With the rapid growth of global communication demand, the limitations of traditional terrestrial networks (TNs) in terms of their coverage, reliability and resource utilization efficiency are becoming increasingly apparent. To address these challenges, this paper proposes a holographic multiple-input multiple-output (MIMO) user-centric cell-free integrated terrestrial and non-terrestrial network (TN-NTN). The proposed architecture is promising to achieve seamless global coverage, high spectral efficiency and robust performance in dynamic multi-user scenarios by combining holographic beamforming, distributed access points (APs) and satellite-ground collaboration. Based on the proposed holographic MIMO assisted user-centric cell-free TN-NTN, first we present the associated channel estimation and time synchronization methods conceived for these dynamic environments, and use statistical modeling as well as distributed optimization techniques for improving the adaptability and synchronization accuracy of the system. Then, we analyze the hybrid beamforming design proposed for this architecture, which combines holographic and digital beamforming for optimizing both signal directionality and interference suppression. We harness statistical modeling and real-time feedback for prompt dynamic adaptation to the violently fluctuating channel conditions. We demonstrate that our system significantly outperforms traditional TN-NTNs in terms of its spectral efficiency and system adaptability. We conclude with the challenges faced by practical hardware limitations, time synchronization and resource allocation, and propose promising future research directions such as hardware optimization, quantum enhanced security, and advanced modulation.

Low-Earth-orbit (LEO) satellites are regarded as a key enabler for 6G communications and localization, due to their large coverage beyond conventional terrestrial networks. In this paper, we propose a downlink LEO base station (BS) bistatic localization framework relying on hybrid beamforming that alleviates the reliance on ultra-fine angle estimation by jointly exploiting time-frequency-spatial observations. A multiple-measurement-vector (MMV) based sparse model is constructed for attaining accurate channel gains and angles from limited pilots and moderate array sizes, where a modified block orthogonal matching pursuit (BOMP) algorithm is proposed to enhance robustness under highly correlated sensing matrices for localization purposes. After geometry-based timing-advance and Doppler pre-compensation at the BS, a two-dimensional (2D) upsampling matched filter having fine delay-Doppler grids is applied to estimate the residual time of arrival (ToA) and Doppler frequency. Then, the final user equipment (UE) position is obtained by intersecting the BS-centered angle-of-arrival (AoA) ray with a bistatic-range ellipse derived from the residual delay. The numerical results under realistic LEO-BS bistatic scenarios demonstrate that the proposed scheme achieves meter-level localization accuracy and highlight the performance gains attained by increasing the number of pilot symbols, subcarriers, and angular resolutions.

We develop a pragmatic multi-user (MU) massive multiple-input multiple-output (MIMO) channel model tailored to the THz band, encompassing factors such as molecular absorption, reflection losses and multipath diffused ray components. Next, we propose a novel semi-blind based channel state information (CSI) acquisition technique i.e. MU whitening decorrelation semi-blind (MU-WD-SB) that exploits the second order statistics corresponding to the unknown data symbols along with pilot vectors. A constrained Cramer-Rao Lower Bound (C-CRLB) is derived to bound the normalized mean square error (NMSE) performance of the proposed semi-blind learning technique. Our proposed scheme efficiently reduces the training overheads while enhancing the overall accuracy of the channel learning process. Furthermore, a novel hybrid receiver combiner framework is devised for MU THz massive MIMO systems, leveraging multiple measurement vector based sparse Bayesian learning (MMV-SBL) that relies on the estimated CSI acquired through our proposed semi-blind technique relying on low resolution analog-to-digital converters (ADCs). Finally, we propose an optimal hybrid combiner based on MMV-SBL, which directly reduces the MU interference. Extensive simulations are conducted to evaluate the performance gain of the proposed MU-WD-SB scheme over conventional training-based and other semi-blind learning techniques for a practical THz channel obtained from the high-resolution transmission (HITRAN) database. The metrics considered for quantifying the improvements include the NMSE, bit error rate (BER) and spectral-efficiency (SE).

