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.

Integrating extremely large antenna arrays (ELAAs) with extremely large reconfigurable intelligent surfaces (XLRISs) in millimeter wave (mmWave) communications places devices in the near-field (NF) region, significantly boosting spectral efficiency (SE). This paper investigates beam focusing for SE maximization by determining the optimal placement of XL-RIS in a multi-input single-output (MISO) system. Specifically, to maximize SE, the transmit beamforming vector at the base station (BS) and the phase-shifting vector at the XL-RIS are jointly optimized. To explore the optimal placement of XL-RIS, we consider three scenarios where the positions of the BS and user are fixed, but the XL-RIS is placed in either the NF or farfield (FF) of both. Since the SE maximization problem is nonconvex with highly coupled variables, we propose an alternating optimization algorithm that decouples the problem into two subproblems: transmit beamforming optimization and phase shift optimization. Both sub-problems are reformulated as convex problems using the semi-definite programming (SDP) technique and are then solved with standard convex optimization tools. Simulation results show that placing the XL-RIS in the NF region of the BS achieves higher SE compared to other configurations.

The strategic deployment of energy storage by cellular operators offers a robust solution to improve the resilience and efficiency of smart grids. Traditionally used to ensure uninterrupted operation of cellular Base Stations (BSs) during power grid outages, these storages can now actively participate in the energy flexibility market. In this study, we examine the potential of BS storages to support smart grid ancillary services by allocating a portion of their capacity to balance demand in the smart grid while ensuring that enhanced Mobile Broadband (eMBB) user requirements, such as data rate, are met. This involves utilizing BS stored energy to feed back into the smart grid during peak demand periods or to balance demand within the smart grid. We analyze the impact of eMBB user communication requirements on smart grid Demand Response Management (DRM), formulating a novel joint cellular communication resource allocation and smart grid energy allocation problem. This problem aims to maximize the participation of BSs in DRM, taking into account battery aging and cycling constraints. Simulation results demonstrate significant improvements in DRM and cost-effectiveness for cellular network operators. For example, if a cellular network with 1,200 BSs reduces its users' data rate requirements from 6 Mbps to 1 Mbps, it can increase DRM participation from 30% to 45%. For a power vacancy of approximately -30 MW, this translates to an increase in power vacancy compensation from 9 MW to around 13.5 MW, exceeding the capacity of a regular wind turbine.

Downlink transmission of a nonorthogonal visible light communication (VLC) system, empowered by autonomous aerial vehicles (AAVs), is studied for coexisting enhanced mobile broadband (eMBB), ultrareliable low-latency communication (URLLC), and massive machine-type communication (mMTC) services. A joint resource allocation problem involving user association, transmit power, and flight trajectory of AAVs is formulated, with the goal of characterizing a multiobjective tradeoff as a weighted sum of the power consumption of each AAV and the perceived Quality of Experience (QoE) of its associated eMBB users, while ensuring the service-specific requirements for eMBB, mMTC, and URLLC are met. Assuming the imperfection of channel state information (CSI), we invoke a generalized Benders decomposition (GBD) methodology, leveraging tools from convex optimization and multiagent deep reinforcement learning to address this problem. We further analytically derive the upper and lower bounds on the reward function for each AAV as a learning agent. Extensive simulations confirm that our proposed method outperforms the single-agent counterpart in the literature, with up to a 22% reduction in power consumption and a 13% gain in perceived QoE. Additionally, compared to the globally optimal brute-force method for AAV-user association, our proposed method experiences only a trivial performance loss in a small-scale scenario.

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.

We study the problem of resource reservation and allocation (RRA), aiming to enable network operators (NOs) to effectively develop the coexistence of enhanced mobile broadband (eMBB) and ultra-reliable low-latency (URLLC) services by acquiring resources–such as spectrum, base stations (BSs), and transmit power of the BSs–from the infrastructure provider (InP). To do this, we quantitatively analyze the impact of spatial-temporal aspects of the network, including the spatial distributions of users and BSs, wireless channel conditions, and user traffic statistics, on resources required to meet users’ requirements using the meta distribution. Since spatial-temporal aspects operate on different time and space scales, we investigate a multi-timescale RRA (MT-RRA) scheme that facilitates collaboration between the InP and NOs to acquire resources through long-timescale RR (LT-RR) over the spatial distributions of users and user traffic statistics and medium-timescale RR (MT-RR) over wireless channel fading. Subsequently, NOs allocate these resources to their eMBB and URLLC users over shorter timescales. To address these problems, an iterative decomposition method is proposed. The MT-RRA scheme leverages the flexibility of the MT-RR, the cost-effectiveness of the LT-RR, and the benefits of spatial-temporal considerations. The simulation results are presented to demonstrate the performance of the proposed MT-RRA scheme and highlight its superiority over single-timescale RRA schemes.

This paper delves into the unexplored frequency-dependent characteristics of beyond diagonal reconfigurable intelligent surfaces (BD-RISs). A generalized practical frequency-dependent reflection model is proposed as a fundamental framework for configuring fully-connected and group-connected RISs in a multi-band multi-base station (BS) multiple-input multiple-output (MIMO) network. Leveraging this practical model, multi-objective optimization strategies are formulated to maximize the received power at multiple users connected to different BSs, each operating under a distinct carrier frequency. By relying on matrix theory and exploiting the symmetric structure of the reflection matrices inherent to BD-RISs, relaxed tractable versions of the challenging problems are achieved for scenarios with obstructed and unobstructed direct channel links. The relaxed solutions are then combined with codebook-based approaches to configure the practical capacitance values for the BD-RISs. Simulation results reveal the frequency-dependent behaviors of different RIS architectures and demonstrate the effectiveness of the proposed schemes. Notably, BD-RISs exhibit high reflection performance across the intended frequency range, remarkably outperforming conventional single-connected RISs. Moreover, the proposed optimization approaches prove effective in enabling the targeted operation of BD-RISs across one or more carrier frequencies. The results also shed light on the potential for harmful interference in the absence of synchronization between RISs and adjacent BSs.

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.

