Cell-free massive multiple-input–multiple-output (CF-mMIMO) has emerged as a key architectural candidate for sixth-generation (6G) wireless networks, in which many distributed access points cooperate to serve users without cell boundaries. When combined with integrated sensing and communication (ISAC), this infrastructure evolves from a pure connectivity layer into a spatially distributed sensing–communication fabric capable of high-rate data delivery and fine-grained environmental perception. This survey provides a structured overview of CF-mMIMO– ISAC systems. We first revisit the fundamentals of CF-mMIMO and ISAC and clarify their synergies and inherent tensions. We then synthesize recent progress along several core design axes: joint maximization of communication sum-rate and sensing signal-to-noise ratio (SNR); physical-layer security and privacy-aware sensing; energy-efficient operation with stringent latency and age-of-information requirements; performance evaluation and scalability under realistic hardware and fronthaul constraints; and integration with enabling technologies such as reconfigurable intelligent surfaces (RISs), movable antennas, orthogonal time–frequency space (OTFS) modulation, and unmanned aerial vehicle (UAV) platforms. Across these themes, we compare optimization-based and learning-based methods, emphasizing how they reshape the rate–sensing trade-off, how sensitive they are to channel state information (CSI) assumptions, and how system-level coordination influences scalability. Finally, we distill cross-cutting lessons and outline open problems in distributed joint sensing–communication design. The survey is intended as both a technical reference and a roadmap for designing CF-mMIMO ISAC frameworks in 6G and beyond.

Seismic deconvolution is essential for extracting layer information from noisy seismic data, but it is an ill-posed problem with nonunique solutions. Inspired by classical optimization approaches, model-based deep learning architectures, such as loop unrolling (LU) methods, unfold the optimization process into iterative steps and learn gradient updates from data. These architectures rely on well-defined forward models, but in real seismic deconvolution scenarios, these models are often inaccurate or unknown. Previous approaches have addressed model uncertainty by training robust networks, either passively or actively. However, these methods require a large number of adversarial examples and diverse data structures, often necessitating retraining for unseen forward model structures, which is resource-intensive. In contrast, we propose a more efficient test-time adaptation (TTA) method for the LU architecture, which refines the forward model during inference. This approach incorporates physical principles into the reconstruction process, enabling higher quality results without the need for costly retraining. The code is available at:https://github.com/InvProbs/A-adaptive-seis-deconv

Underwater monitoring and exploration benefit from Internet of Underwater Things (IoUT). However, the lifetime of IoUT networks is limited due to batteries that require frequent replacement, which is costly and unfeasible in a hostile environment. To maximize IoUT device lifetimes and reduce system costs, we propose on-demand wake-up radios, activated by wake-up calls from deployed surface buoys via acoustic, optical, and magnetic induction communication. Using stochastic geometry tools, we analyze the wake-up scheme’s performance, deriving analytical solutions for success and false wake-up probabilities. We characterize the scheme’s performance under different design parameters and highlight its benefits.

We address the problem of distributed recovery of sparse signals in a resource constrained network. We assume that the network nodes sense a common sparse signal and therefore share an approximately common support. We propose a Bayesian algorithm that performs distributed recovery of the sparse signals and is agnostic to the sparse signal distribution. The algorithm requires nodes in the network to communicate only with their neighbors to estimate the sparse signals and is designed to reduce the communication load between nodes. Simulations have been performed to show that the algorithm requires significantly less communication among the nodes as compared to other algorithms.

Energy and spectral efficiency of Internet of Things (IoT) networks can be improved by integrating energy harvesting (EH), cognitive radio, and nonorthogonal multiple access (NOMA) techniques, while unmanned aerial vehicles (UAVs), on the other hand, are a quick and adaptable entity for improving the coverage performance. In this article, we assess the performance of a UAV-assisted overlay cognitive NOMA (OC-NOMA) system by employing an EH-based IoT-inspired cooperative spectrum sharing transmission (I-CSST) scheme. Herein, an energy-constrained UAV-borne secondary node harvests radio-frequency energy from the primary source and uses it to send both its own information signal and the primary information signal using the NOMA approach. We consider the impact of the imperfect successive interference cancellation in NOMA and the distortion noises caused by hardware impairments (HIs) in signal processing, which are unavoidable in real-world systems. We obtain the complicated expressions of outage probability (OP) for primary and secondary IoT networks using the I-CSST scheme under heterogeneous Rician and Nakagami-${m}$fading channels. We continue to investigate asymptotic analysis for OP in order to gain insightful knowledge on the high signal-to-noise ratio (SNR) slope and practicable diversity order. We also assess the system throughput and energy efficiency for the considered OC-NOMA system. Our results demonstrate the benefits of the suggested I-CSST scheme over the benchmark primary direct transmission and orthogonal multiple access schemes. We create a deep neural network (DNN) architecture for real-time OP prediction in order to combat the complications in model-based approaches.

