Alexandre Graell i Amat

Full Professor

Chalmers University of Technology

Sweden

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About

Professor Alexandre Graell i Amat is a Full Professor at Chalmers University of Technology in Sweden. His research areas encompass massive MIMO systems, LDPC codes, and federated learning, as highlighted in his recent publications. His work also includes contributions to network slicing for IoT and DNA sequencing methodologies, reflecting a broad interest in communications and information theory.

Articles (16)

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2025

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2025

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2023

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2023

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2023

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2022

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2022

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2022

Collaborators (12)

Mikko Valkama

University of Tampere

FINLAND

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Magnus Karlsson

Professor at Chalmers University of Technology

Chalmers University of Technology

SWEDEN

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Giuseppe Durisi

Professor

Chalmers University of Technology

SWEDEN

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Beatriz Soret

Aalborg University

DENMARK

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ALEJANDRO LANCHO

Universidad Carlos III de Madrid

SPAIN

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Michael Lentmaier

Lund University

SWEDEN

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Henk Wymeersch

Professor at Chalmers University of Technology

Chalmers University of Technology

SWEDEN

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Anders Ellersgaard Kalør

Aalborg University

DENMARK

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Peter Andrekson

Professor

Chalmers tekniska högskola

SWEDEN

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Luca Sanguinetti

University of Pisa

ITALY

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Henry Pfister

Associate Professor

Duke University

UNITED STATES

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Fredrik Brännström

Chalmers University of Technology

SWEDEN

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Alexandre Graell i Amat

Research Interests

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About

Articles (16)

Time Versus Frequency Domain DPD for Massive MIMO: Methods and Performance Analysis

LDPC Codes for Quantitative Group Testing With a Non-Binary Alphabet

Uplink Cell-Free Massive MIMO OFDM with Phase Noise-Aware Channel Estimation: Separate and Shared Local Oscillators

FedGT: Identification of Malicious Clients in Federated Learning With Secure Aggregation

Timely Status Updates in Slotted ALOHA Networks With Energy Harvesting

Unsourced Multiple Access With Common Alarm Messages: Network Slicing for Massive and Critical IoT

Successive Cancellation Decoding of Single Parity-Check Product Codes: Analysis and Improved Decoding

Finite-Length Scaling of SC-LDPC Codes With a Limited Number of Decoding Iterations

Improving the Thresholds of Generalized LDPC Codes With Convolutional Code Constraints

Concatenated Codes for Multiple Reads of a DNA Sequence

DSAG: A Mixed Synchronous-Asynchronous Iterative Method for Straggler-Resilient Learning

CodedPaddedFL and CodedSecAgg: Straggler Mitigation and Secure Aggregation in Federated Learning

Unsourced Multiple Access With Random User Activity

Periodicity-Enabled Size Reduction of Symbol Based Predistortion for High-Order QAM

Generalized Spatially-Coupled Parallel Concatenated Codes With Partial Repetition

Model-Based End-to-End Learning for WDM Systems With Transceiver Hardware Impairments

Collaborators (12)

Mikko Valkama

Magnus Karlsson

Giuseppe Durisi

Beatriz Soret

ALEJANDRO LANCHO

Michael Lentmaier

Henk Wymeersch

Anders Ellersgaard Kalør

Peter Andrekson

Luca Sanguinetti

Henry Pfister

Fredrik Brännström

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