• K. Ding and G. Scutari, “From Mixing to Tearing: Graph Decomposition in Decentralized Optimization via Message Passing,” preprint, arXiv:2610.03709, 2026.
• K. Ding, M. Maros, and G. Scutari, “A New Decomposition Paradigm for Graph-structured Nonlinear Programs via Message Passing,” preprint, arXiv:2512.24676, 2025.
• W. Chen, X. Du, G. Scutari, and H. Honnappa, “Is In-Context Reinforcement Learning A Posterior Sampler? An Empirical Study,” preprint, arXiv:2610.28977, 2026.
• X. Chen, I. Kuruzov, and G. Scutari, “A Line-search-free Method for Adaptive Decentralized Optimization,” preprint, arXiv:2605.00711, 2026.
• X. Chen, I. Kuruzov, and G. Scutari, “Adaptive Decentralized Composite Optimization via Three-Operator Splitting,” preprint, arXiv:2602.17545, 2026.

• A. Nedić, J.-S. Pang, G. Scutari, and Y. Sun, Multi-agent Optimization. Lecture Notes in Mathematics, Springer Verlag, CIME Subseries, 2018. [link]

• F. Facchinei, V. Kungurtsev, L. Lampariello, and G. Scutari, “Iteration Complexity of a Fixed-Stepsize SQP Method for Nonconvex Optimization with Convex Constraints,” Numerical Analysis and Optimization, Springer Proceedings in Mathematics & Statistics, pp. 109–120, 2021.
• G. Scutari and Y. Sun, “Parallel and Distributed Successive Convex Approximation Methods for Big-Data Optimization,” in Multi-agent Optimization, Lecture Notes in Mathematics, Springer Verlag, CIME Subseries, pp. 141–308, 2018 [DOI].
• G. Scutari, D. P. Palomar, F. Facchinei, and J.-S. Pang, “Monotone Games for Cognitive Radio Systems,” in Distributed Decision-Making and Control, Eds. Anders Rantzer and Rolf Johansson, Lecture Notes in Control and Information Sciences Series, Springer Verlag, 2011.
• G. Scutari, D. P. Palomar, and S. Barbarossa, “Competitive Optimization of Cognitive Radio MIMO Systems via Game Theory,” in Convex Optimization in Signal Processing and Communications, Eds. D. P. Palomar and Yonina C. Eldar, Cambridge University Press, 2009.
• S. Barbarossa, G. Scutari, and L. Pescosolido, “Distributed Space-Time Coding Techniques for Multihop Networks,” in EURASIP Hindawi Book Series, T. Kaiser and A. Bourdoux, Editors, 2004.

• G. Scutari, F. Facchinei, D. P. Palomar, and J.-S. Pang, “Convex Optimization, Game Theory, and Variational Inequality Theory in Multiuser Communication Systems,” IEEE Signal Processing Magazine, vol. 27, no. 3, pp. 35–49, May 2010 [DOI].
  Top 100 downloaded articles in IEEE among 1.25 million articles available (April-July 2010).
  Top 10 downloaded articles in IEEE SP Magazine (April–August 2010).
• G. Scutari, D. P. Palomar, J.-S. Pang, and F. Facchinei, “Flexible Design for Cognitive Wireless Systems: From Game Theory to Variational Inequality Theory,” IEEE Signal Processing Magazine, vol. 26, no. 5, pp. 107–123, Sept. 2009 [DOI].
  Top 100 downloaded articles in IEEE among 1.25 million articles available (Sept. 2009)
  Top 10 downloaded articles in IEEE SP Magazine (Oct. 2009).
• G. Scutari, D. P. Palomar, and S. Barbarossa, “Cognitive MIMO Radio: Competitive Optimality Design Based on Subspace Projections,” IEEE Signal Processing Magazine, vol. 25, no. 6, pp. 46–59, Nov. 2008 [DOI].
  Top 100 downloaded articles in IEEE among 1.25 million articles available (Jan.–Mar. 2009).
  Top 10 downloaded articles in IEEE SP Magazine (Mar. 2009).
• S. Barbarossa and G. Scutari, “Bio-Inspired Sensor Network Design,” IEEE Signal Processing Magazine, vol. 24, no. 3, pp. 26–35, May 2007 [DOI].

The list includes papers published at the conferences NeurIPS, AAAI, ICML, and AISTATS (acceptance rates around 15%–25%).

• T. Cao, X. Chen, and G. Scutari, “DCatalyst: A Unified Accelerated Framework for Decentralized Optimization,” J. of Machine Learning Research, vol. 27, no. 47, pp. 1–57, 2026.
• X. Chen, T. Cao, and G. Scutari, “Enhancing Convergence of Decentralized Gradient Tracking under the KL Property,” IEEE Trans. on Automatic Control, early access, 2026 [DOI].
• I. Kuruzov, G. Scutari, and A. Gasnikov, “Achieving Linear Convergence with Parameter-Free Algorithms in Decentralized Optimization,” Proc. Conference on Neural Information Processing Systems (NeurIPS), 2024.
• M. Maros, G. Scutari, Y. Sun, G. Cheng, “Decentralized Sparse Linear Regression via Gradient-Tracking,” J. of Machine Learning Research, vol. 26, no. 197, pp. 1–56, 2025. The order of the first three authors is alphabetical.
• Y. Ji, G. Scutari, Y. Sun, and H. Honnappa, “Distributed Sparse Regression via Penalization,” J. of Machine Learning Research, vol. 24, no. 272, pp. 1–62, 2023.
• M. Maros and G. Scutari, “Decentralized Matrix Sensing: Statistical Guarantees and Fast Convergence,” Proc. Conference on Neural Information Processing Systems (NeurIPS), 2023.
• Y. Ji, G. Scutari, Y. Sun, and H. Honnappa, “Distributed (ATC) Gradient Descent for High-Dimension Sparse Regression,” IEEE Trans. on Information Theory, vol. 69, no. 8, pp. 5253–5276, 2023 [DOI].