The terahertz (THz) communication is a key enabler for 6G applications such as ultra-reliable and low-latency communications (URLLCs), as it can provide sufficient spectrum resources for a high data rate and low latency. However, THz communications have poor penetrability with limited coverage. To address this issue, we consider a dual connectivity THz and millimeter-wave (mmWave) heterogeneous network, where mmWave base stations (BSs) and THz BSs are distributed based on a Poisson point process and a Thomas cluster process, respectively, which results in the THz BSs being clustered around the mmWave BSs. This captures the inter-tier spatial dependency and leverages the benefits of both mm Wave and THz links. Employing stochastic geometry, we derive expressions for the reliability probability of URLLC users and the link selection probability for a dual connectivity THz and mm Wave heteroge-neous network. The numerical results validate our analysis with simulation and demonstrate the impact of the maximum delay threshold requirement for URLLC users, density of THz BSs, and molecular absorption noise on the network performance.

This paper studies the Joint Resource block (RB) allocation and Power control (JRP) problem for the coexistence of further enhanced mobile broadband (FeMBB) and extreme ultrareliable low latency (eURLLC) services in rate splitting multiple access-based next-generation (e.g., 5G-Advanced and 6G) wireless networks. In the JRP problem, for each FeMBB user, a minimum data rate requirement is considered, and eURLLC users should meet their latency and reliability requirements. To address the JRP problem, we propose a hybrid deep reinforcement learning (HDRL-JRP) algorithm, in which a double dueling deep Q-network is employed for RB allocation and a deep deterministic policy gradient is used for power control. Via simulation results, the performance of the HDRL-JRP algorithm is demonstrated in terms of total data rate.

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.

We study the problem of end-to-end (E2E) network slicing, i.e., joint slicing of the radio access network (RAN) and core network (CN), for the coexistence of enhanced mobile broadband (eMBB) and ultra-reliable and low latency communication (URLLC) services in future generation cellular (e.g., 5G-Advanced/6G) networks. The E2E resource slicing problem is defined as a mixed-integer non-linear programming problem to minimize the E2E energy consumption and the cost of utilized resources. To overcome the difficulty of solving this problem, we decompose it into two sub-problems, namely, RAN resource allocation (RRA) and CN resource allocation (CRA) problems. In both RRA and CRA problems, the existence of binary variables makes them intractable. To tackle this difficulty, we relax the binary variables by introducing penalty functions. Then, we make the RRA and CRA problems convex by employing the majorization-minimization approximation method. Via simulation results, we compare our proposed joint RAN and CN resource allocation algorithm (JRCRA) with the disjoint solution where RAN and CN resources are allocated to users separately. The joint allocation of resources in the RAN and CN has the advantage that the E2E tolerable latency of users can be flexibly divided between RAN and CN. In contrast, if resources in RAN and CN are allocated separately, a predefined part of the E2E tolerable latency should be considered as the tolerable latency in RAN and CN. The simulation results illustrate that our proposed JRCRA algorithm obtains a 34% improvement in energy consumption and a 24% improvement in cost compared to the disjoint one. Moreover, via simulation results, we illustrate that in comparison with existing algorithms, our proposed JRCRA obtains a higher performance. Besides, simulation results confirm that JRCRA reaches a close performance to the optimal solution.

Downlink transmission of non-orthogonal multiple access visible light communication systems empowered by an unmanned aerial vehicle (UAV) is considered for multiplexing enhanced mobile broadband (eMBB) and massive machine type communication (mMTC) services. Accordingly, a resource allocation problem of joint transmit power control and motion trajectory design of the DAVs is formulated, whose goal is to characterize a multi-objective trade-off as a weighted sum of the UAVs' power consumption and the perceived quality-of-experience (QoE) of eMBB users, while ensuring the eMBB and mMTC service-specific requirements. We leverage an alternative decomposition and tools from convex optimization and actorcritic multi-agent deep reinforcement learning to address this problem in an iterative fashion. We analytically derive the upper-and lower-bounds on the reward of the DAVs as the learning agents and demonstrate that the proposed resource allocation method outperforms the similar scheme in literature, by up to 17% average reduced power consumption, as well as 12% average perceived QoE gain.

Integrating the multitude of emerging internet of things (IoT) applications with diverse requirements in beyond fifth generation (B5G) networks necessitates the coexistence of enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) services. However, bandwidth limited and congested sub-6GHz bands are incapable of fulfilling this coexistence. In this paper, we consider a reconfigurable intelligent surface (RIS)-aided wideband terahertz (THz) communication system to this end. In specific, we formulate a resource management problem, aiming at jointly optimizing the reflection coefficient of the RIS elements and the transmit power of the base station, as well as the wideband THz resource block allocation. To solve this problem, we adopt a supervised learning approach relying on optimization, deep learning and ensemble learning methods. Simulation results show that for an RIS of size$11\times 11$, up to 49% spectral efficiency gain is achieved for the eMBB service compared to the counterparts, while ensuring the reliability and latency requirements of the URLLC service. Further, the ensemble learning model can perform real-time resource management at the expense of up to 1% performance loss, compared to the optimization approach.

In this paper, the downlink transmission of a two-tier heterogeneous network (HetNet) is considered in which a macro base station (MBS) serves the macro users using orthogonal frequency division multiple access (OFDMA) and small base stations (SBSs) serve the small-cell users through multi-carrier non-orthogonal multiple access (MC-NOMA) and joint transmission (JT). In particular, assuming the subcarriers are already allocated to macro users, the problem of scheduling (i.e., joint user association and subcarrier allocation) and power control is studied with the goal of maximizing the total users’ perceived quality-of -experience (QoE) for small-cell users, while a minimum data rate for macro users is guaranteed. To solve the joint optimization problem, a near-optimal and computationally efficient two-phase solution approach is proposed based on the tools from optimization and machine learning (ML). In the first phase, the optimization problem is solved to obtain the scheduling decisions and transmit power variables. In the second phase, the optimized scheduling decisions and transmit power variables serve as training samples for an AlexNet classifier and support vector regressor (SVR), respectively. Simulation results reveal that the integration of JT into MC-NOMA, outperforms the conventional MC-NOMA scheme by up to 24%, 19%, and 21% for the web, video and audio multimedia services, respectively. Compared to a conventional convolutional neural network, our results demonstrate that for the web, video, and audio-services, AlexNet increases the scheduling prediction accuracy up to 14%, 11%, and 17%, while SVR increases the power prediction accuracy up to 8%, 7%, and 12%, respectively.

