  To support emerging applications ranging from holographic communications to
extended reality, next-generation mobile wireless communication systems require
ultra-fast and energy-efficient (UFEE) baseband processors. Traditional
complementary metal-oxide-semiconductor (CMOS)-based baseband processors face
two challenges in transistor scaling and the von Neumann bottleneck. To address
these challenges, in-memory computing-based baseband processors using resistive
random-access memory (RRAM) present an attractive solution. In this paper, we
propose and demonstrate RRAM-based in-memory baseband processing for the widely
adopted multiple-input-multiple-output orthogonal frequency division
multiplexing (MIMO-OFDM) air interface. Its key feature is to execute the key
operations, including discrete Fourier transform (DFT) and MIMO detection using
linear minimum mean square error (L-MMSE) and zero forcing (ZF), in one-step.
In addition, RRAM-based channel estimation as well as mapper/demapper modules
are proposed. By prototyping and simulations, we demonstrate that the
RRAM-based full-fledged communication system can significantly outperform its
CMOS-based counterpart in terms of speed and energy efficiency by $10^3$ and
$10^6$ times, respectively. The results pave a potential pathway for RRAM-based
in-memory computing to be implemented in the era of the sixth generation (6G)
mobile communications.