• V. Kungurtsev, M. Morafah, T. Javidi, and G. Scutari, “Decentralized Asynchronous Non-convex Stochastic Optimization on Directed Graphs,” IEEE Trans. on Control of Network Systems, vol. 10, no. 4, pp. 1796–1804, 2023 [DOI].
• N. Michelusi, G. Scutari, and C.-S. Lee, “Finite-Bit Quantization for Distributed Algorithms with Linear Convergence,” IEEE Trans. on Information Theory, vol. 68, no. 11, pp. 7254–7280, Nov. 2022 [DOI].
• K. Slavakis, G. Shetty, L. Cannelli, G. Scutari, U. Nakarmi, and L. Ying, “Kernel Regression Imputation in Manifolds via Bi-linear Modeling: The Dynamic MRI case,” IEEE Trans. on Computational Imaging, vol. 8, pp. 133–147, 2022 [DOI].
• Y. Sun, G. Scutari, and A. Daneshmand, “Distributed Optimization Based on Gradient-Tracking Revisited: Enhancing Convergence Rate via Surrogation,” SIAM J. on Optimization, vol. 32, no. 2, pp. 354–385, 2022 [DOI].
• F. Facchinei, V. Kungurtsev, L. Lampariello, and G. Scutari, “Diminishing Stepsize Methods for Nonconvex Composite Problems via Ghost Penalties: from the General to the Convex Regular Constrained Case,” Optimization Methods and Software, vol. 37, no. 4, pp. 1242–1268, 2022 [DOI].
• M. Maros and G. Scutari, “Acceleration in Distributed Sparse Regression,” Proc. Conference on Neural Information Processing Systems (NeurIPS 2022), 2022.
• M. Maros and G. Scutari, “DGD²: A Linearly Convergent Distributed Algorithm for High-dimensional Statistical Recovery,” Proc. Conference on Neural Information Processing Systems (NeurIPS 2022), 2022.
• D. Kovalev, A. Beznosikov, E. Borodich, A. Gasnikov, and G. Scutari, “Optimal Gradient Sliding and Its Application to Optimal Distributed Optimization under Similarity,” Proc. Conference on Neural Information Processing Systems (NeurIPS 2022), 2022 (Spotlight, acceptance rate 4%).
• Y. Tian, G. Scutari, T. Cao, and A. Gasnikov, “Acceleration in Distributed Optimization under Similarity,” Proc. International Conference on Artificial Intelligence and Statistics (AISTATS 2022), 2022.
• F. Facchinei, V. Kungurtsev, L. Lampariello, and G. Scutari, “Ghost Penalties in Nonconvex Constrained Optimization: Diminishing Stepsizes and Iteration Complexity,” Mathematics of Operations Research, vol. 46, no. 2, pp. 595–627, 2021 [DOI].
• A. Daneshmand, G. Scutari, P. Dvurechensky, A. Gasnikov, “Newton Method over Networks is Fast up to the Statistical Precision,” Proc. International Conference on Machine Learning (ICML 2021), PMLR vol. 139, pp. 2398–2409, 2021.
• A. Beznosikov, G. Scutari, A. Rogozin, and A. Gasnikov, “Distributed Saddle-Point Problems under Data Similarity,” Proc. Conference on Neural Information Processing Systems (NeurIPS 2021), 2021.
• J. Xu, Y. Tian, Y. Sun, and G. Scutari, “Distributed Algorithms for Composite Optimization: Unified Framework and Convergence Analysis,” IEEE Trans. on Signal Processing, vol. 69, pp. 3555–3570, June 2021 [DOI].
• J. Xu, Y. Tian, Y. Sun, and G. Scutari, “Accelerated Primal-Dual Algorithms for Distributed Smooth Convex Optimization over Networks,” Proc. International Conference on Artificial Intelligence and Statistics (AISTATS 2020), 2020.
• L. Cannelli, F. Facchinei, G. Scutari, and V. Kungurtsev, “Asynchronous Optimization over Graphs: Linear Convergence under Error Bound Conditions,” IEEE Trans. on Automatic Control, vol. 66, no. 10, pp. 4604–4619, Oct. 2021 [DOI].
• C.-S. Lee, N. Michelusi, and G. Scutari, “Limited Rate Distributed Weight-Balancing and Average Consensus over Digraphs,” IEEE Trans. on Automatic Control, vol. 66, no. 10, pp. 4530–4545, Oct. 2021 [DOI].
• I. Notarnicola, Y. Sun, G. Scutari, and G. Notarstefano, “Distributed Big-Data Optimization via Block-wise Gradient Tracking,” IEEE Trans. on Automatic Control, vol. 66, no. 5, pp. 2045–2060, May 2021 [DOI].
• A. Daneshmand, G. Scutari, and V. Kungurtsev, “Second-order Guarantees of Distributed Gradient Algorithms,” SIAM J. on Optimization, vol. 30, no. 4, pp. 3029–3068, 2020 [DOI].
• Y. Tian, Y. Sun, and G. Scutari, “Achieving Linear Convergence in Distributed Asynchronous Multiagent Optimization,” IEEE Trans. on Automatic Control, vol. 65, no. 12, pp. 5264–5279, 2020 [DOI].
• L. Cannelli, F. Facchinei, V. Kungurtsev, G. Scutari, “Asynchronous Parallel Algorithms for Nonconvex Optimization,” Mathematical Programming, vol. 184, pp. 121–154, 2020 [DOI].
• G. Shetty, K. Slavakis, A. Bose, L. Ying, U. Nakarmi, and G. Scutari, “Bi-Linear Modeling of Data Manifolds for Dynamic-MRI recovery,” IEEE Trans. on Medical Imaging, vol. 39, no. 3, pp. 688–702, March 2020 [DOI].
• S. Bagchi, V. Aggarwal, S. Chaterji, F. Douglis, A. El Gamal, J. Han, B. J. Henz, H. Hoffmann, S. Jana, M. Kulkarni, F. X. Lin, K. Marais, P. Mittal, S. Mou, X. Qiu, and G. Scutari, “Vision Paper: Grand Challenges in Resilience: Autonomous System Resilience through Design and Runtime Measures,” IEEE Open Journal of the Computer Society, vol. 1, pp. 155–172, 2020 [DOI].
