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Distributed massive MIMO (D-mMIMO) has been considered for future networks asit holds the potential to offer superior capacity while enabling energy savingsin the network. A D-mMIMO system has multiple arrays. Optimizing the locationsof the arrays is essential for the energy efficiency of the system. In existingworks, array placement has been optimized mostly based on common channelmodels, which rely on a given statistical distribution and Euclidean distancebetween user locations and arrays. These models are justified if applied tosufficiently large cells, where the statistical description of the channel isexpected to fit its empirical condition. However, with the advent of smallcells, this is no longer the case. The channel propagation condition becomeshighly environment-specific. This paper investigates array placementoptimization with different ways of modeling the propagation conditions takingthe environment information (e.g., buildings) into account. We capture theenvironment information via a graph. Two shortest path-based propagation modelsare introduced based on the graph. We validate the performance of the modelswith a ray-tracing simulator considering different signal coverage levels. Thesimulation results demonstrate that higher energy efficiency is achieved byusing the array placements found via the proposed models compared to aEuclidean distance-based propagation model in small cells. The average powersaving at 96% signal coverage, for example, reaches more than 5 dB.

Array Placement in Distributed Massive MIMO for Power Saving considering Environment Information