Georgios B. Giannakis

Georgios B. Giannakis (born February 27, 1958) is a Greek–American Professor, engineer, and inventor. At present he is an Endowed Chair Professor of Wireless Telecommunications, a McKnight Presidential Endowed Chair with the Department of Electrical and Computer Engineering, and Director of the Digital Technology Center at the University of Minnesota.

Georgios B. Giannakis
Born (1958-02-27) 27 February 1958
Piraeus, Greece
NationalityUnited States and Greek
Alma mater
Scientific career
Fields
Institutions
Websitespincom.umn.edu

Giannakis is internationally known for his work in the areas of statistical signal processing, distributed estimation using sensor networks, wireless communications and cross-layer network designs, on topics such as auto-regressive moving average system identification using higher-order statistics,[1][2] principal component filter banks,[3] linear precoding,[4] multicarrier modulation,[5] ultra-wideband communications,[6] cognitive radios, and smart grids. Seminal work includes the development of linear precoding wireless communication systems,[4] which provided a unified approach for designing space-time block codes that achieve data high rates and reliability, and proposal of zero-padding as an alternative to the cyclic prefix for multi-carrier communication systems,[7] which had impact in the multi-band ultra wide band standard.[6] Current research focuses on big data, graph learning, and network science with applications to social, brain and power networks with renewables.

Giannakis has left a substantial academic legacy as an advisor of more than 52 Ph.D. dissertations and mentor of more than 26 postdoctoral researchers at the University of Virginia and the University of Minnesota.

Early life

Born in Piraeus and raised in Corinth, Greece, Giannakis received his MA in electrical engineering from the National Technical University of Athens in 1981, his M.Sc. in electrical engineering from the University of Southern California in 1983, his M.Sc. in mathematics from the University of Southern California in 1986, and his PhD in electrical engineering from the University of Southern California also in 1986.[8] After completing his Ph.D., he started his academic career at the University of Virginia in 1987 and moved to the University of Minnesota in 1999. As a professor, he built a distinguished research group making contributions in many areas including statistical signal processing, wireless communications, sensor and mobile ad hoc networks and data analytics.

Awards and honors

Giannakis is also a co-author of nine best journal paper awards including the IEEE Communications Society's Gugliermo Marconi Prize Paper Award for work on linear precoding,[26] the 2003 IEEE Signal Processing Society's SP Magazine Best Paper Award for a paper on wireless multicarrier communication,[27][28] an IEEE Signal Processing Society's Best Paper Award in 2001 for work on parallel factor analysis in sensor array processing,[29] an IEEE Signal Processing Society's Best Paper Award, 2000 for work on designing filterbank precoders and equalizers.[3]

Invention and Commercialization

Giannakis has 34 US and foreign patents issued in the fields of wireless communications (several related to the 4G LTE standard), cognitive radio sensing, signal processing, power system monitoring, and photovoltaic inverters in residential power distribution. Through those he became a fellow of the US National Academy of Inventors, `…the highest professional distinction accorded to academic inventors who have demonstrated a prolific spirit of innovation…’ Multiple lawsuits were filed by the University of Minnesota against Sprint, T-Mobile, Verizon, and AT&T[30] based on Giannakis’ patents.[31][32][33][34]

Research contributions

Statistical signal processing: theory and applications (1985–1995)

Giannakis established an important result in identifying a linear system with statistically independent input, based only on its output. He showed that non-minimum phase and non-causal auto-regressive moving average models can be uniquely recovered via higher-order statistics (HOS).[1][2] Only zero-, maximum-, or minimum-phase models can be recovered if second-order output statistics are used.[35] Further, he established that HOS guarantee identifiability of systems with noisy inputs (errors-in-variables) and closed-loop systems with correlated Gaussian noise of unknown spectra, as well as multidimensional and multichannel systems with output only data and independent inputs. HOS identify such multi-input multi-output (MIMO) systems by removing the rotational (unitary matrix) ambiguity present with second-order statistics – a basic result that led to the renown tool of independent component analysis and further enabled blind separation of sources received by sensor arrays. Highly regarded are also Giannakis’ identification of linear time-varying systems using basis expansion models including Fourier bases, and optimally chosen wavelet bases and multiresolution depths; HOS-based Gaussianity and linearity tests, detection, estimation, pattern recognition, noise cancellation, object registration, image motion estimation, and the first proof that HOS can estimate directions of arrival of more sources with less antenna elements. Besides non-Gaussian stationary signals, he contributed influential results on consistency and asymptotic normality of HOS for a class of non-stationary and cyclostationary processes. For those, he developed widely applied statistical tests for the presence of cyclostationarity, as well as algorithms for retrieval of harmonics in the presence of multiplicative and additive noise; time series analysis with random and periodic misses; delay-Doppler estimators based on the high-order ambiguity function; multi-component polynomial phase signals for synthetic-aperture radar, and their impact to time-varying image motion estimation.

Wireless communications at the physical layer (1994–2004)

Giannakis and collaborators made fundamental contributions in wireless communication systems. One main contribution was to show how block-based linear precoding could transform a frequency-selective MIMO channel into a set of parallel frequency-flat channels.[4] Another main contribution was to develop a unified approach to design space-time block codes in MIMO channels. Such codes enable maximum diversity and coding gains at full rate (1 symbol per channel use) for any number of transmit-receive antennas.[26] Linear precoding is widely used in commercial wireless systems like IEEE 802.11n[36] and 3GPP LTE.[37] Another seminal contribution resulted in a multicarrier communication technique that is resilient to frequency-selective multi-user and inter-symbol interference. He further designed linear multicarrier precoding combined with a block spreading operation together render the user signature matrix at the receiver well-conditioned, without power control or bandwidth over-expansion.[27] This result shows that block processing of communication signals becomes an important dimension that can improve communication performance without altering power or bandwidth. An additional commercially valuable innovation was the use of zero-padding instead of a cyclic prefix.[7] Using a zero prefix has advantages in the application to multi-band OFDM in ultra wideband because it extends the coverage range by avoiding power back-off at the transmitter.[6][5] Further seminal contributions include the principal component filterbank that benchmarks performance of multiresolution based compression schemes;[38] transmitter-induced cyclostationarity ensuring identifiability of frequency-selective channels even from second-order statistics; optimal training as well as blind estimation and equalization of time- and frequency-selective channels using a basis expansion model;[39] linear multichannel equalizers of nonlinear Volterra channels with memory;[40] and a unifying cyclostationary approach to all-digital (non-) data aided timing and carrier synchronization. Giannakis and collaborators also contributed pioneering approaches to multi-antenna communications that include space-time-frequency-Doppler coded orthogonal frequency division multiplexing systems that attain the maximum diversity order; utilize feedback of the channel mean or correlation to develop optimal transmit-beamformers that markedly outperform maximum receive-SNR designs; and can also afford a highly acclaimed simple and general parameterization that enables quantifiable performance analysis when communicating over wireless single- and multi-antenna fading channels.[41] Additional highly cited results encompass ultra-wideband wireless communications,[42] innovative synchronization algorithms, their performance analyses, and impact to highly accurate positioning systems.[43]

