Total Throughput Research Articles

Optimization of Power Efficient Spatial Division Multiplexed Submarine Cables Using Adaptive Transponders and Machine Learning

Historically, undersea systems capacity increases were primarily based on maximizing the total throughput per fiber. Whilst previously designed to operate at optimum launch power of the fiber, subsea systems are now designed for higher optical power efficiency. Next generation of spatial division multiplexed (SDM) cables require operation at lower signal power enabling repeater pump farming and providing higher reliability and efficiency. To adapt to these power constraints, the benefits of adaptive transponders and the application of machine learning to submarine line design are investigated.

Optimal downlink and uplink design in a wireless powered two‐user indoor communication system

This paper applies the wireless powered communication network (WPCN) to an indoor communication system with two energy harvesting (EH) enabled users. Unlike the existing WPCN works designed for outdoor communications, where each user harvests energy only from the signals transmitted by a dedicatedly deployed hybrid access point (H-AP), due to the short device-to-device distance in the indoor scenario, each user additionally harvests a sufficient amount of energy from the information signals transmitted by the H-AP and the other user. First, the joint downlink and uplink throughput are maximized for the wireless powered indoor communication system. This problem is non-convex. Thus, the authors manage to transform this problem into a convex one and solve it using convex optimization techniques. The solutions reveal that the total throughput increases largely over a reduced device-to-device distance due to the resultant harvested energy from both energy and information signals at each user. However, an unfair downlink versus uplink rate allocation phenomenon is observed. Thus, considering the importance of uplink communication quality for various indoor applications, a new problem is further proposed to maximize the uplink sum-throughput over both users with an additional constraint to ensure a sufficient downlink rate. This problem is also shown to be non-convex and is solved optimally by using a method similar to that in the first problem. Numerical results demonstrate the effectiveness of the proposed approach for improving the downlink versus uplink rate allocation fairness in the indoor wireless-powered communication system.

A Learning-Based Fast Uplink Grant for Massive IoT via Support Vector Machines and Long Short-Term Memory

The current random access (RA) allocation techniques suffer from congestion and high signaling overhead while serving massive machine type communication (mMTC) applications. To this end, 3GPP introduced the need to use fast uplink grant (FUG) allocation in order to reduce latency and increase reliability for smart internet-of-things (IoT) applications with strict QoS constraints. We propose a novel FUG allocation based on support vector machine (SVM), First, MTC devices are prioritized using SVM classifier. Second, LSTM architecture is used for traffic prediction and correction techniques to overcome prediction errors. Both results are used to achieve an efficient resource scheduler in terms of the average latency and total throughput. A Coupled Markov Modulated Poisson Process (CMMPP) traffic model with mixed alarm and regular traffic is applied to compare the proposed FUG allocation to other existing allocation techniques. In addition, an extended traffic model based CMMPP is used to evaluate the proposed algorithm in a more dense network. We test the proposed scheme using real-time measurement data collected from the Numenta Anomaly Benchmark (NAB) database. Our simulation results show the proposed model outperforms the existing RA allocation schemes by achieving the highest throughput and the lowest access delay of the order of 1 ms by achieving prediction accuracy of 98 $\%$ when serving the target massive and critical MTC applications with a limited number of resources.

Resource Allocation in Cognitive Radio-Enabled UAV Communication

The deployment of unmanned aerial vehicles (UAVs) in wireless communications will be constrained in practice by the unavailability of frequency spectrum. Cognitive radio techniques are viewed to offer promising solutions in which a secondary UAV-based network can operate in a frequency band licensed to an existing terrestrial wireless network with minimal interference. We investigate the performance of a cognitive radio-enabled UAV network configuration in which the UAV is allowed to communicate with secondary ground terminals (SGTs) in the underlay mode in the licensed spectrum band. Optimization of the performance of the secondary network is considered in terms of maximizing the total throughput of the network subject to satisfying two constraints. The first constraint is imposed to prevent interference with the primary network while the second constraint ensures that the throughput requirement of each SGT is met. A probabilistic channel model is assumed. The Variables to be determined include the transmission power, the channel time allocations to the SGTs and the route and coordinates of the stationary locations in space in which the UAV will hover and transmit. A heuristic approach is developed in order to arrive at solutions to this extremely complex optimization problem and results of numerical simulations are presented.