• G. Scutari and Y. Sun, “Distributed Nonconvex Constrained Optimization over Time-varying Digraphs,” Mathematical Programming, vol. 176, pp. 497–544, 2019 [DOI].
  Highly cited paper (ISI Web of Knowledge, 2019, 2020).
• A. Daneshmand, Y. Sun, G. Scutari, F. Facchinei, and B. Sadler, “Decentralized Dictionary Learning over Time-Varying Digraphs,” J. of Machine Learning Research, vol. 20, no. 139, pp. 1–62, 2019.
• A. Al-Shuwaili, O. Simeone, A. Bagheri, and G. Scutari, “Joint Uplink/Downlink Optimization for Backhaul-Limited Mobile Cloud Computing with User Scheduling,” IEEE Trans. on Signal and Information Processing over Networks, vol. 3, no. 4, pp. 787–802, Dec. 2017 [DOI].
  Top 50 downloaded articles in IEEE TSP (Feb. 2017–June 2017).
• G. Scutari, F. Facchinei, and L. Lampariello, “Parallel and Distributed Methods for Constrained Nonconvex Optimization – Part I: Theory,” IEEE Trans. on Signal Processing, vol. 65, no. 8, pp. 1929–1944, April 2017 [DOI].
  Top 50 downloaded articles in IEEE TSP (Feb. 2017 – June 2018).
  Highly cited paper (ISI Web of Knowledge, 2018, 2019).
• G. Scutari, F. Facchinei, L. Lampariello, S. Sardellitti, P. Song, “Parallel and Distributed Methods for Constrained Nonconvex Optimization – Part II: Applications in Communication and Machine Learning,” IEEE Trans. on Signal Processing, vol. 65, no. 8, pp. 1945–1960, April 2017 [DOI].
  Top 50 downloaded articles in IEEE TSP (Feb. 2017 – June 2017).
• F. Facchinei, L. Lampariello, and G. Scutari, “Feasible Methods for Nonconvex Nonsmooth Problems with Applications in Green Communications,” Mathematical Programming, vol. 164, pp. 55–90, 2017 [DOI].
• P. Di Lorenzo and G. Scutari, “NEXT: In-Network Nonconvex Optimization,” IEEE Trans. on Signal and Information Processing over Networks, vol. 2, no. 2, pp. 120–136, June 2016 [DOI].
  Top 50 downloaded articles in IEEE TSIPN (Dec. 2016 – May 2018).
  Highly cited paper (ISI Web of Knowledge, 2017, 2018, 2019).
• Y. Yang, G. Scutari, D. Palomar, and M. Pesavento, “A Parallel Stochastic Approximation Method for Nonconvex Multi-Agent Optimization Problems,” IEEE Trans. on Signal Processing, vol. 64, no. 11, pp. 2949–2964, June 2016 [DOI].
• Z. Guan, T. Melodia, and G. Scutari, “To Transmit or Not to Transmit? Distributed Queueing Games for Infrastructureless Wireless Networks,” IEEE/ACM Trans. on Networking, vol. 24, no. 2, pp. 1153–1166, April 2016 [DOI].
• A. Daneshmand, F. Facchinei, V. Kungurtsev, and G. Scutari, “Hybrid Random/Deterministic Parallel Algorithms for Convex and Nonconvex Big Data Optimization,” IEEE Trans. on Signal Processing, vol. 63, no. 15, pp. 3914–3929, August 2015 [DOI].
  Top 50 downloaded articles in IEEE TSP (July 2015).
• S. Sardellitti, G. Scutari, and S. Barbarossa, “Joint Optimization of Radio and Computational Resources for Multicell Mobile-Edge Computing,” IEEE Trans. on Signal and Information Processing over Networks, vol. 1, no. 2, pp. 89–103, June 2015 [DOI].
  Top 50 downloaded articles in IEEE TSIPN (Dec. 2016–June 2017).
  Highly cited paper (ISI Web of Knowledge, 2016–2019).
  2020 IEEE Signal Processing Society Best Paper Award.
• F. Facchinei, G. Scutari, and S. Sagratella, “Parallel Selective Algorithms for Big Data Optimization,” IEEE Trans. on Signal Processing, vol. 63, no. 7, pp. 1874–1889, April 2015 [DOI].
  Top 10 downloaded articles in IEEE TSP (March 2015)
  Top 50 downloaded articles in IEEE TSP (April 2015, May 2015).
• F. Facchinei, J.-S. Pang, and G. Scutari, “Non-Cooperative Games with Minmax Objectives,” Computational Optimization and Applications, vol. 59, no. 1, pp. 85–112, Oct. 2014 [DOI].
• G. Scutari, F. Facchinei, J.-S. Pang, and D. P. Palomar, “Real and Complex Monotone Communication Games,” IEEE Trans. on Information Theory, vol. 60, no. 7, pp. 4197–4231, July 2014 [DOI].
• F. Facchinei, J.-S. Pang, G. Scutari, and L. Lampariello, “VI-constrained Hemivariational Inequalities: Distributed Algorithms and Power Control in Ad-Hoc Networks,” Mathematical Programming, vol. 145, no. 1, pp. 59–96, June 2014 [DOI].
• I. Atzeni, L. G. Ordonez, G. Scutari, D. P. Palomar, and J. R. Fonollosa, “Noncooperative Day-Ahead Bidding Strategies for Demand-Side Expected Cost Minimization with Real-Time Adjustments: A GNEP Approach,” IEEE Trans. on Signal Processing, vol. 62, no. 9, pp. 2397–2412, May 2014 [DOI].
• A. Alvarado, G. Scutari, and J.-S. Pang, “A New Decomposition Method for Multiuser DC-Programming and Its Applications,” IEEE Trans. on Signal Processing, vol. 62, no. 11, pp. 2984–2998, June 2014 [DOI].
• G. Scutari, F. Facchinei, P. Song, D. P. Palomar, and J.-S. Pang, “Decomposition by Partial Linearization: Parallel Optimization of Multi-agent Systems,” IEEE Trans. on Signal Processing, vol. 62, no. 3, pp. 641–656, Feb. 2014 [DOI].