Cross-layer network designs (2003–2008)

The open system interconnection (OSI) model of communication networks comprises multiple design layers. For tractability reasons, each layer was individually optimized, up until it was recognized that joint designs can afford markedly improved performance.  For wireless networks, Giannakis and collaborators were the first to demonstrate how by leveraging channel knowledge at the transmitter, a modulator that adapts to the intended fading channel at the physical (PHY) layer can be fruitfully co-designed with the automatic repeat request (ARQ) strategy at the medium access control (MAC) layer to improve throughput.[44][45] In addition to PHY-MAC, they investigated co-designs involving schedulers with quality of service (QoS) guarantees, as well as queuing with adaptive modulation and coding.[46] They further contributed cross-layer congestion and contention control designs for wireless ad hoc networks,[47] cross-layer optimization of multicast,[44] wireless multihop random access,[47] and wireless cognitive radio networks.

Wireless sensor networks and distributed inference (2004–2012)

Information processing and inference across wirelessly connected low-power and low-cost sensors, have well documented merits in application domains such as environmental sensing for habitat surveillance, intelligent agriculture, and health monitoring using body area networks. Such wireless sensor networks (WSNs) with or without a central computing unit (fusion center) face major challenges due to their limited bandwidth, stringent power to prolong sensor lifetime, the need to cope with nonstationary and spatiotemporally correlated data, synchronization, access, and resource allocation, to carry out the desired distributed inference tasks. Giannakis and his team pioneered energy-efficient sensor scheduling, power-efficient modulations, and bandwidth-constrained estimators,[48] along with relevant fundamental performance bounds,[49] by investigating inference jointly with compression, quantization, and censoring. Surprisingly, even with a few (1-3) bits per sensor sample, the fusion center could attain 90% of the estimation and tracking performance possible with unquantized observations even with a Kalman tracker utilizing just the sign of innovations. Although known as an optimization approach in deterministic settings, Giannakis and his collaborators were also the first to unveil the importance of the alternating direction method of multipliers (ADMM) for fully distributed statistical inference using (ad hoc) WSN processing based on consensus operations.[50] In a series of highly influential results, they contributed static and online ADMM-based approaches for distributed regression and particle filtering for distributed tracking,[51] classification using distributed SVMs,[52] clustering, and dimensionality reduction tailored for WSNs.

Wireless cognitive radio sensing and communications (2007–2017)

The ever-increasing demand for bandwidth to accommodate emerging multimedia applications, and large-scale interconnection of heterogeneous devices, have resulted in an explosive growth of Internet protocol (IP) traffic. This prompted the need for wireless cognitive radio (CR) sensing, communications, and networking that can mitigate the radio-frequency (RF) interference, and judiciously allocate the spectrum, control traffic congestion and routing, as well monitor the network health, flag risks, and overall guarantee secure connectivity. Giannakis and his research team contributed landmark tools for sensing the RF ambiance, propagation channels, and overall provide a succinct depiction of the network state—what is now widely known as cartography of the spectral density, channel gains, path delays, utilization of links, and unveiling anomalies. Whether blind or with training, it was believed that channel estimation requires at least output or input-output data, meaning one must have access to the receive- and perhaps also the transmit-end too. Giannakis bypassed non-cooperative CR transceivers by reformulating channel gain estimation as a function interpolation task using a sufficient number of spatiotemporal samples.[53] He further leveraged structural properties of this (generally dynamic) learning function, namely sparsity,[54][55][56] low rank,[54] space-time correlation, Kriging,[53] and radio tomography-related techniques, to obtain accurate sensing maps even with quantized measurements. Together with his collaborators, they utilized these maps for distributed CR scheduling, dynamic resource management using limited-rate feedback,[57] power control with imperfect exchanges, joint CR sensing and allocation of multichannel CRs, optimal beamforming, statistical routing, cross-layer optimization using interference tweets, and optimal chance-constrained management of orthogonal frequency-division multiple access (OFDMA) radios.

Power systems and smart grid with renewables (2011–2019)

Giannakis and his research group have contributed state-of-the-art signal processing, machine learning, and optimization algorithms tailored for monitoring and managing contemporary power grids.[58] Critical to monitoring have been innovative approaches to power system state estimation, including robust and distributed solvers based on semidefinite programing,[59] and deep neural networks;[60] optimal placement of phasor measurement units to facilitate situational awareness;[61] efficient identification of bad data and power line outages using sparsity to effectively flag power blackouts;[62] and forecasting of demand, real-time load elasticity and pricing for electric vehicle charging, as well as prediction of electricity market prices. Seminal contributions for managing the smart power grid include distributed scheduling even when residential control messages are lost;[63] decentralized optimal power flow for microgrids;[64] active and stochastic reactive power management with renewables (wind and photovoltaics);[65] large-scale demand response for market clearing; patents on optimal dispatch of photovoltaic inverters in residential power distribution;[66] voltage regulation using deep reinforcement learning,[67] and ergodic energy control leveraging resource variability for multiphase distribution grids.[68]

Data science, graph learning, and artificial intelligence (2008–2020)

