This paper investigates the information-theoretic energy efficiency of intelligent reflecting surface (IRS)-aided wireless communication systems, taking into account the statistical quality-of-service (QoS) constraints on delay violation probabilities. Specifically, effective capacity is adopted to capture the maximum constant arrival rate that can be supported by a time-varying service process while fulfilling these statistical QoS requirements. We derive the minimum bit energy required for the IRS-aided wireless communication system under QoS constraints and analyze the spectral efficiency and energy efficiency tradeoff at low but nonzero signal-to-noise ratio (SNR) levels by also characterizing the wideband slope values. Our analysis demonstrates that the energy efficiency for the considered system under statistical QoS constraints can approach that for a system without QoS limitations in the low-SNR regime. Additionally, deploying a sufficiently large number of practical IRS reflecting elements can substantially reduce energy consumption required to achieve desired spectral efficiency performance in the low-power regime, even with limited bit-resolution phase shifters. Besides, we reveal that compared with the results applied to the low-power regime, higher effective capacity performance can be achieved in scenarios with sparse multipath fading while achieving the same minimum bit energy in the wideband regime.

This paper investigates resource allocation design for wideband integrated sensing and communication (ISAC) systems. We aim to minimize the Cramér-Rao Bound (CRB) for target estimation by jointly optimizing subcarrier allocation, digital beamforming matrices, and frequency-independent and frequency-dependent analog beamforming matrices at the base station (BS) adopting a hybrid beamforming structure. We formulate the optimization design as a non-convex mixed-integer nonlinear programming (MINLP) problem, subject to the transmit power budget constraint of the BS, the rate quality-of-service (QoS) constraints for users, and the discrete nature of the analog beamformer. To achieve a globally optimal solution for the complicated design problem, an iterative resource allocation algorithm is proposed by exploiting the generalized Bender's decomposition (GBD) method. Our simulation results demonstrate the crucial importance of simultaneously optimizing all available degrees-of-freedom (DoFs) in wideband ISAC systems jointly and optimally. Besides, our results unveil that deploying true-time-delayer (TTD) units with limited bit-resolution time delays can achieve substantial gains in both communication and sensing performances.

To further improve the transmission efficiency and link stability for free space optical (FSO)-based satellite communication (SatCom) systems when transmitting large-scale remote sensing images, a scheme based on the integration of FSO and semantic communication (FSO-SC) is proposed, which employs a vector quantized variational autoencoder with spatial normalization to extract essential semantic features of images while preserving intricate details. Additionally, theMáalagadistribution model is utilized to simulate FSO channels with diverse turbulence conditions. Moreover, a comparative evaluation between the FSO-SC and traditional systems is conducted through 28 GBaud satellite-ground simulation with three modulation formats considering various effects. Compared to the traditional systems, without incurring additional bits for error corrections, the FSO-SC system achieves a power gain of over 3 dB while enabling transmission at zenith angles over 60°. Moreover, it achieves performance on par with state-of-the-art 4-receiver spatial diversity technology, while offering superior hardware and transmission efficiency. Furthermore, we conduct 10 Gbps real-time satellite-ground equivalent experiments to validate the practicality of the FSO-SC, where it achieves a 60% reduction in communication overhead compared to existing solutions while maintaining comparable received image quality and can reach a minimum receiver sensitivity gain of 4 dB. Simulation and experimental results demonstrate that the proposed FSO-SC scheme achieves high system efficiency and stability, holding promise as a viable solution for future SatCom.

The optical communication system using pulse position modulation (PPM) and superconducting nanowire single-photon detectors (SNSPDs) has attracted considerable attention for deep space applications as it enables high speed data transmission at extremely low average signal power. The deadtime of SNSPD is a critical factor in such systems because it primarily affects the signal detection efficiency. This becomes even more crucial in high-speed systems, where the deadtime can span several symbol periods. We employ the Markov chain model to characterize the high-speed PPM-SNSPD system and investigate its behavior. Analytical expressions for symbol transition probabilities are derived to characterize system-level metrics, including the symbol error rate and achievable code rate. Analysis shows that deadtime introduces memory to the PPM-SNSPD channel, resulting in channel asymmetry. Through experimental verification and simulations, we confirmed the effectiveness of our analysis. In addition, a set of new log-likelihood ratio (LLR) expressions is proposed based on the new model. Compared with the commonly used Poisson LLR expression, our proposed LLR expressions show more than 0.5 dB performance gain.