Passive beamforming in reconfigurable intelligent surfaces (RISs) enables a feasible and efficient way of communication when the RIS reflection coefficients are precisely adjusted. In this paper, we present a framework to track the RIS reflection coefficients with the aid of deep learning from a time-series prediction perspective in a terahertz (THz) communication system. The proposed framework achieves a two-step enhancement over the similar learning-driven counterparts. Specifically, in the first step, we train a liquid state machine (LSM) to track the historical RIS reflection coefficients at prior time steps (known as a time-series sequence) and predict their upcoming time steps. We also fine-tune the trained LSM through Xavier initialization technique to decrease the prediction variance, thus resulting in a higher prediction accuracy. In the second step, we use ensemble learning technique which leverages on the prediction power of multiple LSMs to minimize the prediction variance and improve the precision of the first step. It is numerically demonstrated that, in the first step, employing the Xavier initialization technique to fine-tune the LSM results in at most 26% lower LSM prediction variance and as much as 46% achievable spectral efficiency (SE) improvement over the existing counterparts, when an RIS of size 11×11 is deployed. In the second step, under the same computational complexity of training a single LSM, the ensemble learning with multiple LSMs degrades the prediction variance of a single LSM up to 66% and improves the system achievable SE at most 54%.

This paper focuses on improving the resource allocation algorithm in terms of packet delivery ratio (PDR), i.e., the number of successfully received packets sent by end devices (EDs) in a long-range wide-area network (LoRaWAN). Setting the transmission parameters significantly affects the PDR. Employing reinforcement learning (RL), we propose a resource allocation algorithm that enables the EDs to conFigure their transmission parameters in a distributed manner. We model the resource allocation problem as a multi-armed bandit (MAB) and then address it by proposing a two-phase algorithm named MIX-MAB, which consists of the exponential weights for exploration and exploitation (EXP3) and successive elimination (SE) algorithms. We evaluate the MIX-MAB performance through simulation results and compare it with other existing approaches. Numerical results show that the proposed solution performs better than the existing schemes in terms of convergence time and PDR.

With the emerge of the Internet of Things (IoT), localization within indoor environments has become inevitable and has attracted a great deal of attention in recent years. Several efforts have been made to cope with the challenges of accurate positioning systems in the presence of signal interference. In this paper, we propose a novel deep learning approach through Gradient Boosting Enhanced with Step-Wise Feature Augmentation using Artificial Neural Network (AugBoost-ANN) for indoor localization applications as it trains over labeled data. For this purpose, we propose an IoT architecture using a star network topology to collect the Received Signal Strength Indicator (RSSI) of Bluetooth Low Energy (BLE) modules by means of a Raspberry Pi as an Access Point (AP) in an indoor environment. The dataset for the experiments is gathered in the real world in different periods to match the real environments. Next, we address the challenges of the AugBoost-ANN training which augments features in each iteration of making a decision tree using a deep neural network and the transfer learning technique. Experimental results show more than 8% improvement in terms of accuracy in comparison with the existing gradient boosting and deep learning methods recently proposed in the literature, and our proposed model acquires a mean location accuracy of 0.77 m.

In this paper, we propose an intelligent analog beam selection strategy in a terahertz (THz) band beamspace multiple-input multiple-output (MIMO) system. First inspired by transfer learning, we fine-tune the pre-trained off-the-shelf GoogleNet classifier to learn analog beam selection as a multi-class mapping problem. Simulation results show 83% accuracy for the analog beam selection, which subsequently results in 12% spectral efficiency (SE) gain over the existing counterparts. For a more accurate classifier, we replace the conventional rectified linear unit (ReLU) activation function of the GoogleNet with the recently proposed Swish and retrain the fine-tuned GoogleNet to learn analog beam selection. It is numerically indicated that the fine-tuned Swish-driven GoogleNet achieves 86% accuracy, as well as 18% improvement in achievable SE, over the similar schemes. Eventually, a strong ensembled classifier is developed to learn analog beam selection by sequentially training multiple fine-tuned Swish-driven GoogleNet classifiers. According to the simulations, the strong ensembled model is 90% accurate and yields 27% gain in achievable SE in comparison with prior methods.

Sharing the licensed frequency spectrum among mobile network operators (MNOs) is a promising approach to improve licensed spectrum utilization. In this paper, we model and analyze a non-orthogonal spectrum sharing system consisting of seller and buyer MNOs where buyer MNOs can lease several licensed sub-bands from different seller MNOs. All base stations (BSs) owned by a buyer MNO can also utilize various licensed sub-bands simultaneously, which are also used by other buyer MNOs. To reduce the interference that a buyer MNO imposes on one seller MNO sharing its licensed sub-band, this buyer MNO has a limitation on the maximum interference caused to the corresponding seller MNO’s users. We assume each MNO owns its BSs and users whose locations are modeled as two independent homogeneous Poisson point processes. Applying stochastic geometry, we derive expressions for the downlink signal-to-interference-plus-noise ratio coverage probability and the average rate of both seller and buyer networks. The numerical results validate our analysis with simulation and illustrate the effect of the maximum interference threshold on the total sum rate of the network.

End-to-End (E2E) network slicing enables wireless networks to provide diverse services on a common infrastructure. Each E2E slice, including resources of radio access network (RAN) and core network, is rented to mobile virtual network operators (MVNOs) to provide a specific service to end-users. RAN slicing, which is realized through wireless network virtualization, involves sharing the frequency spectrum and base station antennas in RAN. Similarly, in core slicing, which is achieved by network function virtualization, data center resources such as commodity servers and physical links are shared between users of different MVNOs. In this paper, we study E2E slicing with the aim of minimizing the total energy consumption. The stated optimization problem is non-convex that is solved by a sub-optimal algorithm proposed here. The simulation results show that our proposed joint power control, server and link allocation (JPSLA) algorithm achieves 30% improvement compared to the disjoint scheme, where RAN and core are sliced separately.

In this paper, we propose an accurate two-phase millimeter-Wave (mmWave) beamspace channel tracking mechanism. Particularly in the first phase, we train an extreme reservoir machine (ERM) for tracking the historical features of the mmWave beamspace channel and predicting them in upcoming time steps. Towards a more accurate prediction, we further fine-tune the ERM by means of Xavier initializer technique, whereby the input weights in ERM are initially derived from a zero mean and finite variance Gaussian distribution, leading to 49% degradation in prediction variance of the conventional ERM. The proposed method numerically improves the achievable spectral efficiency (SE) of the existing counterparts, by 13%, when signal-to-noise-ratio (SNR) is 15dB. We further investigate an ensemble learning technique in the second phase by sequentially incorporating multiple ERMs to form an ensembled model, namely adaptive boosting (AdaBoost), which further reduces the prediction variance in conventional ERM by 56%, and concludes in 21% enhancement of achievable SE upon the existing schemes at SNR = 15dB.

