  The order-of-magnitude increase in the dimension of antenna arrays, which
forms extra-large-scale massive multiple-input-multiple-output (MIMO) systems,
enables substantial improvement in spectral efficiency, energy efficiency, and
spatial resolution. However, practical challenges, such as excessive
computational complexity and excess of baseband data to be transferred and
processed, prohibit the use of centralized processing. A promising solution is
to distribute baseband data from disjoint subsets of antennas into parallel
processing procedures coordinated by a central processing unit. This solution
is called subarray-based architecture. In this work, we extend the application
of expectation propagation (EP) principle, which effectively balances
performance and practical feasibility in conventional centralized MIMO detector
design, to fit the subarray-based architecture. Analytical results confirm the
convergence of the proposed iterative procedure and that the proposed detector
asymptotically approximates Bayesian optimal performance under certain
conditions. The proposed subarray-based EP detector is reduced to centralized
EP detector when only one subarray exists. In addition, we propose additional
strategies for further reducing the complexity and overhead of the information
exchange between parallel subarrays and the central processing unit to
facilitate the practical implementation of the proposed detector. Simulation
results demonstrate that the proposed detector achieves numerical stability
within few iterations and outperforms its counterparts.
