arXiv subjects

Amirreza Kiani

Publications and source records attributed to Amirreza Kiani.

4 recordsLinked to original sources

arXiv2026

Scalable Long-Term Beamforming for Massive Multi-User MIMO

Fully digital massive multiple-input multiple-output (MIMO) systems with large numbers (1000+) of antennas offer capacity gains from spatial multiplexing and beamforming, but receivers that scale to these array dimensions face challenges in both channel estimation overhead and digital computation. Long-term beamforming addresses both by projecting the data onto a low-dimensional subspace that can be tracked at a slow time scale from the long-term channel parameters. In this setting, we show how to compute, in closed form, the projection matrix that maximizes a capacity upper bound, using a matrix inverse square root; the same projection is shown to maximize the mean post-projection signal-to-interference-plus-noise ratio (SINR) exactly. Computationally efficient methods are then presented for the matrix computation, realizable with matrix-matrix multiplies and hence amenable to systolic array implementations in hardware. Bounds on the SINR degradation are derived, and ray tracing simulations in a realistic rural uplink setting show a small loss relative to instantaneous minimum mean-square error (MMSE) beamforming when the covariance is accurately estimated. The efficient Gram-domain form of the instantaneous MMSE receiver applies the maximum-ratio combining reduction before an inverse whose dimension is the total number of streams. Against this baseline, the method estimates and refreshes beamforming coefficients three orders of magnitude less often and decouples the real-time path across users. With the conjugate-gradient solve and a rank-one projection, its total arithmetic cost is 2% higher at the ten-user operating point and lower above a stream-dimension crossover that we characterize.

eess.SP↗

arXiv2026

Low-rank Preconditioning in Beamspace Domain For Massive MU-MIMO Long-Term Beamforming

Long-term beamforming substantially reduces the channel estimation and inversion overhead of conventional massive MU-MIMO receivers; yet, its construction still hinges on the inversion of a large Hermitian matrix, whose condition number deteriorates with the per-user SNR dynamic range. When this inversion is approximated in hardware via the conjugate gradient (CG) algorithm, the deterioration directly inflates the iteration count and, consequently, the energy and latency budget. We propose a hardware-friendly low-rank preconditioning framework that targets exactly this bottleneck. The preconditioner is constructed from the top eigenpairs of the long-term covariance matrix through a randomized complex eigenvalue decomposition (RC-EVD), whose inner QR factorizations are realized via a Cholesky-based scheme (QRC), confining the dominant cost to generalized matrix multiplication (GEMM) and small triangular solves that map naturally onto systolic arrays. We further show that performing the preconditioned CG inversion in the beamspace domain induces sparsification of the system matrix and provides additional convergence acceleration at negligible transformation cost. Ray-tracing simulations confirm that the joint scheme reduces the required CG iteration count by two to three while matching the post-equalization SINR of the exact inversion.

eess.SP↗

arXiv2026

Interference Suppression for Massive MU-MIMO Long-Term Beamforming with Matrix Inversion Approximation

Long-term beamforming (LTBF) is a widely-used scalable alternative to instantaneous multi-user MIMO processing that leverages slowly varying spatial channel statistics. VLSI implementations require matrix inversion that become computationally challenging for massive MIMO systems with large number of antennas. In this work, we show that dominant interferers significantly degrade the numerical conditioning of the LTBF covariance matrix, leading to severe performance loss in finite-precision implementations of polynomial and conjugate gradient (CG) based inversion methods. To address this issue, we propose a subspace nulling approach that operates solely on long-term channel statistics and acts as an implicit preconditioning step for LTBF. By projecting the received signal onto the orthogonal complement of the dominant interference subspace, the proposed method reduces the eigenvalue spread of the covariance matrix and improves numerical stability. Through ray-tracing simulations in a realistic 5G scenario, we demonstrate that the proposed method substantially reduces the number of CG iterations required to achieve near-optimal performance across floating-point and fixed-point implementations while preserving the low-overhead nature of LTBF.

eess.SP↗

arXiv2016

Passivation-sensitive exciton finestructure produces excess Stokes shifts in colloidal quantum dots

The excitonic finestructure of colloidal quantum dots (CQDs) is comprised of a manifold of transitions, of which only the lowest are populated and contribute to photoluminescence. This leads to a Stokes shift in emission relative to absorption. Here we show experimentally that the Stokes shift in Pb and Cd-based chalcogenide CQDs is correlated with the degree of surface passivation, and develop a model that explains how coupling to the surface affects the core electronic states. Dark and bright transitions can reorder and split, increasing the Stokes shift even without the formation of deep traps. Our findings resolve the highly-debated topic of excess Stokes shifts in PbS nanocrystals as due to parity-forbidden transitions instead of traps. We predict that the Stokes shift in PbS can be eliminated via core stoichiometry control, a critical step towards enhancing the open circuit voltage in quantum dot solar cells.

cond-mat.mes-hall↗