  Signal processing in wireless communications, such as precoding, detection,
and channel estimation, are basically about solving inverse matrix problems,
which, however, are slow and inefficient in conventional digital computers,
thus requiring a radical paradigm shift to achieve fast, real-time solutions.
Here, for the first time, we apply the emerging analog matrix computing (AMC)
to the linear precoding of massive MIMO. The real-valued AMC concept is
extended to process complex-valued signals. In order to adapt the MIMO channel
models to RRAM conductance mapping, a new matrix inversion circuit is
developed. In addition, fully analog dataflow and optimized operational
amplifiers are designed to support AMC precoding implementation. Simulation
results show that the zero-forcing precoding is solved within 20 ns for a
16x128 MIMO system, which is two orders of magnitude faster than the
conventional digital approach. Meanwhile, the energy efficiency is improved by
50x.
