Energy-efficient flexible and fixed antenna selection methods for XL-MIMO systems

Abstract

Massive multiple-input multiple-output (M-MIMO) is a key technology for 5G networks which consists on equipping the base station (BS) with hundreds or thousands of antennas. Increasing the antenna separation is paramount in order to make real advantage from an array dimension of the order of thousands of antennas. One potential approach is the integration of the antenna array into large structures, which is referred to as extra-large-scale MIMO (XL-MIMO). However, when employing extremely large arrays, centralized processing architectures face a challenging complexity. A promising solution is to divide the antenna array into disjoint units, referred to as subarrays, with individual processing units. In this paper, we modify the M-MIMO channel model aiming to take two implications of the extreme array dimensions into account: the spherical wavefronts, which is called near-field propagation, and the concentration of the majority of energy received from a specific user on a small portion of the array, namely spatial non-stationarity. Considering the latter, it is not efficient from an energy perspective to activate the antennas with lower channel gains to transmit/receive the signal to/from a given user. Thus, antenna selection methods are quite important in the considered scenario. Two antenna selection (AS) methods for XL-MIMO are proposed in this paper: the adjustable-flexible antenna selection (FAS), and the fixed subarray selection (FSS). Numerical results demonstrate that, by judiciously selecting the antennas subset used to detect the signal of each user in the uplink (UL), the number of active antennas can be reduced considerably in a scenario with up to 64 active users, reducing the power consumption without compromising the throughput. The EE is hugely increased in such scenario. Finally, the FSS scheme achieved the same performance of FAS, while having a simpler hardware implementation.