Array spatial thinning for interference mitigation by semidefinite programming. Nosrati, H., Aboutanios, E., & Smith, D. B. In 2017 25th European Signal Processing Conference (EUSIPCO), pages 2230-2234, Aug, 2017.
Array spatial thinning for interference mitigation by semidefinite programming [pdf]Paper  doi  abstract   bibtex   
We study the problem of interference mitigation in a phased array, where a subset containing k out of a total of N receivers creates a virtual spatial null for an incoming interference. The signal-of-interest and interference are represented by their corresponding steering vectors, and an optimum subarray is chosen such that the two vectors are as orthogonal as possible. This optimization is a binary quadratic non-convex minimization. We propose a semidefinite programming method to find suboptimal solutions using an optimal randomized sampling strategy. We show that the proposed method provides solutions as good as an exhaustive search with a cubic computational complexity. Furthermore, the proposed algorithm outperforms existing methods by solving the problem in a higher dimensionality.
@InProceedings{8081606,
  author = {H. Nosrati and E. Aboutanios and D. B. Smith},
  booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
  title = {Array spatial thinning for interference mitigation by semidefinite programming},
  year = {2017},
  pages = {2230-2234},
  abstract = {We study the problem of interference mitigation in a phased array, where a subset containing k out of a total of N receivers creates a virtual spatial null for an incoming interference. The signal-of-interest and interference are represented by their corresponding steering vectors, and an optimum subarray is chosen such that the two vectors are as orthogonal as possible. This optimization is a binary quadratic non-convex minimization. We propose a semidefinite programming method to find suboptimal solutions using an optimal randomized sampling strategy. We show that the proposed method provides solutions as good as an exhaustive search with a cubic computational complexity. Furthermore, the proposed algorithm outperforms existing methods by solving the problem in a higher dimensionality.},
  keywords = {antenna phased arrays;array signal processing;computational complexity;concave programming;interference suppression;matrix algebra;minimisation;signal sampling;steering vectors;signal-of-interest;incoming interference;virtual spatial null;phased array;interference mitigation;array spatial thinning;optimal randomized sampling strategy;semidefinite programming method;binary quadratic nonconvex minimization;optimum subarray;Signal processing algorithms;Correlation;Phased arrays;Programming;Interference;Minimization;Optimization;Array thinning;antenna selection;binary quadratic constrained programming;semidefinite programming;convex optimization},
  doi = {10.23919/EUSIPCO.2017.8081606},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570347711.pdf},
}
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