  2015 IEEE Signal Processing Society Young Author Best Paper Award.
  Highly cited paper (ISI Web of Knowledge, 2016, 2017, 2018, 2019, 2020).
• Y. Yang, F. Rubio, G. Scutari, and D. P. Palomar, “Multi-Portfolio Optimization: A Potential Game Approach,” IEEE Trans. on Signal Processing, vol. 61, no. 22, pp. 5590–5602, Nov. 2013 [DOI].
  Top 10 downloaded articles in IEEE TSP (Oct. 2013).
• Y. Yang, G. Scutari, P. Song, and D. P. Palomar, “Robust MIMO Cognitive Radio under Interference Temperature Constraints,” IEEE Journal on Selected Areas in Communications, vol. 31, no. 11, pp. 2465–2482, Nov. 2013 [DOI].
• G. Scutari and J.-S. Pang, “Joint Sensing and Power Allocation in Nonconvex Cognitive Radio Games: Nash Equilibria and Distributed Algorithms,” IEEE Trans. on Information Theory, vol. 59, no. 7, pp. 4626–4661, July 2013 [DOI].
• I. Atzeni, L. G. Ordonez, G. Scutari, D. P. Palomar, and J. R. Fonollosa, “Demand-Side Management via Distributed Energy Generation and Storage Optimization,” IEEE Transactions on Smart Grid, vol. 4, no. 2, pp. 866–876, June 2013 [DOI].
  Top 50 downloaded articles in IEEE TSG (Jan. 2017, April 2017).
  Highly cited paper (ISI Web of Knowledge, 2016, 2017, 2018, 2019, 2020).
• I. Atzeni, L. G. Ordonez, G. Scutari, D. P. Palomar, and J. R. Fonollosa, “Noncooperative and Cooperative Optimization of Distributed Energy Generation and Storage in the Demand-Side of the Smart Grid,” IEEE Trans. on Signal Processing, vol. 61, no. 10, pp. 2454–2472, May 2013 [DOI].
  Top 10 downloaded articles in IEEE TSP (June, July 2013).
• J.-S. Pang and G. Scutari, “Joint Sensing and Power Allocation in Nonconvex Cognitive Radio Games: Quasi Nash Equilibria,” IEEE Trans. on Signal Processing, vol. 61, no. 9, pp. 2366–2382, May 2013 [DOI].
• J.-S. Pang and G. Scutari, “Nonconvex Games with Side Constraints,” SIAM J. on Optimization, vol. 21, no. 4, pp. 1491–1522, Dec. 2011 [DOI].
• J. Wang, G. Scutari, and D. P. Palomar, “Robust MIMO Cognitive Radio via Game Theory,” IEEE Transactions on Signal Processing, vol. 59, no. 3, pp. 1182–1201, March 2011 [DOI].
  Top 10 downloaded articles in IEEE TSP (March 2011).
• J.-S. Pang, G. Scutari, D. P. Palomar, and F. Facchinei, “Design of Cognitive Radio Systems Under Temperature-Interference Constraints: A Variational Inequality Approach,” IEEE Trans. on Signal Processing, vol. 58, no. 6, pp. 3251–3271, June 2010 [DOI].
• G. Scutari and D. P. Palomar, “MIMO Cognitive Radio: A Game-Theoretical Approach,” IEEE Trans. on Signal Processing, vol. 58, no. 2, pp. 761–780, Feb. 2010 [DOI].
  Top 10 downloaded articles in IEEE TSP (Jan.-March 2010).
• G. Scutari, D. P. Palomar, and S. Barbarossa, “The MIMO Iterative Waterfilling Algorithm,” IEEE Trans. on Signal Processing, vol. 57, no. 5, pp. 1917–1935, May 2009 [DOI].
  Top 10 downloaded articles in IEEE TSP (May – July 2009).
• G. Scutari, D. P. Palomar, and S. Barbarossa, “Competitive Design of Multiuser MIMO Systems based on Game Theory: A Unified View,” IEEE J. on Selected Areas in Communications: Special Issue on Game Theory, vol. 26, no. 7, pp. 1089–1103, Sept. 2008 [DOI].
• J.-S. Pang, G. Scutari, F. Facchinei, and C. Wang, “Distributed Power Allocation with Rate Constraints in Gaussian Parallel Interference Channels,” IEEE Trans. on Information Theory, vol. 54, no. 8, pp. 3471–3489, Aug. 2008 [DOI].
• G. Scutari and S. Barbarossa, “Distributed Consensus Over Wireless Sensor Networks Affected by Multipath Fading,” IEEE Trans. on Signal Processing, vol. 56, no. 8, pp. 4100–4110, Aug. 2008 [DOI].
• G. Scutari, D. P. Palomar, and S. Barbarossa, “Asynchronous Iterative Water-Filling for Gaussian Frequency-Selective Interference Channels,” IEEE Trans. on Information Theory, vol. 54, no. 7, pp. 2868–2878, July 2008 [DOI].
• G. Scutari, S. Barbarossa, and L. Pescosolido, “Distributed Decision Through Self-Synchronizing Sensor Networks in the Presence of Propagation Delays and Asymmetric Channels,” IEEE Trans. on Signal Processing, vol. 56, no. 4, pp. 1667–1684, April 2008 [DOI].
• G. Scutari, D. P. Palomar, and S. Barbarossa, “Optimal Linear Precoding Strategies for Wideband Noncooperative Systems Based on Game Theory – Part I: Nash Equilibria,” IEEE Trans. on Signal Processing, vol. 56, no. 3, pp. 1230–1249, March 2008 [DOI].
  Highly cited paper (ISI Web of Knowledge, 2008, 2009).
• G. Scutari, D. P. Palomar, and S. Barbarossa, “Optimal Linear Precoding Strategies for Wideband Noncooperative Systems Based on Game Theory – Part II: Algorithms,” IEEE Trans. on Signal Processing, vol. 56, no. 3, pp. 1250–1267, March 2008 [DOI].
