11,282 research outputs found
Efficient Fast-Convolution-Based Waveform Processing for 5G Physical Layer
This paper investigates the application of fast-convolution (FC) filtering
schemes for flexible and effective waveform generation and processing in the
fifth generation (5G) systems. FC-based filtering is presented as a generic
multimode waveform processing engine while, following the progress of 5G new
radio standardization in the Third-Generation Partnership Project, the main
focus is on efficient generation and processing of subband-filtered cyclic
prefix orthogonal frequency-division multiplexing (CP-OFDM) signals. First, a
matrix model for analyzing FC filter processing responses is presented and used
for designing optimized multiplexing of filtered groups of CP-OFDM physical
resource blocks (PRBs) in a spectrally well-localized manner, i.e., with narrow
guardbands. Subband filtering is able to suppress interference leakage between
adjacent subbands, thus supporting independent waveform parametrization and
different numerologies for different groups of PRBs, as well as asynchronous
multiuser operation in uplink. These are central ingredients in the 5G waveform
developments, particularly at sub-6-GHz bands. The FC filter optimization
criterion is passband error vector magnitude minimization subject to a given
subband band-limitation constraint. Optimized designs with different guardband
widths, PRB group sizes, and essential design parameters are compared in terms
of interference levels and implementation complexity. Finally, extensive coded
5G radio link simulation results are presented to compare the proposed approach
with other subband-filtered CP-OFDM schemes and time-domain windowing methods,
considering cases with different numerologies or asynchronous transmissions in
adjacent subbands. Also the feasibility of using independent transmitter and
receiver processing for CP-OFDM spectrum control is demonstrated
A machine learning route between band mapping and band structure
The electronic band structure (BS) of solid state materials imprints the
multidimensional and multi-valued functional relations between energy and
momenta of periodically confined electrons. Photoemission spectroscopy is a
powerful tool for its comprehensive characterization. A common task in
photoemission band mapping is to recover the underlying quasiparticle
dispersion, which we call band structure reconstruction. Traditional methods
often focus on specific regions of interests yet require extensive human
oversight. To cope with the growing size and scale of photoemission data, we
develop a generic machine-learning approach leveraging the information within
electronic structure calculations for this task. We demonstrate its capability
by reconstructing all fourteen valence bands of tungsten diselenide and
validate the accuracy on various synthetic data. The reconstruction uncovers
previously inaccessible momentum-space structural information on both global
and local scales in conjunction with theory, while realizing a path towards
integrating band mapping data into materials science databases
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