49,904 research outputs found
Artin Conjecture for p-adic Galois Representations of Function Fields
For a global function field K of positive characteristic p, we show that
Artin conjecture for L-functions of geometric p-adic Galois representations of
K is true in a non-trivial p-adic disk but is false in the full p-adic plane.
In particular, we prove the non-rationality of the geometric unit root
L-functions.Comment: Remove the condition 6|k in Lemma 3.8; final versio
Sidelobe Suppression for Capon Beamforming with Mainlobe to Sidelobe Power Ratio Maximization
High sidelobe level is a major disadvantage of the Capon beamforming. To
suppress the sidelobe, this paper introduces a mainlobe to sidelobe power ratio
constraint to the Capon beamforming. it minimizes the sidelobe power while
keeping the mainlobe power constant. Simulations show that the obtained
beamformer outperforms the Capon beamformer.Comment: 8 pages, 2 figure
Enhanced Compressive Wideband Frequency Spectrum Sensing for Dynamic Spectrum Access
Wideband spectrum sensing detects the unused spectrum holes for dynamic
spectrum access (DSA). Too high sampling rate is the main problem. Compressive
sensing (CS) can reconstruct sparse signal with much fewer randomized samples
than Nyquist sampling with high probability. Since survey shows that the
monitored signal is sparse in frequency domain, CS can deal with the sampling
burden. Random samples can be obtained by the analog-to-information converter.
Signal recovery can be formulated as an L0 norm minimization and a linear
measurement fitting constraint. In DSA, the static spectrum allocation of
primary radios means the bounds between different types of primary radios are
known in advance. To incorporate this a priori information, we divide the whole
spectrum into subsections according to the spectrum allocation policy. In the
new optimization model, the minimization of the L2 norm of each subsection is
used to encourage the cluster distribution locally, while the L0 norm of the L2
norms is minimized to give sparse distribution globally. Because the L0/L2
optimization is not convex, an iteratively re-weighted L1/L2 optimization is
proposed to approximate it. Simulations demonstrate the proposed method
outperforms others in accuracy, denoising ability, etc.Comment: 23 pages, 6 figures, 4 table. arXiv admin note: substantial text
overlap with arXiv:1005.180
Sidelobe Suppression for Robust Beamformer via The Mixed Norm Constraint
Applying a sparse constraint on the beam pattern has been suggested to
suppress the sidelobe of the minimum variance distortionless response (MVDR)
beamformer recently. To further improve the performance, we add a mixed norm
constraint on the beam pattern. It matches the beam pattern better and
encourages dense distribution in mainlobe and sparse distribution in sidelobe.
The obtained beamformer has a lower sidelobe level and deeper nulls for
interference avoidance than the standard sparse constraint based beamformer.
Simulation demonstrates that the SINR gain is considerable for its lower
sidelobe level and deeper nulling for interference, while the robustness
against the mismatch between the steering angle and the direction of arrival
(DOA) of the desired signal, caused by imperfect estimation of DOA, is
maintained too.Comment: 10 pages, 3 figures; accepted by Wireless Personal Communication
A Robust Beamformer Based on Weighted Sparse Constraint
Applying a sparse constraint on the beam pattern has been suggested to
suppress the sidelobe level of a minimum variance distortionless response
(MVDR) beamformer. In this letter, we introduce a weighted sparse constraint in
the beamformer design to provide a lower sidelobe level and deeper nulls for
interference avoidance, as compared with a conventional MVDR beamformer. The
proposed beamformer also shows improved robustness against the mismatch between
the steering angle and the direction of arrival (DOA) of the desired signal,
caused by imperfect estimation of DOA.Comment: 4 pages, 2 figure
Geodesic-Einstein metrics and nonlinear stabilities
In this paper, we introduce notions of nonlinear stabilities for a relative
ample line bundle over a holomorphic fibration and define the notion of a
geodesic-Einstein metric on this line bundle, which generalize the classical
stabilities and Hermitian-Einstein metrics of holomorphic vector bundles. We
introduce a Donaldson type functional and show that this functional attains its
absolute minimum at geodesic-Einstein metrics, and we also discuss the
relations between the existence of geodesic-Einstein metrics and the nonlinear
stabilities of the line bundle. As an application, we will prove that a
holomorphic vector bundle admits a Finsler-Einstein metric if and only if it
admits a Hermitian-Einstein metric, which answers a problem posed by S.
Kobayashi.Comment: 21 pages, the final version, to appear in Transactions of the
American Mathematical Societ
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