Graph Neural Networks (GNNs) have emerged as a powerful framework for modeling complex interconnected systems, hence making them particularly well-suited to address the growing challenges of next-generation Internet of Things (NG-IoT) networks. Despite increasing interest in this area, existing studies remain fragmented, and there is a lack of comprehensive guidance on how GNNs can be systematically applied to NG-IoT systems. As NG-IoT systems evolve toward 6G, they incorporate diverse technologies such as massive MIMO, reconfigurable intelligent surfaces (RIS), terahertz (THz) communication, satellite systems, mobile edge computing (MEC), and ultra-reliable low-latency communication (URLLC). These advances promise unprecedented connectivity, sensing, and automation but also introduce significant complexity, requiring new approaches for scalable learning, dynamic optimization, and secure, decentralized decision-making. This survey provides a comprehensive and forward-looking exploration of how GNNs can empower NG-IoT environments structured as ten open research questions that span the relevant theoretical foundations, practical deployments, and emerging integration pathways. We commence by exploring the fundamental paradigms of GNNs and articulating the motivation for their use in NG-IoT networks. Besides, to further justify their suitability, we intrinsically connect GNNs for the first time with the family of low-density parity-check (LDPC) codes, modeling the NG-IoT as dynamic constrainted graphs where GNNs harness belief propagation for convergence and interpretability through density evolution and EXIT charts. We highlight the distinct roles of node-, edge-, and graph-level tasks in tackling key challenges and demonstrate the GNNs’ ability to overcome the limitations of traditional optimization methods. Following this, we examine the application of GNNs across core NG-enabling technologies and their integration with distributed frameworks to support privacy preservation and distributed intelligence. We then delve into the challenges posed by adversarial attacks, offering insights into defense mechanisms to secure GNN-based NG-IoT networks. Lastly, we examine how GNNs can be integrated with emerging technologies like integrated sensing and communication (ISAC), satellite-air-ground-sea integrated networks (SAGSIN), and quantum computing. Our findings highlight the transformative potential of GNNs in improving efficiency, scalability, and security within NG-IoT systems, paving the way for future advances. Finally, we summarize the key lessons learned throughout the paper and outline promising future research directions, along with a set of design guidelines aimed at facilitating the development of efficient, scalable, and secure GNN models tailored for NG-IoT applications.

Future integrated sensing and communication (ISAC) networks are expected to operate in dense multi-cell environments, where multiple base stations (BSs) share their time-frequency resources for communication and sensing. In such scenarios, the delay–Doppler (DD) sensing performance is strongly affected by random finite-alphabet orthogonal frequency-division multiplexing (OFDM) symbols, power allocation, receive filtering, and interference. This paper develops a modulation- and receive-filter-aware framework for the sensing-interference management in multi-cell OFDM-ISAC systems. Starting from a discrete-time OFDM sensing model, we derive closed-form signal-to-interference-plus-noise ratio (SINR) expressions for each range–Doppler bin under matched filtering (MF) and reciprocal filtering (RF). The analysis reveals distinct interference structures: MF depends on fourth-order constellation moments and power-overlap terms, whereas RF is governed by inverse-symbol-power and ratio-type interference terms. Based on these expressions, we obtain sensing-oriented power allocation structures, including a ramped water-filling solution for MF and a square-root allocation rule for RF. Furthermore, we jointly optimize the finite-alphabet constellation selection and power allocation under realistic communication and power constraints, and obtain tractable mixed-integer convex formulations for both MF and RF. Additionally, we study spectrum-overlap coordination in multi-cell scenarios and reveal the distinct MF/RF preferences for shared and orthogonalized tones. Furthermore, we extend the interference model to inter-cell propagation delays exceeding the cyclic prefix (CP), and show how the resultant delay violation redistributes the nominal interference spectrum into a delay-distorted effective spectrum...

