ﻻ يوجد ملخص باللغة العربية
We propose a new scheme to enhance the physical-layer security of wireless single-input single-output orthogonal-frequency division-multiplexing (OFDM) transmissions from an electric vehicle, Alice, to the aggregator, Bob, in the presence of an eavesdropper, Eve. To prevent information leakage to Eve, Alice exploits the wireless channel randomness to extract secret key symbols that are used to encrypt some data symbols which are then multiplexed in the frequency domain with the remaining unencrypted data symbols. To secure the unencrypted data symbols, Alice transmits an artificial-noise (AN) signal superimposed over her data signal. We propose a three-level optimization procedure to increase the average secrecy rate of this wiretap channel by optimizing the transmit power allocation between the encrypted data symbols, unencrypted data symbols and the AN symbols. Our numerical results show that the proposed scheme achieves considerable secrecy rate gains compared to the benchmark cases
We investigate the physical-layer security of indoor hybrid parallel power-line/wireless orthogonal-frequency division-multiplexing (OFDM) communication systems. We propose an artificial-noise (AN) aided scheme to enhance the systems security in the
This paper investigates artificial noise injection into the temporal and spatial dimensions of a legitimate wireless communication system to secure its transmissions from potential eavesdropping. We consider a multiple-input single-output (MISO) orth
We investigate the physical layer security of uplink single-carrier frequency-division multiple-access (SC-FDMA) systems. Multiple users, Alices, send confidential messages to a common legitimate base-station, Bob, in the presence of an eavesdropper,
The unique information ($UI$) is an information measure that quantifies a deviation from the Blackwell order. We have recently shown that this quantity is an upper bound on the one-way secret key rate. In this paper, we prove a triangle inequality fo
The partial information decomposition (PID) is a promising framework for decomposing a joint random variable into the amount of influence each source variable Xi has on a target variable Y, relative to the other sources. For two sources, influence br