62 research outputs found
Optimization Methods for Designing Sequences with Low Autocorrelation Sidelobes
Unimodular sequences with low autocorrelations are desired in many
applications, especially in the area of radar and code-division multiple access
(CDMA). In this paper, we propose a new algorithm to design unimodular
sequences with low integrated sidelobe level (ISL), which is a widely used
measure of the goodness of a sequence's correlation property. The algorithm
falls into the general framework of majorization-minimization (MM) algorithms
and thus shares the monotonic property of such algorithms. In addition, the
algorithm can be implemented via fast Fourier transform (FFT) operations and
thus is computationally efficient. Furthermore, after some modifications the
algorithm can be adapted to incorporate spectral constraints, which makes the
design more flexible. Numerical experiments show that the proposed algorithms
outperform existing algorithms in terms of both the quality of designed
sequences and the computational complexity
Sparse Filter Design Under a Quadratic Constraint: Low-Complexity Algorithms
This paper considers three problems in sparse filter design, the first involving a weighted least-squares constraint on the frequency response, the second a constraint on mean squared error in estimation, and the third a constraint on signal-to-noise ratio in detection. The three problems are unified under a single framework based on sparsity maximization under a quadratic performance constraint. Efficient and exact solutions are developed for specific cases in which the matrix in the quadratic constraint is diagonal, block-diagonal, banded, or has low condition number. For the more difficult general case, a low-complexity algorithm based on backward greedy selection is described with emphasis on its efficient implementation. Examples in wireless channel equalization and minimum-variance distortionless-response beamforming show that the backward selection algorithm yields optimally sparse designs in many instances while also highlighting the benefits of sparse design.Texas Instruments Leadership University Consortium Progra
New Spectrally Constrained Sequence Sets With Optimal Periodic Cross-Correlation
Spectrally constrained sequences (SCSs) play an important role in modern communication and radar systems operating over non-contiguous spectrum. Despite numerous research attempts over the past years, very few works are known on the constructions of optimal SCSs with low cross-correlations. In this paper, we address such a major problem by introducing a unifying framework to construct unimodular SCS families using circular Florentine rectangles (CFRs) and interleaving techniques. By leveraging the uniform power allocation in the frequency domain for all the admissible carriers (a necessary condition for beating the existing periodic correlation lower bound of SCSs), we present a tighter correlation lower bound and show that it is achievable by our proposed SCS families including multiple SCS sets with zero correlation zone properties
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