The evolution of 6G has brought distributed multi-input multi-output (D-MIMO) near-field communications into the spotlight, owing to their potential to significantly enhance wireless capacity and spectral efficiency. This study delves into exploring the optimal characteristics of distributed antenna deployment in D-MIMO systems through the optimization of access point (AP) placement and subarray topology. To guarantee the stability of system configuration, we derive an approximate ergodic sum rate with high accuracy under near-field spherical wavefront propagation that serves as the optimization criterion. Confronting the non-convex challenges, we adopt an approach integrating successive convex approximation (SCA), gradient descent, and alternating optimization methods, which enables us to attain a near-optimal solution with low complexity. Numerical simulations highlight the significant superiority of our proposal to traditional baseline approaches, revealing key characteristics and providing crucial insights for AP deployment and array layout optimization. In densely clustered user scenarios, APs should be deployed near the user aggregation centroid, which may not align with the geometric center. For dispersed user distributions, AP deployment positions should be adjusted more flexibly to regional statistical characteristics rather than confined to central points to accommodate specific user distribution features. Moreover, the array topology exhibits a pattern of alternating dense and sparse distribution, diverging from the attributes observed in centralized MIMO (C-MIMO) architectures, where a consistent medium density is flanked by regions of sparsity.

In this paper, we investigate the energy efficiency (EE) of large-scale distributed multiple-input multiple-output (D-MIMO) systems. Firstly, we derive an analytical closed-form expression of the lower bound on the ergodic capacity for D-MIMO systems, and the asymptotic performance for the large-scale antenna arrays systems over K fading channels is proposed. Utilizing these results, we elaborate on the EE optimization problem with transmitted power constraint. To tackle this multi-objective optimization problem, we propose an one-dimension iterative search algorithm with low complexity to obtain the optimal number of receive antennas, transmitted power and achievable sum rate with optimum EE. Simulation results verify the performance gain of the proposed scheme along with the increasing number of transmit antennas.

In this paper, we investigate the energy efficiency (EE) of massive multiple-input multiple-output (MIMO) systems with linear maximum ratio combining (MRC) and zero-forcing (ZF) receivers. To be practical, the baseband power consumption with algorithm computational complexity (ACC) is taken into account. Utilizing this model, we maximize the global optimal number of receiver antenna and sum rate with optimal EE by fixed the number of users for different receivers, respectively. In parallel, the low complexity iterative algorithm is proposed to reap the optimal values. Simulation results indicate that the EE improves with the number of users for the two receivers.