  Highly cited paper (ISI Web of Knowledge, 2008, 2009).
• S. Barbarossa and G. Scutari, “Decentralized Maximum-Likelihood Estimation for Sensor Networks Composed of Nonlinearly Coupled Dynamical Systems,” IEEE Trans. on Signal Processing, vol. 55, no. 7, pp. 3456–3470, July 2007 [DOI].
• G. Scutari and S. Barbarossa, “Distributed Space-time Coding for Regenerative Relay Networks,” IEEE Trans. on Wireless Communications, vol. 4, no. 5, pp. 2387–2399, Sept. 2005 [DOI].
• O. Simeone, U. Spagnolini, G. Scutari, and Y. Bar-Ness, “Physical-layer Distributed Synchronization in Wireless Networks and Applications,” Physical Communication, pp. 67–83, March 2008.

• X. Chen, I. Kuruzov, and G. Scutari, “A Line-search-free Method for Adaptive Decentralized Optimization,” Proc. of the 60th Asilomar Conference on Signals, Systems, and Computers, Oct. 25–28, 2026, Pacific Grove, CA, USA.
  Best Student Paper Award (1st Place).
• X. Chen, I. Kuruzov, G. Scutari, and A. Gasnikov, “A Parameter-free Decentralized Algorithm for Composite Convex Optimization,” Proc. of the 2025 IEEE 64th Conference on Decision and Control (CDC25), pp. 1544–1551, Dec. 2025 [DOI].
• M. Maros and G. Scutari, “A Unified View of Decentralized Algorithms for Sparse Linear Regression,” Proc. of the 2023 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP23), Dec. 10–13, 2023, Los Suenos, Costa Rica.
• A. Agafonov, P. Dvurechensky, G. Scutari, A. Gasnikov, D. Kamzolov, A. Lukashevich, and A. Daneshmand, “An Accelerated Second-Order Method for Distributed Stochastic Optimization,” Proc. of the 60th IEEE Conference on Decision and Control (CDC), 14–17 Dec. 2021, Austin, TX, USA.
• J. Xu, Y. Tian, Y. Sun, and G. Scutari, “A Unified Algorithmic Framework for Distributed Composite Optimization,” Proc. of the 59th IEEE Conference on Decision and Control (CDC20), Dec. 14–18, 2020, (online conference because of COVID-19).
• G. Shetty, K. Slavakis, U. Nakarmi, G. Scutari, and L. Ying, “Kernel Bi-Linear Modeling for Reconstructing Data on Manifolds: The Dynamic-MRI Case,” Proc. of the 28th European Signal Processing Conference (EUSIPCO 2020), pp. 1482–1486, 2020 [DOI].
• J. Xu, Y. Sun, Y. Tian, and G. Scutari, “A Unified Contraction Analysis of a Class of Distributed Algorithms for Composite Optimization,” Proc. of the 2019 IEEE Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP19), Dec. 15–18, 2019, Guadeloupe, West Indies.
• C.-S. Lee, N. Michelusi, and G. Scutari, “Distributed Quantized Weight-Balancing and Average Consensus over Digraphs,” Proc. of the 57th IEEE Conference on Decision and Control (CDC18), Dec. 17–19, 2018, Fontainebleau, Miami Beach, FL, USA.
• Y. Tian, Y. Sun, G. Scutari, and B. Du, “ASY-SONATA: Achieving Linear Convergence in Distributed Asynchronous Multiagent Optimization,” Proc. of the 56th Allerton Conference on Communication, Control, and Computing (Allerton 2018), Oct. 2–5, 2018, Monticello, IL, USA.
• A. Daneshmand, G. Scutari, and V. Kungurtsev, “Second Order Guarantees of Gradient Algorithms over Networks,” Proc. of the 56th Annual Allerton Conference on Communication, Control, and Computing, Oct. 3–5, 2018, Monticello, IL, USA.
• I. Notarnicola, Y. Sun, G. Scutari, and G. Notarstefano, “Distributed Big-Data Optimization via Block-Iterative Convexification and Averaging,” Proc. of the 56th IEEE Conference on Decision and Control, Dec. 12–15, 2017, Melbourne, Australia.
• I. Notarnicola, Y. Sun, G. Scutari, and G. Notarstefano, “Distributed Big-Data Optimization via Block Communications,” Proc. of the 2017 IEEE Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP17), Dec. 10–13, 2017, Curaçao, Dutch Antilles.
  Best Student Paper Award.
• L. Cannelli, F. Facchinei, V. Kungurtsev, and G. Scutari, “Essentially Cyclic Asynchronous Nonconvex Large-Scale Optimization,” Proc. of the 2017 IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC 2017), July 3–6, 2017, Sapporo, Japan.
  Best Paper Award runner-up.
• Y. Sun and G. Scutari, “Distributed Nonconvex Optimization for Sparse Representation,” Proc. of the 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP17), March 5–9, 2017, New Orleans, USA.
• A. Mokhtari, A. Koppel, G. Scutari, and A. Ribeiro, “Large-scale Nonconvex Stochastic Optimization by Doubly Stochastic Successive Convex Approximation,” Proc. of the 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP17), March 5–9, 2017, New Orleans, USA.
• A. Daneshmand, Y. Sun, G. Scutari, and F. Facchinei, “D2L: Decentralized Dictionary Learning over Dynamic Networks,” Proc. of the 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP17), March 5–9, 2017, New Orleans, USA.
• L. Cannelli, F. Facchinei, V. Kungurtsev, and G. Scutari, “Asynchronous Parallel Nonconvex Large-scale Optimization,” Proc. of the 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP17), March 5–9, 2017, New Orleans, USA.
• A. Daneshmand, G. Scutari, and F. Facchinei, “Distributed Dictionary Learning,” Proc. of the 50th Asilomar Conference on Signals, Systems, and Computers, Nov. 6–9, 2016, Pacific Grove, CA, USA.
• L. Cannelli, G. Scutari, F. Facchinei, and V. Kungurtsev, “Parallel Asynchronous Lock-Free Algorithms for Nonconvex Big-Data Optimization,” Proc. of the 50th Asilomar Conference on Signals, Systems, and Computers, Nov. 6–9, 2016, Pacific Grove, CA, USA.