Graph Neural Networks (GNNs) are eminently suitable for wireless resource management, thanks to their scalability, but they still face computational challenges in large-scale, dense networks on classical computers. The integration of quantum computing with GNNs offers a promising pathway for enhancing computational efficiency, because they may reduce the model complexity. This is achieved by leveraging the quantum advantages of parameterized quantum circuits (PQCs), while retaining the expressive power of GNNs. However, existing pure quantum message passing models remain constrained by the limited number of qubits, hence limiting the scalability of their application to wireless systems. As a remedy, we conceive a Scalable Quantum Message Passing Graph Neural Network (SQM-GNN) relying on a quantum message passing architecture. To address the aforementioned scalability issue, we decompose the graph into subgraphs and apply a shared PQC to each local subgraph. Importantly, the model incorporates both node and edge features, facilitating the full representation of the underlying wireless graph structure. We demonstrate the efficiency of SQM-GNN on a device-to-device (D2D) power control task, where it outperforms both classical GNNs and heuristic baselines. These results highlight SQM-GNN as a promising direction for future wireless network optimization.

Next Generation (NG) networks move beyond simply connecting devices to creating an ecosystem of connected intelligence, especially with the support of generative Artificial Intelligence (AI) and quantum computation. These systems are expected to handle large-scale deployments and high-density networks with diverse functionalities. As a result, there is an increasing demand for efficient and intelligent algorithms that can operate under uncertainty from both propagation environments and networking systems. Traditional optimization methods often depend on accurate theoretical models of data transmission, but in real-world NG scenarios, they suffer from high computational complexity in large-scale settings. Stochastic Optimization (SO) algorithms, designed to accommodate extremely high density and extensive network scalability, have emerged as a powerful solution for optimizing wireless networks. This includes various categories that range from model-based approaches to learning-based approaches. These techniques are capable of converging within a feasible time frame while addressing complex, large-scale optimization problems. However, there is currently limited research on SO applied for NG networks, especially the upcoming Sixth-Generation (6G). In this survey, we emphasize the relationship between NG systems and SO by eight open questions involving the background, key features, and lesson learned. Overall, our study starts by providing a detailed overview of both areas, covering fundamental and widely used SO techniques, spanning from single to multi-objective signal processing. Next, we explore how different algorithms can solve NG challenges, such as load balancing, optimizing energy efficiency, improving spectral efficiency, or handling multiple performance trade-offs. Lastly, we highlight the challenges in the current research and propose new directions for future studies.

The significant progress of quantum sensing technologies offer numerous radical solutions for measuring a multitude of physical quantities at an unprecedented precision. Among them, Rydberg atomic quantum receivers (RAQRs) emerge as an eminent solution for detecting the electric field of radio frequency (RF) signals, exhibiting great potential in assisting classical wireless communications and sensing. So far, most experimental studies have aimed for the proof of physical concepts to reveal its promise, while the practical signal model of RAQR-aided wireless communications and sensing remained under-explored. Furthermore, the performance of RAQR-based wireless receivers and their advantages over classical RF receivers have not been fully characterized. To fill these gaps, we introduce the RAQR to the wireless community by presenting an end-to-end reception scheme. We then develop a corresponding equivalent baseband signal model relying on a realistic reception flow. Our scheme and model provide explicit design guidance to RAQR-aided wireless systems. We next study the performance of RAQR-aided wireless systems based on our model, and compare them to classical RF receivers. The results show that Doppler broadening-free RAQRs are capable of achieving a substantial received signal-to-noise ratio (SNR) gain of over 27 decibel (dB) and 40 dB in the photon shot limit and standard quantum limit regimes, respectively.

This letter considers a fluid antenna system (FAS)-aided rate-splitting multiple access (RSMA) approach for downlink transmission. In particular, a base station (BS) equipped with a single traditional antenna system (TAS) uses RSMA signaling to send information to several mobile users (MUs) each equipped with FAS. To understand the achievable performance, we first present the distribution of the equivalent channel gain based on the joint multivariate t-distribution and then derive a compact analytical expression for the outage probability (OP). Moreover, we obtain the asymptotic OP in the high signal-to-noise ratio (SNR) regime. Numerical results show that combining FAS with RSMA significantly outperforms TAS and conventional multiple access schemes, such as non-orthogonal multiple access (NOMA), in terms of OP. The results also indicate that FAS can be the tool that greatly improves the practicality of RSMA.

