Massive Multiple Input Multiple Output (mMIMO) Antenna Systems

Massive Multiple Input Multiple Output (mMIMO) antenna systems will be an important technology for future generation, e.g., B5G, 6G, mobile telecommunication networks to meet emerging throughput, reliability, and latency requirements. Directional transmission becomes increasingly important for exploiting spatial multiplexing in future mobile and wireless networks [1].

In an mMIMO system, signals are transmitted simultaneously across multiple antenna arrays, allowing the system to steer the directional behavior by adjusting the phases and amplitudes of the transmitted signals.

However, the large number of antennas in mMIMO systems makes real-time optimization of these parameters highly complex and computationally demanding.

Machine Learning (ML) concepts such as Reinforcement Learning (RL) and Deep Learning (DL) can be used to not only estimate but also predict channel conditions and to determine parameters for rapid beam forming. Federated Learning (FL) allows ML to be performed in a distributed way [2]. This approach is promising since many user devices can be involved in the learning process, but FL for channel estimation and beam forming is not well explored and understood.

In the field of managing rapid beam forming, channel estimation plays a crucial role [3]. Recent research has explored the use of graph neural networks (GNNs) for different challenges in mMIMO systems. This approach leverages the potential of representing the channel as a graph, where multi-dimensional channels are divided into distinct groups, each represented as nodes and connected based on various characteristics. GNNs have gained popularity for their effectiveness in enhancing channel estimation and simulation in mMIMO systems. Our simulations and results are conducted using Sionna, a Python-based, open-source library designed for physical-layer research in next-generation communication systems [4].

Traditional RF-based beam forming for mmWave communication requires estimating channel characteristics, generating appropriate precoding weights, and sweeping through MIMO code elements. This process is highly complex, incurs significant overhead, and is time-intensive.

Integrated Sensing and Communication will play a crucial role in 6G systems by utilizing multi-sensor data, such as GPS, imagery, and LiDAR, to create a comprehensive situational awareness model [5].

This model can predict optimal sectors and expedite beam training. However, transmitting large amounts of raw data from multi-modal sensors to a central server can be impractical due to bandwidth constraints and privacy concerns. FL offers a promising solution, enabling multiple vehicles to collaboratively train models while preserving data privacy by not sharing raw data, thus reducing the burden on control channels [6]. This work aims to develop a hybrid machine learning framework incorporating federated learning, split learning, reinforcement learning, etc., to optimize accuracy, latency, and reliability for efficient multi-modal beam forming in 6G communication systems.

References

[1] V. Ardianto Nugroho and B. M. Lee, "A Survey of Federated Learning for mmWave Massive MIMO," in IEEE Internet of Things Journal, vol. 11, no. 16, pp. 27167-27183, 15 Aug.15, 2024.

[2] B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in Artificial intelligence and statistics. PMLR, 2017, pp. 1273–1282.

[3] D. Góez et al., “Computational Efficiency of Deep Learning-Based Super Resolution Methods for 5G-NR Channel Estimation,” in 2024 IEEE Latin-American Conference on Communications (LATINCOM), Medellin, Colombia: IEEE, Nov. 2024, pp. 1–7.

[4] L. Shan, Y. Hu, W. Shan, F. Zhang, and M. Chen, “MAGLN: Multi-Attention Graph Learning Network for Channel Estimation in Multi-User SIMO,” in 2023 28th Asia Pacific Conference on Communications (APCC), Sydney, Australia: IEEE, Nov. 2023, pp. 1–6.

[5] N. González-Prelcic et al., "The Integrated Sensing and Communication Revolution for 6G: Vision, Techniques, and Applications," in Proceedings of the IEEE, vol. 112, no. 7, pp. 676-723, July 2024.

[6] B. Salehi, J. Gu, D. Roy, and K. Chowdhury, “Flash: Federated learning for automated selection of high-band mmwave sectors,” in IEEE INFOCOM 2022-IEEE Conference on Computer Communications. IEEE, 2022, pp. 1719–1728.