UAV Relay-Assisted Emergency Communications in IoT Networks: Resource Allocation and Trajectory Optimization

Unmanned aerial vehicle (UAV) communication has emerged as a prominent technology for emergency communications (e.g., natural disaster) in the Internet of Things (IoT) networks to enhance the ability of disaster prediction, damage assessment, and rescue operations promptly. A UAV can be deployed as a flying base station (BS) to collect data from time-constrained IoT devices and then transfer it to a ground gateway (GW). In general, the latency constraint at IoT devices and UAV’s limited storage capacity highly hinder practical applications of UAV-assisted IoT networks. In this paper, full-duplex (FD) radio is adopted at the UAV to overcome these challenges. In addition, half-duplex (HD) scheme for UAV-based relaying is also considered to provide a comparative study between two modes (viz., FD and HD). Herein, a device is considered to be successfully served if its data is collected by the UAV and conveyed to GW timely during flight time. In this context, we aim to maximize the number of served IoT devices by jointly optimizing bandwidth, power allocation, and the UAV trajectory while satisfying each device’s requirement and the UAV’s limited storage capacity. The formulated optimization problem is troublesome to solve due to its non-convexity and combinatorial nature. Towards appealing applications, we first relax binary variables into continuous ones and transform the original problem into a more computationally tractable form. By leveraging inner approximation framework, we derive newly approximated functions for non-convex parts and then develop a simple yet efficient iterative algorithm for its solutions. Next, we attempt to maximize the total throughput subject to the number of served IoT devices. Finally, numerical results show that the proposed algorithms significantly outperform benchmark approaches in terms of the number of served IoT devices and system throughput.

Real-Time Arterial-Friendly Ramp Metering System

To balance the benefits between the freeway and arterial users and also to prevent on-ramp queue spillbacks, the authors have developed an arterial-friendly local ramp metering control (AF-ramp) system for time-of-day operations during recurrent congestion. This study presents the real-time version of the AF-ramp (named RAF-ramp) system, with a lane-group-based macroscopic traffic module for predicting traffic state and for executing control strategies, aiming at maximizing the total throughput from the control area, comprising the ramp meter and nearby local intersection signals. Recognizing the discrepancy in the dynamic nature between ramp traffic and arterial flows, the RAF-ramp system with its embedded traffic state prediction and monitoring mechanism can trigger the concurrent optimization of both controls when justified to do so, or only dynamically adjust the ramp metering rate under the pre-optimized local signal environment. The results of extensive simulation experiments have confirmed that the proposed system outperforms the widely-applied real-time ramp control model, ALINEA/Q, under various experimental traffic scenarios, because the produced control strategies can effectively utilize the freeway’s weaving capacity and also best coordinate neighboring intersections’ signals to maximize the entire network’s performance. Such a real-time arterial-friendly ramp metering system, addressing both the time-varying freeway dynamics and the concerns of local traffic users, may well serve as an effective tool for contending with bottlenecks at freeway interchanges.

Efficient models for enhancing the link adaptation performance of LTE/LTE-A networks

Link adaptation (LA) is the ability to adapt the modulation scheme (MS) and the coding rate of the error correction in accordance with the quality of the radio link. The MS plays an important role in enhancing the performance of LTE/LTE-A, which is typically dependent on the received signal to noise ratio (SNR). However, using the SNR to select the proper MSs is not enough given that adaptive MSs are sensitive to error. Meanwhile, non-optimal MS selection may seriously impair the system performance and hence degrades LA. In LTE/ LTE-A, the LA system must be designed and optimized in accordance with the characteristics of the physical (e.g., MSs) and MAC layers (e.g., Packet loss) to enhance the channel efficiency and throughput. Accordingly, this study proposes using two LA models to overcome the problem. The first model, named the cross-layer link adaptation (CLLA) model, is based on the downward cross-layer approach. This model is designed to overcome the accuracy issue of adaptive modulation in existing systems and improve the channel efficiency and throughput. The second model, named the Markov decision process over the CLLA (MDP-CLLA) model, is designed to improve on the selection of modulation levels. Besides that, our previous contribution, namely the modified alpha-Shannon capacity formula, is adopted as part of the MDP-CLLA model to enhance the link adaptation of LTE/LTE-A. The effectiveness of the proposed models is evaluated in terms of throughput and packet loss for different packet sizes using the MATLAB and Simulink environments for the single input single output (SISO) mode for transmissions over Rayleigh fading channels. In addition, phase productivity, which is defined as the multiplication of the total throughput for a specific modulation with the difference between adjacent modulation SNR threshold values, is used to determine the best model for specific packet sizes in addition to determine the optimal packet size for specific packet sizes among models. Results generally showed that the throughput improved from 87.5 to 89.6% for (QPSK rightarrow 16-QAM) and from 0 to 43.3% for (16-QAM rightarrow 64-QAM) modulation transitions, respectively, using the CLLA model when compared with the existing system. Moreover, the throughput using the MDP-CLLA model was improved by 87.5–88.6% and by 0–43.2% for the (QPSK rightarrow 16-QAM)and (16-QAM rightarrow 64-QAM) modulation transitions, respectively, when compared with the CLLA model and the existing system. Results were also validated for each model via the summation of the phase productivity for every modulation at specific packet sizes, followed by the application one-way analysis of variance (ANOVA) statistical analysis with a post hoc test, to prove that the MDP-CLLA model improves with best high efficiency than the CLLA model and the existing system.