With documented expertise in statistics and optimization tools, Giannakis' research team contributed innovative solutions to challenging science and engineering problems by capitalizing on the data deluge, while jointly leveraging physics-guided and data-driven models. Their key novelties have markedly advanced machine learning with data collected at distributed agents, and offered learning models that account for nonlinear data dependencies, structures, dynamics, and outliers. They were the first to develop consensus-based distributed (gossip) schemes for classification, sparse regression, and clustering using the alternating direction method of multipliers (ADMM);[69] and pioneered a solver based on judiciously designed cluster-heads to speed up decentralized optimization.[70] Sparsity and low rank were the data structures they exploited early on to develop online estimators of sparse signals;[71] to cope with perturbed compressive sampling using sparse total least-squares,[72] and also insightfully link compressive sensing with robust statistics, simply because data outliers are sparse.[73] This link led to major results on robust smoothing of dynamical signals via outlier constraints; sparse polynomial regression models; robust nonparametric regression via sparsity control; robust principal component analysis, robust multi-dimensional scaling, and robust clustering schemes.[74] Prompted by the NP-hard task of reconstructing a signal from its magnitude, they also developed state-of-the-art algorithms to solve random systems of quadratic equations.[75] They further established identifiability of models comprising a low-rank matrix plus a compressed matrix.[76] This result is not only intriguing by itself (it can find the summands from the sum), but also impactful to unveiling network traffic anomalies, and accelerating dynamic magnetic resonance imaging at desirable resolution levels. Another seminal contribution to nonlinear learning models was to enable a nonparametric basis function pursuit via sparse kernel-based learning,[77] what led to the first approach to tensor completion and extrapolation with applications to spectrum cartography, network flow prediction, and imputation of gene expression data.

A cornerstone of data science is learning from big data, where the latter refers to the volume (dimensionality and number) of data, their velocity (of streaming data), and variety (multimodality).[78] To extract the sought information that often resides in small subspaces, and cope with subsampled or missing data, Giannakis and collaborators put forth an online censoring approach for large-scale regressions and trackers,[79] where only informative data are retained for learning. Instead of censoring, they also adopted a limited number of random data projections (sketches) and validated whether they contain informative data, before employing them for (subspace) clustering to obtain desirable performance-complexity tradeoffs.[80] They further introduced linear subspace learning and imputation schemes for streaming tensors; online categorical subspace learning; and kernel-based nonlinear subspace trackers on a budget.[81]

Graphs underpin the structure and operation of networks everywhere: from the Internet to the power grid, financial markets, social media, gene regulation, and brain functionality. Whether graph edges capture physical interconnections or interdependencies among nodes or variables, learning a graph and carrying out inference of processes on a graph, are two tasks of paramount importance in data science, network science, and applications. Giannakis and collaborators established conditions to first identify topologies of directed graphs using sparse linear or nonlinear,[82] and static or dynamic structural equation models.[83] These models relate endogenous nodal variables with or without exogenous inputs, under sparsity and low-rank constraints. Multilayer graphs, as well as evolving graphs with memory (such as those emerging with generally nonlinear structural vector autoregressive models) are viewed as exogenous inputs. If the latter are not available, results of Giannakis' team show how to "blindly" identify directed graph topologies by decomposing tensor statistics of nodal data obtained under dynamic graph changes.[84] They further employed such graphs as prior information to offer a unifying graph kernel-based approach to statistical inference of (non) stationary processes over graphs.[85] Whether for interpolation, denoising, or extrapolation, their innovation accounts for dynamic and/or nonlinear interdependencies of nodal processes. These are instrumental in practice to predict partially observed dynamic processes over communication networks;[86] to estimate IP traffic and map anomalies in such networks; to infer functions over brain networks, as well as regulatory processes by leveraging genetic perturbations on gene networks; and even track cascades over social networks under smooth or switching dynamics. To cope with large-scale graphs, they further developed canonical correlation analysis tools for graph data; data adaptive active sampling strategies; node embeddings with adaptive similarities; and random walk driven adaptive diffusions that can outperform state-of-the-art graph convolutional neural networks.[87]

Giannakis and collaborators have also contributed to the resurgence of artificial intelligence (AI), and specifically to the areas of crowdsourcing, ensemble learning, interactive learning, and the associated performance analyses. Highly acclaimed results include blind and active multi-class meta-learning with categorical information from unequally reliable learners with possibly correlated and sequential data;[88] random feature-based online multi-kernel learning in environments with unknown dynamics;[89] and a Bayesian approach via ensemble (non)Gaussian processes for online learning with scalability, robustness, and uncertainty quantification through regret analyses. Additional major advances include (deep) reinforcement learning as applied to adaptive caching in hierarchical content delivery networks.[90] The novel caching schemes account for space-time content popularity in future-generation communication networks, and also dynamic storage pricing.

Selected books and book chapters
  • G. B. Giannakis, Y. Hua, P. Stoica, L. Tong, Editors, Signal Processing Advances in Wireless and Mobile Communications, Vol. 1: Trends in Channel Est. and Equalization, Prentice Hall, 2000.
  • G. B. Giannakis, Y. Hua, P. Stoica, L. Tong, Editors, Signal Processing Advances in Wireless and Mobile Communications, Vol. 2: Trends in Single- and Multi-User Systems, Prentice Hall, Inc., 2000.
  • G. B. Giannakis, Z. Liu, X. Ma, and S. Zhou, Space-Time Coding for Broadband Wireless Communications, John Wiley & Sons, Inc., 2007.
  • V. Kekatos, G. Wang, H. Zhu, and G. B. Giannakis, "PSSE redux: Convex relaxation, decentralized, robust, and dynamic approaches", Chapter in Advances in Electric Power and Energy; M. El-Hawary Editor, 2018.
  • G. Mateos and G. B. Giannakis, "Robust PCA by controlling sparsity in model residuals", Chapter in T. Bouwmans, E. Zahzah, and N. Aybat, Editors, CRC Press, 2017.
  • G. B. Giannakis, G. Mateos, I. D. Schizas, H. Zhu, and Q. Ling, "Decentralized learning for wireless communications and networking", Chapter in Splitting Methods... by R. Glowinski, S. Osher, and W. Yin, Editors, NY, Springer, 2016.
  • X. Ma and G. B. Giannakis, "Communicating over Wireless Doubly-Selective Channels", Chapter in Space-Time Wireless..., H. Boelcskei, D. Gesbert, C.B. Papadias and A.-J. van der Veen Eds., Cambridge U. Press, 2006.
  • Z. Tian, T. Davidson, X. Luo, X. Wu and G. B. Giannakis, "Ultra-Wideband Pulse-Shaper Design", Chapter in UWB Wireless Communications, H. Arslan and Y. Chen, Wiley 2005.
  • G. B. Giannakis, "Statistical Signal Processing", Chapter in DSP, V. K. Madisetti, D. Williams, Editors-in-Chief, CRC Press, 1998.
  • G. B. Giannakis, "Trends in Spectral Analysis: Higher-Order and Cyclic Statistics", Chapter in Digital Signal Proc. Tech., P. Papamichalis and R. Kerwin, Eds., pp. 74–97, vol. CR57, 1995.

