Communication reliability, as defined by 3GPP, is the probability of achieving a desired quality of service (QoS). Traditionally, this metric is evaluated by averaging the QoS success indicator over spatiotemporal random variables. Recently, the meta distribution (MD) has emerged as a two-level analysis tool that characterizes system-level reliability as a function of link-level reliability thresholds. However, existing MD studies have two limitations. First, they focus exclusively on spatial and temporal randomness corresponding to node distribution and fading channels, respectively, leaving stochastic behaviors in other domains largely unexplored. Second, they are restricted to first-order MDs with two randomness levels, restricting applicability to scenarios requiring higher-order MD characterization. To address these gaps, we propose a hierarchical framework for higher-order MD reliability in wireless networks, where each layer’s success probability is formulated and fed into the next layer, yielding overall MD reliability at the highest level. We apply this framework to wireless networks by capturing three levels of temporal dynamics representing fast, slow, and static random elements, and provide a comprehensive second-order MD reliability analysis for two application scenarios. The effectiveness of the proposed approach is demonstrated via these representative scenarios, supported by detailed analytical and numerical evaluations. Our results highlight the value of hierarchical MD representations across multiple domains and reveal the significant influence of inner-layer target reliabilities on overall performance.

In dynamic metasurface antennas (DMAs) architecture, multiple radiating metamaterial elements are embedded onto a microstrip, with each microstrip connected to a dedicated radio frequency (RF) chain. Therefore, this architecture results in reduced cost and power consumption. This paper proposes a dynamic hybrid beamforming architecture that utilizes a switching network connected to the DMA structure to select efficient microstrips, thereby maximizing the achievable rate while minimizing RF power consumption in near-field communication systems. To model the RF power consumption, a binary diagonal matrix—referred to as the microstrip selection matrix—is defined, where the number of non-zero diagonal elements indicates the number of microstrips required to serve the user. Specifically, we jointly optimize the microstrip selection matrix, the transmit beamforming vectors, and the configurable weights of the DMA’s metamaterial elements, subject to the maximum power budget constraint, the integer constraints of the microstrip selection, and the Lorentzian circle constraint associated with the DMA elements. The optimization problem is non-convex due to the coupling between continuous and discrete decision variables, which makes it challenging to solve. In this regard, we employ a combination of the alternating optimization method, the quadratic transform, and the augmented Lagrangian technique to address these challenges. Simulation results demonstrate that the proposed algorithm outperforms conventional hybrid beamforming approaches in DMA architectures with respect to both achievable rate and RF power consumption.

Communication reliability is typically assessed by averaging the QoS success indicator over spatial and temporal variables. The meta distribution (MD) has recently emerged as a powerful two-level analysis framework, providing insights into system-level (outer) reliability relative to link-level (inner) thresholds. While prior studies focus on first-order spatiotemporal MD reliability, applications beyond this structure remain unexplored. This work introduces a second-order MD reliability analysis framework and applies it to spatial-spectral-temporal MD analysis for frequency-hopping THz communication. Numerical results show how inner-layer target reliabilities in temporal/spectral domains affect overall spatial MD reliability. It is also shown that adopting a non-uniform frequency-hopping pattern enhances spatial MD reliability but reduces resiliency and increases jamming risk.

The stabilizability of wireless networked control systems (WN CSs) is a deterministic binary valued parameter proven to hold if the communication data rate is higher than the sum of the logarithm of unstable eigenvalues of the open-loop control system. In this analysis, it is assumed that the communication system provides a fixed deterministic trans-mission rate between the sensors and controllers. Due to the stochastic parameters of communication channels, such as small-scale fading, the instantaneous rate is an intrinsically stochastic parameter. In this sense, it is a common practice in the literature to use the deterministic ergodic rate in analyzing the asymptotic stabilizability. Theoretically, there exists no work in the literature investigating how the ergodic rate can be incorporated into the analysis of asymptotic stabilizability. Considering the stochastic nature of channel parameters, we introduce the concept of probability of stabilizability by interconnecting communication link reliability with the system's unstable eigenvalues and derive a closed-form expression that quantifies this metric. Numerical results are provided to visualize how communication and control systems' parameters affect the probability of stabilizability of the overall system.