The throughput calibration of the VERITAS telescopes

Context. The response of imaging atmospheric Cherenkov telescopes to incident γ-ray-initiated showers in the atmosphere changes as the telescopes age due to exposure to light and weather. These aging processes affect the reconstructed energies of the events and γ-ray fluxes. Aims. This work discusses the implementation of signal calibration methods for the Very Energetic Radiation Imaging Telescope Array System (VERITAS) to account for changes in the optical throughput and detector performance over time. Methods. The total throughput of a Cherenkov telescope is the product of camera-dependent factors, such as the photomultiplier tube gains and their quantum efficiencies, and the mirror reflectivity and Winston cone response to incoming radiation. This document summarizes different methods to determine how the camera gains and mirror reflectivity have evolved over time and how we can calibrate this changing throughput in reconstruction pipelines for imaging atmospheric Cherenkov telescopes. The implementation is validated against seven years of observations with the VERITAS telescopes of the Crab Nebula, which is a reference object in very-high-energy astronomy. Results. Regular optical throughput monitoring and the corresponding signal calibrations are found to be critical for the reconstruction of extensive air shower images. The proposed implementation is applied as a correction to the signals of the photomultiplier tubes in the telescope simulation to produce fine-tuned instrument response functions. This method is shown to be effective for calibrating the acquired γ-ray data and for recovering the correct energy of the events and photon fluxes. At the same time, it keeps the computational effort of generating Monte Carlo simulations for instrument response functions affordably low.

Beyond Equal-Power Sparse NOMA: Two User Classes and Closed-Form Bounds on the Achievable Region.

Non-orthogonal multiple access (NOMA) is a promising technology for future beyond-5G wireless networks, whose fundamental information-theoretic limits are yet to be fully explored. Considering regular sparse code-domain NOMA (with a fixed and finite number of orthogonal resources allocated to any designated user and vice versa), this paper extends previous results by the authors to a setting comprising two classes of users with different power constraints. Explicit rigorous closed-form analytical inner and outer bounds on the achievable rate (total class throughput) region in the large-system limit are derived and comparatively investigated in extreme-SNR regimes. The inner bound is based on the conditional vector entropy power inequality (EPI), while the outer bound relies on a recent strengthened version of the EPI. Valuable insights are provided into the potential performance gains of regular sparse NOMA in practically oriented settings, comprising, e.g., a combination of low-complexity devices and broadband users with higher transmit power capabilities, or combinations of cell-edge and cell-center users. The conditions for superior performance over dense code-domain NOMA (taking the form of randomly spread code-division multiple access), as well as a relatively small gap to the ultimate performance limits, are identified. The proposed bounds are also applicable for the analysis of interference networks, e.g., Wyner-type cellular models.

Resource Allocation for Throughput Maximization in Cognitive Radio Network with NOMA

Spectrum resources are the precious and limited natural resources. In order to improve the utilization of spectrum resources and maximize the network throughput, this paper studies the resource allocation of the downlink cognitive radio network with non-orthogonal multiple access (CRN-NOMA). NOMA, as the key technology of the fifth-generation communication (5G), can effectively increase the capacity of 5G networks. The optimization problem proposed in this paper aims to maximize the number of secondary users (SUs) accessing the system and the total throughput in the CRN-NOMA. Under the constraints of total power, minimum rate, interference and SINR, CRN-NOMA throughput is maximized by allocating optimal transmission power. First, for the situation of multiple sub-users, an adaptive optimization method is proposed to reduce the complexity of the optimization solution. Secondly, for the optimization problem of nonlinear programming, a maximization throughput optimization algorithm based on Chebyshev and convex (MTCC) for CRN-NOMA is proposed, which converts multi-objective optimization problem into single-objective optimization problem to solve. At the same time, the convergence and time complexity of the algorithm are verified. Theoretical analysis and simulation results show that the algorithm can effectively improve the system throughput. In terms of interference and throughput, the performance of the sub-optimal solution is better than that of orthogonal-frequency-division-multiple-access (OFDMA). This paper provides important insights for the research and application of NOMA in future communications.