Selected publications
  • S. Gezici, Z. Tian, G. B. Giannakis, H. Kobayashi, A. V. Molisch, H. V. Poor and Z. Sahinoglu, "Localization via Ultra-Wideband Radios", IEEE Signal Processing Magazine, vol. 22, no. 4, pp. 70–84, July 2005.
  • L. Yang, and G. B. Giannakis, "Ultra-Wideband Communications: An Idea whose Time has Come", IEEE Signal Processing Magazine, vol. 21, no. 6, pp. 26–54, November 2004.
  • Q. Liu, S. Zhou, and G. B. Giannakis, "Cross-Layer Combining of Adaptive Modulation and Coding with Truncated ARQ over Wireless Links", IEEE Trans. on Wireless Communications, vol. 3, no. 5, pp. 1746–1755, September 2004.
  • Z. Wang, and G. B. Giannakis, "A Simple and General Parameterization Quantifying Performance in Fading Channels", IEEE Transactions on Communications, vol. 51, no. 8, pp. 1389–1398, August 2003.
  • P. Xia, and G. B. Giannakis, "Design and Analysis of Transmit-Beamforming based on Limited-Rate Feedback", IEEE Transactions on Signal Processing, vol. 54, no. 5, pp. 1853–1863, May 2006.
  • G. B. Giannakis, P. Anghel and Z. Wang, "Generalized Multi-Carrier CDMA: Unification and Equalization", EURASIP Journal of Applied Signal Processing, pp. 743–756, February 2005.
  • Y. Xin, Z. Wang, and G. B. Giannakis, "Space-Time Diversity Systems based on Linear Constellation Precoding", IEEE Transactions on Wireless Communications, vol. 2, no. 2, pp. 294–309, March 2003.
  • N. D. Sidiropoulos, R. Bro, and G. B. Giannakis, "Parallel Factor Analysis in Sensor Array Processing", IEEE Transactions on Signal Processing, vol. 48, pp. 2377–2388, August 2000.
  • Z. Wang and G. B. Giannakis, "Wireless Multicarrier Communications: Where Fourier Meets Shannon", IEEE Signal Processing Magazine, vol. 17, pp. 29–48, May 2000.
  • A. Scaglione, G. B. Giannakis, and S. Barbarossa, "Redundant Filterbank Precoders and Equalizers Part I: Unification and Optimal Designs", IEEE Transactions on Signal Processing, vol. 47, pp. 1988– 2006, July 1999.
  • M. K. Tsatsanis and G. B. Giannakis, "Principal component filter banks for optimal multiresolution analysis", IEEE Transactions on Signal Processing, vol. 43, pp. 1766–1777, August 1995.
  • G. B. Giannakis and J. M. Mendel, "Identification of non-minimum phase systems using higher-order statistics", IEEE Transactions on Acoustics Speech and Signal Processing, vol. 37, pp. 360–377, March 1989.

Selected patents
  • G. B. Giannakis, and X. Ma, "Estimating Frequency-offsets and Multi-antenna Channels in MIMO-OFDM Systems", US Patent no. US 10,700,800 B2; issued June 30, 2020; impacted LTE (3GPP Tech. Spec.36.211, Sec.6.10).
  • S. Dhople, G. B. Giannakis, and E. Dall’Anese, "Decentralized Optimal Dispatch of Photovoltaic Inverters in Power Distribution Systems", US Patent no. 10,139,800 B2, issued Nov. 27, 2018.
  • G. B. Giannakis, and H. Zhu, "State Estimation of Electrical Power Networks using Semidefinite Relaxation", US Patent no. 9,863,985, issued January 9, 2018.
  • G. B. Giannakis, E. Dall'Anese, J. A. Bazerque, H. Zhu, and G. Mateos, "Robust Parametric Power Spectrum Density Map Construction", US Patent no. 9,363,679, issued June 7, 2016; RF maps for wireless cognitive radios.
  • G. B. Giannakis, G. Mateos, and J. A. Bazerque, "Non-parametric Power Spectral Density Map Construction", US Patent no. 9,191,831, issued November 17, 2015.
  • G. B. Giannakis, Y. Xin, and Z. Wang, "Wireless communication system having linear encoder", US Patent no. RE45,230, issued Nov. 4, 2014; complex-field codes that combat fading effects to ensure fast-reliable wireless links.
  • G. B. Giannakis, P. Xia and S. Zhou, "Bandwidth- and Power-Efficient Multi-Carrier Multiple Access for Uplink Broadband Wireless Communications", US Patent no. 7,672,384, issued March 2, 2010.

