Papers
Topics
Authors
Recent
Detailed Answer
Quick Answer
Concise responses based on abstracts only
Detailed Answer
Well-researched responses based on abstracts and relevant paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses
Gemini 2.5 Flash
Gemini 2.5 Flash 97 tok/s
Gemini 2.5 Pro 49 tok/s Pro
GPT-5 Medium 21 tok/s Pro
GPT-5 High 18 tok/s Pro
GPT-4o 92 tok/s Pro
GPT OSS 120B 468 tok/s Pro
Kimi K2 175 tok/s Pro
2000 character limit reached

Mutual Information Optimization for SIM-Based Holographic MIMO Systems (2403.18307v2)

Published 27 Mar 2024 in cs.IT, eess.SP, and math.IT

Abstract: In the context of emerging stacked intelligent metasurface (SIM)-based holographic MIMO (HMIMO) systems, a fundamental problem is to study the mutual information (MI) between transmitted and received signals to establish their capacity. However, direct optimization or analytical evaluation of the MI, particularly for discrete signaling, is often intractable. To address this challenge, we adopt the channel cutoff rate (CR) as an alternative optimization metric for the MI maximization. In this regard, we propose an alternating projected gradient method (APGM), which optimizes the CR of a SIM-based HMIMO system by adjusting signal precoding and the phase shifts across the transmit and receive SIMs on a layer-by-layer basis. Simulation results indicate that the proposed algorithm significantly enhances the CR, achieving substantial gains, compared to the case with random SIM phase shifts, that are proportional to those observed for the corresponding MI. This justifies the effectiveness of using the channel CR for the MI optimization. Moreover, we demonstrate that the integration of digital precoding, even on a modest scale, has a significant impact on the ultimate performance of SIM-aided systems.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (14)
  1. M. Di Renzo et al., “Smart radio environments empowered by reconfigurable intelligent surfaces: How it works, state of research, and the road ahead,” IEEE Journal on Selected Areas in Communications, vol. 38, no. 11, pp. 2450–2525, 2020.
  2. N. Shlezinger et al., “Dynamic metasurface antennas for 6G extreme massive MIMO communications,” IEEE Wireless Communications, vol. 28, no. 2, pp. 106–113, 2021.
  3. M. Di Renzo and M. D. Migliore, “Electromagnetic signal and information theory,” IEEE BITS the Information Theory Magazine, 2024, Early Access.
  4. J. An et al., “Stacked intelligent metasurface-aided MIMO transceiver design,” arXiv preprint arXiv:2311.09814, 2023.
  5. C. Liu et al., “A programmable diffractive deep neural network based on a digital-coding metasurface array,” Nature Electronics, vol. 5, no. 2, pp. 113–122, 2022.
  6. J. An et al., “Two-dimensional direction-of-arrival estimation using stacked intelligent metasurfaces,” arXiv preprint arXiv:2402.08224, 2024.
  7. Q.-U.-A. Nadeem et al., “Hybrid digital-wave domain channel estimator for stacked intelligent metasurface enabled multi-user miso systems,” arXiv preprint arXiv:2309.16204, 2023.
  8. N. U. Hassan et al., “Efficient beamforming and radiation pattern control using stacked intelligent metasurfaces,” IEEE Open Journal of the Communications Society, vol. 5, pp. 599–611, 2024.
  9. J. An et al., “Stacked intelligent metasurfaces for multiuser downlink beamforming in the wave domain,” arXiv preprint arXiv:2309.02687, 2023.
  10. ——, “Stacked intelligent metasurfaces for efficient holographic MIMO communications in 6G,” IEEE Journal on Selected Areas in Communications, 2023.
  11. A. Papazafeiropoulos et al., “Achievable rate optimization for stacked intelligent metasurface-assisted holographic MIMO communications,” arXiv preprint arXiv:2402.16415, 2024.
  12. N. S. Perović et al., “Optimization of the cut-off rate of generalized spatial modulation with transmit precoding,” IEEE Transactions on Communications, vol. 66, no. 10, pp. 4578–4595, 2018.
  13. ——, “Optimization of RIS-aided MIMO systems via the cutoff rate,” IEEE Wireless Communications Letters, vol. 10, no. 8, pp. 1692–1696, 2021.
  14. X. Lin et al., “All-optical machine learning using diffractive deep neural networks,” Science, vol. 361, no. 6406, pp. 1004–1008, 2018.
Citations (5)
List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Summary

We haven't generated a summary for this paper yet.

Ai Generate Text Spark Streamline Icon: https://streamlinehq.com

Paper Prompts

Sign up for free to create and run prompts on this paper using GPT-5.

Dice Question Streamline Icon: https://streamlinehq.com

Follow-up Questions

We haven't generated follow-up questions for this paper yet.

X Twitter Logo Streamline Icon: https://streamlinehq.com

Don't miss out on important new AI/ML research

See which papers are being discussed right now on X, Reddit, and more:

“Emergent Mind helps me see which AI papers have caught fire online.”

Philip

Philip

Creator, AI Explained on YouTube