positioning. We demonstrate its usefulness by imaging individual vortices in high Tc La1.85Sr0.15CuO4 films and superconducting networks, and magnetic bubble domains.

Raman imaging with near‐field scanning optical microscopy

Raman spectroscopy in conjunction with near‐field scanning optical microscopy is used to image Rb‐doped KTiOPO4 within a spectral feature with high spatial resolution. We present Raman spectra as well as the first Raman images obtained in the near field. Differences between near‐field and far‐field Raman measurements are discovered and discussed.

Origins and effects of thermal processes on near‐field optical probes

An aluminum‐coated tapered fiber probe, as used in near‐field scanning optical microscopy (NSOM), is heated by the light coupled into it. This can destroy the probe or may modify the sample, which can be problematic or used as a tool. To study these thermal effects, we couple modulated visible light of various power through probes. Simultaneously coupled infrared light senses the thermal effects. We report their magnitude, their spatial and temporal scales, and real‐time probe damage observations. A model describes the experimental data, the mechanisms for induced IR variation, and their relative importance.

Nano-Raman Spectroscopy and Imaging with a Near-Field Scanning Optical Microscope

Raman spectroscopy was performed using a near-field scanning optical microscope. This avoids the diffraction limit inherent in conventional optical microscopy techniques involving far-field optical components, and allows volumes significantly smaller than the cube of the wavelength to be investigated. The small sample volume coupled with the light-starved nature of the Raman effect itself makes such nano-Raman studies difficult. A near-field Raman microscope is described and results showing near-field effects in an investigation of Rb-doped KTP are presented. An image taken within a Raman feature demonstrates that nano-Raman imaging is indeed possible if the near-field instrument has considerable long-term stability, and that several unique aspects of the near-field data recommend this approach.

Long range prediction and reduced feedback for mobile radio adaptive OFDM systems

Adaptive orthogonal frequency division multiplexing (AOFDM) modulation is a promising technique for achieving high data rates required for wireless multimedia services. To accomplish efficient adaptive channel loading, the channel state information (CSI) needs to be fed back to the transmitter. Since the fading channel varies rapidly for fast vehicle speeds, long range fading prediction (LRP) is required for mobile radio AOFDM to insure reliable adaptation. We use past channel observations to predict future CST and perform adaptive bit and power allocation for the OFDM system. We derive the minimum mean-square-error (MMSE) long-range channel prediction that utilizes the time and frequency domain correlation functions of the Rayleigh fading channel. Since the channel statistics are usually unknown, robust prediction methods that do not require the knowledge of the correlation functions are developed. Statistical model of the prediction error is created and used in the design of reliable adaptive modulation. In addition, several methods that significantly reduce the feedback load for mobile radio AOFDM systems are developed and compared. We use a standard sum-of-sinusoids model and our realistic physical model to validate performance of proposed methods. Simulation results demonstrate reliable performance and robustness of the proposed techniques, thus validating feasibility of AOFDM for rapidly varying mobile radio channels

Reliable adaptive modulation aided by observations of another fading channel

Adaptive transmission techniques, such as adaptive modulation and coding, adaptive power control, adaptive transmitter antenna diversity, etc., generally require precise channel estimation and feedback of channel state information (CSI). For fast vehicle speeds, reliable adaptive transmission also requires long-range prediction of future CSI, since the channel conditions are rapidly time variant. In this paper, we propose using past channel observations of one carrier to predict future CSI and perform adaptive modulation without feedback for another correlated carrier. We derive the minimum mean-square error (MMSE) long-range channel prediction that uses the time- and frequency-domain correlation function of the Rayleigh fading channel. An adaptive MMSE prediction method is also proposed. A statistical model of the prediction error that depends on the frequency and time correlation is developed and is used in the design of reliable adaptive modulation methods. We use a standard stationary fading channel model (Jakes model) and a novel physical channel model to test our algorithm. Significant gains relative to nonadaptive techniques are demonstrated for sufficiently correlated channels and realistic prediction range.

Surface enhancement in near-field Raman spectroscopy

The intensity and selection rules of Raman spectra change as a metal surface approaches the sample. We study the distance dependence of the new Raman modes with a near-field scanning optical microscope (NSOM). The metal-coated NSOM probe provides localized illumination of a metal surface with good distance control. Spectra are measured as the probe approaches the surface, and the changes elucidated with difference spectra. Comparisons to a theoretical model for Raman excitation by evanescent light near the probe tip indicate that while the general trends are well described, the data show oscillations about the model.

Scanning Hall-Probe Microscopy of a Vortex and Field Fluctuations in La1.85Sr0.15CuO4 Films

A high-resolution scanning Hall-probe microscope is used to spatially resolve vortices in high-temperature superconducting La1.85Sr0.15CuO4 films. At low magnetic fields, a disordered vortex arrangement is observed. A fit to the surface field of an individual vortex is consistent with one flux quantum, and is used to determine the local penetration depth and its temperature dependence. At higher fields, magnetic fluctuations are observed and compared to a collective pinning model. For films grown with the c-axis tilted from the surface normal, oval vortices are observed.

A physical model for wireless channels to provide insights for long range prediction

Algorithms that predict the wireless channel for up to a few wavelengths cannot be adequately tested with stationary models. Ray-tracing or FDTD methods do not provide insights into the relationship between reflector configurations and the performance of long-range prediction. Therefore, we present a novel model that: (1) creates non-stationary datasets to test our previously proposed adaptive long range prediction algorithm, which enables practical realization of adaptive transmission techniques; (2) classifies the reflector geometries that have typical or most severe parameter variations, so that the reflector configurations for test datasets can be appropriately chosen; (3) provides limits on the speed of adaptation needed for an algorithm to predict the channel significantly into the future, and thereby reveal the timing of future deep fades, etc.; (4) illuminates the origins of the temporal and statistical properties of measured data. The algorithm performs similarly on channels given by the physical model or actual measured data, but differently on a channel simulated by the stationary Jakes model. The insights of the model accurately describe the performance of the algorithm in several scattering environments when prediction is employed with adaptive power control and adaptive modulation. Moreover, we study limits of the long-range prediction at frequencies other than the observed frequency, of importance in correlated uplink and downlink transmission, orthogonal frequency division multiplexing (OFDM) and frequency-hopping systems.