References
  1. Giannakis, G. B.; Mendel, J. M. (March 1989). "Identification of nonminimum phase systems using higher order statistics". IEEE Transactions on Acoustics, Speech, and Signal Processing. 37 (3): 360–377. doi:10.1109/29.21704. ISSN 0096-3518.
  2. Giannakis, G. B.; Swami, A. (March 1990). "On estimating noncausal nonminimum phase ARMA models of non-Gaussian processes". IEEE Transactions on Acoustics, Speech, and Signal Processing. 38 (3): 478–495. doi:10.1109/29.106866. ISSN 0096-3518.
  3. Scaglione, A.; Giannakis, G. B.; Barbarossa, S. (July 1999). "Redundant filterbank precoders and equalizers. I. Unification and optimal designs". IEEE Transactions on Signal Processing. 47 (7): 1988–2006. doi:10.1109/78.771047. ISSN 1053-587X.
  4. Scaglione, A.; Stoica, P.; Barbarossa, S.; Giannakis, G. B.; Sampath, H. (May 2002). "Optimal designs for space-time linear precoders and decoders". IEEE Transactions on Signal Processing. 50 (5): 1051–1064. CiteSeerX 10.1.1.16.9100. doi:10.1109/78.995062. ISSN 1053-587X.
  5. Batra, Anuj; Giannakis, G. B. (May 2000). "Wireless multicarrier communications". IEEE Signal Processing Magazine. 17 (3): 29–48. doi:10.1109/79.841722. ISSN 1053-5888.
  6. Batra, A; Balakrishnan, J; Aiello, G; Foerster, J; Dabak, A (September 2004). "Design of multiband OFDM system for realistic UWB channel environments". IEEE Transactions on Microwave Theory and Techniques. 52 (9): 2123–2138. CiteSeerX 10.1.1.330.5178. doi:10.1109/TMTT.2004.834184. S2CID 16835205.
  7. Muquet, B.; Wang, Zhengdao; Giannakis, G. B.; Courville, M. de; Duhamel, P. (December 2002). "Cyclic prefixing or zero padding for wireless multicarrier transmissions?". IEEE Transactions on Communications. 50 (12): 2136–2148. CiteSeerX 10.1.1.12.6811. doi:10.1109/TCOMM.2002.806518. ISSN 0090-6778.
  8. Georgios B. Giannakis degrees at umn.edu. Accessed September 5, 2013
  9. "Academy of Europe: Giannakis Georgios". www.ae-info.org. Retrieved 2020-07-28.
  10. "Athanasios Papoulis Award".
  11. "European Academy of Sciences".
  12. "Signal Processing Society Awardees".
  13. "U of M Professor Georgios Giannakis to be inducted into the National Academy of Inventors".
  14. "Prof. Georgios Giannakis Receives the 2019 IEEE Communications Society Education Award".
  15. "Georgios Giannakis awarded prestigious McKnight Presidential Endowed Chair".
  16. "IEEE Fourier Award for Signal Processing Recipients".
  17. "The composition of the Council Foundation".
  18. "EURASIP Fellows".
  19. "Individual Technical".
  20. "Georgios B. Giannakis".
  21. "IEEE Signal Processing Society Technical Achievement Award" (PDF).
  22. "IEEE Fellows Directory".
  23. "World's Top Computer Scientists: H-Index Computer Science Ranking". www.guide2research.com. Retrieved 2020-07-08.
  24. "G.B. Giannakis Google scholar".
  25. "31 Greek researchers among the world's most influential scientific minds".
  26. Xin, Yan; Wang, Zhengdao; Giannakis, G. B. (March 2003). "Space-time diversity systems based on linear constellation precoding". IEEE Transactions on Wireless Communications. 2 (2): 294–309. CiteSeerX 10.1.1.12.8101. doi:10.1109/TWC.2003.808970. ISSN 1536-1276.
  27. Wang, Zhendao; Giannakis, G. B. (May 2000). "Wireless multicarrier communications". IEEE Signal Processing Magazine. 17 (3): 29–48. doi:10.1109/79.841722. ISSN 1053-5888.
  28. "IEEE Signal Processing Society Signal Processing Magazine Best Paper Award" (PDF).
  29. Sidiropoulos, N. D.; Bro, R.; Giannakis, G. B. (August 2000). "Parallel factor analysis in sensor array processing". IEEE Transactions on Signal Processing. 48 (8): 2377–2388. CiteSeerX 10.1.1.21.4217. doi:10.1109/78.852018. ISSN 1053-587X.
  30. "AT&T, Verizon, Others Flouted U. Of Minn. Patents, Suit Says".
  31. US Grant 7,251,768, Georgios Giannakis & Shengli Zhou, "Wireless communication system having error-control coder and linear precoder", published February 5, 2004, issued July 31, 2007, assigned to Regents of the University of Minnesota (Minneapolis, MN)
  32. US Grant 8,588,317, Georgios Giannakis, Xiaoli Ma & Xiaoli Ma, "Estimating frequency-offsets and multi-antenna channels in MIMO OFDM systems", published November 19, 2013, issued November 19, 2013, assigned to Regents of the University of Minnesota (Minneapolis, MN)
  33. US Grant 8,718,185, Georgios Giannakis, Xiaoli Ma & Xiaoli Ma, "Estimating frequency-offsets and multi-antenna channels in MIMO OFDM systems", published 2014-05-06, issued 2014-05-06, assigned to Regents of the University of Minnesota (Minneapolis, MN)
  34. US Grant 8,774,309, Georgios Giannakis, Xiaoli Ma & Xiaoli Ma, "Estimating frequency-offsets and multi-antenna channels in MIMO OFDM systems", published 2014-07-08, issued 2014-07-08, assigned to Regents of the University of Minnesota (Minneapolis, MN)
