  This paper investigates the multiple-input-multiple-output (MIMO) massive
unsourced random access in an asynchronous orthogonal frequency division
multiplexing (OFDM) system, with both timing and frequency offsets (TFO) and
non-negligible user collisions. The proposed coding framework splits the data
into two parts encoded by sparse regression code (SPARC) and low-density parity
check (LDPC) code. Multistage orthogonal pilots are transmitted in the first
part to reduce collision density. Unlike existing schemes requiring a
quantization codebook with a large size for estimating TFO, we establish a
\textit{graph-based channel reconstruction and collision resolution
(GB-CR$^2$)} algorithm to iteratively reconstruct channels, resolve collisions,
and compensate for TFO rotations on the formulated graph jointly among multiple
stages. We further propose to leverage the geometric characteristics of signal
constellations to correct TFO estimations. Exhaustive simulations demonstrate
remarkable performance superiority in channel estimation and data recovery with
substantial complexity reduction compared to state-of-the-art schemes.
