  Massive multiple input multiple output(MIMO)-based fully-digital receive
antenna arrays bring huge amount of complexity to both traditional direction of
arrival(DOA) estimation algorithms and neural network training, which is
difficult to satisfy high-precision and low-latency applications in future
wireless communications. To address this challenge, two estimators called OPSC
and OSAP-CBAM-CNN are proposed in this paper. The computational complexity of
the traditional DOA algorithm is first considered to be reduced by dividing the
total set of antennas into multiple overlapped subarrays uniformly, each
subarray crosses each other proportionally and performs DOA estimation to
generate coarse angles, and all angles are coherently combined to get the
better estimation, the final DOA estimation can given by maximum likelihood
alternating projection(ML-AP) in a very small range, which has a better
performance than the direct partitioning of subarrays. To further reduce the
complexity of traditional estimation algorithms, deep neural networks(DNN) are
utilized to offline train the relationship between the received signal
covariance matrix and the estimated angles. Due to the high complexity of the
training network based on large-scale arrays, in the OSAP-CBAM-CNN method, the
complex network is divided into several smaller networks based on the
overlapped subarray to give rough DOA estimations, followed by coherent
combining and AP algorithm to get the final DOA estimation. Simulation results
show that as the number of antennas goes to large-scale, the proposed methods
can achieve a remarkable complexity reduction over conventional ML-AP
algorithm.
