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Integrated Sensing, Localization, and Communication in Holographic MIMO-enabled Wireless Network: A Deep Learning Approach
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  • APURBA ADHIKARY ,
  • Md. Shirajum Munir ,
  • Avi Deb Raha ,
  • Yu Qiao ,
  • Zhu Han ,
  • Choong Seon Hong
APURBA ADHIKARY
Kyung Hee University

Corresponding Author:[email protected]

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Md. Shirajum Munir
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Avi Deb Raha
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Choong Seon Hong
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Abstract

The impending sixth-generation wireless communication networks are anticipated to guarantee mass connectivity, high integration, and lower power consumption for generating the required beamforming. To achieve these goals, an artificial intelligence (AI) framework is proposed by utilizing holographic MIMO-assisted integrated sensing, localization, and communication. The proposed AI framework ensures lower power consumption to activate the minimum number of grids from the holographic grid array for the generation of holographic beamforming. An optimization problem is formulated to maximize the signal-to-interference-plus-noise ratio received by the users, which in turn maximizes the utility function for sensing considering the user distances, beampattern gains, sensingcommunication loss, and dense locations controlling parameter. A novel AI-based framework is proposed to solve the formulated NP-hard optimization problem by decomposing it into two subproblems: the sensing problem and the communication resource allocation problem. First, a variational autoencoder (VAE) based mechanism is devised to solve the sensing problem mitigating the disputes to obtain the users’ exact location. Second, a sequential neural network-based scheme is utilized to allocate the communication resources to the heterogeneous users for generating the desired beamforming based on the findings of the VAE-based mechanism. Moreover, an extreme case power allocation strategy is presented once a large number of users enter the system. The extreme case power allocation strategy applies when the total power prediction exceeds the total system power for allocating the communication resources to the users. Finally, simulation results validate that the proposed AI-based framework outperforms the long short-term memory method with a cumulative power savings of 34.02% taking the ground truth power into account. Therefore, the proposed AI framework generates effective beamforming to serve the communication users.
2023Published in IEEE Transactions on Network and Service Management on pages 1-1. 10.1109/TNSM.2023.3292269