PAPR-Aware Artificial Noise for Secure Massive MIMO Downlink

Idowu Ajayi (1) , Yahia Medjahdi (2, 3) , Rafik Zayani (4) , Lina Mroueh (1) , Fatima Kaddour (5)

1 ISEP - Institut Supérieur d'Electronique de Paris
2 CERI SN - IMT Nord Europe - Centre for Digital Systems
3 IMT Nord Europe - Ecole nationale supérieure Mines-Télécom Lille Douai
4 CEA-LETI - Commissariat à l'énergie atomique et aux énergies alternatives - Laboratoire d'Electronique et de Technologie de l'Information
5 ANFR - Agence Nationale des Fréquences

This paper introduces a new approach to providing secure physical-layer massive MIMO based communications that can improve the energy efficiency of the system. This is achieved by synthesizing orthogonal artificial noise (AN) that on one hand has to be constrained to lie in the null space of the legitimate users channels while it should lie in the range space of the eavesdroppers channel, and on the other hand reduces the peak-to-average power ratio (PAPR) of the transmitted signals. Indeed, low PAPR signals are friendly to more efficient and low-cost hardware, improving thus the energy consumption of massive MIMO systems. Specifically, this paper investigates a new PAPR-aware precoding scheme that is based on the use of AN to enhance the secrecy performance of massive MIMO while reducing the PAPR of transmitted signals and guaranteeing excellent transmission quality towards legitimate users. The scheme is formulated as a convex optimization problem that can be resolved via steepest gradient descent (GD). Then, we developed a new iterative algorithm, referred to as PAPR-Aware-Secure-mMIMO, that makes use of instantaneous information to solve the optimization problem. Simulation results show the efficiency of our proposed algorithm in terms of PAPR reduction and secrecy, which is also studied with respect to power distribution between useful signal and AN, PAPR targets and the number of BS antennas.

Mots clés: artificial noise (AN); massive multiple-input multiple-output (m-MIMO); peak-to-average power ratio (PAPR); physical layer security (PLS); power allocation; steepest gradient descent (GD); zero-forcing (ZF) precoding

Domaines: Sciences de l'information et de la communication

Idowu Ajayi, Yahia Medjahdi, Rafik Zayani, Lina Mroueh, Fatima Kaddour. PAPR-Aware Artificial Noise for Secure Massive MIMO Downlink. IEEE Access, 2022, 10, pp.68482 - 68490. ⟨10.1109/ACCESS.2022.3186695⟩. ⟨hal-03708452⟩