Pseudo-Noise Superposition for Finite-Alphabet Physical Layer Security

Physical-layer security based on pseudo-noise (PN) superposition is a promising approach for mitigating eavesdropping in future wireless systems. However, under Shannon's capacity formulation with Gaussian signaling, achieving secrecy typically requires allocating substantial transmit power to PN, resulting in a significant reduction in achievable information rate and limiting practical applicability. This limitation is alleviated when finite-alphabet modulation schemes, such as M-ary Quadrature Amplitude Modulation ($M$-QAM), are employed, as expected in practical 6G transceivers. In this work, we analyze the information rate performance of PN-assisted systems under $M$-QAM signaling using mutual information and derive the corresponding achievable secrecy rate. The impact of PN power allocation on both the legitimate user and the eavesdropper is investigated across different modulation orders and channel conditions. Monte Carlo simulations are conducted to evaluate system behavior under varying user and eavesdropper channel conditions and to examine how PN power allocation influences secrecy performance. The results show that, at sufficiently high signal-to-noise ratio (SNR), the information rate becomes largely insensitive to PN power allocation, enabling near-perfect secrecy with $M$-QAM modulation-highlighting a key departure from Shannon-capacity-based secrecy analyses and underscoring the practicality of finite-alphabet security mechanisms for 6G wireless systems.

Null-Shaping for Interference Mitigation in LEO Satellites Under Location Uncertainty

Radio frequency interference (RFI) poses a growing challenge to satellite communications, particularly in uplink channels of Low Earth Orbit (LEO) systems, due to increasing spectrum congestion and uncertainty in the location of terrestrial interferers. This paper addresses the impact of RFI source position uncertainty on beamforming-based interference mitigation. First, we analytically characterize how geographic uncertainty in RFI location translates into angular deviation as observed from the satellite. Building on this, we propose a robust null-shaping framework to increase resilience in the communication links by incorporating the probability density function (PDF) of the RFI location uncertainty into the beamforming design via stochastic optimization. This allows adaptive shaping of the antenna array's nulling pattern to enhance interference suppression under uncertainty. Extensive Monte Carlo simulations, incorporating realistic satellite orbital dynamics and various RFI scenarios, demonstrate that the proposed approach achieves significantly improved mitigation performance compared to conventional deterministic designs.

Interference Mitigation in LEO Constellations with Limited Radio Environment Information

This research paper delves into interference mitigation within Low Earth Orbit (LEO) satellite constellations, particularly when operating under constraints of limited radio environment information. Leveraging cognitive capabilities facilitated by the Radio Environment Map (REM), we explore strategies to mitigate the impact of both intentional and unintentional interference using planar antenna array (PAA) beamforming techniques. We address the complexities encountered in the design of beamforming weights, a challenge exacerbated by the array size and the increasing number of directions of interest and avoidance. Furthermore, we conduct an extensive analysis of beamforming performance from various perspectives associated with limited REM information: static versus dynamic, partial versus full, and perfect versus imperfect. To substantiate our findings, we provide simulation results and offer conclusions based on the outcomes of our investigation.