We characterize three near-field sub-regions for phased array antennas by elaborating on the boundaries Fraunhofer, radial-focal, and non-radiating distances. The Fraunhofer distance which is the boundary between near and far field has been well studied in the literature on the principal axis (PA) of single-element center-fed antennas, where PA denotes the axis perpendicular to the antenna surface passing from the antenna center. The results are also valid for phased arrays if the PA coincides with the boresight, which is not commonly the case in practice. In this work, we completely characterize the Fraunhofer distance by considering various angles between the PA and the boresight. For the radial-focal distance, below which beamfocusing is feasible in the radial domain, a formal characterization of the corresponding region based on the general model of near-field channels (GNC) is missing in the literature. We investigate this and elaborate that the maximum-ratio-transmission (MRT) beamforming based on the simple uniform spherical wave (USW) channel model results in a radial gap between the achieved and the desired focal points. While the gap vanishes when the array size N becomes sufficiently large, we propose a practical algorithm to remove this gap in the non-asymptotic case when N is not very large. Finally, the non-radiating distance, below which the reactive power dominates active power, has been studied in the literature for single-element antennas. We analytically explore this for phased arrays and show how different excitation phases of the antenna array impact it. We also clarify some misconceptions about the non-radiating and Fresnel distances prevailing in the literature.

Three-dimensional (3D) spot beamfocusing (SBF), in contrast to conventional angular-domain beamforming, concentrates radiating power within a very small volume in both radial and angular domains in the near-field zone. Recently the implementation of channel-state-information (CSI)-independent machine learning (ML)-based approaches have been developed for effective SBF using extremely large-scale programmable metasurface (ELPMs). These methods involve dividing the ELPMs into subarrays and independently training them with Deep Reinforcement Learning to jointly focus the beam at the desired focal point (DFP). This paper explores near-field SBF using ELPMs, addressing challenges associated with lengthy training times resulting from independent training of subarrays. To achieve a faster CSI-independent solution, inspired by the correlation between the beamfocusing matrices of the subarrays, we leverage transfer learning techniques. First, we introduce a novel similarity criterion based on the phase distribution image (PDI) of subarray apertures. Then we devise a subarray policy propagation scheme that transfers the knowledge from trained to untrained subarrays. We further enhance learning by introducing quasi-liquid layers as a revised version of the adaptive policy reuse technique. We show through simulations that the proposed scheme improves the training speed about 5 times. Furthermore, for dynamic DFP management, we devised a DFP policy blending process, which augments the convergence rate up to 8-fold.

This paper presents the characterization of near-field propagation regions for phased array antennas, with a particular focus on the propagation boundaries defined by Fraunhofer and Fresnel distances. These distances, which serve as critical boundaries for understanding signal propagation behavior, have been extensively studied and characterized in the literature for single-element antennas. However, the direct application of these results to phased arrays, a common practice in the field, is argued to be invalid and non-exact. This work calls for a deeper understanding of near-field propagation to accurately characterize such boundaries around phased array antennas. More specifically, for a single-element antenna, the Fraunhofer distance is dF= 2D2sin2(0)/λ where D represents the largest dimension of the antenna, λ is the wavelength and θ denotes the observation angle. We show that for phased arrays, dFexperiences a fourfold increase (i.e., dF= 8D2sin2(θ)/λ) provided that $\left| {\theta - \frac{\pi }{2}} \right| > {\theta ^F}$ (which holds for most practical scenarios), where θFis a small angle whose value depends on the number of array elements, and for the case $\left| {\theta - \frac{\pi }{2}} \right| \leq {\theta ^F}$, we have dF∈ [2D2/λ, 8D2cos2(θF)/λ], where the precise value is obtained according to some square polynomial function ${{\tilde F}}\left( \theta \right)$. Besides, we also prove that the Fresnel distance for phased array antennas is given by ${d^{\text{N}}} = 1.75\sqrt {{D^3}/\lambda }$ which is $\sqrt 8$ times greater than the corresponding distance for a conventional single-element antenna with the same dimension.

As 6G goes toward applying extremely large-scale antenna arrays (ELAAs) as well as very high operating frequencies, the near-field propagation region expands to even hundreds of meters. This provides conventional communication systems with new challenges as well as opportunities. In this context, effectively managing the complexities of near-field communication, and harnessing its unique features require a deep understanding of near-field signal behavior, particularly when initiated from ELAAs. The radial beamfocusing through ELAAs is one of the important unique near-field features which has been less analytically explored and characterized in the literature so far. In this paper, we formally define and characterize the radial focal region and establish the conditions for achieving a near-field focal point in the radial domain. We analytically reveal that employing a maximum ratio beamformer leads to a radial focal gap between the desired and achievable focal points. We analytically prove that when the number of arrays becomes extremely large through ELAAs, the gap is eliminated. In addition, for non-ELAA scenarios where the number of array elements is not very high, we present a beamforming algorithm to remove the gap and precisely create a radial focal point at the desired location. The achieved outcomes are verified through numerical results for single-focal and multi-focal scenarios.

