  In this work, we consider the use of model-driven deep learning techniques
for massive multiple-input multiple-output (MIMO) detection. Compared with
conventional MIMO systems, massive MIMO promises improved spectral efficiency,
coverage and range. Unfortunately, these benefits are coming at the cost of
significantly increased computational complexity. To reduce the complexity of
signal detection and guarantee the performance, we present a learned conjugate
gradient descent network (LcgNet), which is constructed by unfolding the
iterative conjugate gradient descent (CG) detector. In the proposed network,
instead of calculating the exact values of the scalar step-sizes, we explicitly
learn their universal values. Also, we can enhance the proposed network by
augmenting the dimensions of these step-sizes. Furthermore, in order to reduce
the memory costs, a novel quantized LcgNet is proposed, where a low-resolution
nonuniform quantizer is integrated into the LcgNet to smartly quantize the
aforementioned step-sizes. The quantizer is based on a specially designed soft
staircase function with learnable parameters to adjust its shape. Meanwhile,
due to fact that the number of learnable parameters is limited, the proposed
networks are easy and fast to train. Numerical results demonstrate that the
proposed network can achieve promising performance with much lower complexity.
