Design of Energy-Efficient Artificial Noise for Physical Layer Security in Visible Light Communications

TL;DR

This paper explores energy-efficient artificial noise schemes in visible light communications to enhance physical layer security, comparing SISO and MISO methods under different eavesdropper CSI scenarios.

Contribution

It introduces novel AN schemes for VLC security, analyzing their energy efficiency and proposing low-complexity design methods for different CSI conditions.

Findings

Selective AN-aided SISO achieves twice the SEE of MISO when Eves' CSI is unknown.

MISO outperforms SISO by 30% in SEE when Eves' CSI is known.

Proposed designs improve physical layer security energy efficiency in VLC systems.

Abstract

This paper studies the design of energy-efficient artificial noise (AN) schemes in the context of physical layer security in visible light communications (VLC). Two different transmission schemes termed $\textit{selective AN-aided single-input single-output (SISO)}$ and $\textit{AN-aided multiple-input single-output (MISO)}$ are examined and compared in terms of secrecy energy efficiency (SEE). In the former, the closest LED luminaire to the legitimate user (Bob) is the information-bearing signal's transmitter. At the same time, the rest of the luminaries act as jammers transmitting AN to degrade the channels of eavesdroppers (Eves). In the latter, the information-bearing signal and AN are combined and transmitted by all luminaries. When Eves' CSI is unknown, an indirect design to improve the SEE is formulated by maximizing Bob's channel's energy efficiency. A low-complexity design based on the zero-forcing criterion is also proposed. In the case of known Eves' CSI, we study the design that maximizes the minimum SEE among those corresponding to all eavesdroppers. At their respective optimal SEEs, simulation results reveal that when Eves' CSI is unknown, the selective AN-aided SISO transmission can archive twice better SEE as the AN-aided MISO does. In contrast, when Eves' CSI is known, the AN-aided MISO outperforms by 30%.