We propose a novel approach to smart spot-beamforming (SBF) in the Fresnel zone leveraging extremely large-scale programmable metasurfaces (ELPMs). A smart SBF scheme aims to adaptively concentrate the aperture's radiating power exactly at a desired focal point (DFP) in the 3D space utilizing some Machine Learning (ML) method. This offers numerous advantages for next-generation networks including ultra-high-speed wireless communication, location-based multiple access (LDMA), efficient wireless power transfer (WPT), interference mitigation, and improved information security. SBF necessitates ELPMs with precise channel state information (CSI) for all ELPM elements. However, obtaining exact CSI for ELPMs is not feasible in all environments; we alleviate this by developing a novel CSI-independent ML scheme based on the TD3 deep-reinforcement-learning (DRL) method. While the proposed ML-based scheme is well-suited for relatively small-size arrays, the computational complexity is unaffordable for ELPMs. To overcome this limitation, we introduce a modular highly scalable structure composed of multiple sub-arrays, each equipped with a TD3-DRL optimizer. This setup enables collaborative optimization of the radiated power at the DFP, significantly reducing computational complexity while enhancing learning speed. The proposed structure's benefits in terms of 3D spot-like power distribution, convergence rate, and scalability are validated through simulation results.

Transmit antenna selection (TAS) at base station has been widely studied and employed in many communication networks (such as those using massive multiple-input multiple-output (MIMO) systems). Thanks to the small size of microstrip antenna elements applicable to millimeter-wave (mmWave) frequencies, the implementation of TAS in small user equipments (UEs) employing switchable directional antennas is recently becoming popular. In this paper, we consider device-to-device (D2D) communications underlaying a cellular network wherein each D2D UE is equipped with switchable transmit antennas. By employing Generalized Bender’s Decomposition (GBD) algorithm, we obtained the solution to the problem of globally optimal transmit antenna selection and channel allocation to D2D UEs together with transmit powers of cellular and D2D UEs. Although we reformulated the non-convex primal subproblem of the proposed GBD-based method into convex form, we further managed to obtain the corresponding closed-form solution through analytical manipulations; this extensively increased (at least 60 times) the execution speed of the proposed method as shown in the simulation results.

We develop a comprehensive framework to characterize and optimize the performance of a unmanned aerial vehicle (UAV)-assisted D2D network, where D2D transmissions underlay cellular transmissions. Different from conventional non-line-of-sight (NLoS) terrestrial transmissions, aerial transmissions are highly likely to experience line-of-sight (LoS). As such, characterizing the performance of mixed aerial-terrestrial networks with accurate fading models is critical to precise network performance characterization and resource optimization. We first characterize closed-form expressions for a variety of performance metrics such as frame decoding error probability (referred to as reliability), outage probability, and ergodic capacity of users. The terrestrial and aerial transmissions may experience either LoS Rician fading or NLoS Nakagami-m fading with a certain probability. Based on the derived expressions, we formulate a hierarchical bi-objective mixed-integer-nonlinear-programming (MINLP) problem to minimize the total transmit power of all users and maximize the aggregate throughput of D2D users subject to quality-of-service (QoS) measures (i.e., reliability and ergodic capacity) of cellular users. We model the proposed problem as a bi-partite one-to-many matching game. To solve this problem, we first obtain the optimal closed-form power allocations for each D2D and cellular user on any possible subchannel, and then incorporate them to devise efficient subchannel and power allocation algorithms. Complexity analysis of the proposed algorithms is presented. Numerical results verify the accuracy of our derived expressions and reveal the significance of aerial relays compared to ground relays in increasing the throughput of D2D pairs especially for distant D2D pairs.

In an underlay cognitive radio network (CRN), in order to guarantee that all primary users (PUs) achieve their target-signal-to-interference-plus-noise ratios (target-SINRs), the interference caused by all secondary users (SUs) to the primary receiving-points should be controlled. To do so, the feasible cognitive interference region (FCIR), i.e., the region for allowable values of interference at all of the primary receiving-points, which guarantee the protection of the PUs, needs to be formally characterized. In the state-of-the-art interference management schemes for underlay CRNs, it is considered that all PUs are protected if the cognitive interference for each primary receiving-point is lower than a maximum threshold, the so called interference temperature limit (ITL) for the corresponding receiving-point. This is assumed to be fixed and independent of ITL values for other primary receiving-points, which corresponds to a box-like FCIR. In this paper, we characterize the FCIR for uplink transmissions in cellular CRNs and for direct transmissions in ad-hoc CRNs. We show that the FCIR is in fact a polyhedron (i.e., the maximum feasible cognitive interference threshold for each primary receiving-point is not a constant, and it depends on that for the other primary receiving-points). Therefore, in practical interference management algorithms, it is not proper to consider a constant and independent ITL value for each of the primary receiving-points. This finding would significantly affect the design of practical interference management schemes for CRNs. To demonstrate this, based on the characterized FCIR, we propose two power control algorithms to find the maximum number of admitted SUs and the maximum aggregate throughput of the SUs in infeasible and feasible CRNs, respectively. For two distinct objectives, our proposed interference management schemes outperform the existing ones. The numerical results also demonstrate how the assumption of fixed ITL values leads to poor performance measures in CRNs.

