Stacked intelligent metasurfaces (SIMs) have recently emerged as a promising architecture for large-scale beamforming systems, including cell-free massive MIMO (CF-mMIMO), due to their cost-effective wave-domain signal processing capabilities. However, existing algorithms for the joint optimization of digital and SIM-enabled wave-domain beamforming typically incur prohibitive computational complexity. In this work, we propose an efficient alternating optimization (AO) algorithm for weighted sum-rate maximization in SIM-assisted CF-mMIMO systems employing hybrid digital-wave beamforming. Unlike prior approaches that rely on general-purpose optimization solvers or per-element gradient ascent methods, the proposed algorithm updates the digital and wave-domain beamforming variables on a per-access point (AP) or per-SIM-layer basis, enabling closed-form updates at each step. Numerical results demonstrate that the proposed algorithm reduces the computational complexity by more than 99% compared to existing algorithms while achieving nearly identical sum-rate performance.

As the dense deployment of access points (APs) in cell-free massive multiple-input multiple-output (CF-mMIMO) systems presents significant challenges, per-AP coverage can be expanded using large-scale antenna arrays (LAAs). However, this approach incurs high implementation costs and substantial fronthaul demands due to the need for dedicated RF chains for all antennas. To address these challenges, we propose a hybrid beamforming framework that integrates wave-domain beamforming via stacked intelligent metasurfaces (SIM) with conventional digital processing. By dynamically manipulating electromagnetic waves, SIM-equipped APs enhance beamforming gains while significantly reducing RF chain requirements. We formulate a joint optimization problem for digital and wave-domain beamforming along with fronthaul compression to maximize the weighted sum-rate for both uplink and downlink transmission under finite-capacity fronthaul constraints. Given the high dimensionality and non-convexity of the problem, we develop alternating optimization-based algorithms that iteratively optimize digital and wave-domain variables. Numerical results demonstrate that the proposed hybrid schemes outperform conventional hybrid schemes, that rely on randomly set wave-domain beamformers or restrict digital beamforming to simple power control. Moreover, the proposed scheme employing sufficiently deep SIMs achieves near fully-digital performance with fewer RF chains in the high signal-to-noise ratios regime.

With the evolution of multiple-input multiple-output (MIMO) technology toward extremely large (XL) MIMO systems comprising hundreds of, or more, antennas, this work investigates scalable and fronthaul-efficient reception design for the uplink of cell-free (CF) XL-MIMO systems. In such systems, the uplink signals transmitted by mobile user equipments (UEs) are jointly decoded at a central processing unit (CPU) connected to distributed access points (APs) via finite-capacity fronthaul links.We address the joint optimization of linear transform matrices, used by the APs to reduce the signal dimension and fronthaul load, and fronthaul compression strategies to maximize the uplink sum-rate. A fractional programming (FP)-based iterative algorithm is first developed, followed by a reduced-complexity variant, termed accelerated FP (A-FP), along with its decentralized implementation whose fronthaul overhead remains independent of the number of AP antennas. Numerical results show that the proposed A-FP scheme significantly reduces computational complexity compared to FP implemented with general-purpose solvers, while substantially outperforming scalable baseline schemes that rely solely on local channel state information.

We investigate a downlink communication system with multiple low Earth orbit (LEO) satellites, where inter-satellite links (ISLs) enable cooperative transmission. To address the difficulty of acquiring instantaneous channel state information (CSI), we exploit statistical CSI and formulate a precoding optimization problem to maximize the ergodic sumrate. To overcome the complexity of ergodic rate computation, we approximate the ergodic rates and propose a weighted minimum mean squared error-based algorithm. Numerical results demonstrate that the proposed method achieves performance close to a benchmark scheme based on perfect CSI and significantly outperforms non-cooperative transmission.

This letter presents an efficient beamforming solution for a multi-user integrated sensing and communication (ISAC) system. The problem of optimizing ISAC beamforming vectors is formulated to maximize a weighted sum of communication sum-rate and radar sensing signal-to-interference-plus-noise ratio (SINR), subject to arbitrary per-group power constraints (PGPCs). To address the non-convexity, we employ fractional programming (FP) approach, transforming the original problem into a series of convex subproblems with fixed auxiliary variables. To avoid dependence on convex optimization tools, we propose an accelerated FP (A-FP) scheme that partitions the beamforming vectors into block variables, which are sequentially optimized with closed-form solutions for each block. Numerical results demonstrate that the A-FP scheme achieves almost identical performance to the FP scheme, while significantly reducing computational complexity.

