543 research outputs found
A Generalized Framework on Beamformer Design and CSI Acquisition for Single-Carrier Massive MIMO Systems in Millimeter Wave Channels
In this paper, we establish a general framework on the reduced dimensional
channel state information (CSI) estimation and pre-beamformer design for
frequency-selective massive multiple-input multiple-output MIMO systems
employing single-carrier (SC) modulation in time division duplex (TDD) mode by
exploiting the joint angle-delay domain channel sparsity in millimeter (mm)
wave frequencies. First, based on a generic subspace projection taking the
joint angle-delay power profile and user-grouping into account, the reduced
rank minimum mean square error (RR-MMSE) instantaneous CSI estimator is derived
for spatially correlated wideband MIMO channels. Second, the statistical
pre-beamformer design is considered for frequency-selective SC massive MIMO
channels. We examine the dimension reduction problem and subspace (beamspace)
construction on which the RR-MMSE estimation can be realized as accurately as
possible. Finally, a spatio-temporal domain correlator type reduced rank
channel estimator, as an approximation of the RR-MMSE estimate, is obtained by
carrying out least square (LS) estimation in a proper reduced dimensional
beamspace. It is observed that the proposed techniques show remarkable
robustness to the pilot interference (or contamination) with a significant
reduction in pilot overhead
Resource Allocation for Near-Field Communications: Fundamentals, Tools, and Outlooks
Extremely large-scale multiple-input-multiple output (XL-MIMO) is a promising
technology to achieve high spectral efficiency (SE) and energy efficiency (EE)
in future wireless systems. The larger array aperture of XL-MIMO makes
communication scenarios closer to the near-field region. Therefore, near-field
resource allocation is essential in realizing the above key performance
indicators (KPIs). Moreover, the overall performance of XL-MIMO systems heavily
depends on the channel characteristics of the selected users, eliminating
interference between users through beamforming, power control, etc. The above
resource allocation issue constitutes a complex joint multi-objective
optimization problem since many variables and parameters must be optimized,
including the spatial degree of freedom, rate, power allocation, and
transmission technique. In this article, we review the basic properties of
near-field communications and focus on the corresponding "resource allocation"
problems. First, we identify available resources in near-field communication
systems and highlight their distinctions from far-field communications. Then,
we summarize optimization tools, such as numerical techniques and machine
learning methods, for addressing near-field resource allocation, emphasizing
their strengths and limitations. Finally, several important research directions
of near-field communications are pointed out for further investigation
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