Fast and Robust LRSD-based SAR/ISAR Imaging and Decomposition

The earlier works in the context of low-rank-sparse-decomposition (LRSD)-driven stationary synthetic aperture radar (SAR) imaging have shown significant improvement in the reconstruction-decomposition process. Neither of the proposed frameworks, however, can achieve satisfactory performance when facing a platform residual phase error (PRPE) arising from the instability of airborne platforms. More importantly, in spite of the significance of real-time processing requirements in remote sensing applications, these prior works have only focused on enhancing the quality of the formed image, not reducing the computational burden. To address these two concerns, this article presents a fast and unified joint SAR imaging framework where the dominant sparse objects and low-rank features of the image background are decomposed and enhanced through a robust LRSD. In particular, our unified algorithm circumvents the tedious task of computing the inverse of large matrices for image formation and takes advantage of the recent advances in constrained quadratic programming to handle the unimodular constraint imposed due to the PRPE. Furthermore, we extend our approach to ISAR autofocusing and imaging. Specifically, due to the intrinsic sparsity of ISAR images, the LRSD framework is essentially tasked with the recovery of a sparse image. Several experiments based on synthetic and real data are presented to validate the superiority of the proposed method in terms of imaging quality and computational cost compared to the state-of-the-art methods.

Energy-Efficient Federated Learning with Relay-Assisted Aggregation in IIoT Networks

This paper presents an energy-efficient transmission framework for federated learning (FL) in industrial Internet of Things (IIoT) environments with strict latency and energy constraints. Machinery subnetworks (SNs) collaboratively train a global model by uploading local updates to an edge server (ES), either directly or via neighboring SNs acting as decode-and-forward relays. To enhance communication efficiency, relays perform partial aggregation before forwarding the models to the ES, significantly reducing overhead and training latency. We analyze the convergence behavior of this relay-assisted FL scheme. To address the inherent energy efficiency (EE) challenges, we decompose the original non-convex optimization problem into sub-problems addressing computation and communication energy separately. An SN grouping algorithm categorizes devices into single-hop and two-hop transmitters based on latency minimization, followed by a relay selection mechanism. To improve FL reliability, we further maximize the number of SNs that meet the roundwise delay constraint, promoting broader participation and improved convergence stability under practical IIoT data distributions. Transmit power levels are then optimized to maximize EE, and a sequential parametric convex approximation (SPCA) method is proposed for joint configuration of system parameters. We further extend the EE formulation to the imperfect channel state information (ICSI). Simulation results demonstrate that the proposed framework significantly enhances convergence speed, reduces outage probability from 10-2 in single-hop to 10-6 and achieves substantial energy savings, with the SPCA approach reducing energy consumption by at least 2x compared to unaggregated cooperation and up to 6x over single-hop transmission.

Power Efficient Cooperative Communication within IIoT Subnetworks: Relay or RIS?

The forthcoming sixth-generation (6G) industrial Internet-of-Things (IIoT) subnetworks are expected to support ultra-fast control communication cycles for numerous IoT devices. However, meeting the stringent requirements for low latency and high reliability poses significant challenges, particularly due to signal fading and physical obstructions. In this paper, we propose novel time division multiple access (TDMA) and frequency division multiple access (FDMA) communication protocols for cooperative transmission in IIoT subnetworks. These protocols leverage secondary access points (sAPs) as Decode-and-Forward (DF) and Amplify-and-Forward (AF) relays, enabling shorter cycle times while minimizing overall transmit power. A classification mechanism determines whether the highest-gain link for each IoT device is a single-hop or two-hop connection, and selects the corresponding sAP. We then formulate the problem of minimizing transmit power for DF/AF relaying while adhering to the delay and maximum power constraints. In the FDMA case, an additional constraint is introduced for bandwidth allocation to IoT devices during the first and second phases of cooperative transmission. To tackle the nonconvex problem, we employ the sequential parametric convex approximation (SPCA) method. We extend our analysis to a system model with reconfigurable intelligent surfaces (RISs), enabling transmission through direct and RIS-assisted channels, and optimizing for a multi-RIS scenario for comparative analysis. Simulation results show that our cooperative communication approach reduces the emitted power by up to 7.5 dB while maintaining an outage probability and a resource overflow rate below $10^{-6}$. While the RIS-based solution achieves greater power savings, the relay-based protocol outperforms RIS in terms of outage probability.

Secure SWIPT in STAR-RIS Aided Downlink MISO Rate-Splitting Multiple Access Networks

Recently, simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have emerged as a novel technology that facilitates sustainable communication by providing 360 coverage and new degrees-of-freedom (DoF) for manipulating signal propagation as well as simultaneous wireless information and power transfer (SWIPT). Inspired by these applications, this paper presents a novel STAR-RIS-aided secure SWIPT system for downlink multiple input single output (MISO) Rate-Splitting multiple access (RSMA) networks. The transmitter concurrently communicates with the information receivers (IRs) and sends energy to untrusted energy receivers (UERs). UERs are also able to wiretap the IR streams. The paper assumes that the channel state information (CSI) of the IRs is known at the transmitter. However, only imperfect CSI (ICSI) for the UERs is available at the transmitter. The paper aims to maximize the achievable worst-case sum secrecy rate (WCSSR) of the IRs under a total transmit power constraint, a sum energy constraint for the UERs, and constraints on the transmission and reflection coefficients by jointly optimizing the precoders and the transmission and reflection beamforming at the STAR-RIS. The formulated problem is non-convex with intricately coupled variables, and to tackle this challenge a suboptimal two-step iterative algorithm based on the sequential parametric convex approximation (SPCA) method is proposed. Specifically, the precoders and the transmission and reflection beamforming vectors are optimized alternatingly. Simulations are conducted to show that the proposed RSMA-based algorithm in a STAR-RIS aided network can improve the secrecy of the confidential information and the overall spectral efficiency.