Abstract
Lens antenna arrays have become attractive for mmWave MIMO systems due to their energy-focusing and path-division properties. However, when the signals cannot be well differentiated at different array elements due to their similar incident angles, we cannot estimate their DoAs separately. In this paper, we present a DoA estimation algorithm for this situation. Our algorithm has three main steps: a special version of root multiple signal classification (MUSIC) for lens antenna arrays, outlier detection, and clustering. Numerical results show that our algorithm can achieve good performance even with a large number of signal sources, a large number of array elements, and a small number of snapshots.
Highlights
A LENS antenna array is an advanced antenna design for millimeter wave multiple-input multipleoutput (MIMO) systems [1]–[3]
Since the position of the focal point is determined by the direction-of-arrival (DoA) of the incident EM wave, the waves which come from sufficiently separated directions have distinct energy distributions on the lens antenna array
The numerical results show that our algorithm can achieve a good performance even with a large number of signal sources, a large number of array elements, and a small number of snapshots
Summary
A LENS antenna array is an advanced antenna design for millimeter wave (mmWave) multiple-input multipleoutput (MIMO) systems [1]–[3]. Since the position of the focal point is determined by the direction-of-arrival (DoA) of the incident EM wave, the waves which come from sufficiently separated directions have distinct energy distributions on the lens antenna array. By the energy-focusing property, the signals with separated DoAs can be regarded as received by different array elements, as Fig. 1 shows. Because most of the energy is received by a small group of array elements, we can only connect them to radio frequency (RF) chains for further processing and ignore the rest. This makes a lens antenna array a good candidate for mmWave MIMO systems because both the hardware complexity and the power consumption are significantly reduced
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