Integrated sensing and communication (ISAC) is emerging as a key enabler for spectrum-efficient and hardware-converged wireless networks. However, classical radar systems within ISAC architectures face fundamental limitations under low signal power and high-noise conditions. This paper proposes a novel framework that embeds quantum illumination radar into a base station to simultaneously support full-duplex classical communication and quantum-enhanced target detection. The resulting integrated quantum sensing and classical communication (IQSCC) system is optimized via a sum-rate maximization formulation subject to radar sensing constraints. The non-convex joint optimization of transmit power and beamforming vectors is tackled using the successive convex approximation technique. Furthermore, we derive performance bounds for classical and quantum radar protocols under the statistical detection theory, highlighting the quantum advantage in low signal-to-interference-plus-noise ratio regimes. Simulation results demonstrate that the proposed IQSCC system achieves a higher communication throughput than the conventional ISAC baseline while satisfying the sensing requirement.

This paper studies intelligent reflecting surface (IRS)-assisted free space optical (FSO) quantum communication systems. An IRS can relax the requirement of a line-of-sight (LoS) link between Alice and Bob for FSO quantum key distribution (QKD) applications. The IRS focuses the laser beam toward Bob by introducing a phase shift to the incoming incident beam from Alice. Both discrete variable QKD (DV-QKD) and continuous variable QKD (CV-QKD) protocols for IRS-assisted QKD systems are studied. For DV-QKD, we characterize the average quantum bit error rate (QBER) and the achievable secret key rate (SKR) under a log-normal fading turbulence model. For CV-QKD, we present the ultimate quantum information-theoretic bounds for the SKR and entanglement distribution rates. We also characterize the achievable SKR of a practical CV-QKD protocol implemented using Gaussian coherent states. Our results reveal that Alice and Bob can generate a quantum secure key even if the LoS link between them is blocked by utilizing the IRS. Furthermore, with a quadratic phase shift profile, the IRS-assisted link achieves a higher SKR as compared to the direct LoS link and a relay-assisted link for large transmission distances.

Radio tomographic imaging (RTI) is a device-free sensing technology that can image the radio frequency (RF) attenuation of physical objects in the environment. RTI uses received signal strength (RSS) information from a wireless communication network (WCN) to perform image reconstruction. However it requires a dense WCN consisting of a large number of nodes making it difficult to apply in realistic WCN. In this paper we investigate the performance of RTI when reconfigurable intelligent surfaces (RISs) are integrated into the WCN for integrated sensing and communication (ISAC). This approach can potentially enable the use of RTI in realistic WCN without a large number of nodes. Theoretical and numerical comparisons based on the Cramér-Rao Lower Bound (CRLB) are provided to verify the improvement on sensing performance brought by RIS. The scaling behavior of the reconstruction error as a function of the number of RIS element groups is also derived. Simulations are conducted to validate the proposed RIS-RTI system with a limited number of nodes. The results indicate that incorporating RIS can reduce the necessary number of nodes by more than a half while maintaining high-quality reconstruction compared to a network without RIS.

We study the secret key rate (SKR) of a multiple-input multiple-output (MIMO) continuous variable quantum key distribution (CVQKD) system operating at terahertz (THz) frequencies, accounting for the effects of channel estimation. We propose a practical channel estimation scheme for the THz MIMO CVQKD system which is necessary to realize transmit-receive beamforming between Alice and Bob. We characterize the input-output relation between Alice and Bob during the key generation phase, by incorporating the effects of additional noise terms arising due to the channel estimation error and detector noise. Furthermore, we analyze the SKR of the system and study the effect of channel estimation error and overhead. Our simulation results reveal that the SKR may degrade significantly as compared to the SKR upper bound that assumes perfect channel state information, particularly at large transmission distances.

We consider multi-antenna wireless systems employing large intelligent surfaces (LIS); a new physical layer technology for improving coverage and energy efficiency by intelligently controlling the propagation environment. In practice, achieving the promised gains of LIS requires accurate channel estimation. Recent solutions have been presented based on the simple, but sub-optimal, least-squares (LS) approach. Here, we propose an improved channel estimator based on the minimum mean-squared-error (MMSE) criterion. While a closed-form MMSE solution is intractable, we obtain an approximate MMSE solution by employing a deep learning-based denoising convolutional neural network (DnCNN) that takes as input the noisy LS channel estimate, and produces a cleaned channel matrix at its output. Simulation results show that the proposed DnCNN-based estimator achieves a 3 dB improvement in mean squared error compared with the existing LS approach.

A new digital modulation scheme, termed frequency-phase keying (FPK), that combines frequency-shift keying (FSK) and phase-shift keying (PSK), is presented. Optimized signal designs for $M$ -ary FPK that jointly select the phase and frequency of the modulating signal in order to minimize the union bound on the symbol error probability with maximum likelihood reception are obtained. Solutions for baseband communication in additive white Gaussian noise channels and for passband communication in Rayleigh fading with receive diversity are derived. In contrast to conventional FSK and PSK, and to existing hybrid FSK/PSK schemes, the designed FPK signal constellations have the desirable property that the maximum correlation coefficient between any two signals is negative. We demonstrate that the proposed FPK solutions yield improved error performance for both baseband and passband communication.