The cooperation between unmanned aerial vehicles (UAVs) and ground mobile-edge computing (MEC) servers in processing tasks is becoming one of the main research trends of MEC networks. Despite the advantages of UAV-assisted MEC, it is restricted by the limited battery capacity and sensitive energy consumption of UAVs. Unlike the previous works where UAVs are allowed to either process tasks locally or offload them to ground MEC servers, in this article, we propose a multihop task routing solution for Internet of Things (IoT) networks in which a UAV can also relay to another UAV with better connection to a ground MEC server. Furthermore, the UAV can make benefit of existing intelligent reflective surfaces (IRSs) to further improve task offloading and reduce energy consumption. We show that the problem of minimizing the total energy of UAVs is NP-hard, and we propose a graph-based heuristic solution to solve it. Simulation results show that the proposed graph-based solution outperforms the traditional no UAV–UAV relaying scheme, especially when IRSs are deployed. Furthermore, a convolutional neural network (CNN) is devised to reduce the delay of finding the decisions for the UAVs at the centralized coordinator. Simulations show that the CNN achieves very close energy consumption performance and a remarkable reduction in execution time compared to the graph-based heuristic solution.

For large-scale seismic exploration in areas that lack even basic infrastructure, wired geophones are impractical because of the huge effort involved and their high deployment and operating costs. A network of wireless geophones capable of recording and transmitting data could be an inexpensive solution. However, a typical seismic survey can generate hundreds of terabytes of raw seismic data per day. It takes a huge amount of energy to transmit this massive amount of data from geophones to the on-site data collection center, thus making the transformation from pre-wired to wireless geophones a significant challenge. To reduce data traffic to the data center without putting additional strain on the geophone, a standalone and lightweight compressive sensing (CS) method is proposed in this work. The method takes advantage of the inherent sparsity in the seismic data to enable the geophone to sense data in a compressed manner. This significantly reduces the amount of data that needs to be recorded/transmitted by the geophone, making it energy efficient. However, instead of employing conventional optimization-based CS reconstruction methods, we propose an efficient implementation of a deep convolutional neural network (DCNN). This network processes the compressed data received at the collection center without any a priori assumptions about the underlying seismic signal statistics, making it appropriate for a wide range of seismic data. The use of CS for energy-efficient sensing and transmission combined with powerful DCNN for reconstruction yields a system that could achieve signal-to-noise ratio (SNR) of around 30 dB with a compression gain of 16 on a field data set. Finally, when compared with other methods, the proposed approach demonstrates significant superiority in maximizing compression gain and reconstruction quality for both synthetic and real field data sets.

Recent advances in the Internet of Things (IoT) are giving rise to a proliferation of interconnected devices, allowing the use of various smart applications. The enormous number of IoT devices generates a large volume of data that requires further intelligent data analysis and processing methods such as deep learning (DL). Notably, DL algorithms, when applied to the Industrial IoT (IIoT), can provide various new applications, such as smart assembling, smart manufacturing, efficient networking, and accident detection and prevention. Motivated by these numerous applications, in this article, we present the key potentials of DL in IIoT. First, we review various DL techniques, including convolutional neural networks, autoencoders, and recurrent neural networks, as well as their use in different industries. We then outline a variety of DL use cases for IIoT systems, including smart manufacturing, smart metering, and smart agriculture. We delineate several research challenges with the effective design and appropriate implementation of DL-IIoT. Finally, we present several future research directions to inspire and motivate further research in this area.

