AI Article Synopsis
- The paper introduces a recurrent neural network (RNN) to tackle the adaptive beamforming problem, aiming to reduce sidelobe interference.
- It frames the issue as a convex optimization problem using a linear array model and ensures that RNN optimizes the weight values based on the array's state and plane wave data.
- The algorithm is shown to be stable and effective in converging to an optimal solution, especially in scenarios with array mismatches, outperforming other optimization methods in finding precise solutions despite large-scale constraints.
Article Abstract
In this paper, a recurrent neural network (RNN) is proposed for solving adaptive beamforming problem. In order to minimize sidelobe interference, the problem is described as a convex optimization problem based on linear array model. RNN is designed to optimize system's weight values in the feasible region which is derived from arrays' state and plane wave's information. The new algorithm is proven to be stable and converge to optimal solution in the sense of Lyapunov. So as to verify new algorithm's performance, we apply it to beamforming under array mismatch situation. Comparing with other optimization algorithms, simulations suggest that RNN has strong ability to search for exact solutions under the condition of large scale constraints.
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| http://dx.doi.org/10.1016/j.neunet.2016.04.010 | DOI Listing |
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