The long-range and low energy consumption re-quirements in Internet of Things (IoT) applications have led to a new wireless communication technology known as Low Power Wide Area Network (LPWANs). In recent years, the Long Range (LoRa) protocol has gained a lot of attention as one of the most promising technologies in LPWAN. Choosing the right combination of transmission parameters is a major challenge in the LoRa networks. In LoRa, an Adaptive Data Rate (ADR) mechanism is executed to configure each End Device's (ED) trans-mission parameters, resulting in improved performance metrics. In this paper, we propose a link-based ADR approach that aims to configure the transmission parameters of EDs by making a decision without taking into account the history of the last received packets, resulting in a relatively low space complexity approach. In this study, we present four different scenarios for assessing performance, including a scenario where mobile EDs are considered. Our simulation results show that in a mobile scenario with high channel noise, our proposed algorithm's Packet Delivery Ratio (PDR) is 2.8 times outperforming the original ADR and 1.35 times that of other relevant algorithms.

Network function virtualization (NFV) is a promising technology to make 5G networks flexible and agile. NFV decreases operators’ OPEX and CAPEX by decoupling the physical hardware from the functions they perform. In NFV, users’ service request can be viewed as a service function chain (SFC) consisting of several virtual network functions (VNFs) which are connected through virtual links. Resource allocation in NFV is done through a centralized authority called NFV Orchestrator (NFVO). This centralized authority suffers from some drawbacks such as single point of failure and security. Blockchain (BC) technology is able to address these problems by decentralizing resource allocation. The drawbacks of NFVO in NFV architecture and the exceptional BC characteristics to address these problems motivate us to focus on NFV resource allocation to users’ SFCs without the need for an NFVO. To this end, we assume there are two types of users: users who send SFC requests (SFC requesting users) and users who perform mining process (miner users). For SFC requesting users, we formulate NFV resource allocation (NFV-RA) problem as a multi-objective problem to minimize the energy consumption and utilized resource cost, simultaneously. To address this problem, we propose an Approximation-based Resource Allocation algorithm (ARA) using Majorization-Minimization approximation method to convexify NFV-RA problem. Furthermore, due to the high complexity of ARA algorithm, we propose a low complexity Hungarian-based Resource Allocation (HuRA) algorithm using Hungarian algorithm for server allocation. Through the simulation results, we show that our proposed ARA and HuRA algorithms achieve near-optimal performance with lower computational complexity. Also, ARA algorithm outperforms the existing algorithms in terms of number of active servers, energy consumption, and average latency. Moreover, the mining process is the foundation of BC technology. In wireless networks, mining is performed by resource-limited mobile users. Since the mining process requires high computational complexity, miner users cannot perform it alone. So, in this article, we assume that miner users can perform mining process with participating of other users. For mining process, the problem of minimizing the energy consumption and cost of users’ processing resources is formulated as a linear programming problem that can be optimally solved in polynomial time.

In this paper, we study the problem of minimizing the uplink aggregate transmit power subject to the users' minimum data rate and peak power constraint on each sub-channel for multi-cell wireless networks. To address this problem, a distributed sub-optimal joint power and rate control algorithm called JPRC is proposed, which is applicable to both non-orthogonal frequency-division multiple access (NOMA) and orthogonal frequency-division multiple access (OFDMA) schemes. Employing JPRC, each user updates its transmit power using only local information. Simulation results illustrate that the JPRC algorithm can reach a performance close to that obtained by the optimal solution via exhaustive search, with the NOMA scheme achieving a 59% improvement on the aggregate transmit power over the OFDMA counterpart. It is also shown that the JPRC algorithm can outperform existing distributed power control algorithms.

Cyber-physical systems (CPSs) form from convergence of computation, networking, and control of physical processes. In this article, the focus is on an industrial CPS consisting of many control plants and rate constrained (RC) users communicating over a single-cell orthogonal frequency-division multiple access network. We formulate a multiobjective mixed integer nonlinear optimization problem that seeks to determine the next maximum acceptable sampling moment of control plants and to minimize the energy usage in the uplinks and downlinks while taking into account the dynamics and intended performance of control plants, the quality of service of RC users, and power and subcarrier constraints. To address this problem, a decoupling approach is taken, leading to a Pareto solution. Our simulations show that the proposed scheme decreases the total power consumption in the CPS to a significant degree.

In this article, we present a new approach for managing the interference on cellular users through optimum mode selection between half-duplex (HD) and in-band full-duplex (IBFD) in such a way that device-to-device (D2D) throughput is maximized, while the quality of service (QoS) of the cellular users is guaranteed in terms of delay. To present a comprehensive view and analyse of the proposed approach in a large network, we use Poisson point process that enables us to model a large network with random parameters in such a way that is highly compatible with reality. Also, to model the cellular users' delay, we use the queuing theory and Markov processes. Unlike other related works, the mode selection between HD and IBFD is considered as a decision variable and its optimal value is obtained. The results show that in comparison with the related works, our proposed approach leads to improvements in the D2D throughput, while through proper interference management, the impact on the cellular users' throughput is negligible. Moreover, the QoS of the cellular users is guaranteed by keeping the delay below a certain threshold.

We present a resource management framework for load-coupled downlink cellular OFDMA networks considering the load factor of an individual base station (BS) per resource block (RB), i.e., the number of adjacent sub-carriers (SCs), as the variable of interest in the resource management problem. The load factor of a BS per RB, which corresponds to the fraction of active SCs in the BS per RB, is an indicator of the level of resource consumption, and it affects the interference caused to that RB reused in other BSs, and thereby, results in a load-coupled OFDMA system. We first propose two distributed schemes to minimize: (i) the total load factor of the BSs (which would in turn increase the number of supportable users in the system), and (ii) the total downlink transmit power level of the BSs. Then, we derive the necessary and sufficient conditions for checking the feasibility of given target-rate requirements (also referred to as demand vector) for users. Accordingly, an iterative and distributed scheme is proposed to check the feasibility of a given demand vector. Next, for a priority-based load-coupled network, we propose a priority-based gradual removal algorithm to support the maximal number of low-priority users while satisfying the demands of the high-priority users. To evaluate the performance of our proposed schemes for resource management and admission control in load-coupled OFDMA networks, the theoretical investigations are complemented with Monte Carlo simulations.