DBF-Based Fusion Control of Transmit Power and Beam Directivity for Flexible Resource Allocation in HTS Communication System Toward B5G

The demand for satellite communication increases with digitalization and globalization in the era of 5G and beyond; therefore, research and development of high-throughput satellites (HTSs) to increase the communication capacity and improve the flexibility of satellite communication systems is underway. Additionally, digital beamforming (DBF), which can control the directivity of multiple beams through high-speed digital signal processing, has received significant attention as a technology to improve the flexibility of resource allocation with HTSs. The principal control parameters in the DBF-based HTS, including the transmission power, beam gain, and placement location of each beam, cause alterations in the positional characteristics of the total throughput in the coverage area of an HTS. However, there are no power resource allocation models with dual control of these control parameters, based on the above positional characteristic. This study clarifies the effect of such control parameters on the total throughput in the coverage area of an HTS. We demonstrate a method for improving the flexibility of resource allocation to satisfy the geographic distribution of traffic requirements by constructing a power resource allocation model with a DBF-based fusion control in the HTS communication system. We evaluated the effectiveness of our proposed method through a simulation analysis using exponential annealing.

REQIBA: Regression and Deep Q-Learning for Intelligent UAV Cellular User to Base Station Association

Unmanned Aerial Vehicles (UAVs) are emerging as important users of next-generation cellular networks. By operating in the sky, UAV users experience very different radio conditions than terrestrial users, due to factors such as strong Line-of-Sight (LoS) channels (and interference) and Base Station (BS) antenna misalignment. As a consequence, the UAVs may experience significant degradation to their received quality of service, particularly when they are moving and are subject to frequent handovers. The solution is to allow the UAV to be aware of its surrounding environment, and intelligently connect into the cellular network taking advantage of this awareness. In this paper we present REgression and deep Q-learning for Intelligent UAV cellular user to Base station Association (REQIBA), a solution that allows a UAV flying over an urban area to intelligently connect to underlying BSs, using information about the received signal powers, the BS locations, and the surrounding building topology. We demonstrate how REQIBA can as much as double the total UAV throughput, when compared to heuristic association schemes similar to those commonly used by terrestrial users. We also evaluate how environmental factors such as UAV height, building density, and throughput loss due to handovers impact the performance of our solution.

Flow Management Mechanism in Software-Defined Network

Software-defined networking (SDN) is a paradigm shift in modern networking. However, centralised controller architecture in SDN imposed flow setup overhead issue as the control plane handles all flows regardless of size and priority. Existing frameworks strictly reduce control plane overhead and it does not focus on rule placement of the flows itself. Furthermore, existing frameworks do not focus on managing elephant flows like RTSP. Thus, the proposed mechanism will use the flow statistics gathering method such as random packet sampling to determine elephant flow and microflow via a pre-defined threshold. This mechanism will ensure that the control plane works at an optimum workload because the controller only manages elephant flows via reactive routing and rule placement respectively. Reactive routing has reduced link bandwidth usage below the pre-defined threshold. Furthermore, rule placement has increased average throughput and total transfer to 238%. Meanwhile, the data plane switches will be able to forward microflows via multipath wildcard routing without invoking controller in greater responding time by 85 ms faster in two Transmission Control Protocol (TCP) traffic and achieved 11% and 12% higher total transfer size and throughput respectively. Hence, the controller’s workload reduced significantly to 48% in two TCP traffic.