We investigate a movable antenna (MA)-enabled multiuser multiple-input single-output (MU-MISO) downlink system. In particular, we propose a deep learning-based algorithm comprising two deep neural networks (DNNs), where each DNN determines either the MA positions or key features of beamforming vectors. These DNNs are jointly trained to maximize the sum-rate performance. The effectiveness of the proposed method is demonstrated through numerical results.

This work investigates a collaborative sensing and data collection system in which multiple unmanned aerial vehicles (UAVs) sense an area of interest and transmit images to a cloud server (CS) for processing. To accelerate the completion of sensing missions, including data transmission, the sensing task is divided into individual private sensing tasks for each UAV and a common sensing task that is executed by all UAVs to enable cooperative transmission. Unlike existing studies, we explore the use of an advanced cell-free multiple-input multiple-output (MIMO) network, which effectively manages inter-UAV interference. To further optimize wireless channel utilization, we propose a hybrid transmission strategy that combines time-division multiple access (TDMA), non-orthogonal multiple access (NOMA), and cooperative transmission. The problem of jointly optimizing task splitting ratios and the hybrid TDMA-NOMA-cooperative transmission strategy is formulated with the objective of minimizing mission completion time. Extensive numerical results demonstrate the effectiveness of the proposed task allocation and hybrid transmission scheme in accelerating the completion of sensing missions.

This paper proposes a hybrid analog-digital beamforming scheme for cell-free massive multiple-input multiple-output (CF-mMIMO) systems. Unlike fully-digital schemes, each AP uses a limited number of RF chains and applies analog beamforming under constant modulus constraints. The digital beamforming vectors are optimized via the weighted minimum mean square error (WMMSE) algorithm, while the analog phase shifters are updated using gradient descent. Simulation results show that the proposed scheme achieves sum-rate performance close to the fully-digital case as the number of RF chains increases, demonstrating its efficiency and hardware scalability.

Stacked intelligent metasurfaces (SIMs), which are composed of multi-layer programmable metasurfaces, support beamforming in the wave domain, utilizing a limited number of radio frequency (RF) chains. This work investigates the application of SIMs in the downlink of cell-free massive multiple-input multiple-output systems with finite-capacity fronthaul links. Specifically, we address the joint optimization of fronthaul compression and hybrid digital and wave-domain beamforming. To tackle the resulting highly non-convex problem, an alternating optimization algorithm is proposed, which iteratively optimizes digital processing and wave beamforming variables. Numerical results demonstrate that the proposed method outperforms baseline schemes relying solely on digital or wave beamforming, achieving near fully-digital performance with a few RF chains, assuming a sufficiently high signal-to-noise ratio (SNR).

We investigate inter-satellite cooperative transmission in a multiple low-Earth orbit (LEO) satellite communication system to enhance spectral efficiency. Specifically, we design multiple-input multiple-output (MIMO) precoding at LEO satellites for cooperative rate-splitting multiple access (RSMA). Given the difficulty of acquiring instantaneous channel state information (iCSI) due to long delays and Doppler effects, we formulate an ergodic max-min fairness rate (MMFR) maximization problem based on statistical CSI (sCSI). To address the challenge of ergodic rate evaluation, we approximate the problem using closed-form upper bounds and develop a weighted minimum mean squared error-based algorithm to obtain a stationary point. Simulation results demonstrate that the proposed sCSI-based RSMA scheme approaches iCSI-based performance and significantly outperforms conventional space-division multiple access.

