Energy-Efficient Secure Cell-Free Massive MIMO for Internet of Things: A Hybrid CNN–LSTM-Based Deep-Learning Approach

Ali Vaziri, Pardis Sadatian Moghaddam, Mehrdad Shoeibi and Masoud Kaveh ()
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Ali Vaziri: Department of Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ 07030, USA
Pardis Sadatian Moghaddam: Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA
Mehrdad Shoeibi: The WPI Business School, Worcester Polytechnic Institute, Worcester, MA 01609, USA
Masoud Kaveh: Department of Information and Communication Engineering, Aalto University, 02150 Espoo, Finland

Future Internet, 2025, vol. 17, issue 4, 1-29

Abstract: The Internet of Things (IoT) has revolutionized modern communication systems by enabling seamless connectivity among low-power devices. However, the increasing demand for high-performance wireless networks necessitates advanced frameworks that optimize both energy efficiency (EE) and security. Cell-free massive multiple-input multiple-output (CF m-MIMO) has emerged as a promising solution for IoT networks, offering enhanced spectral efficiency, low-latency communication, and robust connectivity. Nevertheless, balancing EE and security in such systems remains a significant challenge due to the stringent power and computational constraints of IoT devices. This study employs secrecy energy efficiency (SEE) as a key performance metric to evaluate the trade-off between power consumption and secure communication efficiency. By jointly considering energy consumption and secrecy rate, our analysis provides a comprehensive assessment of security-aware energy efficiency in CF m-MIMO-based IoT networks. To enhance SEE, we introduce a hybrid deep-learning (DL) framework that integrates convolutional neural networks (CNN) and long short-term memory (LSTM) networks for joint EE and security optimization. The CNN extracts spatial features, while the LSTM captures temporal dependencies, enabling a more robust and adaptive modeling of dynamic IoT communication patterns. Additionally, a multi-objective improved biogeography-based optimization (MOIBBO) algorithm is utilized to optimize hyperparameters, ensuring an improved balance between convergence speed and model performance. Extensive simulation results demonstrate that the proposed MOIBBO-CNN–LSTM framework achieves superior SEE performance compared to benchmark schemes. Specifically, MOIBBO-CNN–LSTM attains an SEE gain of up to 38% compared to LSTM and 22% over CNN while converging significantly faster at early training epochs. Furthermore, our results reveal that SEE improves with increasing AP transmit power up to a saturation point (approximately 9.5 Mb/J at P AP max = 500 mW), beyond which excessive power consumption limits efficiency gains. Additionally, SEE decreases as the number of APs increases, underscoring the need for adaptive AP selection strategies to mitigate static power consumption in backhaul links. These findings confirm that MOIBBO-CNN–LSTM offers an effective solution for optimizing SEE in CF m-MIMO-based IoT networks, paving the way for more energy-efficient and secure IoT communications.

Keywords: internet of things; energy efficiency; cell-free massive MIMO; secrecy rate; deep learning (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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