Satellite- and Cache-Assisted UAV: A Joint Cache Placement, Resource Allocation, and Trajectory Optimization for 6G Aerial Networks

This paper considers Low Earth Orbit (LEO) satellite- and cache-assisted unmanned aerial vehicle (UAV) communications for content delivery in terrestrial networks, which shows great potential for next-generation systems to provide ubiquitous connectivity and high capacity. Specifically, caching is provided by the UAV to reduce backhaul congestion, and the LEO satellite supports the UAV’s backhaul link. In this context, we aim to maximize the minimum achievable throughput per ground user (GU) by jointly optimizing cache placement, the UAV’s resource allocation, and trajectory while cache capacity and flight time are limited. The formulated problem is challenging to solve directly due to its non-convexity and combinatorial nature. To find a solution, the problem is decomposed into three sub-problems: (1) cache placement optimization with fixed UAV resources and trajectory, followed by (2) the UAV resources optimization with fixed cache placement vector and trajectory, and finally, (3) we optimize the UAV trajectory with fixed cache placement and UAV resources. Based on the solutions of sub-problems, an efficient alternating algorithm is proposed utilizing the block coordinate descent (BCD) and successive convex approximation (SCA) methods. Simulation results show that the max-min throughput and total achievable throughput enhancement can be achieved by applying our proposed algorithm instead of other benchmark schemes.

Double Deep Q-Network Based Distributed Resource Matching Algorithm for D2D Communication

Device-to-Device (D2D) communication with short communication distance is an efficient way to improve spectrum efficiency and mitigate interference. To realize the optimal resource configuration including wireless channel matching and power allocation, a distributed resource matching scheme is proposed based on deep reinforcement learning(DRL). The reward is defined as the difference of achieve rate of D2D users and the consumed power, which is limited by the Signal to Interference plus Noise Ratio (SINR) of the other cellular users on the current channel. The proposed algorithm maximizes the D2D throughput and energy efficiency in a distributed manner, without online coordination and message exchange between users. The considered resource allocation problem is formulated as a random non-cooperative game with multiple players (D2D pairs), where each player is a learning agent, whose task is to learn its best strategy based on locally observed information, multi-user communication resource matching algorithm is proposed based on a Double Deep Q-network (DDQN), where the total cellular throughput and user energy efficiency could converge to the Nash equilibrium (NE) under the mixed strategy. Simulation results show that the proposed algorithm can improve the communication rate and energy efficiency of each user by selecting the optimal strategy, and has better convergence performance compared with existing schemes.

Service-Oriented LSTM Multi-Criteria RAT Selection Scheme for Vehicle-to-Infrastructure Communication

The evolution towards future Intelligent Transportation Systems (ITS) is significantly influenced by enabling telematics and safety services for vehicular use-cases, especially for autonomous driving. These vehicular use-cases have their own network requirements, with a variety of instantaneous requirements and dynamic characterizations that make delivering data via a single Radio Access Technology (RAT) severely challenging. By utilizing current and future network context and vehicular use-case requirements, the RAT selection scheme must select the optimum network that guarantees the continuity and high throughput data transfer. Moreover, the consideration of on-time stochastic queue backlog of all use-cases in each RATs, while maximizing throughput is important for a reliable vehicular use-case deployment. In this paper, we developed a Service-Oriented Joint LSTM Multi-Criteria (SOLMC) RAT selection scheme for Vehicle-to-Infrastructure (V2I) networks. The objective of this scheme is to maximize the overall network throughput while reducing the current stochastic queue backlog in deployed RATs. LSTM prediction technique is used as a network filter process that rejects the worst channel quality before the initialization RAT selection scheme. Transmission capacity, connectivity time, data delivery cost, and queue backlog are the decision criteria in our proposed SOLMC RAT scheme. In particular, to characterize the importance of these criteria factors, we construct Analytical Hierarchy Process (AHP) for each use-case. Extensive simulations are carried out under different network context conditions. The results demonstrate that the SOLMC scheme can significantly achieve up to 47.5% network throughput maximization and 20.42% packet delivery ratio improvement, using lower queue length compared to nearest-RAT-based selection. In addition, implementing LSTM in SOLMC improves the total average throughput by up to 3.1% and 6% packet delivery ratio.

Optimal downlink power allocation schemes for OFDM-NOMA-based Internet of things

With the continuous development of fifth-generation technology, the number of mobile terminal Internet of Things devices has increased exponentially. How to effectively improve the throughput of fifth-generation systems has become a challenge. In the Internet of Things networks, ultra-dense networks and non-orthogonal multiple access technology have drawn extensive attention in recent years, because they can achieve multiplexing from the space domain and power domain. To improve the throughput of the system, this article combines non-orthogonal multiple access with ultra-dense networks technology and considers the orthogonal frequency division multiplexing non-orthogonal multiple access–based ultra-dense networks with multiple base stations and multiple Internet of Things devices. In particular, first, we build the network model and channel model. Second, we construct the downlink transmission rate maximizing problem subject to the total power. Then, to solve this problem, we divide it into three sub-problems: device grouping, inter-sub-band power allocation, and intra-sub-band power allocation problems. Solving these sub-problems, we obtain the optimal power allocation schemes by jointly employing channel-state sorting–pairing algorithm, water-filling algorithm, and convex optimization theory. Finally, numerical simulations are conducted to validate the performance of our proposed optimal downlink power allocation scheme. Experimental results show that the total throughput of the system has been significantly improved.