Next generation cellular networks will consist of multiple tiers of cells and users associated with different network tiers may have different priorities (e.g., macrocell-picocell- femtocell networks with macro tier prioritized over pico tier, which is again prioritized over femto tier). Designing efficient joint power and admission control (JPAC) algorithms for such networks under a cochannel deployment (i.e., underlay) scenario is of significant importance. Feasibility checking of a given target signal-to-noise-plus-interference ratio (SINR) vector is generally the most significant contributor to the complexity of JPAC algorithms in single/multitier underlay cellular networks. This is generally accomplished through iterative strategies whose complexity is either unpredictable or of O(M3), when the well-known relationship between the SINR vector and the power vector is used, where M is the number of users/links. In this paper, we derive a novel relationship between a given SINR vector and its corresponding uplink/downlink power vector based on which the feasibility checking can be performed with a complexity of O(B3+ MB), where B is the number of base stations. This is significantly less compared to O(M3) in many cellular wireless networks since the number of base stations is generally much lower than the number of users/links in such networks. The developed novel relationship between the SINR and power vector not only substantially reduces the complexity of designing JPAC algorithms, but also provides insights into developing efficient but low-complexity power update strategies for prioritized multitier cellular networks. We propose two such algorithms and through simulations, we show that our proposed algorithms outperform the existing ones in prioritized cellular networks.

In an underlay cognitive radio network (CRN), in order to guarantee protection of primary users (PUs) (i.e., all PUs achieve their target signal-to-interference-plus-noise ratios [SINRs]), the interference caused by all secondary users (SUs) to the primary receiving-points should be controlled. To do so, the feasible cognitive interference region (FCIR), i.e., the region for allowable values of interference at all of the primary receiving-points, which guarantee protecting the PUs, needs to be formally characterized. In the state-of-the-art interference management schemes for underlay CRNs, it is considered that all PUs are protected if the cognitive interference for each primary receiving-point is lower than a maximum threshold, the so called interference temperature limit (ITL) for the corresponding receiving-point. This is assumed to be fixed and independent of ITL values for other primary receiving-points. This corresponds to a box-like FCIR, which is not correct. In this paper, we analytically obtain the FCIR and show that, the FCIR is a polyhedron (i.e., the maximum feasible cognitive interference threshold for each primary receiving-point is not constant, and it depends on that for other primary receiving-points). Therefore, in practical interference management algorithms, it is not proper to consider a constant and independent ITL value for each of the primary receiving-points. This finding would significantly affect the design of practical interference management schemes for CRNs.

We investigate the problem of designing efficient and low-complexity centralized algorithms for joint power and admission control in a cellular cognitive radio network (CRN) which coexists with a primary radio network (PRN) in a spectrum underlay fashion. We first derive a simple one-to-one relation between the signal-to-interference-plus-noise ratio (SINR) vector and its corresponding power vector of all users of the CRN and PRN, and based on this we propose two new admission metrics. Then, in an infeasible system, where the minimum acceptable target-SINRs for all primary and secondary users are not simultaneously reachable, two centralized algorithms are proposed. These algorithms aim at removing the minimal number of secondary users (based on the proposed admission metrics), subject to the constraint that all primary users are supported with their target-SINRs. In an infeasible system, our proposed algorithms outperform other existing algorithms in terms of complexity and secondary users' outage ratio. Furthermore, for a feasible system, where all secondary users can be admitted along with all primary users, by using our derived one-to-one relation between SINR and power vector, we solve the problems of maximizing aggregate throughput and max-min quality-of-service (QoS) for secondary users, both subject to the constraint that all primary and secondary users are supported with their minimum target-SINRs.

In the well-known distributed target-SINR-tracking power control algorithm (TPC), when the system is infeasible, some users transmit at the maximum power without obtaining their target-SINRs. This imposes extra interference on other users and results in an increased number of non-supported users. Several ideas have been proposed to alleviate this deficiency, most of which employ a removal scheme for users who cannot attain their target-SINRs. In this paper, we propose a distributed multiple target-SINRs tracking power control algorithm (MTPC) in which instead of a single target-SINR (as is the case in existing target-SINR tracking schemes), depending on the channel condition, each user dynamically selects its target-SINR from a set of predefined desired target-SINRs. If a user cannot attain a targetSINR, instead of stopping transmission, it may keep transmitting with a lower target-SINR (and hence, lower data rate). We show that our proposed power update function has at least one fixedpoint and all of its fixed-points are Pareto and energy efficient, i.e., at the equilibrium, any unilateral increase in the targetSINR of a user leads to a degradation in the target-SINR of at least one other user (i.e., Pareto-efficiency) and furthermore the transmit power vector corresponds to the minimum aggregate power needed for attaining the equilibrium target-SINR vector (i.e., energy efficiency).