Multiuser MIMO Communication

Multiple-Input-Multiple-Output (MIMO) communication, hence the use of multiple antennas at the transmitter and receiver side along with sophisticated analog and digital signal processing techniques, is a key technology in modern cellular and wireless communication systems. Besides the frequency and time dimension, the use of MIMO side brings in the space dimension as an additional resource to enhance data rates, enable user separation, and improve energy efficiency of the transmissions. Spatial precoding techniques use channel information at the transmitter side to simultaneously open multiple data streams over the same radio resource to multiantenna users, and to significantly boost the data rates in the network. In addition, the information along all resource dimensions (time, frequency, space) can be encoded with space-time coding techniques to provide reliable communication link. Furthermore, robust transmit and receive beamforming techniques can be used to steer information beams to desired users while suppressing noise and interference from undesired users.

Recently, the next generation of energy-efficient linear precoding techniques have been designed that completely avoid the concept interference. In these techniques the designated received symbol at the mobile communication users is pre-computed already before the transmission takes place. It has the effect that received signals are pre-equalized at the transmitter side, thus the communication system achieves a better quality of service.

In future wireless networks massive connectivity between devices will play a dominant role. To coordinate interference in such large scale networks with local channel information available within the network, machine learning techniques for resources allocation in wireless networks become an important area of research.


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Further Reading

Schynol, Lukas; Pesavento, Marius: Coordinated Sum-Rate Maximization in Multicell MU-MIMO With Deep Unrolling. In: IEEE Journal on Selected Areas in Communications 2023, ISSN: 1558-0008, doi:10.1109/JSAC.2023.3242716,

Pesavento, Marius; Bahlke, Florian: Machine Learning for Optimal Resource Allocation. In: Machine Learning for Future Wireless Communications 2020, John Wiley & Sons, Ltd Chichester, UK, [Book section]

Hegde, Ganapati; Masouros, Christos; Pesavento, Marius: Interference Exploitation-based Hybrid Precoding With Robustness Against Phase Errors. In: IEEE Transactions on Wireless Communications 2019, 18, ISSN: 1558-2248, doi:10.1109/TWC.2019.2917064, [Article]

Yang, Yang; Pesavento, Marius; Chatzinotas, Symeon; Ottersten, Björn: Energy Efficiency Optimization in MIMO Interference Channels: A Successive Pseudoconvex Approximation Approach. In: IEEE Transactions on Signal Processing 2019, 67, ISSN: 1941-0476, doi:10.1109/TSP.2019.2923141, [Article]

Bahlke, Florian; Ramos-Cantor, Oscar D.; Henneberger, Steffen; Pesavento, Marius: Optimized Cell Planning for Network Slicing in Heterogeneous Wireless Communication Networks. In: IEEE Communications Letters 2018, 22, ISSN: 1558-2558, doi:10.1109/LCOMM.2018.2841866, [Article

Law, Ka Lung; Masouros, Christos; Pesavento, Marius: Transmit Precoding for Interference Exploitation in the Underlay Cognitive Radio Z-channel. In: IEEE Transactions on Signal Processing 2017, 65, ISSN: 1941-0476, doi:10.1109/TSP.2017.2695448, [Article]

Cheng, Yong; Pesavento, Marius; Philipp, Anne: Joint Network Optimization and Downlink Beamforming for CoMP Transmissions Using Mixed Integer Conic Programming. In: IEEE Transactions on Signal Processing 2013, 61, ISSN: 1941-0476, doi:10.1109/TSP.2013.2261993, [Article]