Biblio
Unmanned Aerial Vehicles (UAVs) are drawing enormous attention in both commercial and military applications to facilitate dynamic wireless communications and deliver seamless connectivity due to their flexible deployment, inherent line-of-sight (LOS) air-to-ground (A2G) channels, and high mobility. These advantages, however, render UAV-enabled wireless communication systems susceptible to eavesdropping attempts. Hence, there is a strong need to protect the wireless channel through which most of the UAV-enabled applications share data with each other. There exist various error correction techniques such as Low Density Parity Check (LDPC), polar codes that provide safe and reliable data transmission by exploiting the physical layer but require high transmission power. Also, the security gap achieved by these error-correction techniques must be reduced to improve the security level. In this paper, we present deep learning (DL) enabled punctured LDPC codes to provide secure and reliable transmission of data for UAVs through the Additive White Gaussian Noise (AWGN) channel irrespective of the computational power and channel state information (CSI) of the Eavesdropper. Numerical result analysis shows that the proposed scheme reduces the Bit Error Rate (BER) at Bob effectively as compared to Eve and the Signal to Noise Ratio (SNR) per bit value of 3.5 dB is achieved at the maximum threshold value of BER. Also, the security gap is reduced by 47.22 % as compared to conventional LDPC codes.
Transmission techniques based on channel coding with feedback are proposed in this paper to enhance the security of wireless communications systems at the physical layer. Reliable and secure transmission over an additive noise Gaussian wiretap channel is investigated using Bose-Chaudhuri-Hocquenghem (BCH) and Low-Density Parity-Check (LDPC) channel codes. A hybrid automatic repeat-request (HARQ) protocol is used to allow for the retransmission of coded packets requested by the intended receiver (Bob). It is assumed that an eavesdropper (Eve) has access to all forward and feedback transmitted packets. To limit the information leakage to Eve, retransmitted packets are subdivided into smaller granular subpackets. Retransmissions are stopped as soon as the decoding process at the legitimate (Bob) receiver converges. For the hard decision decoded BCH codes, a framework to compute the frame error probability with granular HARQ is proposed. For LDPC codes, the HARQ retransmission requests are based on received symbols likelihood computations: the legitimate recipient request for the retransmission of the set of bits that are more likely to help for successful LDPC decoding. The performances of the proposed techniques are assessed for nul and negative security gap (SG) values, that is when the eavesdropper's channel benefits from equal or better channel conditions than the legitimate channel.
Next generation 5G wireless networks pose several important security challenges. One fundamental challenge is key management between the two communicating parties. The goal is to establish a common secret key through an unsecured wireless medium. In this paper, we introduce a new physical layer paradigm for secure key exchange between the legitimate communication parties in the presence of a passive eavesdropper. The proposed method ensures secrecy via pre-equalization and guarantees reliable communications by the use of Low Density Parity Check (LDPC) codes. One of the main findings of this paper is to demonstrate through simulations that the diversity order of the eavesdropper will be zero unless the main and eavesdropping channels are almost correlated, while the probability of key mismatch between the legitimate transmitter and receiver will be low. Simulation results demonstrate that the proposed approach achieves very low secret key mismatch between the legitimate users, while ensuring very high error probability at the eavesdropper.