No Arabic abstract
Physical layer security is investigated over mixture Gamma (MG) distributed fading channels with discrete inputs. By the Gaussian quadrature rules, closed-form expressions are derived to characterize the average secrecy rate (ASR) and secrecy outage probability (SOP), whose accuracy is validated by numerical simulations. To show more properties of the finite-alphabet signaling, we perform an asymptotic analysis on the secrecy metrics in the large limit of the average signal-to-noise ratio (SNR) of the main channel. Leveraging the Mellin transform, we find that the ASR and SOP converge to some constants as the average SNR increases and we derive novel expressions to characterize the rates of convergence. This work establishes a unified and general analytical framework for the secrecy performance achieved by discrete inputs.
In this work, we propose a new physical layer security framework for optical space networks. More precisely, we consider two practical eavesdropping scenarios: free-space optical (FSO) eavesdropping in the space and FSO eavesdropping in the air. In the former, we assume that a high altitude platform station (HAPS) is trying to capture the confidential information from the low earth orbit (LEO) satellite, whereas in the latter, an unmanned aerial vehicle (UAV) eavesdropper is trying to intercept the confidential information from the HAPS node. To quantify the overall performance of both scenarios, we obtain closed-form secrecy outage probability (SOP) and probability of positive secrecy capacity (PPSC) expressions and validate with Monte Carlo simulations. Furthermore, we provide important design guidelines that can be helpful in the design of secure non-terrestrial networks.
We study the problem of securely communicating a sequence of information bits with a client in the presence of multiple adversaries at unknown locations in the environment. We assume that the client and the adversaries are located in the far-field region, and all possible directions for each adversary can be expressed as a continuous interval of directions. In such a setting, we develop a periodic transmission strategy, i.e., a sequence of joint beamforming gain and artificial noise pairs, that prevents the adversaries from decreasing their uncertainty on the information sequence by eavesdropping on the transmission. We formulate a series of nonconvex semi-infinite optimization problems to synthesize the transmission strategy. We show that the semi-definite program (SDP) relaxations of these nonconvex problems are exact under an efficiently verifiable sufficient condition. We approximate the SDP relaxations, which are subject to infinitely many constraints, by randomly sampling a finite subset of the constraints and establish the probability with which optimal solutions to the obtained finite SDPs and the semi-infinite SDPs coincide. We demonstrate with numerical simulations that the proposed periodic strategy can ensure the security of communication in scenarios in which all stationary strategies fail to guarantee security.
The present paper is devoted to the evaluation of energy detection based spectrum sensing over different multipath fading and shadowing conditions. This is realized by means of a unified and versatile approach that is based on the particularly flexible mixture gamma distribution. To this end, novel analytic expressions are firstly derived for the probability of detection over MG fading channels for the conventional single-channel communication scenario. These expressions are subsequently employed in deriving closed-form expressions for the case of square-law combining and square-law selection diversity methods. The validity of the offered expressions is verified through comparisons with results from respective computer simulations. Furthermore, they are employed in analyzing the performance of energy detection over multipath fading, shadowing and composite fading conditions, which provides useful insighs on the performance and design of future cognitive radio based communication systems.
The integration of unmanned aerial vehicles (UAVs) into the terrestrial cellular networks is envisioned as one key technology for next-generation wireless communications. In this work, we consider the physical layer security of the communications links in the millimeter-wave (mmWave) spectrum which are maintained by UAVs functioning as base stations (BS). In particular, we propose a new precoding strategy which incorporates the channel state information (CSI) of the eavesdropper (Eve) compromising link security. We show that our proposed precoder strategy eliminates any need for artificial noise (AN) transmission in underloaded scenarios (fewer users than number of antennas). In addition, we demonstrate that our nonlinear precoding scheme provides promising secrecy-rate performance even for overloaded scenarios at the expense of transmitting low-power AN.
We suggest secure Vehicle-to-Vehicle communications in a secure cluster. Here, the security cluster refers to a group of vehicles having a certain level or more of secrecy capacity. Usually, there are many difficulties in defining secrecy capacity, but we define vehicular secrecy capacity for the vehicle defined only by SNR values. Defined vehicular secrecy capacity is practical and efficient in achieving physical layer security in V2V. Typically, secrecy capacity may be changed by antenna related parameters, path related parameters, and noise related parameters. In addition to these conventional parameters, we address unique vehicle-related parameters, such as vehicle speed, safety distance, speed limit, response time, etc. in connection with autonomous driving. We confirm the relationship between vehicle-related secrecy parameters and secrecy capacity through modeling in highway and urban traffic situations. These vehicular secrecy parameters enable real-time control of vehicle secrecy capacity of V2V communications. We can use vehicular secrecy capacity to achieve secure vehicle communications from attackers such as quantum computers. Our research enables economic, effective and efficient physical layer security in autonomous driving.