  Best Paper Award runner-up (track D). [here]
• Y. Sun, G. Scutari, and D. Palomar, “Distributed Nonconvex Multiagent Optimization Over Time-Varying Networks,” Proc. of the 50th Asilomar Conference on Signals, Systems, and Computers, Nov. 6–9, 2016, Pacific Grove, CA, USA.
• P. Song, G. Scutari, F. Facchinei, and L. Lampariello, “D3M: Distributed Multi-Cell Multigroup Multicasting,” Proc. of the 2016 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP16), March 20–25, 2016, Shanghai, China.
• P. Di Lorenzo and G. Scutari, “Distributed Nonconvex Optimization Over Time-Varying Networks,” Proc. of the 2016 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP16), March 20–25, 2016, Shanghai, China.
• P. Di Lorenzo and G. Scutari, “Distributed Nonconvex Optimization Over Networks,” Proc. of the 6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, pp. 229–232, Dec. 13–16, 2015, Cancun, Mexico.
• L. Cannelli, P. Scarponi, G. Scutari, and L. Ying, “A Parallel Algorithm for Compressed Sensing Dynamic MRI Reconstruction,” Proc. of the 23rd Annual Meeting and Exhibition, International Society for Magnetic Resonance in Medicine (ISMRM), May 30–June 5, 2015, Toronto, Ontario, Canada.
• S. Sardellitti, S. Barbarossa, and G. Scutari, “Distributed Mobile Cloud Computing: Joint Optimization of Radio and Computational Resources,” Proc. of the 2014 IEEE Global Communications Conference, pp. 1505–1510, Dec. 8–12, 2014, Austin, TX, USA.
• A. Daneshmand, F. Facchinei, V. Kungurtsev, and G. Scutari, “Flexible Selective Parallel Algorithms for Big Data Optimization,” Proc. of the 48th Asilomar Conference on Signals, Systems, and Computers, pp. 3–7, Nov. 2–5, 2014, Pacific Grove, CA, USA.
• S. Sardellitti, G. Scutari, and S. Barbarossa, “Distributed Joint Optimization of Radio and Computational Resources for Mobile Cloud Computing,” Proc. of the 3rd IEEE International Conference on Cloud Networking, pp. 211–216, Oct. 8–10, 2014, Luxembourg.
• M. Mardani, L. Ying, G. Scutari, K. Slavakis, and G. Giannakis, “Dynamic MRI Using Subspace Tensor Tracking,” Proc. of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, August 26–30, 2014, Chicago, Illinois, USA.
• F. Facchinei, S. Sagratella, and G. Scutari, “Flexible Parallel Algorithms for Big Data Optimization,” Proc. of the 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP14), pp. 7208–7212, May 4–9, 2014, Florence, Italy.
• G. Scutari, F. Facchinei, L. Lampariello, and P. Song, “Parallel and Distributed Methods for Nonconvex Optimization,” Proc. of the 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP14), pp. 840–844, May 4–9, 2014, Florence, Italy.
• S. Sardellitti, G. Scutari, and S. Barbarossa, “Joint Cell Selection and MIMO Precoding in Heterogeneous Networks via Successive Convex Approximation,” Proc. of the 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP14), pp. 850–854, May 4–9, 2014, Florence, Italy.
• Y. Yang, G. Scutari, and D. Palomar, “Parallel Stochastic Decomposition Algorithms for Multi-Agent Systems,” Proc. of the 14th IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC13), pp. 180–184, June 16–19, 2013, Darmstadt, Germany.
• G. Scutari, F. Facchinei, P. Song, D. Palomar, and J.-S. Pang, “Decomposition by Partial Linearization in Multiuser Systems,” Proc. of the 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP13), pp. 4424–4428, May 26–31, 2013, Vancouver, Canada.
• I. Atzeni, L. Ordonez, G. Scutari, D. Palomar, and J. Fonollosa, “Cooperative Day-Ahead Bidding Strategies for Demand-side Expected Cost Minimization,” Proc. of the 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP13), pp. 5224–5228, May 26–31, 2013, Vancouver, Canada.
• Z. Guan, T. Melodia, and G. Scutari, “Distributed Queueing Games in Interference-limited Wireless Networks,” Proc. of the 2013 IEEE International Conference on Communications (ICC13), pp. 1810–1815, June 9–13, 2013, Budapest, Hungary.
• I. Atzeni, L. Ordonez, G. Scutari, D. Palomar, and J. Fonollosa, “Day-Ahead Bidding Strategies for Demand-Side Expected Cost Minimization,” Proc. of the 2012 IEEE Smart Grid Communications, pp. 91–96, Nov. 5–8, 2012, Tainan, Taiwan.
• G. Scutari, F. Facchinei, and J.-S. Pang, “Equilibrium Selection in MIMO Communication Games,” Proc. of the 13th IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC12), pp. 80–84, June 17–20, 2012, Cesme, Turkey.
• G. Scutari, F. Facchinei, J.-S. Pang, and L. Lampariello, “Equilibrium Selection in Power Control Games on the Interference Channel,” Proc. of the 2012 IEEE International Conference on Computer Communications, pp. 675–683, March 25–30, 2012, Orlando, Florida.
• G. Scutari, D. Palomar, F. Facchinei, and J.-S. Pang, “Distributed Dynamic Pricing for MIMO Interfering Multiuser Systems: A Unified Approach,” Proc. of the 5th International Conference on Network Games, Control and Optimization, Oct. 12–14, 2011, Paris, France.
• G. Scutari and J.-S. Pang, “Joint Sensing and Power Allocation in Nonconvex Cognitive Radio Games: Quasi-Nash Equilibria,” Proc. of the 17th International Conference on Digital Signal Processing, July 6–8, 2011, Corfu, Greece.
• Y. Yang, F. Rubio, G. Scutari, and D. Palomar, “Multi-Portfolio Optimization: A Potential Game Approach,” Proc. of the 2nd International ICST Conference on Game Theory for Networks, pp. 1–5, April 16–18, 2011, Shanghai, China.
