The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Optimization Methods for Designing Sequences With Low Autocorrelation Sidelobes
Open accessAbstract
Unimodular sequences with low autocorrelation are desired in many applications, especially in radar systems and code-division multiple access (CDMA) communication systems. In this paper, we propose a new algorithm to design unimodular sequences with low autocorrelation via directly minimizing the integrated sidelobe level (ISL) of the autocorrelation. The algorithm is derived based on the general framework of majorization-minimization (MM) algorithms and thus shares the monotonic property of such methods, and two acceleration schemes have been considered to accelerate the overall convergence. In addition, the proposed 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 ones in terms of both the merit factors of designed sequences and the computational complexity.
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Cites
Two-Point Step Size Gradient Methods
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Convex Analysis and Optimization
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A Unified Convergence Analysis of Block Successive Minimization Methods for Nonsmooth Optimization
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Cognitive radar: a way of the future
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Signal Design for Good Correlation: For Wireless Communication, Cryptography, and Radar
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New Algorithms for Designing Unimodular Sequences With Good Correlation Properties
Stoica, P., He, H., & Li, J. (2009) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2009.2012562 · Cited by 475
Simple and Globally Convergent Methods for Accelerating the Convergence of Any EM Algorithm
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Decomposition by Partial Linearization: Parallel Optimization of Multi-Agent Systems
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Self-citationSieves for low autocorrelation binary sequences
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Cited by
Majorization-Minimization Algorithms in Signal Processing, Communications, and Machine Learning
Sun, Y., Babu, P., & Palomar, D. P. (2017) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2016.2601299 · Cited by 1,820
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Metadata
- Authors
- Song, J., Babu, P., & Palomar, D. P.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 2015-08
- Volume
- 63
- Issue
- 15
- Pages
- 3998-4009
- ISSN
- 1053-587X (ISSN-L)
- 1941-0476
- OpenAlex
- W2962834015
- Cited by
- 382
- References
- 16
- FWCI
- 42.77
- Type
- journal article
- Language
- en
- Open access
- green
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