Zobrazeno 1 - 10
of 513
pro vyhledávání: '"Rao, Bhaskar D."'
Autor:
Chen, Kuan-Lin, Rao, Bhaskar D.
Localizing more sources than sensors with a sparse linear array (SLA) has long relied on minimizing a distance between two covariance matrices and recent algorithms often utilize semidefinite programming (SDP). Although deep neural network (DNN)-base
Externí odkaz:
http://arxiv.org/abs/2408.16605
Autor:
Pote, Rohan R., Rao, Bhaskar D.
We propose a novel sensing approach for the beam alignment problem in millimeter wave systems using a single Radio Frequency (RF) chain. Conventionally, beam alignment using a single phased array involves comparing beamformer output power across diff
Externí odkaz:
http://arxiv.org/abs/2404.07604
Autor:
Sant, Aditya, Rao, Bhaskar D.
Detection for one-bit massive MIMO systems presents several challenges especially for higher order constellations. Recent advances in both model-based analysis and deep learning frameworks have resulted in several robust one-bit detector designs. Our
Externí odkaz:
http://arxiv.org/abs/2305.15543
Deep neural networks (DNNs) have greatly benefited direction of arrival (DoA) estimation methods for speech source localization in noisy environments. However, their localization accuracy is still far from satisfactory due to the vulnerability to non
Externí odkaz:
http://arxiv.org/abs/2302.10147
Autor:
Gopal, Govind R., Rao, Bhaskar D.
We examine the problem of uplink cell-free access point (AP) placement in the context of optimal throughput. In this regard, we formulate two main placement problems, namely the sum rate and minimum rate maximization problems, and discuss the challen
Externí odkaz:
http://arxiv.org/abs/2211.13356
A deep neural network using rectified linear units represents a continuous piecewise linear (CPWL) function and vice versa. Recent results in the literature estimated that the number of neurons needed to exactly represent any CPWL function grows expo
Externí odkaz:
http://arxiv.org/abs/2210.07236
Autor:
Pote, Rohan R., Rao, Bhaskar D.
We consider the parametric data model employed in applications such as line spectral estimation and direction-of-arrival estimation. We focus on the stochastic maximum likelihood estimation (MLE) framework and offer approaches to estimate the paramet
Externí odkaz:
http://arxiv.org/abs/2210.03266
We show that a new design criterion, i.e., the least squares on subband errors regularized by a weighted norm, can be used to generalize the proportionate-type normalized subband adaptive filtering (PtNSAF) framework. The new criterion directly penal
Externí odkaz:
http://arxiv.org/abs/2111.08952
Models recently used in the literature proving residual networks (ResNets) are better than linear predictors are actually different from standard ResNets that have been widely used in computer vision. In addition to the assumptions such as scalar-val
Externí odkaz:
http://arxiv.org/abs/2111.05496
Autor:
Pote, Rohan R., Rao, Bhaskar D.
Sparse signal recovery algorithms like sparse Bayesian learning work well but the complexity quickly grows when tackling higher dimensional parametric dictionaries. In this work we propose a novel Bayesian strategy to address the two dimensional harm
Externí odkaz:
http://arxiv.org/abs/2102.08515