Search Results for author: Alva Kosasih

Found 10 papers, 1 papers with code

Parametric Near-Field Channel Estimation for Extremely Large Aperture Arrays

no code implementations31 Jan 2024 Alva Kosasih, Özlem Tuğfe Demir, Emil Björnson

Accurate channel estimation is critical to fully exploit the beamforming gains when communicating with extremely large aperture arrays.

Nonlinear Distortion Radiated from Large Arrays and Active Reconfigurable Intelligent Surfaces

no code implementations23 Jan 2024 Nikolaos Kolomvakis, Alva Kosasih, Emil Björnson

In this paper, we study the distortion created by nonlinear amplifiers in both ELAAs and active RIS.

Optimal Dual-Polarized Planar Arrays for Massive Capacity Over Point-to-Point MIMO Channels

no code implementations4 Dec 2023 Amna Irshad, Alva Kosasih, Emil Björnson, Luca Sanguinetti

We optimize the rank and condition number by identifying the optimal antenna spacing in dual-polarized planar antenna arrays with imperfect isolation.

Exploiting the Depth and Angular Domains for Massive Near-Field Spatial Multiplexing

no code implementations5 Jul 2023 Parisa Ramezani, Alva Kosasih, Amna Irshad, Emil Björnson

This article presents the foundational properties of communication in the radiative near-field region and then exemplifies how these properties enable two unprecedented spatial multiplexing schemes: depth-domain multiplexing of multiple users and angular multiplexing of data streams to a single user.

Finite Beam Depth Analysis for Large Arrays

no code implementations21 Jun 2023 Alva Kosasih, Emil Björnson

Furthermore, it is sufficient to characterize the BD for a broadside transmitter, as the beam pattern with a non-broadside transmitter can be approximated by that of a smaller/projected array with a broadside transmitter.

Untrained Neural Network based Bayesian Detector for OTFS Modulation Systems

no code implementations8 May 2023 Hao Chang, Alva Kosasih, Wibowo Hardjawana, Xinwei Qu, Branka Vucetic

In this paper, we propose an untrained DNN based on the deep image prior (DIP) and decoder architecture, referred to as D-DIP that replaces the MMSE denoiser in the iterative detector.

Bayesian Neural Network Detector for an Orthogonal Time Frequency Space Modulation

no code implementations27 Jun 2022 Alva Kosasih, Xinwei Qu, Wibowo Hardjawana, Chentao Yue, Branka Vucetic

The orthogonal time-frequency space (OTFS) modulation is proposed for beyond 5G wireless systems to deal with high mobility communications.

Bayesian Inference

Graph Neural Network Aided MU-MIMO Detectors

1 code implementation19 Jun 2022 Alva Kosasih, Vincent Onasis, Vera Miloslavskaya, Wibowo Hardjawana, Victor Andrean, Branka Vucetic

Multi-user multiple-input multiple-output (MU-MIMO) systems can be used to meet high throughput requirements of 5G and beyond networks.

Bayesian-based Symbol Detector for Orthogonal Time Frequency Space Modulation Systems

no code implementations27 Oct 2021 Xinwei Qu, Alva Kosasih, Wibowo Hardjawana, Vincent Onasis, Branka Vucetic

Our simulation results show that in contrast to the state-of-the-art OTFS detectors, the proposed detector is able to achieve a BER of less than $10^{-5}$, when SNR is over $14$ dB, under high ICI environments.

Improving Cell-Free Massive MIMO Detection Performance via Expectation Propagation

no code implementations27 Oct 2021 Alva Kosasih, Vera Miloslavskaya, Wibowo Hardjawana, Victor Andrean, Branka Vucetic

The simulation results show that the proposed detector achieves significant improvements in terms of the bit-error rate and sum spectral efficiency performances as compared to the ones of the state-of-the-art CF detectors.

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