Suitability of Conventional 1D Noise Subspace Algorithms for DOA Estimation using Large Arrays at Millimeter Wave Band

Array signal processing has attracted the interest of the scientific community for the past several decades. An Array of sensor elements (be it microphones, hydrophones, antenna elements, piezoelectric sensors) achieves better performance than a single element would. Antenna arrays are made up of antenna elements which can be arranged in a variety of configurations (with respect to the geometry, inter-element spacing, etc.). The Direction of Arrival (DOA) estimation is a signal processing technique that can be used at a receiving array to find the directions of the incoming signals that impinge on the antenna array. Beamforming is a technique that can be used to focus the transmit energy towards or to collect the received energy from certain desired directions. A smart antenna system is one which can perform DOA estimation as well as beamforming. The correctness of DOA estimation algorithms is a major contributing factor in the performance of smart antenna systems. Since beamforming is a key enabler for Millimeter Wave (mmWave) and fifth-generation cellular (5G); DOA estimation assumes much importance in future wireless communications. MmWave allows the use of large arrays owing to the small wavelengths. In this paper, we have studied the suitability of DOA estimation algorithms using Eigen decomposition methods which include the Pisarenko Harmonic Decomposition (PHD), Multiple Signal Classification (MUSIC), Modified MUSIC and Root-MUSIC at 30 GHz, which is a proposed frequency in 5G. MATLAB simulations throw light upon the various factors affecting DOA estimation accuracy and it is found that the above conventional methods are very much suited for the mmWave frequencies.