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.

Paper doi abstract bibtex

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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