To ensure coherent signal processing across distributed Access Points (APs) in Cell-Free Massive Multiple-Input Multiple-Output (CF-mMIMO) systems, a fronthaul connection between the APs and a Central Processor (CP) is imperative. We consider a fronthaul network employing parallel radio stripes. In this system, APs are grouped into multiple segments where APs within each segment are sequentially connected through a radio stripe. This fronthaul topology strikes a balance between standard star and bus topologies, which deploy parallel or serial connections of all APs. Our focus lies in designing the uplink signal processing for a CF-mMIMO system with parallel radio stripes. We tackle the challenge of finite-capacity fronthaul links by addressing the design of In-Network Processing (INP) strategies at APs. These strategies involve linearly combining received signals and compressing the combining output for fronthaul transmission, aiming to maximize the sum-rate performance. Given the high complexity and the stringent requirement for global Channel State Information (CSI) in jointly optimizing INP strategies across all APs, we propose an efficient sequential design approach. Numerical results demonstrate that the proposed sequential INP design achieves a sum-rate gain of up to 82.92% compared to baseline schemes.

The optimization of cooperative beamforming vectors in cell-free massive MIMO (mMIMO) systems is presented where multi-antenna access points (APs) support downlink data transmission of multiple users. Albeit the successes of the weighted minimum mean squared error (WMMSE) algorithm and their variants, they lack careful investigations about computational complexity that scales with the number of antennas and APs. We propose a generalized and reduced WMMSE (G-R-WMMSE) approach whose complexity is significantly lower than conventional WMMSE. We partition the set of beamforming coefficients into subvectors, with each subvector corresponding to a specific AP. Such a partitioning approach decomposes the original WMMSE problem across individual APs. By leveraging the Lagrange duality analysis, a closed-form solution can be derived for each subproblem, which substantially reduces the computation burden. Additionally, we present a parallel execution of the proposed G-R-WMMSE with adaptive step sizes, aiming at further reducing the time complexity. Numerical results validate that the proposed G-R-WMMSE schemes achieve over 99% complexity savings compared to the conventional WMMSE scheme while maintaining almost the same performance.

In the domain of Computer-Aided Diagnosis (CAD) systems, the accurate identification of cancer lesions is paramount, given the life-threatening nature of cancer and the complexities inherent in its manifestation. This task is particularly arduous due to the often vague boundaries of cancerous regions, compounded by the presence of noise and the heterogeneity in the appearance of lesions, making precise segmentation a critical yet challenging endeavor. This study introduces an innovative, an iterative feedback mechanism tailored for the nuanced detection of cancer lesions in a variety of medical imaging modalities, offering a refining phase to adjust detection results. The core of our approach is the elimination of the need for an initial segmentation mask, a common limitation in iterative-based segmentation methods. Instead, we utilize a novel system where the feedback for refining segmentation is derived directly from the encoder-decoder architecture of our neural network model. This shift allows for more dynamic and accurate lesion identification. To further enhance the accuracy of our CAD system, we employ a multi-scale feedback attention mechanism to guide and refine predicted mask subsequent iterations. In parallel, we introduce a sophisticated weighted feedback loss function. This function synergistically combines global and iteration-specific loss considerations, thereby refining parameter estimation and improving the overall precision of the segmentation. We conducted comprehensive experiments across three distinct categories of medical imaging: colonoscopy, ultrasonography, and dermoscopic images. The experimental results demonstrate that our method not only competes favorably with but also surpasses current state-of-the-art methods in various scenarios, including both standard and challenging out-of-domain tasks. This evidences the robustness and versatility of our approach in accurately identifying cancer lesions across a spectrum of medical imaging contexts. Our source code can be found at https://github.com/dewamsa/EfficientFeedbackNetwork.

Weeds pose a significant threat to crops and must be effectively managed. Manual weed management is labour intensive and time-consuming, underscoring the need for efficient solutions. Utilising robotic systems presents a promising alternative. These systems require advanced vision-based capabilities to accurately distinguish between crops and weeds. To enhance the robot’s vision performance for automatic differentiation, this study proposes a method that jointly trains a segmentation model using both labelled and unlabelled data. The labelled data is trained in a supervised manner, while the unlabelled data is incorporated through an entropy minimisation mechanism, promoting learning from diverse data sources. To further enhance generalisation across different fields, we employ instance selective whitening, which enables the network to become style-agnostic and focus on contextual features. Our proposed method achieved a mean Intersection over Union (mIoU) of 0.938 in in-domain experiments, and 0.644 and 0.565 in out-domain experiments on two distinct datasets. These results demonstrate the robust performance of our method across various agricultural datasets, underscoring its potential for practical applications in precision agriculture. This approach not only improves the accuracy of robotic weed detection but also offers a scalable solution for diverse agricultural environments.