In this paper, we develop a resource allocation framework to optimize the downlink transmission of a backhaul-aware multi-cell cognitive radio network (CRN) which is enabled with multi-carrier non-orthogonal multiple access (MC-NOMA). The considered CRN is composed of a single macro base station (MBS) and multiple small BSs (SBSs) that are referred to as the primary and secondary tiers, respectively. For the primary tier, we consider orthogonal frequency division multiple access (OFDMA) scheme and also Quality of Service (QoS) to evaluate the user satisfaction. On the other hand in secondary tier, MCNOMA is employed and the user satisfaction for web, video and audio as popular multimedia services is evaluated by Quality-of-Experience (QoE). Furthermore, each user in secondary tier can be served simultaneously by multiple SBSs over a subcarrier via Joint Transmission (JT). In particular, we formulate a joint optimization problem of power control and scheduling (i.e., user association and subcarrier allocation) in secondary tier to maximize total achievable QoE for the secondary users. An efficient resource allocation mechanism has been developed to handle the non-linear form interference and to overcome the non-convexity of QoE serving functions. The scheduling and power control policy leverage on Augmented Lagrangian Method (ALM). Simulation results reveal that proposed solution approach can control the interference and JT-NOMA improves total perceived QoE compared to the existing schemes.

Nowadays, different technologies have been pro-posed for low-power wide-area networks (LPWAN). The long-range wide-area network (LoRaWAN) as one of the LPWAN technologies is designed for the internet of things (IoT) communications which covers thousands of things in a wide range network, with low battery power consumption. The adaptive data rate (ADR) algorithm is a mechanism in LoRaWAN for configuring transmission parameters in a node with the aim of improving the quality of communication between the node and the gateway. Our studies in this paper show that applying the original ADR algorithm in a network with a large number of nodes and in the presence of channel noise results in significantly decreased packet delivery ratio and increased energy consumption. In this paper, we propose an improved version of the original ADR algorithm which dynamically and almost irrespectively of the number of nodes configures transmission parameters including the spreading factor and transmit power of the nodes in LoRa networks with variable channel conditions. In the proposed algorithm, the ordered weighted averaging (OWA) is employed as a decision-making method capable of considering the channel condition in configuring the transmission parameters. Using OWA makes the proposed algorithm perform well at all channel conditions as demonstrated by our numerical results. Also, simulation results show that the packet delivery ratio of the proposed algorithm in a sub-urban scenario with high channel noise is 4 and 1.5 times of the original ADR and other algorithms, respectively, while it consumes the least energy compared to the others.

This paper aims at investigating the problem of energy efficiency (EE) maximization for uplink multi-cell networks via a joint design of sub-channel assignment, power control, and antenna selection. We study the problem under two practical scenarios. In the first scenario, known as conventional antenna selection (CAS), there is only one radio frequency (RF) chain available at the mobile user and all the sub-channels for each user can be assigned to one of the antennas. For the second scenario, known as generalized antenna selection (GAS), the number of RF chains is equal to the number of antennas and the messages of each user can transmit over its assigned sub-channels via different antennas. The resource allocation design is formulated as a multi-objective optimization problem (MOOP) and then converted into a single objective optimization problem (SOOP) via the weighted Tchebycheff method. The considered problem is a mixed integer nonlinear programming (MINLP) which is generally intractable. To address this problem, a penalty function is introduced to handle the binary variable constraints. In order to obtain a computationally efficient suboptimal solution, the majorization minimization (MM) approach is proposed where a surrogate function serves as the lower bound of the objective function. Furthermore, we propose another low-complexity practical algorithm to further reduce the computational cost. Simulation results demonstrate the superiority of the proposed method and unveil an interesting trade-off between EE and SE for two considered scenarios.

The problem of joint power and sub-channel allocation to maximize energy efficiency (EE) and spectral efficiency (SE) simultaneously in in-band full-duplex (IBFD) orthogonal frequency-division multiple access (OFDMA) network is addressed considering users' QoS in both uplink and downlink. The resulting optimization problem is a non-convex mixed integer non-linear program (MINLP) which is generally difficult to solve. In order to strike a balance between the EE and SE, we restate this problem as a multi-objective optimization problem (MOOP) which aims at maximizing system's throughput and minimizing system's power consumption, simultaneously. To this end, the ε-constraint method is adopted to transform the MOOP into single objective optimization problem (SOOP). The underlying problem is solved via an efficient solution based on the majorization minimization (MM) approach. Furthermore, in order to handle binary subchannel allocation variable constraints, a penalty function is introduced. Simulation results unveil interesting tradeoffs between EE and SE.

Dual Connectivity (DC) has been proposed by Third Generation Partnership Project (3GPP), in order to address the small coverage areas and outage of users and improve the mobility robustness and rate of users in Heterogeneous Networks (HetNets). In the HetNet with DC, each user is assigned a Macro eNode Base Station (MeNB) and a Small eNode Base Station (SeNB) and transmits data to both eNode Base Stations (eNBs), simultaneously. In this paper, we present a power splitting scheme for the HetNet with DC; to maximize the total rate of the users while not exceeding the maximum transmit power of each user. In our proposed power splitting scheme, a Deep Reinforcement Learning (DRL) approach is taken based on the actor-critic model on continuous state-action spaces. Simulation results demonstrate that our power splitting scheme outperforms the baseline approaches in terms of total rate of users and fairness.

In cellular-D2D networks, users can select the communication mode either direct and form D2D links or indirect and communicate with BS. In former case, users should perform pairing selection and choose their pairs. The main focus in this paper is proposing an analytical framework by using tools from stochastic geometry to address these two issues, i.e. i) mode selection for the user devices to be established in either cellular or D2D mode, which is done based on received power from BS influenced by a bias factor, and ii) investigation of choosing nth-nearest neighbor as the serving node for the receiver of interest, by considering full-duplex (FD) radios as well as half-duplex (HD) in the D2D links. The analytic and simulation results demonstrate that even though the bias factor determines the throughput of each mode, it does not have any influence on the system sum throughput. Furthermore, we demonstrate that despite of suffering from self-interference, FD-D2D results in higher system sum throughput as well as higher coverage probability in comparison to its counterpart, namely purely HD-D2D network.

