Evaluation of Channel Performance in XL-Multiple-input Multipleoutput: Far-Field vs Near-Field

These extremely large-scale multiple-input multiple-output (XL-MIMO) systems will be able to provide the high data rates required for future 6G. Well accurate channel state information will ensure successful precoding. Currently, most low pilot overhead channel estimating algorithms depend primarily on angular domain channel sparsity based on the classical near-field planar-wavefront assumption. This assumption fails since in XL-MIMO, a significant near-field spherical-wavefront feature occurs. As a result, the performance of current far-field channel estimation approaches will drop steeply. This dissertation presents research into near-field channel estimation using polar-domain sparsity, thereby tackling this problem. In contrast to the conventional angular-domain model, which accepts only the angular part, here presented a polar domain representative, which considers the distance and angular data simultaneously. Because the near-field channel with a polar domain is equally sparse, so near-field XL-MIMO channel estimation methods for both on- and off-grid environments are used. For effective near-field estimate, the on-grid parallel-domain simultaneous orthogonal matching pursuit (P-SOMP) is required. technique is shown first. For even greater estimation accuracy, simultaneous iterative grid-less weighted off-grid polar domain (P-SIGW) approach is also suggested Finally, simulation results are provided to validate the effectiveness of our methods.