Deijany Rodriguez-Linares
Signal Processing,. sampling frequency synchronization,. linearization,. and equalization.
Ph.D. candidate in Communication Systems focusing on hardware-aware, low-complexity signal processing for modern wireless systems.
About Me
PhD researcher in signal processing working on estimation, compensation, synchronization, linearization, and equalization, with a strong focus on robustness under non-ideal hardware effects and noisy conditions. Develops hardware-aware, low-complexity algorithms, including neural-network–inspired linearizers, alongside classical model-based techniques.
Quick Facts
Current Position
Ph.D. Candidate, Communication Systems — Linköping University
Expected Graduation
April 17, 2026
Core Expertise
Signal Processing · Estimation & Synchronization · Hardware-Aware DSP · Linearization · Equalization
Tools & Languages
Python · MATLAB · C++ · PyTorch · NumPy · SciPy · Linux
Languages
Skills
My expertise lies in DSP for communication systems, with a strong emphasis on hardware-aware, low-complexity algorithm design under practical hardware constraints. I combine classical methods with learning-inspired techniques to address hardware effects in modern wireless systems.
Beyond-5G Baseband Signal Processing
Ph.D. research on low-complexity, hardware-aware baseband signal processing algorithms within the ELLIIT project Baseband Processing for Beyond 5G Wireless.
Sampling-Frequency Synchronization
Development of efficient sampling-frequency offset estimation and compensation methods for wideband communication systems under strict hardware constraints.
Nonlinear System Modeling
Modeling and analysis of nonlinear and mixed-signal systems for communication applications, with emphasis on implementable representations and performance–complexity trade-offs.
ADC Linearization
Design of low-complexity, learning-inspired linearizers for ADCs and data converters, covering memoryless and frequency-dependent nonlinearities before and after sampling under hardware constraints.
DAC Equalization
Equalization of DAC frequency-response distortions using linear-phase FIR filters across multiple Nyquist bands.
Model-Order & Complexity Analysis
Derivation of closed-form model-order and complexity predictions using symbolic regression and structured optimization.
Learning-Inspired Signal Processing
Use of learning-inspired and unsupervised methods to tune models, discover structure, and improve robustness when labeled data is limited.
Teaching assistant
Teaching assistant for Signal Processing for Communications, Analog Filters, and Digital Filters.
Experience
Research and engineering experience in hardware-aware signal processing, computational modeling, and applied machine learning, combined with university-level teaching and scientific research.
Linköping University
Ph.D. Candidate — Communication Systems
Division of Communication Systems, Department of Electrical Engineering
Key Achievements
- Ph.D. research on low-complexity, hardware-aware baseband signal processing algorithms (ELLIIT project: Baseband Processing for Beyond 5G Wireless).
- Development of efficient sampling-frequency offset estimation and compensation methods for wideband communication systems.
- Design of low-complexity, learning-inspired linearizers for ADCs and data converters, addressing memoryless and frequency-dependent nonlinearities before and after sampling.
- Equalization of DAC frequency-response distortions using linear-phase FIR filters across multiple Nyquist bands.
- Derivation of closed-form model-order and complexity predictions using symbolic regression and structured optimization.
- Focus on implementability, complexity analysis, and hardware-constrained algorithm design.
University of Havana
Researcher and Teaching Assistant
Higher Institute of Technologies and Applied Sciences (InSTEC)
Key Achievements
- Researched deep learning combined with Monte Carlo radiation transport to improve prediction accuracy for low-probability interaction events.
- Teaching assistant for Numerical Mathematics II and Fundamentals of Medical Physics.
Center for State Control of Medicines, Equipment and Medical Devices (CECMED)
Medical Physicist — QA and Modeling
Cuban regulatory authority for medicines and medical devices
Key Achievements
- Developed image processing for tumor detection and classification.
- Performed Monte Carlo simulation of radiation transport for dose calculation and verification in treatment planning systems.
- Conducted dose plan verification, beam model validation, and participation in clinical quality assurance audits.
Publications
(Last 3 Years)
Journal Articles
- D. R. Linares, O. Moryakova, and H. Johansson, “Joint sampling frequency offset estimation and compensation algorithms based on the Farrow structure,” IEEE Open J. Signal Process., 2026. Manuscript in preparation (journal extension of DSP 2025 conference paper; 12-page draft).
- D. Rodriguez-Linares, O. Moryakova, and H. Johansson, “Efficient Computation of Time-Index Powered Weighted Sums Using Cascaded Accumulators,” IEEE Signal Process. Lett., 2026. Accepted (minor revision). arXiv: 2509.15069
- D. R. Linares and H. Johansson, “Low-Complexity Frequency-Dependent Linearizers Based on Parallel Bias-Modulus and Bias-ReLU Operations,” IEEE Access, vol. 13, pp. 209796–209812, 2025. DOI: 10.1109/ACCESS.2025.3642613
- D. R. Linares, H. Johansson, and Y. Wang, “Order Estimation of Linear-Phase FIR Filters for DAC Equalization in Multiple Nyquist Bands,” IEEE Signal Process. Lett., vol. 31, pp. 2955–2959, 2024. DOI: 10.1109/LSP.2024.3483008
- A. Quiñones-EspĂn, M. Perez-Diaz, R. EspĂn-Coto, D. R. Linares, and J. D. Lopez-Cabrera, “Automatic detection of breast masses using deep learning with YOLO approach,” Health Technol., vol. 13, no. 6, pp. 915–923, 2023. DOI: 10.1007/s12553-023-00783-x
Conference Proceedings
- D. R. Linares, O. Moryakova, and H. Johansson, “Joint Sampling Frequency Offset Estimation and Compensation Based on the Farrow Structure,” in Proc. 25th Int. Conf. Digit. Signal Process. (DSP), 2025, pp. 1–5. DOI: 10.1109/DSP65409.2025.11074995
- D. R. Linares and H. Johansson, “Digital Linearizer Based on 1-Bit Quantizations,” in Proc. IEEE 24th Int. Conf. Commun. Technol. (ICCT), 2024, pp. 1659–1663. DOI: 10.1109/ICCT62411.2024.10946352
- D. R. Linares and H. Johansson, “Low-Complexity Memoryless Linearizer for Analog-to-Digital Interfaces,” in Proc. 24th Int. Conf. Digit. Signal Process. (DSP), 2023, pp. 1–5. DOI: 10.1109/DSP58604.2023.10167765
Awards & Recognition
Excellent Oral Presentation Award (Session Award) — IEEE ICCT 2024
ICTP Travel Award & Full Scholarship (2017, 2018) — Abdus Salam ICTP
National Scholarship (Ministry of Education, Cuba) (2010–2015)
Let's Connect
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Contact Information
Let's connect! I'm always interested in hearing about new projects and opportunities.