Efficient Bit Loading Algorithm for OFDM-NOMA Systems With BER Constraints

This paper proposes a novel bit loading algorithm to multicarrier non-orthogonal multiple access (NOMA) systems to maximize the system total throughput while satisfying the users’ individual quality of service (QoS) constraints. Although bit loading is generally a non-deterministic polynomial-time (NP)-hard problem, even for orthogonal multiple access (OMA), the mutual interference between the users, and the dependence of power coefficients and modulation orders are additional challenges that add substantial complexity to the optimization problem. Therefore, in this paper, we propose an efficient bit loading algorithm for multicarrier NOMA systems and compare the complexity and throughput with a corresponding OMA system. The obtained results show that for the case of NOMA, bit loading provides an additional degree of freedom that allows non-uniform spectrum sharing among the users. In particular scenarios, the system may experience hybrid modes of operation, where the system switches between NOMA and OMA. The obtained numerical results show that NOMA can outperform OMA by 100% in terms of spectral efficiency, for a two-user scenario. The complexity of the loading process for NOMA is noticeably higher than OMA, which is due to the high complexity associated with the bit error rate (BER) computation in the case of NOMA.

Reliability and robust resource allocation for Cache-enabled HetNets: QoS-aware mobile edge computing

Mobile edge computing is a key feature of next-generation mobile networks aimed at providing a variety of services for different applications by performing related processing tasks closer to the user equipment. Due to the high frame loss rate during multi-hop transmissions in wireless edge computing networks, the problem of reliability and system safety will be of great importance. Data caching is a necessity to provide reliable edge computing and, at the same time a major consumer of energy. So, we propose a reliable mobile edge computing system using active caching and robust resource allocation approaches. This paper presents an energy-efficient stochastic network calculus framework to control edge-computing data flows for active data-caching systems. In accordance with the entrance processes of different QoS-class data streams, closed-form problems were formulated to determine the correlation between resource utilization and the violation probability of each data flow. Also, in the access layer, this paper proposes a reliable user association and resource allocation approach which maximizes the overall energy efficiency of cache-enabled cellular networks in addition to improving system safety. In this energy-cooperative approach, the power can be shared among the cells using a grid network. The simulation results show that the proposed approach can effectively increase the network reliability and power efficiency while guaranteeing the acceptable fairness level for uniform and hotspot user distribution models. The numerical results also exhibit considerable power saving for different traffic models in addition to increasing the total network throughput by up to 25%.

Energy-efficient opportunistic multi-carrier NOMA-based resource allocation for beyond 5G (B5G) networks

The interplay between the non-orthogonal multiple access (NOMA) and the opportunistic cognitive radio (CR)-based orthogonal frequency multiple access (OFDMA) has been recently realized as a promising paradigm to support the unprecedented massive connectivity demands of future beyond fifth-generation (B5G) wireless communication systems. In such systems, which are called multi-carrier NOMA CR-based systems, each licensed band reserved for primary users can be opportunistically utilized based on power-domain NOMA to serve a group of secondary users simultaneously. An important challenge in this domain is how to provide energy-efficient resource allocation techniques that attempt to strike a balance between the total throughput (i.e., the achieved sum-rate) and the power required to achieve that rate while satisfying network QoS demands and being aware of the unique characteristics of the CR operating environment. In this paper, we propose an energy-efficient resource allocation technique for multi-carrier NOMA CR-based systems, which aims at maximizing the overall energy efficiency (EE) of the system under a set of CR and NOMA constraints. The EE maximization problem is shown to be a fractional non-convex optimization, which is, in general, hard to optimize. To deal with the fractional and the non-convexity nature of the formulated EE maximization problem, we exploit the Dinkelbach’s algorithm to transfer the EE problem to a parameterized optimization problem. Then we use an iterative optimization approach to obtain the solution for the EE maximization problem. Simulation results reveal that this EE maximization-based resource allocation technique outperforms the existing resource allocation techniques in terms of the overall EE of the system while striking a good balance between the sum-rate and the transmit power consumption.