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Found 10 papers, 3 papers with code
Joint Location Sensing and Channel Estimation for IRS-Aided mmWave ISAC Systems
Based on the estimation results from the first phase, we formulate a Cram\'{e}r-Rao bound (CRB) minimization problem for optimizing IRS reflection coefficients, and through proper reformulations, a low-complexity manifold-based optimization algorithm is proposed to solve this problem.
Rethinking IoU-based Optimization for Single-stage 3D Object Detection
Since Intersection-over-Union (IoU) based optimization maintains the consistency of the final IoU prediction metric and losses, it has been widely used in both regression and classification branches of single-stage 2D object detectors.
Channel Tracking and Prediction for IRS-aided Wireless Communications
Based on the proposed transmission protocol, we propose a two-stage channel tracking and prediction (2SCTP) scheme to obtain the direct and reflected channels with low channel training overhead, which is achieved by exploiting the temporal correlation of the time-varying channels.
Improving 3D Object Detection with Channel-wise Transformer
Though 3D object detection from point clouds has achieved rapid progress in recent years, the lack of flexible and high-performance proposal refinement remains a great hurdle for existing state-of-the-art two-stage detectors.
Two-Stage Stochastic Optimization via Primal-Dual Decomposition and Deep Unrolling
Then we propose a PDD-based stochastic successive convex approximation (PDD-SSCA) algorithmic framework to find KKT solutions for two-stage stochastic optimization problems.
Channel Estimation for IRS-aided Multiuser Communications with Reduced Error Propagation
Intelligent reflecting surface (IRS) has emerged as a promising paradigm to improve the capacity and reliability of a wireless communication system by smartly reconfiguring the wireless propagation environment.
A PDD Decoder for Binary Linear Codes With Neural Check Polytope Projection
Linear Programming (LP) is an important decoding technique for binary linear codes.
Energy Efficiency Optimization for Millimeter Wave System with Resolution-Adaptive ADCs
This letter investigates the uplink of a multi-user millimeter wave (mmWave) system, where the base station (BS) is equipped with a massive multiple-input multiple-output (MIMO) array and resolution-adaptive analog-to-digital converters (RADCs).
ADMM-based Decoder for Binary Linear Codes Aided by Deep Learning
Inspired by the recent advances in deep learning (DL), this work presents a deep neural network aided decoding algorithm for binary linear codes.
Learned Conjugate Gradient Descent Network for Massive MIMO Detection
Furthermore, in order to reduce the memory costs, a novel quantized LcgNet is proposed, where a low-resolution nonuniform quantizer is integrated into the LcgNet to smartly quantize the aforementioned step-sizes.