• J. Wang, G. Scutari, and D. P. Palomar, “Robust MIMO Cognitive Radio via Game Theory,” Proc. of the 2010 IEEE International Symposium on Information Theory (ISIT10), pp. 2073–2077, June 13–18, 2010, Austin, Texas, USA.
• J.-S. Pang, G. Scutari, D. P. Palomar, and F. Facchinei, “Design of Cognitive Radio Systems Under Temperature-Interference Constraints: A Variational Inequality Approach,” Proc. of the 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP10), pp. 2997–2998, March 14–19, 2010, Sheraton Dallas Hotel, Dallas, Texas, USA.
• S. Barbarossa, S. Sardellitti, and G. Scutari, “Joint Optimization of Detection Thresholds and Power Allocation for Opportunistic Access in Multicarrier Cognitive Radio Networks,” Proc. of the Third IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP09), pp. 404–407, Dec. 13–16, 2009, Radisson Aruba Resort, Casino & Spa Aruba, Dutch Antilles.
• S. Barbarossa, S. Sardellitti, and G. Scutari, “Joint Optimization of Detection Thresholds and Power Allocation in Multiuser Wideband Cognitive Radios,” Proc. of the 2009 Conference on Cognitive Systems with Interactive Sensors, pp. 16–18, Nov. 16–18, 2009, Paris, France.
• S. Barbarossa, G. Scutari, and T. Battisti, “Cooperative Sensing for Cognitive Radio Using Decentralized Projection Algorithms,” Proc. of the 10th IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC09), pp. 116–120, June 21–24, 2009, Perugia, Italy.
• G. Scutari, D. P. Palomar, and S. Barbarossa, “Competitive Optimization of Cognitive Radio MIMO Systems via Game Theory,” Proc. of the 1st International Conference on Game Theory for Networks (GameNets 2009), pp. 452–461, May 13–15, 2009, Boğaziçi University, Istanbul, Turkey.
• S. Barbarossa, G. Scutari, and T. Battisti, “Distributed Signal Subspace Projection Algorithms with Maximum Convergence Rate for Sensor Networks with Topological Constraints,” Proc. of the 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP09), pp. 2893–2896, April 19–24, 2009, Taipei, Taiwan.
• S. Barbarossa, T. Battisti, L. Pescosolido, S. Sardellitti, and G. Scutari, “Distributed Spatial Smoothing Algorithms for Wireless Sensor Networks Having Fast Convergence and Robustness Against Coupling Noise,” Proc. of the 10th IEEE International Symposium on Spread Spectrum Techniques and Applications (ISSSTA08), pp. 7–11, Aug. 25–28, 2008, Bologna, Italy.
• G. Scutari, D. P. Palomar, and S. Barbarossa, “MIMO Cognitive Radio: A Game-Theoretical Approach,” Proc. of the 9th IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC08), pp. 426–430, July 7–9, 2008, Recife, Brazil.
• L. Pescosolido, S. Barbarossa, and G. Scutari, “Average Consensus Algorithms Robust Against Channel Noise,” Proc. of the 9th IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC08), pp. 261–265, July 7–9, 2008, Recife, Brazil.
• L. Pescosolido, S. Barbarossa, and G. Scutari, “Radar Sensor Networks with Distributed Detection Capabilities,” Proc. of the 2008 IEEE Radar Conference, 5 pages, May 26–30, 2008, Sheraton Golf Parco dei Medici, Rome, Italy.
• A. Fasano and G. Scutari, “The Effect of Additive Noise on Consensus Achievement in Wireless Sensor Networks,” Proc. of the 2008 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP08), pp. 2277–2280, March 30–April 4, 2008, Las Vegas, NV, USA.
• G. Scutari, D. P. Palomar, and S. Barbarossa, “Competitive Design of Multiuser MIMO Interference Systems Based on Game Theory: A Unified Framework,” Proc. of the 2008 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP08), pp. 5376–5379, March 30–April 4, 2008, Caesars Palace, Las Vegas, Nevada, USA.
• G. Scutari and S. Barbarossa, “Optimal Distributed Decision over Wireless Sensor Networks Affected by Multipath Fading,” Proc. of the 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP07), pp. 245–248, Dec. 12–14, 2007, St. Thomas, Virgin Islands, USA.
• G. Scutari, D. P. Palomar, and S. Barbarossa, “Optimal Decentralized Linear Precoding for Wideband Non-Cooperative Interference Systems Based on Game Theory,” Proc. of the 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP07), pp. 85–88, Dec. 12–14, 2007, St. Thomas, Virgin Islands, USA.
• O. Simeone and G. Scutari, “Pulse-coupled distributed PLLs in Heterogeneous Wireless Networks,” Proc. of the 41st Asilomar Conference on Signals, Systems & Computers, pp. 1770–1775, Nov. 4–7, 2007, Pacific Grove, CA, USA.
• G. Scutari, S. Barbarossa, and L. Pescosolido, “Distributed Decision Through Self-Synchronizing Sensor Networks in the Presence of Propagation Delays and Asymmetric Channels,” Proc. of the 8th IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC07), June 17–20, 2007, Helsinki, Finland.
• G. Scutari, D. P. Palomar, and S. Barbarossa, “Distributed Totally Asynchronous Iterative Waterfilling for Wideband Interference Channel with Time/Frequency Offset,” Proc. of the 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP07), vol. 4, pp. 1325–1328, April 15–20, 2007, Honolulu, Hawaii, USA.
• S. Barbarossa, G. Scutari, and A. Swami, “Achieving Consensus in Self-Organizing Wireless Sensor Networks: The Impact of Network Topology on Energy Consumption,” Proc. of the 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP07), vol. 2, pp. II-841–II-844, April 15–20, 2007, Honolulu, Hawaii, USA.
• G. Scutari, D. P. Palomar, and S. Barbarossa, “Asynchronous Iterative Waterfilling for Gaussian Frequency-Selective Interference Channels: A Unified Framework,” Proc. of the 2007 Information Theory and Applications Workshop (ITA07), pp. 349–358, Jan. 29–Feb. 2, 2007, La Jolla, CA, USA.
