Research
RESEARCH TOPICS
My research lies at the intersection of optimization, learning, signal processing, and wireless communications.
Interpretable Models in Machine Learning
Models at the heart of machine learning and artificial intelligence. ML follows the black-box model (i.e., no interpretability). Interpreabilty is achieved by designing model which facilitates finding causal relations. We instantiate these relations by finding sufficient conditions which bind (in the sense of implications in mathematical logic) outcomes of specific random variables and extract valuable relations from the data. We also propose an efficient algorithm for computing the model.
We also investigate applications of this framework in: recommendation systems, bioinformatics (DNA analysis), and beamforming for large mmWave MIMO
SELECTED RELEVANT WORK:
- H. Ghauch, H. Shokri, M. Skoglund, C. Fischione, A. Sayed, "Learning Kolmogorov Models for Binary Random Variables", ICML 2018
- H. Ghauch, M. Skoglund, C. Fischione, A. Sayed, "Learning elementary representations of random variables", Journal of Machine Learning Research (in preparation)
Learning-based Resource Allocation
Many wireless communication systems are highly correlated: millimeter-wave communication, line-of-sight channels. We leverage this inherent property using machine learning to perform resource allocation, based on position information. This drastically reduces the overhead needed estimating the large number of channels in the network
SELECTED RELEVANT WORK:
- S. Imtiaz, H. Ghauch, G. Koudouridis, J. Gross, "Random forests for resource allocation in 5G cloud radio access networks based on position information", EURASIP Journal on Wireless Communications and Networking, May 2017
- S Imtiaz, H. Ghauch, M. Rahman, G. Koudouridis, J. Gross, "Learning-Based Resource Allocation Scheme for TDD-Based 5G CRAN System" , in Proc of ACM MSWIM 2016, Malta
- S Imtiaz, H. Ghauch, G. Koudouridis, J. Gross, " Random Forests Resource Allocation for 5G Systems: Performance and Robustness Study", IEEE WCNC 2018
Optimization for mmWave Communication
mmWave bands have ~200 times more spectrum than traditional cellular systems. Hundreds of antennas are needed at the transmitter and receiver (to combat the sever pathloss of mmWave channels), making the power consumption too high. Instead, a Hybrid Analog-Digital Architecture was proposed instead (overview slide)
Our works address several fundamental problems such as efficient channels estimation, algorithmic solution to the hybrid precoding problem, and channel tracking in case of mobility
SELECTED RELEVANT WORK (picking up on google scholar):
- H. Ghauch, T. Kim, M. Skoglund, M. Bengtsoon, “Blind Subspace Estimation and Decomposition in large millimeter-wave MIMO systems”, in IEEE Journal of Selected Topics in Signal Processing, vol. 10, no. 3, pp. 528-542, April 2016.
- H. Ghauch, M. Bengtsson, T. Kim, M. Skoglund, “Subspace Estimation and Decomposition for Hybrid Analog-Digital Millimetre-Wave MIMO systems”, Proc of IEEE SPAWC 2015, Stockholm, Sweden
- J. He, T. Kim, H. Ghauch, K. Liu, G. Wang, "Millimeter Wave MIMO Channel tracking systems", in Proc. of IEEE GLOBECOM, Austin, TX, USA, 2014
- Subspace Estimation and Decomposition in Large millimeter-wave MIMO systems (slides)
Low-overhead Coordination in 5G Cellular Networks
5G cellular systems require ever increasing data rates. Distributed coordination among base station increases network throughput. However, the coordination overhead is a limiting factor.
Our works proposed several classes of distributed optimization with fast convergence, to reduce the coordination overhead (overview slide)
SELECTED RELEVANT WORK:
- H. Ghauch, T. Kim, M. Bengtsson, M. Skoglund, " Separability and Sum-rate Maximization in Sub-28 GHz Millimeter-wave MIMO systems", Journal of Selected Areas in Communications, April 2017
- H. Ghauch, T. Kim, M. Bengtsson, M. Skoglund “Distributed Low-overhead schemes for Multi-stream MIMO Interference Channels ”, in IEEE Trans. on Signal Processing, April 2015
- R. Mochaourab, R. Brandt, H. Ghauch, M. Bengtsson, "Overhead-Aware Distributed CSI Selection in the MIMO Interference Channels", in proc of EUSIPCO '15, Nice, France
- H. Ghauch, R. Mochaourab, M. Bengtsson, M. Skoglund, "Distributed Precoding and User Selection in MIMO Interfering Networks", in Proc of IEEE CAMSAP '15, Cancun, Mexico
Optimizing Cloud Radio-Access Networks
Most of the gains in data rates are from densification (more antennas/base stations). Cloud RAN allows for tight coordination of antennas, in a cheap manner (because it is centralized).
Our works investigate several optimization techniques to address problems such as beamforming design, and user assignment (overview slide)
SELECTED RELEVANT WORK:
- H. Ghauch, M. Rahman, S. Imtiaz, J. Gross, M. Skoglund, C. Qvarfordt " User Assignment in Antenna Domain Systems: Algorithms and Bounds ", Trans. on Wireless Communications
- H. Ghauch, M. Rahman, S. Imtiaz, J. Gross, "Cooperation and Antenna-Domain Formation in Cloud RAN systems", in Proc of IEEE ICC 2016, Kuala Lampur, Malaysia
- H. Ghauch, C. Papadias, “Interference Alignment: A one sided approach", in Proc of IEEE GLOBECOM 2011, Houston, TX, USA