In this paper, we present a centralized power allocation scheme to maximize system throughput, subject to users' QoS requirements in both uplink and downlink, for an inband full-duplex (IBFD) multi-tier network, considering both cross-tier interference and self-interference. As the result of these two types of interference, the objective function as well as QoS constraints are non-convex functions with respect to transmit power, which makes the optimization problem hard to tackle. We deal with this non-convexity by deriving a concave lower bound for each of rate functions, based on which an efficient iterative algorithm is developed. Our simulation results demonstrate the effectiveness of our proposed power control scheme for IBFD networks and system throughput enhancement up to 62%.

To improve the spectral efficiency in long-term evolution systems, the resource blocks (RBs) are shared among different cells/base stations (BSs) resulting in interference among the cells/BSs on each RB, although all the sub-carriers (SCs) in an RB may not be used in a cell. Defining the load of a given BS per RB as the fraction of the active SCs in that RB, in this paper, we present a generalized signal-to-interference-and-noise-ratio (SINR) model for downlink users on a given RB. This model considers both the transmit powers of the BSs and the loads of the cells over that RB. Under this load-coupled SINR model, to study the feasibility of a given rate demand vector for users, we formulate an optimization problem of minimizing the total load of the BSs on the RBs. Then, for two different scenarios of feasible and infeasible demand vectors, respectively, we study the load management problem (i.e., minimizing the total load of the BSs on the RBs) and admission control problem (i.e., finding the sub-set of users with maximum cardinality whose demands can be concurrently satisfied), respectively. Our theoretical investigations, which provide guidelines for designing radio resource management methods for load-coupled OFDMA networks, are complemented through Monte Carlo simulations.

The problem of joint load management and admission control (JLAC) in underlay OFDMA-based cognitive radio networks (CRNs) is studied. The fraction of the active sub-carriers in a base station (BS) in each resource block (RB) is defined as the load factor of the BS per RB. In the JLAC problem, we simultaneously minimize the secondary users' outage ratio and the total load factors of the BSs via the RBs, subject to the constraint that the primary users are protected. This problem is a NP-hard problem. We first relax it into a convex optimization problem. Then, by employing the optimal solution to the relaxed JLAC problem, we derive a heuristic algorithm to gradually remove the most adversative secondary users imposing the most interference to the primary users. The performance of the proposed algorithm is studied in terms of the outage ratio of secondary users and the total load factors of the BSs via the RBs through extensive simulations.

In this paper, we investigate the joint admission control and resource allocation problem for collaborative computation under fading channels. We develop an Internet-of-Things (IoT) framework in which establishing Device-to-Device (D2D) communications, resource-poor wearable Source Mobile Terminals (SMTs) may offload their computations to resource-rich Processing Mobile Terminals (PMTs), or execute them locally, so as to save energy. Considering the offloading scenario, first, a probabilistic admission control algorithm is proposed for Mobile Terminals (MTs) taking both the deadline and energy harvesting constraints into account. Then, the joint CPU clock frequency/transmit power allocation and collaborative pair selection problem for MTs is addressed mathematically. For local execution scenario, optimal CPU clock frequencies are obtained for SMTs. Finally, based on energy consumption and outage imposed by each scenario, SMTs decide whether to offload their computations or execute them locally. Simulation results demonstrate that the proposed D2D-aided Collaborative Mobile Cloud (DCMC) approach attains a near-optimal energy expenditure in a semi-feasible system while effectively mitigating outage ratio of MTs.

In this letter, we investigate the problem of sum-power minimization for the users in orthogonal frequency division multiple access (OFDMA) networks subject to the target rate constraint. To address this problem, two fast water-filling (WF) methods are developed under two scenarios: without and with considering the peak-power constraint on the subchannels, which are referred to as FWF and FWF-PP, respectively. We show that our proposed methods converge to the optimal solution, while the computational complexity is reduced compared to the existing WF methods. Our results are also confirmed via simulations.

This paper studies two power control problems in energy harvesting wireless networks where one hybrid base station (HBS) and all user equipments (UEs) are operating in in-band full-duplex mode. We consider minimizing the aggregate power subject to the quality of service requirement constraint, and maximizing the aggregate throughput. We address these two problems by proposing two distributed power control schemes for controlling the uplink transmit power by the UEs and the downlink energy harvesting signal power by the HBS. In our proposed schemes, the HBS updates the downlink transmit power level of the energy-harvesting signal so that each UE is enabled to harvest its required energy for powering the operating circuit and transmitting its uplink information signal with the power level determined by the proposed schemes. We show that our proposed power control schemes converge to their corresponding unique fixed points starting from any arbitrary initial transmit power. We will show that our proposed schemes well address the stated problems, which is also demonstrated by our extensive simulation results.

In this paper, we investigate the resource allocation problem to improve the performance of Device-to-Device (D2D) communications underlaying downlink cellular networks. We aim to optimize the sum-rate of D2D communications while fulfilling Quality of Service (QoS) requirements of prioritized cellular network. To achieve this, we formulate the problem as a Knapsack utility maximization problem for each cellular resource and generalize a recursive algorithm based on dynamic programming as a solution. In order to improve the spectrum utilization we exploit multiuser diversity in which, a cellular frequency resource is allowed to be shared by multiple D2D users (DUs) and a DU is allowed to reuse more than one frequency resource. The simulation results demonstrate that the proposed scheme leads to near-optimal performance on D2D sum-rate.

In a D2D-enabled cellular system, not only can communications be relayed through the base station, but also closely located pairs are allowed to communicate directly over a D2D link, to support short-range data-intensive services. In this paper, we first formally state the problem of system throughput maximization subject to a given feasible lower bound for the users' SINRs in a D2D-enabled cellular system, and then propose a distributed joint power control and mode selection algorithm to address it. In our proposed algorithm, each transmitter with the potential of benefiting from D2D communications, selects its communication mode by comparing the effective interferences received at its serving base station and corresponding D2D receiver. Then, based on the selected mode, transmit power levels are iteratively updated in a way that the QoS requirements of all users are met, and the system throughput is enhanced as well. The simulation results show that our proposed distributed algorithm not only guarantees the minimum acceptable SINRs for all users, but also outperforms existing distributed schemes in terms of system throughput.