  35. Mendel, Jerry M. (March 1991). "Tutorial on higher-order statistics (spectra) in signal processing and system theory: Theoretical results and some applications". Proceedings of the IEEE. 79 (3): 278–305. doi:10.1109/5.75086.
  36. "IEEE 802.11n standard". IEEE. Retrieved July 26, 2017.
  37. 3GPP Technical Specification 36.211; Sections 6.3.3, 6.3.4, and 6.10
  38. Tsatsanis, M.K.; Giannakis, G.B. (1995). "Principal component filter banks for optimal multiresolution analysis". IEEE Transactions on Signal Processing. 43 (8): 1766–1777. doi:10.1109/78.403336. ISSN 1053-587X.
  39. Giannakis, G.B.; Tepedelenlioglu, C. (October 1998). "Basis expansion models and diversity techniques for blind identification and equalization of time-varying channels". Proceedings of the IEEE. 86 (10): 1969–1986. doi:10.1109/5.720248.
  40. Giannakis, G.B.; Serpedin, E. (1996). "Blind equalizers of multichannel linear-quadratic FIR Volterra channels". Proceedings of 8th Workshop on Statistical Signal and Array Processing. IEEE Comput. Soc. Press: 371–374. doi:10.1109/ssap.1996.534893. ISBN 0-8186-7576-4. S2CID 124873969.
  41. Wang, Zhengdao; Giannakis, G.B. (August 2003). "A simple and general parameterization quantifying performance in fading channels". IEEE Transactions on Communications. 51 (8): 1389–1398. doi:10.1109/tcomm.2003.815053. ISSN 0090-6778.
  42. Yang, L.; Giannakis, G.B. (November 2004). "Ultra-wideband communications - An idea whose time has come". IEEE Signal Processing Magazine. 21 (6): 26–54. doi:10.1109/MSP.2004.1359140. ISSN 1053-5888.
  43. Gezici, S.; Zhi, T.; Giannakis, G.B.; Kobayashi, H.; Molisch, A.F.; Poor, H.V.; Sahinoglu, Z. (July 2005). "Localization via ultra-wideband radios: a look at positioning aspects for future sensor networks". IEEE Signal Processing Magazine. 22 (4): 70–84. doi:10.1109/MSP.2005.1458289. ISSN 1053-5888. S2CID 2174942.
  44. Rajawat, Ketan; Gatsis, Nikolaos; Giannakis, Georgios B. (October 2011). "Cross-Layer Designs in Coded Wireless Fading Networks With Multicast". IEEE/ACM Transactions on Networking. 19 (5): 1276–1289. arXiv:1003.5239. doi:10.1109/tnet.2011.2109010. ISSN 1063-6692. S2CID 8871776.
  45. Liu, Q.; Zhou, S.; Giannakis, G.B. (September 2004). "Cross-Layer Combining of Adaptive Modulation and Coding With Truncated ARQ Over Wireless Links". IEEE Transactions on Wireless Communications. 3 (5): 1746–1755. doi:10.1109/twc.2004.833474. ISSN 1536-1276. S2CID 7439785.
  46. Liu, Qingwen; Zhou, Shengli; Giannakis, G.B. (May 2005). "Queuing with adaptive modulation and coding over wireless links: cross-Layer analysis and design". IEEE Transactions on Wireless Communications. 4 (3): 1142–1153. doi:10.1109/twc.2005.847005. ISSN 1536-1276. S2CID 9287319.
  47. Kliazovich, Dzmitry; Granelli, Fabrizio (November 2006). "Cross-layer congestion control in ad hoc wireless networks". Ad Hoc Networks. 4 (6): 687–708. doi:10.1016/j.adhoc.2005.08.001. ISSN 1570-8705.
  48. Ribeiro, A.; Giannakis, G.B. (2006). "Bandwidth-constrained distributed estimation for wireless sensor Networks-part I: Gaussian case". IEEE Transactions on Signal Processing. 54 (3): 1131–1143. doi:10.1109/TSP.2005.863009. ISSN 1941-0476. S2CID 16223482.
  49. Zhu, Hao; Cano, Alfonso; Giannakis, Georgios (June 2010). "Distributed consensus-based demodulation: algorithms and error analysis". IEEE Transactions on Wireless Communications. 9 (6): 2044–2054. doi:10.1109/twc.2010.06.090890. ISSN 1536-1276. S2CID 1708666.
  50. Schizas, Ioannis D.; Ribeiro, Alejandro; Giannakis, Georgios B. (2008). "Consensus in ad hoc WSNs with noisy links - Part I: Distributed estimation of deterministic signals". IEEE Transactions on Signal Processing. 56 (1): 350–364. doi:10.1109/TSP.2007.906734. ISSN 1053-587X. S2CID 17406788.
  51. Ribeiro, Alejandro; Schizas, Ioannis D.; Roumeliotis, Stergios I.; Giannakis, Georgios B. (2010). "Kalman Filtering in Wireless Sensor Networks: Reducing communication cost in state-estimation problems". IEEE Control Systems Magazine. 30 (2): 66–86. doi:10.1109/MCS.2009.935569. ISSN 1066-033X. S2CID 8025516.
  52. A, ForeroPedro; CanoAlfonso; B, GiannakisGeorgios (2010). "Consensus-Based Distributed Support Vector Machines". The Journal of Machine Learning Research.
  53. Emiliano, Dall’Anese; Kim, Seung-Jun; Giannakis, Georgios B. (March 2011). "Channel Gain Map Tracking via Distributed Kriging". IEEE Transactions on Vehicular Technology. 60 (3): 1205–1211. doi:10.1109/TVT.2011.2113195. S2CID 9488427.
  54. Lee, Donghoon; Kim, Seung-Jun; Giannakis, Georgios B. (September 2017). "Channel Gain Cartography for Cognitive Radios Leveraging Low Rank and Sparsity". IEEE Transactions on Wireless Communications. 16 (9): 5953–5966. doi:10.1109/TWC.2017.2717822. S2CID 2594515.
  55. Bazerque, J.A.; Giannakis, G.B. (March 2010). "Distributed Spectrum Sensing for Cognitive Radio Networks by Exploiting Sparsity". IEEE Transactions on Signal Processing. 58 (3): 1847–1862. doi:10.1109/TSP.2009.2038417. ISSN 1053-587X. S2CID 10628871.
