Optimization Methods for Designing Sequences With Low Autocorrelation Sidelobes

By Junxiao Song, Prabhu Babu, D. P. Palomar. IEEE Transactions on Signal Processing, 2014

341 citations recorded by the source corpus.

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.

Subfields: Aerospace Engineering, Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/4ee563d6aac174dddedee203ebfd5820e5a976e5

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Citations in the corpus

Enabling Secure Wireless Communications via Intelligent Reflecting Surfaces (2019, background): “Proof: Because of the convexity, we have the following inequality where c is a constant and ( b ) applies [16, Lemma 1].”
Majorization-Minimization Algorithms in Signal Processing, Communications, and Machine Learning (2017, methodology): “Therefore, we can apply inequality (25) twice with M being a scaled identity matrix, yielding a linear surrogate function that has a closed-form minimizer in X [31].”
Asymptotic Max-Min SINR Analysis of Reconfigurable Intelligent Surface Assisted MISO Systems (2019, background): “alue of A. Since rank(ggH) = 1, so, ~v = g: (25) To ﬁnd the v that satisﬁes the constraint in (23b), we solve the following problem: (P5) min jvnj=1;n=1;:::;N j~v vj2 (26a) The solution of (P5) is [24], v = exp(jarg(~v)): (27) A similar problem has been solved in [5] that deals with a LIS-assisted s”
Reconfigurable Intelligent Surfaces for Energy Efficiency in Wireless Communication (2018, methodology): “Moreover, upon convergence, the ﬁrst-order optimality properties of problem (25) are satisﬁed [10], [52]–[55].”