Two-tier networks consisting of macrocell and femtocells are provided as a solution to increase capacity and improve indoor coverage of cellular networks. In two-tier networks, when the spectrum is shared by macrocell and all of femtocells, the cross-tier and co-tier interference should be taken into account. In this paper, we investigate the power control problem of minimizing the aggregate transmit power for macro-tier and the joint power control and sub-channel allocation problem of maximizing the total rate for femto-tier, both in uplink transmission, while the cross-tier and co-tier interference are both taken into account. Then, to solve these problems, we propose distributed sub-optimal algorithms for both macro and femto-tier. Finally, simulation results are presented to confirm out performance of our proposed algorithm and to compare with the existing algorithm.

In this paper, the region of SINRs vector for a given user on its assigned sub-carriers (SCs) which corresponds to a given target-rate, is characterized. In doing so, given the user's data rate, the boundary of region of the SINRs vector corresponding to data rate for the user on its assigned SCs is obtained. We show that for a given preference of the user, the point laid on that boundary corresponds to the minimum SINRs vectors for providing the given data rate for the user. Therefore, characterizing the region of SINRs vector corresponding to the target-rate for each users on their assigned SCs and at the same time characterizing the feasible SINRs region on each SCs (which is a traditional problem and addressed already) enable us to study if a given target-rate vector is admissible or not.

In addition to providing high data rate, Device-to-Device (D2D) communication reduces the transmit power of users and improve spatial spectrum reuse significantly. In this paper, we formulate the joint power control and mode selection problem for D2D communications underlaying LTE-A network for minimizing the aggregate transmit power of users subject to a minimum target throughput for each cellular and D2D user, and propose a distributed joint mode selection and power control scheme for addressing it. In our proposed distributed scheme, each D2D user selects its mode on each of its assigned resource blocks (RBs), based on the effective interference on that RB, and each cellular and D2D user adjusts its transmit power level on its assigned RBs so that its target throughput is achieved. The mode selection by D2D users and the power control by cellular and D2D users in our proposed scheme are performed in an iterative manner. The simulation results demonstrate the effectiveness and superiority of our proposed method over two extreme methods (i.e., force D2D and force cellular methods), in terms of power consumption.

In this paper, we focus on energy efficiency in cognitive radio networks (CRNs). This is formally stated as the problem of maximizing the energy efficiency (defined as the ratio of the total rate of secondary users (SUs) to total energy consumption), which is a nonlinear integer programming, and then a polynomial time heuristic algorithm is presented to address it. In contrast to existing schemes, in our proposed scheme, each SU is allowed to transmit on multiple channels. After the channel assignment, according to the number of channels assigned to each SU and their conditions, the transmission power of SUs on each assigned channel is adjusted. Moreover, the SUs, who do not have any data to transmit or no channel is assigned to them, transit into the sleep-mode, which would significantly reduce the energy consumption. Our simulation results indicate that our proposed algorithm (MCAS) outperforms existing algorithms for CRNs in terms of energy efficiency, throughput and energy consumption.

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.

We propose a distributed opportunistic medium access control (MAC) scheme for maximizing the expected aggregate throughput in a multichannel wireless network such as a clustered orthogonal frequency-division multiple access (OFDMA) network. In our proposed scheme, each user attempts to send only on its best channel and transmits if the best-channel gain is higher than a given threshold, which is dynamically updated depending on previous idle and collision situations. In this way, with our proposed scheme, in a homogeneous system where the channel fading distribution is identical for all users, the best user for each channel is obtained in a distributed manner. We also obtain the optimal values of the thresholds so that the probability of successful transmission is maximized and a minimal number of transmission opportunities are wasted (e.g., due to collision or idle transmissions). In the asymptotic limit of a large number of users and sufficiently long transmission slot duration, we show that, in comparison with the optimal centralized scheme, the throughput loss for our proposed scheme goes to zero. Furthermore, we extend our distributed opportunistic MAC scheme for a homogeneous system to that for a heterogeneous system where the channel fading distribution is heterogeneous across users. Throughput performances and signaling overhead are analyzed for the proposed distributed MAC schemes and compared with those of the existing schemes. Simulation results show that our proposed schemes significantly improve the average aggregate throughput when compared with the existing schemes.

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 present a distributed power control algorithm to address the uplink interference management problem in cognitive radio networks where the underlaying secondary users (SUs) share the same licensed spectrum with the primary users (PUs) in multi-cell environments. Since the PUs have a higher priority of channel access compared to the SUs, minimal number of SUs should be gradually removed, subject to the constraint that all primary users are supported with their target signal-to-interference-plus-noise ratios (SINRs), which is assumed feasible. In our proposed algorithm, each primary user rigidly tracks its target-SINR by employing the conventional target-SINR tracking power control algorithm (TPC). Each transmitting SU employs the TPC as long as the total received power at the primary receiver is below a given threshold; otherwise, it decreases its transmit power in proportion to the ratio between the given threshold and the total received power at the primary receiver, which is referred to as the total received-power-temperature. We show that our proposed distributed power-update function has at least one fixed-point. We also show that our proposed algorithm not only improves the number of supported SUs but also guarantees that all primary users are supported with their (feasible) target-SINRs. Finally, we also propose an enhanced power control algorithm that achieves zero-outage for PUs and a better outage ratio for SUs. To this end, we provide a robust power control method that considers the uncertainties in channel gains.

Resource reuse schemes in relay-enhanced cooperative orthogonal frequency-division multiple access (OFDMA) networks have been well studied in the literature from an interference mitigation perspective and with cell-partitioning being the basis. In this paper however, we study dynamic intra-cell subcarrier reuse in cooperative OFDMA networks in which the users are allowed to share any subcarrier in the relay links provided that the resultant intra-cell interference is managed; an option not available in non-cooperative networks. Specifically, when the target data-rates of the users are not reachable in a conventional cooperative OFDMA network, dynamic intra-cell subcarrier reuse enables the users to achieve their target data-rates, even when the number of users surpasses the number of subcarriers. We formally define the problem of maximizing the system sum-rate subject to per node power and target data-rate constraints in a cooperative OFDMA single-cell network in which subcarriers are allowed to be reused in the relay links. After analyzing the problem in the dual domain and deriving the performance bounds, we propose a suboptimal resource allocation algorithm where subcarriers and links (relay and/or direct) are assigned to the users. Simulation results show that by employing our resource allocation algorithm, the system sum-rate and the outage ratio are significantly improved, specially in an overpopulated network.

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).