Electromagnetic (EM) fields constitute the fundamental physical medium carrying information in wireless communications and sensing. It is therefore critically important to investigate the fundamental structure of wireless signals and information under the physical laws governing EM wave propagation. In this letter, we derive the spatial bandwidth of harmonic EM fields in a physically intuitive manner to assess the degrees of freedom (DoFs) of EM fields. Based on the resultant DoF analysis, spatial sampling algorithms are developed to determine antenna placement for EM field measurements, using both global and local spatial bandwidths, respectively. Numerical simulations are conducted to demonstrate the validity of the proposed sampling algorithms.

Accurate channel state information (CSI) underpins reliable and efficient wireless communication. However, acquiring CSI via pilot estimation incurs substantial overhead, especially in massive multiple-input multiple-output (MIMO) systems operating in high-Doppler environments. By leveraging the growing availability of environmental sensing data, this treatise investigates pilot-free channel inference that estimates complete CSI directly from multimodal observations, including camera images, LiDAR point clouds, and GPS coordinates. In contrast to prior studies that rely on predefined channel models, we develop a data-driven framework that formulates the sensing-to-channel mapping as a cross-modal flow matching problem. The framework fuses multimodal features into a latent distribution within the channel domain, and learns a velocity field that continuously transforms the latent distribution toward the channel distribution. To make this formulation tractable and efficient, we reformulate the problem as an equivalent conditional flow matching objective and incorporate a modality alignment loss, while adopting low-latency inference mechanisms to enable real-time CSI estimation. In experiments, we build a procedural data generator based on Sionna and Blender to support realistic modeling of sensing scenes and wireless propagation. System-level evaluations demonstrate significant improvements over pilot- and sensing-based benchmarks in both channel estimation accuracy and spectral efficiency for the downstream beamforming task.

Quantum communication holds the potential to revolutionize information transmission by enabling secure data exchange that exceeds the limits of classical systems. One of the key performance metrics in quantum information theory, namely the Holevo bound, quantifies the amount of classical information that can be transmitted reliably over a quantum channel. However, computing and optimizing the Holevo bound remains a challenging task due to its dependence on both the quantum input ensemble and the quantum channel. In order to maximize the Holevo bound, we propose a unified projected gradient ascent algorithm to optimize the quantum channel given a fixed input ensemble. We provide a detailed complexity analysis for the proposed algorithm. Simulation results demonstrate that the proposed quantum channel optimization yields higher Holevo bounds than input ensemble optimization.

Ultra-reliable low-latency communication (URLLC) is becoming a cornerstone of wireless networks, supporting applications that require stringent reliability and latency, such as industrial automation, remote surgery, immersive virtual reality, and Vehicle-to-Everything (V2X) communication. Hyper Reliable Low Latency Communication (HRLLC), which is envisioned as a Next-Generation (NG) technology, has even made stringent reliability and latency requirements. Achieving the required reliability in the face of low-latency constraints requires robust error correction schemes. This paper introduces the Universal Generalized Flip Decoder (UGFD) concept conceived for efficiently decoding short codes. UGFD leverages multiple sources of enhancing reliability, such as the Channel State Information (CSI) and the associated Log-Likelihood Ratios (LLRs), and allows the adjustment of control variables for flexibly balancing error-correction performance vs. complexity. Our simulation results demonstrate that UGFD is capable of outperforming the state-of-the-art decoders. Hence it is eminently suitable for NG applications, including mMTC (massive Machine-Type Communications) and V2X communication.