This paper investigates robust resource allocation design for active intelligent reflecting surface (IRS)-aided cognitive rate-splitting multiple access (C-RSMA) systems. In particular, an active IRS is deployed to shape a favorable wireless communication environment for enhancing the system performance. We aim to maximize the system energy efficiency by jointly optimizing the common rate allocations for the users, the transmit beamforming vectors at the coordinated base stations, and the active beamforming matrix at the IRS. We formulate the design as a non-convex optimization problem taking into account the discrete nature of the IRS elements and the transmit power budget constraints of the base stations as well as the active IRS. To tackle the non-convex design problem, a computationally effective iterative suboptimal algorithm is proposed by exploiting the block coordinate descent method, the generalized S-Procedure, the successive convex approximation, and the Dinkelbach’s approach. Simulation results reveal a non-trivial tradeoff between the system energy efficiency and the number of the IRS elements. Moreover, our results unveil that active IRS elements equipped with limited bit-resolution of discrete amplifiers and phase shifters is sufficient to achieve a significant gain in system energy efficiency.

This paper investigates resource allocation design for wideband integrated sensing and communication (ISAC) systems. To tackle the severe propagation attenuation issue in designing high-frequency ISAC systems, we adopt the hybrid beamformer at the transmitter to achieve substantial beamforming gains by generating highly directional beams. However, the well-known beam-split effect introduces multiple spatial directions at each subcarrier, due to the employment of wider bandwidth and a larger number of antennas, which may lead to system performance degradation. Fortunately, the notion of a true-time-delayer (TTD) has emerged as a crucial solution for compensating for the beam split by generating frequency-dependent phase shifts. To fully unleash its potential, we aim to minimize the Cramér-Rao Bound (CRB) for target estimation by jointly optimizing subcarrier allocation, digital beamforming matrices, and frequency-independent and frequency-dependent analog beamforming matrices at base station (BS). We formulate the optimization design as a non-convex mixed-integer non-linear programming (MINLP) problem, subject to the transmit power budget constraint of the BS, the rate quality-of-service (QoS) constraints for users, and the discrete nature of the analog beamformer. To achieve a globally optimal solution for the complex design problem, an iterative resource allocation algorithm is proposed by exploiting the generalized Bender’s decomposition (GBD) method. Moreover, we develop a computationally-efficient suboptimal algorithm to strike an effective balance between system performance and complexity. Our simulation results demonstrate the crucial importance of simultaneously optimizing all available degrees-of-freedom (DoFs) in wideband ISAC systems jointly and optimally. Furthermore, our proposed schemes are able to significantly improve the sensing accuracy over the traditional alternating optimization (AO) scheme adopted in existing solutions. Besides, our results unveil that deploying TTD units with limited bit-resolution time delays can achieve substantial gains in both communication and sensing performances.

This paper investigates robust resource allocation design for active intelligent reflecting surface (IRS)-aided cog-nitive rate-splitting multiple access (C-RSMA) systems. In particular, an active IRS is deployed to shape a favorable wireless communication environment for enhancing the system performance. We aim to maximize the system energy efficiency by jointly optimizing the common rate allocations for the users, the transmit beamforming vectors at the coordinated base stations, and the active beamforming matrix at the IRS. We formulate the resource allocation design as a non-convex optimization problem taking into account the discrete nature of the IRS elements and the transmit power budget constraints of the base stations as well as the active IRS. To tackle the non-convex design problem, we propose a computationally effective iterative suboptimal algorithm. Simulation results reveal a nontrivial tradeoff between the system energy efficiency and the number of the IRS elements. Moreover, our results unveil that active IRS elements equipped with limited bit-resolution of discrete amplifiers and phase shifters is sufficient to achieve a significant gain in the system energy efficiency.

This paper studies the robust resource allocation design for secure intelligent reflecting surface (IRS)-aided wireless-powered communication networks (WPCN). Specifically, deploying an IRS can establish favorable end-to-end radio propagation environment for achieving the desired performance gain in secure wireless-powered systems. We aim to minimize the total hybrid base station (HBS) transmit power by jointly designing the active transmitting and receiving beamforming at the HBS, and the passive beamforming at the IRS taking into account the secrecy rate outage requirement and the harvested power constraints of the legitimate devices. To handle the optimization problem, we propose an efficient iterative suboptimal algorithm, which attains a Karush-Kuhn-Tucker (KKT) solution of the transformed problem. Simulation results unveil that the proposed scheme can dramatically reduce the HBS transmit power over various baseline schemes. Also, our results show the superiority of IRS-aided secure communication in wireless-powered systems.