• S. Barbarossa, G. Scutari, and A. Swami, “Distributed Detection and Estimation in Decentralized Sensor Networks: An Overview,” Proc. of the 14th European Signal Processing Conference, Sept. 4–8, 2006, Florence, Italy.
• G. Scutari, D. P. Palomar, and S. Barbarossa, “Simultaneous Iterative Water-Filling for Gaussian Frequency-Selective Interference Channels,” Proc. of the 2006 IEEE International Symposium on Information Theory (ISIT06), pp. 600–604, July 9–14, 2006, Seattle, WA, USA.
• G. Scutari, D. P. Palomar, and S. Barbarossa, “Asynchronous Iterative Water-Filling for Gaussian Frequency-Selective Interference Channels: A Unified Framework,” Proc. of the 7th IEEE Workshop on Signal Processing Advances in Wireless Communications, July 2–5, 2006, Cannes, France.
• G. Scutari, S. Barbarossa, and L. Pescosolido, “Optimal Decentralized Estimation Through Self-Synchronizing Networks in the Presence of Propagation Delays,” Proc. of the 7th IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC06), July 2–5, 2006, Cannes, France.
• G. Scutari, S. Barbarossa, and D. P. Palomar, “Potential Games: A Framework for Vector Power Control Problems with Coupled Constraints,” Proc. of the 2006 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP06), vol. 4, pp. 241–244, May 14–19, 2006, Toulouse, France.
  Best Student Paper Award.
• S. Barbarossa, G. Scutari, and L. Pescosolido, “Global stability of a Population of Mutually Coupled Oscillators Reaching Global Maximum Likelihood Estimate Through a Decentralized Approach,” Proc. of the 2006 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP06), vol. 4, pp. 385–388, May 14–19, 2006, Toulouse, France.
• L. Pescosolido, S. Barbarossa, and G. Scutari, “Decentralized Detection and Localization Through Sensor Networks Designed as a Population of Self-Synchronizing Oscillators,” Proc. of the 2006 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP06), vol. 4, pp. 981–984, May 14–19, 2006, Toulouse, France.
• S. Barbarossa, L. Pescosolido, D. Ludovici, and G. Scutari, “Cooperative Wireless Networks based on Distributed Space-time Coding,” Proc. of the 2004 International Workshop on Wireless Ad-Hoc Networks, pp. 30–34, May 31–June 3, 2004, Oulu, Finland.
• G. Scutari, S. Barbarossa, and D. Ludovici, “On the Maximum Achievable Rates in Wireless Meshed Networks: Centralized versus Decentralized Solutions,” Proc. of the 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP04), vol. 4, pp. 573–576, May 17–21, 2004, Montreal, Quebec, Canada.
• S. Barbarossa and G. Scutari, “Distributed Space-Time Coding Strategies for Wideband Multihop Networks: Regenerative vs. non-Regenerative Relays,” Proc. of the 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP04), vol. 4, pp. 501–504, May 17–21, 2004, Montreal, Quebec, Canada.
• S. Barbarossa and G. Scutari, “Distributed Space-Time Coding for Multihop Networks,” Proc. of the 2004 IEEE International Conference on Communications (ICC04), vol. 2, pp. 916–920, June 20–24, 2004, Paris, France.
• G. Scutari, S. Barbarossa, and D. Ludovici, “Cooperation Diversity in Multihop Wireless Networks Using Opportunistic Driven Multiple Access,” Proc. of the 4th IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC03), pp. 170–174, June 15–18, 2003, Rome, Italy.
• S. Barbarossa and G. Scutari, “Optimal Rate Allocation for Multiple Access Wideband Systems with Rate Profile Constraint and Adaptive Power Control,” Proc. of the 4th IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC03), pp. 115–119, June 15–18, 2003, Rome, Italy.
• S. Barbarossa and G. Scutari, “Cooperative Diversity Through Virtual Arrays in Multihop Networks,” Proc. of the 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP03), vol. 4, pp. 209–212, April 6–10, 2003, Hong Kong, China.
• G. Scutari, G. Paccapeli, and S. Barbarossa, “Concatenated Space-Time Block Coding with Optimal Trade-off between Maximum Diversity and Coding Gains,” Proc. of the 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP03), vol. 4, pp. 333–336, April 6–10, 2003, Hong Kong, China.
• G. Scutari and S. Barbarossa, “Generalized Water-Filling for Multiple Transmit Antenna Systems,” Proc. of the 2003 IEEE International Conference on Communications (ICC03), vol. 3, pp. 2668–2672, May 11–15, 2003, Anchorage, Alaska, USA.
• S. Barbarossa, G. Scutari, and G. Paccapeli, “Concatenated Space-Time Block Coding with Maximum Diversity Gain,” Proc. of the 2003 IEEE International Conference on Communications (ICC03), vol. 3, pp. 2129–2133, May 11–15, 2003, Anchorage, Alaska, USA.
• S. Barbarossa and G. Scutari, “On the Maximum Common Rate in Multiple Access Channels,” Proc. of the 11th European Signal Processing Conference, pp. 1–4, Sept. 3–6, 2002, Toulouse, France.
• S. Barbarossa, G. Scutari, and A. Swami, “MUI-FREE CDMA Systems Incorporating Space-Time Coding and Channel Shortening,” Proc. of the 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP02), pp. III-2213 – III-2216, May 13–17, 2002, Orlando, Florida, USA.
• S. Barbarossa, G. Scutari, and D. Scamolla, “Efficient Space-Time Coding for Wideband CDMA Systems Using Variable-Length Cyclic Prefix,” Proc. of the 2002 European Wireless Conference, Feb. 25–28, 2002, Florence, Italy.

• G. Scutari and Daniel P. Palomar, “Multiple-Input, Multiple-Output Cognitive Radio,” US Patent 8,706,041, April 22, 2014. Licensed.

• Gesualdo Scutari, “Competition and Cooperation in Wireless Communication Networks,” PhD. Dissertation, University of Rome, “La Sapienza,” Rome, Italy, March 2005.