Extremely large-scale multiple-input and multiple-output (XL-MIMO) exhibit substantial spatial multiplexing capabilities owing to their high degree of freedom. As the number of antenna elements increases, it becomes more practically suitable to utilize cost-effective antennas equipped with low-resolution RF chains. However, hardware impairments (HWIs) associated with these cost-effective antennas lead to performance saturation in the high signal-to-noise ratio (SNR) region, which cannot be mitigated by merely increasing the transmit power. To address these challenges, we propose a hierarchical receive beamforming method for XL-MIMO based near-field cell-free networks with HWIs. Specifically, the antenna array of each access point (AP) is partitioned into multiple sub-arrays, with each sub-array independently harnessing the minimum mean-square error (MMSE) receive beamforming algorithm. The local data estimates at each AP are then optimized using the results from all sub-arrays, and the central processing unit (CPU) performs its final information recovery by integrating these local estimates. Our theoretical analysis shows that the proposed hierarchical receive beamforming method achieves a higher ergodic sum-rate than the state-of-the-art (SoA) scheme in XL-MIMO systems in the face of HWIs.

A novel Gaussian mixture model (GMM)–aided sparse Bayesian learning (SBL) framework is proposed for channel state information (CSI) estimation in orthogonal time-frequency space (OTFS) modulated systems. The key attribute of the proposed algorithm lies in casting CSI recovery as an SBL inference problem, where posterior distributions are iteratively refined under a hierarchical GMM prior. Using this approach, the sparsity-inducing variances beneficially promote sparsity in the delay–Doppler (DD) domain, while additionally augmenting the capability of SBL to exploit channel statistics more effectively. Moreover, to fully exploit the GMM's ability to approximate arbitrary probability density functions and model complex multipath fading scenarios, the channel statistics are represented using a complex Gaussian mixture. Simultaneously, the method leverages time-domain (TD) pilots without requiring wasteful DD domain guard intervals, thereby ensuring low pilot overhead and high spectral efficiency. The CSI recovered is subsequently applied in a linear minimum mean square error (MMSE) detector for reliable data detection. To benchmark performance, the Oracle-MMSE and the Bayesian Cramér-Rao lower bound (BCRLB) are also derived. Our simulation results demonstrate significant performance improvement over the state-of-the-art sparse estimation methods.

A multipath channel impulse response (CIR) estimator is proposed by leveraging the simultaneous sparsity inherent in the multipath CIR across multiple measurement vectors (MMV) for asymmetrically clipped direct current-biased optical OFDM (ADO-OFDM) visible light communication (VLC) systems. A detailed multipath CIR model is formulated to characterize specular and diffuse optical reflections present in VLC channels. We begin by formulating the system model of the ADO-OFDM-VLC system. Following this, we briefly revisit the traditional channel estimation (CE) techniques, along with the class of compressive sensing (CS)-based CE schemes. Specifically, the FOCal Underdetermined System Solver (FOCUSS), its MMV-based extension (MFOCUSS), and orthogonal matching pursuit (OMP) algorithms are considered, as they effectively exploit the sparsity structure present in the multipath CIR of VLC channels. Furthermore, we introduce an enhanced estimation technique namely, the simultaneous sparse OMP (SOMP), which effectively utilizes the simultaneous sparsity observed in the delay-domain CIR across MMVs, particularly relevant to the non-line-of-sight (NLoS) components of the VLC channel. In addition, an advanced MMV-based Bayesian learning (MBL) framework is proposed to further reduce pilot overhead by exploiting both time and delay-domain sparsity of the CIR. For benchmarking, the Oracle-based minimum mean square error (O-MMSE), Oracle-based least square LS (O-LS), and the Bayesian Cramer-Rao lower bound (BCRLB) are utilized. Simulation results confirm that the proposed MMV-based MBL approach significantly outperforms conventional and existing CS-based techniques, including SOMP, MFOCUSS, and Bayesian learning (BL) methods, in terms of normalized mean square error (NMSE), pilot overhead, bit error rate (BER), and outage probability (OP).