A distributed priority-based power and admission control algorithm is presented to address the priority-based gradual removal problem in cellular wireless networks. We assume that there exist two classes of priority for users (high-priority users versus low-priority users) and minimal number of low-priority users should be gradually removed, subject to the constraint that all high-priority users are supported with their target signal-to-interference-plus-noise ratios (SINRs) which is assumed feasible. In our proposed algorithm, each high-priority user rigidly tracks its target-SINR by employing the conventional target-SINR tracking power control algorithm, and each transmitting low-priority user tracks its target-SINR as long as its required transmit power is below a threshold, otherwise it temporarily removes itself. Each removed low-priority user resumes its transmission if the required transmit power to reach its target-SINR goes below a given threshold which is different from the former. Of these two thresholds, whose values are analytically obtained, the former is provided by the base station and the latter is obtained in a distributed manner as a function of the former. We show that the distributed power-update function corresponding to our proposed algorithm has at least one fixed-point which is not unique in general. The convergence point, where our proposed algorithm potentially converges to, depends on initial transmit power levels of users. We also show that our proposed algorithm, at each of its fixed-points, not only provides all high-priority users with their (feasible) target-SINRs but also guarantees that no low-priority user is erroneously removed (i.e., no additional low priority user can be supported along with currently supported users). Furthermore, for the special case of tracking a common target-SINR by all low-priority users, we show that our proposed algorithm minimizes the outage-ratio of low-priority users subject to zero-outage-ratio of high-priority users. Simulation results confirm our analytical developments and show that our proposed priority-based power and admission control algorithm solves the priority-based gradual removal problem efficiently.

In the well-known distributed target-SIR-tracking power control algorithm (TPC), when the system is infeasible (a constrained power vector does not exist to attain target-SIRs), all non-supported users (those who cannot obtain their target-SIRs) transmit at their maximum power. Such users inefficiently consume their energy, and cause interference to others, which increases the number of non-supported users. To deal with this, some non-supported users should decrease their transmit power (the gradual removal problem). We present a distributed power control scheme with gradual soft removal, by which either TPC or OPC (opportunistic power control) is used, depending on whether the ratio of interference-to-path-gain is below or above a threshold that is chosen by each user in a distributed manner. We show that our algorithm converges to a unique fixed-point in both feasible and infeasible systems, and that when the system is infeasible, it results in less outage with significantly less consumed power, as compared to TPC. We also provide a game theoretic analysis of our algorithm by introducing a new pricing when users are selfish. As our algorithm is fully distributed and requires only local information, it can be applied to both cellular and ad hoc networks. Simulation results confirm our analysis.

We formally define the gradual removal problem in wireless networks, where the smallest number of users should be removed due to infeasibility of the target-SIR requirements for all users, and present a distributed power-control algorithm with temporary removal and feasibility check (DFC) to address it. The basic idea is that any transmitting user whose required transmit power for reaching its target-SIR exceeds its maximum power is temporarily removed, but resumes its transmission if its required transmit power goes below a given threshold obtained in a distributed manner. This enables users to check the feasibility of system in a distributed manner. The existence of at least one fixed-point in DFC is guaranteed, and at each equilibrium, all transmitting users reach their target-SIRs consuming the minimum aggregate transmit power. Furthermore, in contrast to the existing algorithms, no user is unnecessarily removed by DFC, i.e., DFC is Pareto and energy-efficient. We also show that when target-SIRs are the same for all users, DFC minimizes the outage probability. Simulation results confirm our analytical developments and show that DFC significantly outperforms the existing schemes in addressing the gradual removal problem in terms of convergence, outage probability, and power consumption.

We propose a scheme for maintaining the requested SIR of each user under uncertainty of system parameters in the power control of interference limited wireless networks. In doing so, we keep the outage probability of users below their predefined threshold with minimal power consumption. To reduce the complexity, we apply the notion of chance constraint robust optimization to the outage probability. This approach preserves the convexity of the problem and maintains its tractability. For solving the reformulated problem, a distributed probabilistic robust power algorithm is developed based on the standard interference function and local convergence, which utilizes infrequent message passing. We derive the conditions for the convergence of our algorithm, and prove the optimality of the equilibrium.

In the opportunistic power control algorithm (OPC), designed in each user tries to keep the product of its transmit power and its experienced effective interference to a constant, called the target signal-interference product (SIP). This increases the transmit power when the channel is good and reduces it when the channel is poor (opportunism). It has been shown that the OPC always converges to a fixed point irrespective of whether the power is constrained (where there is no upper bound on transmit power) or unconstrained (where an upper bound on transmit power is taken into account). It has also been shown via simulation that the throughput achieved by the unconstrained OPC is significant (as compared to other existing distributed schemes), and is an increasing function of the target-SIP set by users, in the sense that a higher target-SIP results in a higher throughput. In this paper, we show that in contrast to the unconstrained OPC, when the constrained OPC is applied, not only the throughput is not necessarily increased as the target-SIPs increase, but it may even decrease if some users set their target-SIPs at high values. Furthermore, we propose a heuristic solution to determine the target-SIPs by users in a distributed manner. Our simulation results show that the throughput achieved by our distributed setting of the target-SIPs very closely approaches the maximum achievable throughput by the constrained OPC, and is very close to the global optimum value.

In the well-known distributed target-SIR tracking power control algorithm, when the target-SIR requirements are not reachable for all users, all non-supported users (those who do not reach their target SIRs) transmit at their maximum power. Such users inefficiently consume their energies, and introduce unnecessary interference to others, which in turn unnecessarily increases the number of non-supported users. To deal with this, the smallest number of users should be removed due to infeasibility of the system (gradual removal problem). We present a new distributed constrained power control (DCPC) algorithm to address the gradual removal problem. The basic idea is that any transmitting user whose required transmit power for reaching its target-SIR exceeds its maximum power is temporarily removed. Each temporarily removed user resumes its transmission if its required transmit power for reaching its target-SIR goes below a given threshold (lower than its maximum power). This threshold is determined by each removed user in a distributed manner using only local information. We will show that our proposed algorithm has at least one-fixed point (i.e., its convergence can be guaranteed), and at the equilibrium where the algorithm converges, all transmitting users (the users whose transmit powers are greater than zero) reach their target SIRs consuming the minimum aggregate transmit power. Furthermore, in contrast to the existing DCPC algorithms, no user is unnecessarily removed in our proposed scheme, i.e., it is efficient. Our simulation results confirm our analytic developments and show that our scheme outperforms the existing DCPCs in addressing the gradual removal problem, in terms of convergence, outage probability and power consumption.