This paper studies the robust resource allocation design for secure intelligent reflecting surface (IRS)-aided wireless powered communication networks (WPCN). In particular, deploying an IRS can establish favorable end-to-end radio propagation environment for achieving the desired performance gain in secure wireless-powered systems. We aim to minimize the total hybrid base station (HBS) transmit power by jointly designing the active transmitting and receiving beamforming at the HBS, the passive beamforming at the IRS, and the transmit power of each wireless-powered device (WD) and jammer node (JN). We formulate a non-convex optimization problem for the robust resource allocation design taking into account the secrecy rate requirement of the WDs and the power budgets for both the WDs and the JNs. To handle this intractable problem, we propose a computationally efficient iterative suboptimal algorithm exploiting the block coordinate descent approach, the successive convex approximation, and the penalty method, which attains a Karush-Kuhn-Tucker (KKT) solution of the transformed problem. Also, we reveal that the optimal energy beamforming matrices are rank-one sharing the same spatial direction. Simulation results unveil that the proposed scheme is able to dramatically reduce the HBS transmit power over various baseline schemes adopting existing solutions. Besides, our results show the superiority of introducing IRS for secure communication in wireless-powered systems.

This paper investigates resource allocation design for intelligent reflecting surface (IRS)-aided joint processing coordinated multipoint (JP-CoMP) downlink cellular networks with underlaying device-to-device (D2D) communications. In particular, the IRS is employed to establish favorable communication channel conditions and to mitigate the malignant interference caused by D2D devices. We aim to maximize the total weighted system sum-rate by jointly designing the cellular user (CU) association, the active beamforming at the base stations (BSs), the passive beamforming at the IRS, and the transmit power of each D2D transmitter. The resource allocation design is formulated as a non-convex optimization problem while taking into account the quality of service requirement of CUs and the power allocations for both CUs and D2D pairs. We propose a computationally efficient suboptimal iterative algorithm, which is guaranteed to converge to a Karush-Kuhn-Tucker (KKT) solution of the design problem. Simulation results demonstrate that the proposed scheme can significantly improve the system sum-rate over various baseline schemes adopting existing solutions. Also, our results confirm the superiority of introducing IRS for managing interference in wireless communication systems.

This paper investigates resource allocation design for intelligent reflecting surface (IRS)-aided joint processing coordinated multipoint (JP-CoMP) downlink cellular networks with underlaying device-to-device (D2D) communications. In particular, an IRS is employed to establish favorable communication channel conditions and to mitigate the malignant interference caused by D2D devices. We aim to maximize the system sum-rate by jointly designing the cellular user (CU) association, the active beamforming at the base stations (BSs), the passive beamforming at the IRS, and the transmit power of each D2D transmitter (DT). The resource allocation design is formulated as a non-convex optimization problem while taking into account the quality of service (QoS) requirement of CUs, the power allocations for both CUs and D2D pairs, and the limited backhaul capacity. To handle the non-convex optimization problem, we propose a computationally efficient iterative algorithm exploiting the big-M formulation, the penalty method, and the successive convex approximation, which is guaranteed to converge to a Karush-Kuhn-Tucker (KKT) solution. Simulation results demonstrate that the proposed scheme can increase the system sum-rate by 70% and 20% compared with the schemes with no IRS and random phase shifts, respectively, when the minimum required SINR of CUs is 5 dB. Additionally, our results confirm the superiority of introducing IRS for harnessing interference in wireless communication systems.

Aiming at the data transmission demand of large space information network, we propose a network bandwidth scheduling mechanism based on credit value sorting for FC-AE-1553 network based on passive optical network technology. The business types of FC-AE-1553 network include cyclical business, strong timeliness business and burst business. In this paper, a multi service FC network simulation platform is constructed, and the scheduling mechanism is analyzed by combining theoretical analysis with simulation development. The results show that, under the typical working condition at the 32 nodes, the network throughput can reach 3.44Gbps, the average time delay of the burst traffic is 58ms, and the average time delay of the strong timeliness burst service is 23us.