This work studies an over-the-air-fog computation (AirFogComp) system, wherein Internet-of- Things (IoT) devices collaboratively learn a machine learning model by communicating with a central server (CS) through a network of access points (APs). Considering the finite capacity of fronthaul links between APs and the CS, we address the challenge of jointly optimizing linear precoding at the IDs, linear processing, and quantization noise covariance matrices at the APs, along with linear combining at the CS. The objective is to minimize the mean squared error (MSE) of the target vector, which is defined as a weighted sum of the local model vectors. To tackle this optimization problem, we propose an iterative block coordinated descent (BCD) algorithm. Numerical experiments demonstrate the rapid convergence of the proposed algorithm and its superior performance compared to baseline schemes.

Cloud Radio Access Network (C-RAN) refers to the virtualization of base station functionalities by means of cloud computing. With centralized processing at the cloud, the amount of hardware and infrastructure in a network can be reduced and it allows for operators to save expenses needed to deploy and maintain the network. Along with high spectral and energy efficiency, C-RAN becomes a key technology evolving to 5G network and it is also expected to play a critical role in the next generation of wireless network. In this paper, theoretical expressions are derived for throughput performance at Medium Access Control (MAC) layer with and without Cloud Radio Random-Access (CRRA). We first develop a CRRA protocol by accounting for the Multi-Packet Reception (MPR) capabilities afforded thanks to the deployment of C-RAN. We then analyze the throughput performance based on error exponent analysis. Numerical results show the performance advantages of C-RAN and verify theoretical expressions.

Multi-tier computing employing computation of-floading to edge servers (ESs) or cloud servers (CSs) offers a solution for challenges for intensive tasks at mobile devices having limited computing resources. We tackle a joint design to minimize completion time, dividing tasks into subtasks processed by local servers, ESs, and CSs. We jointly optimize task splitting ratios and transmission strategies. Numerical results confirm the effectiveness of the proposed multi-tier computing systems.

In the upcoming 6G era, multiple access (MA) will play an essential role in achieving high throughput performances required in a wide range of wireless applications. Since MA and interference management are closely related issues, the conventional MA techniques are limited in that they cannot provide near-optimal performance in universal interference regimes. Recently, rate-splitting multiple access (RSMA) has been gaining much attention. RSMA splits an individual message into two parts: a common part, decodable by every user, and a private part, decodable only by the intended user. Each user first decodes the common message and then decodes its private message by applying successive interference cancellation (SIC). By doing so, RSMA not only embraces the existing MA techniques as special cases but also provides significant performance gains by efficiently mitigating inter-user interference in a broad range of interference regimes. In this article, we first present the theoretical foundation of RSMA. Subsequently, we put forth four key benefits of RSMA: spectral efficiency, robustness, scalability, and flexibility. Upon this, we describe how RSMA can enable ten promising scenarios and applications along with future research directions to pave the way for 6G.

A sequential fronthaul network, referred to as radio stripes, is a promising fronthaul topology of cell-free MIMO systems. In this setup, a single cable suffices to connect access points (APs) to a central processor (CP). Thus, radio stripes are more effective than conventional star fronthaul topology which requires dedicated cables for each of APs. Most of works on radio stripes focused on the uplink communication or downlink energy transfer. This work tackles the design of the downlink data transmission for the first time. The CP sends compressed information of linearly precoded signals to the APs on fronthaul. Due to the serial transfer on radio stripes, each AP has an access to all the compressed blocks which pass through it. Thus, an advanced compression technique, called Wyner-Ziv (WZ) compression, can be applied in which each AP decompresses all the received blocks to exploit them for the reconstruction of its desired precoded signal as side information. The problem of maximizing the sum-rate is tackled under the standard point-to-point (P2P) and WZ compression strategies. Numerical results validate the performance gains of the proposed scheme.