  56. Tian, Zhi; Giannakis, Georgios B. (April 2007). "Compressed Sensing for Wideband Cognitive Radios". 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07. 4: IV–1357–IV-1360. doi:10.1109/ICASSP.2007.367330. ISBN 978-1-4244-0727-9. S2CID 15068636.
  57. Kim, Seung-Jun; Giannakis, Georgios B. (May 2011). "Optimal Resource Allocation for MIMO Ad Hoc Cognitive Radio Networks". IEEE Transactions on Information Theory. 57 (5): 3117–3131. doi:10.1109/TIT.2011.2120270. ISSN 0018-9448. S2CID 15032887.
  58. Giannakis, Georgios B.; Kekatos, Vassilis; Gatsis, Nikolaos; Kim, Seung-Jun; Zhu, Hao; Wollenberg, Bruce F. (2013). "Monitoring and Optimization for Power Grids: A Signal Processing Perspective". IEEE Signal Processing Magazine. 30 (5): 107–128. arXiv:1302.0885. doi:10.1109/MSP.2013.2245726. ISSN 1558-0792. S2CID 2491099.
  59. Zhu, Hao; Giannakis, Georgios B. (2014). "Power System Nonlinear State Estimation Using Distributed Semidefinite Programming". IEEE Journal of Selected Topics in Signal Processing. 8 (6): 1039–1050. doi:10.1109/JSTSP.2014.2331033. ISSN 1941-0484. S2CID 16032161.
  60. Zhang, Liang; Wang, Gang; Giannakis, Georgios B. (2019). "Real-Time Power System State Estimation and Forecasting via Deep Unrolled Neural Networks". IEEE Transactions on Signal Processing. 67 (15): 4069–4077. arXiv:1811.06146. doi:10.1109/TSP.2019.2926023. ISSN 1941-0476. S2CID 53433892.
  61. Kekatos, Vassilis; Giannakis, Georgios B.; Wollenberg, Bruce (2012). "Optimal Placement of Phasor Measurement Units via Convex Relaxation". IEEE Transactions on Power Systems. 27 (3): 1521–1530. doi:10.1109/TPWRS.2012.2185959. ISSN 1558-0679. S2CID 14315556.
  62. Zhu, Hao; Giannakis, Georgios B. (2012). "Sparse Overcomplete Representations for Efficient Identification of Power Line Outages". IEEE Transactions on Power Systems. 27 (4): 2215–2224. doi:10.1109/TPWRS.2012.2192142. ISSN 1558-0679. S2CID 11897055.
  63. Gatsis, Nikolaos; Giannakis, Georgios B. (2012). "Residential Load Control: Distributed Scheduling and Convergence With Lost AMI Messages". IEEE Transactions on Smart Grid. 3 (2): 770–786. doi:10.1109/TSG.2011.2176518. ISSN 1949-3061. S2CID 674732.
  64. Dall'Anese, Emiliano; Dhople, Sairaj V.; Giannakis, Georgios B. (2014). "Optimal dispatch of photovoltaic inverters in residential distribution systems". 2014 IEEE PES General Meeting | Conference Exposition: 1. arXiv:1307.3751. doi:10.1109/PESGM.2014.6939035. ISBN 978-1-4799-6415-4. S2CID 52318633.
  65. Kekatos, Vassilis; Wang, Gang; Conejo, Antonio; Giannakis, Georgios (2015). "Stochastic reactive power management in microgrids with renewables". 2015 IEEE Power Energy Society General Meeting: 1. arXiv:1409.6758. doi:10.1109/PESGM.2015.7286375. ISBN 978-1-4673-8040-9. S2CID 6827664.
  66. Zhang, Yu; Gatsis, Nikolaos; Giannakis, Georgios B. (2013). "Robust Energy Management for Microgrids With High-Penetration Renewables". IEEE Transactions on Sustainable Energy. 4 (4): 944–953. arXiv:1207.4831. doi:10.1109/TSTE.2013.2255135. ISSN 1949-3037. S2CID 10963015.
  67. Yang, Qiuling; Wang, Gang; Sadeghi, Alireza; Giannakis, Georgios B.; Sun, Jian (2020). "Two-Timescale Voltage Control in Distribution Grids Using Deep Reinforcement Learning". IEEE Transactions on Smart Grid. 11 (3): 2313–2323. arXiv:1904.09374. doi:10.1109/TSG.2019.2951769. ISSN 1949-3061. S2CID 208617841.
  68. Wang, Gang; Kekatos, Vassilis; Conejo, Antonio J.; Giannakis, Georgios B. (2016). "Ergodic Energy Management Leveraging Resource Variability in Distribution Grids". IEEE Transactions on Power Systems. 31 (6): 4765–4775. arXiv:1508.00654. doi:10.1109/TPWRS.2016.2524679. ISSN 1558-0679. S2CID 21927.
  69. A, ForeroPedro; CanoAlfonso; B, GiannakisGeorgios (August 2010). "Consensus-Based Distributed Support Vector Machines". The Journal of Machine Learning Research. doi:10.1145/1791212.1791218. S2CID 555634.
  70. Ma, Meng; Giannakis, Georgios B. (October 2018). "Graph-aware Weighted Hybrid ADMM for Fast Decentralized Optimization". 2018 52nd Asilomar Conference on Signals, Systems, and Computers. IEEE: 1881–1885. doi:10.1109/acssc.2018.8645558. ISBN 978-1-5386-9218-9. S2CID 67864918.
  71. Angelosante, Daniele; Bazerque, Juan Andrés; Giannakis, Georgios B. (July 2010). "Online Adaptive Estimation of Sparse Signals: Where RLS Meets the $\ell_1$-Norm". IEEE Transactions on Signal Processing. 58 (7): 3436–3447. doi:10.1109/tsp.2010.2046897. ISSN 1053-587X. S2CID 1670277.
  72. Z., Hao; Leus, G.; Giannakis, G. B. (May 2011). "Sparsity-Cognizant Total Least-Squares for Perturbed Compressive Sampling". IEEE Transactions on Signal Processing. 59 (5): 2002–2016. arXiv:1008.2996. doi:10.1109/tsp.2011.2109956. ISSN 1053-587X. S2CID 2016796.
  73. Kekatos, V.; Giannakis, G. B. (July 2011). "From Sparse Signals to Sparse Residuals for Robust Sensing". IEEE Transactions on Signal Processing. 59 (7): 3355–3368. arXiv:1011.0450. doi:10.1109/TSP.2011.2141661. ISSN 1941-0476. S2CID 16981922.