In this paper, we consider the problem of joint timing-offset and channel estimation for amplify-and-forward (AF) two-way relay networks (TWRNs). This problem is solved for generic pulse-shaping filters, taking into account the filter truncation in practical communication and considering both pilot-based and semi-blind estimation strategies. Beginning with pilot-based estimation, we propose a novel Maximum-likelihood joint timing-offset and channel estimator, as well as an alternative estimator based on the special properties of Zadoff-Chu sequences. The first algorithm offers high accuracy, almost overlapping with the Cramer-Rao bound (CRB), while the second offers very low computational complexity. We then develop a semi-blind estimator based on the expectation maximization (EM) framework, exploiting the underlying Hidden Markov Model to apply Baum-Welch forward-backward recursion. The semi-blind CRB is also obtained as an indicator of the best achievable performance. Using simulations, we show that the semi-blind algorithm yields superior accuracy to pilot-based estimation, as well as improved symbol-error-rates and performs very close to the semi-blind CRB. Additionally, a low-complexity approximate EM algorithm is proposed for the case of rectangular pulses. Finally, we consider the possibility of errors in integer-offset estimation and propose pilot-based and semi-blind generalized likelihood ratio test (GLRT) schemes for correcting such errors.

In this paper, we propose a novel patch-based image denoising algorithm using collaborative support-agnostic sparse reconstruction. In the proposed collaborative scheme, similar patches are assumed to share the same support taps. For sparse reconstruction, the likelihood of a tap being active in a patch is computed and refined through a collaboration process with other similar patches in the similarity group. This provides a very good patch support estimation, hence enhancing the quality of image restoration. Performance comparisons with state-of-the-art algorithms, in terms of PSNR and SSIM, demonstrate the superiority of the proposed algorithm.

By virtue of large antenna arrays, massive MIMO systems have a potential to yield higher spectral and energy efficiency in comparison with the conventional MIMO systems. This paper addresses uplink channel estimation in massive MIMO-OFDM systems with frequency selective channels. We propose an efficient distributed minimum mean square error (MMSE) algorithm that can achieve near optimal channel estimates at low complexity by exploiting the strong spatial correlation among antenna array elements. The proposed method involves solving a reduced dimensional MMSE problem at each antenna followed by a repetitive sharing of information through collaboration among neighboring array elements. To further enhance the channel estimates and/or reduce the number of reserved pilot tones, we propose a data-aided estimation technique that relies on finding a set of most reliable data carriers. Furthermore, we use stochastic geometry to quantify the pilot contamination, and in turn use this information to analyze the effect of pilot contamination on channel MSE. The simulation results validate our analysis and show near optimal performance of the proposed estimation algorithms.

This paper presents a novel narrowband interference (NBI) mitigation scheme for SC-FDMA systems. The proposed scheme exploits the frequency domain sparsity of the unknown NBI signal and adopts a low complexity Bayesian sparse recovery procedure. In practice, however, the sparsity of the NBI is destroyed by a grid mismatch between NBI sources and SC-FDMA system. Towards this end, an accurate grid mismatch model is presented and a sparsifying transform is utilized to restore the sparsity of the unknown signal. Numerical results are presented that depict the suitability of the proposed scheme for NBI mitigation.

We propose a method for estimation of sparse frequency selective channels within MIMO-OFDM systems. These channels are independently sparse and share a common support. The method estimates the impulse response for each channel observed by the antennas at the receiver. Estimation is performed in a coordinated manner by sharing minimal information among neighboring antennas to achieve results better than many contemporary methods. Simulations demonstrate the superior performance of the proposed method.

A fast matching pursuit method using a Bayesian approach is introduced for block-sparse signal recovery. This method performs Bayesian estimates of block-sparse signals even when the distribution of active blocks is non-Gaussian or unknown. It is agnostic to the distribution of active blocks in the signal and utilizes a priori statistics of additive noise and the sparsity rate of the signal, which are shown to be easily estimated from data and no user intervention is required. The method requires a priori knowledge of block partition and utilizes a greedy approach and order-recursive updates of its metrics to find the most dominant sparse supports to determine the approximate minimum mean square error (MMSE) estimate of the block-sparse signal. Simulation results demonstrate the power and robustness of our proposed estimator.