The bit error rate (BER) of Zak orthogonal time-frequency space (Zak-OTFS) modulation using zero-forcing (ZF) equalization over doubly dispersive wireless channels is analyzed. A closed-form BER expression is derived by exploiting the block-circulant structure of the Zak-OTFS channel matrix in the delay-Doppler (DD) domain, enabling an eigenvalue-based representation through discrete Fourier transform diagonalization. To assess its robustness under practical conditions, the analysis is further extended to account for imperfect channel state information (CSI) using an estimation error model. Increasing the number of paths enhances the diversity gain and strengthens effective eigenvalues, hence improving BER performance. In the crystalline regime, the eigenvalue spread narrows, yielding stable and predictable BER. The results demonstrate that Zak-OTFS outperforms the multicarrier OTFS (MC-OTFS) and maintains consistent BER in high-mobility scenarios. However, the channel estimation errors reduce the effective SNR and distort the DD-domain eigenvalues, hence causing higher error floors at high SNR.

The intrinsic integration of Rydberg atomic receivers into wireless communication systems is proposed, by harnessing the principles of quantum physics in wireless communications. More particularly, we conceive a pair of Rydberg atomic receivers, one incorporates a local oscillator (LO), referred to as an LO-dressed receiver, while the other operates without an LO and is termed an LO-free receiver. The appropriate wireless model is developed for each configuration, elaborating on the receiver's responses to the radio frequency (RF) signal, on the potential noise sources, and on the signal-to-noise ratio (SNR) performance. The developed wireless model conforms to the classical RF framework, facilitating compatibility with established signal processing methodologies. Next, we investigate the associated distortion effects that might occur, specifically identifying the conditions under which distortion arises and demonstrating the boundaries of linear dynamic ranges. This provides critical insights into its practical implementations in wireless systems. Finally, extensive simulation results are provided for characterizing the performance of wireless systems, harnessing this pair of Rydberg atomic receivers. Our results demonstrate that LO-dressed systems achieve a significant SNR gain of approximately 40 50 dB over conventional RF receivers in the standard quantum limit regime. This SNR head-room translates into reduced symbol error rates, enabling efficient and reliable transmission with higher-order constellations.

We conceive a novel channel estimation and data detection scheme for OTFS-modulated faster-than-Nyquist (FTN) transmission over doubly selective fading channels, aiming for enhancing the spectral efficiency and Doppler resilience. The delay-Doppler (DD) domain's input-output relationship of OTFS-FTN signaling is derived by employing a root-raised cosine (RRC) shaping filter. More specifically, we design our DD-domain channel estimator for FTN-based pilot transmission, where the pilot symbol interval is lower than that defined by the classic Nyquist criterion. Moreover, we propose a reduced-complexity linear minimum mean square error equalizer, supporting noise whitening, where the FTN-induced inter-symbol interference (ISI) matrix is approximated by a sparse one. Our performance results demonstrate that the proposed OTFS-FTN scheme is capable of enhancing the achievable information rate, while attaining a comparable BER performance to both that of its Nyquist-based OTFS counterpart and to other FTN transmission schemes, which employ the same RRC shaping filter.

Integrated Sensing and Communication (ISAC) requires the development of a waveform capable of efficiently supporting both communication and sensing functionalities. This paper proposes a novel waveform that combines the benefits of both the orthogonal frequency division multiplexing (OFDM) and the Chirp waveforms to improve both the communication and sensing performance within an ISAC framework. Hence, a new architecture is proposed that utilizes the conventional communication framework while leveraging the parameters sensed at the receiver (Rx) for enhancing the communication performance. We demonstrate that the affine addition of OFDM and chirp signals results in a near constant-envelope OFDM waveform, which effectively reduces the peak-to-average power ratio (PAPR), a key limitation of traditional OFDM systems. Using the OFDM framework for sensing in the conventional fashion requires the allocation of some resources for sensing, which in turn reduces communication performance. As a remedy, the proposed affine amalgam facilitates sensing through the chirp waveform without consuming communication resources, thereby preserving communication efficiency. Furthermore, a novel technique of integrating the chirp signal into the OFDM framework at the slot-level is proposed to enhance the accuracy of range estimation. The results show that the OFDM signal incorporated with chirp has better autocorrelation properties, improved root mean square error (RMSE) of range and velocity, and lower PAPR. Finally, we characterize the trade-off between communications and sensing performance.