  74. Mateos, G.; Giannakis, G. B. (October 2012). "Robust PCA as Bilinear Decomposition With Outlier-Sparsity Regularization". IEEE Transactions on Signal Processing. 60 (10): 5176–5190. arXiv:1111.1788. doi:10.1109/TSP.2012.2204986. ISSN 1941-0476. S2CID 3100452.
  75. Wang, G.; Giannakis, G. B.; Eldar, Y. C. (February 2018). "Solving Systems of Random Quadratic Equations via Truncated Amplitude Flow". IEEE Transactions on Information Theory. 64 (2): 773–794. doi:10.1109/TIT.2017.2756858. ISSN 1557-9654. S2CID 382743.
  76. Mardani, M.; Mateos, G.; Giannakis, G. B. (February 2013). "Dynamic Anomalography: Tracking Network Anomalies Via Sparsity and Low Rank". IEEE Journal of Selected Topics in Signal Processing. 7 (1): 50–66. arXiv:1208.4043. doi:10.1109/JSTSP.2012.2233193. ISSN 1941-0484. S2CID 8379547.
  77. Bazerque, Juan Andres; Giannakis, Georgios B. (July 2013). "Nonparametric Basis Pursuit via Sparse Kernel-Based Learning: A Unifying View with Advances in Blind Methods". IEEE Signal Processing Magazine. 30 (4): 112–125. doi:10.1109/msp.2013.2253354. ISSN 1053-5888. S2CID 11973124.
  78. Slavakis, Konstantinos; Giannakis, Georgios B.; Mateos, Gonzalo (September 2014). "Modeling and Optimization for Big Data Analytics: (Statistical) learning tools for our era of data deluge". IEEE Signal Processing Magazine. 31 (5): 18–31. doi:10.1109/MSP.2014.2327238. ISSN 1558-0792. S2CID 16794388.
  79. Berberidis, Dimitris; Kekatos, Vassilis; Giannakis, Georgios B. (October 2016). "Online Censoring for Large-Scale Regressions with Application to Streaming Big Data". IEEE Transactions on Signal Processing. 64 (15): 3854–3867. doi:10.1109/TSP.2016.2546225. ISSN 1941-0476. PMC 5198787. PMID 28042229.
  80. Traganitis, Panagiotis A.; Slavakis, Konstantinos; Giannakis, Georgios B. (June 2015). "Sketch and Validate for Big Data Clustering". IEEE Journal of Selected Topics in Signal Processing. 9 (4): 678–690. arXiv:1501.05590. doi:10.1109/JSTSP.2015.2396477. ISSN 1941-0484. S2CID 2298975.
  81. Mardani, Morteza; Mateos, Gonzalo; Giannakis, Georgios B. (June 2015). "Subspace Learning and Imputation for Streaming Big Data Matrices and Tensors". IEEE Transactions on Signal Processing. 63 (10): 2663–2677. doi:10.1109/TSP.2015.2417491. ISSN 1941-0476. S2CID 8134310.
  82. Cai, Xiaodong; Bazerque, Juan Andrés; Giannakis, Georgios B. (2013-05-23). "Inference of Gene Regulatory Networks with Sparse Structural Equation Models Exploiting Genetic Perturbations". PLOS Computational Biology. 9 (5): e1003068. doi:10.1371/journal.pcbi.1003068. ISSN 1553-7358. PMC 3662697. PMID 23717196.
  83. Giannakis, Georgios B.; Shen, Yanning; Karanikolas, Georgios Vasileios (May 2018). "Topology Identification and Learning over Graphs: Accounting for Nonlinearities and Dynamics". Proceedings of the IEEE. 106 (5): 787–807. doi:10.1109/jproc.2018.2804318. ISSN 0018-9219. S2CID 13740314.
  84. Shen, Yanning; Giannakis, Georgios B.; Baingana, Brian (2019-10-15). "Nonlinear Structural Vector Autoregressive Models With Application to Directed Brain Networks". IEEE Transactions on Signal Processing. 67 (20): 5325–5339. doi:10.1109/tsp.2019.2940122. ISSN 1053-587X. PMC 6779157. PMID 31592214.
  85. Romero, Daniel; Ma, Meng; Giannakis, Georgios B. (February 2017). "Kernel-Based Reconstruction of Graph Signals". IEEE Transactions on Signal Processing. 65 (3): 764–778. doi:10.1109/TSP.2016.2620116. ISSN 1941-0476. S2CID 11959872.
  86. Forero, Pedro A.; Rajawat, Ketan; Giannakis, Georgios B. (July 2014). "Prediction of Partially Observed Dynamical Processes Over Networks via Dictionary Learning". IEEE Transactions on Signal Processing. 62 (13): 3305–3320. doi:10.1109/TSP.2014.2325798. ISSN 1941-0476. S2CID 18431953.
  87. Berberidis, Dimitris; Nikolakopoulos, Athanasios N.; Giannakis, Georgios B. (March 2019). "Adaptive Diffusions for Scalable Learning Over Graphs". IEEE Transactions on Signal Processing. 67 (5): 1307–1321. arXiv:1804.02081. doi:10.1109/TSP.2018.2889984. ISSN 1941-0476. S2CID 4692126.
  88. Traganitis, Panagiotis A.; Pagès-Zamora, Alba; Giannakis, Georgios B. (September 2018). "Blind Multiclass Ensemble Classification". IEEE Transactions on Signal Processing. 66 (18): 4737–4752. doi:10.1109/TSP.2018.2860562. hdl:2117/120513. ISSN 1941-0476. S2CID 49907089.
  89. Shen, Yanning; Tianyi, Chen; Giannakis, Georgios B. (January 2019). "Random feature-based online multi-kernel learning in environments with unknown dynamics". The Journal of Machine Learning Research. 20: 1–36.
  90. Sadeghi, Alireza; Wang, Gang; Giannakis, Georgios B. (December 2019). "Deep Reinforcement Learning for Adaptive Caching in Hierarchical Content Delivery Networks". IEEE Transactions on Cognitive Communications and Networking. 5 (4): 1024–1033. arXiv:1902.10301. doi:10.1109/TCCN.2019.2936193. ISSN 2332-7731. S2CID 195886353.
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