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Summary: The solution of resource allocation is based on NOMA and OMA structures that are suboptimal to satisfy the demanding QoS and higher data rate requirements compared to EE requirements in 5G cellular networks. In this work, the resource allocation problem in the hybrid MC‐NOMA system is solved. The system achieves the trade‐off between spectral and energy efficiency (EE) with the lowest rate of user requirements. The system's resource allocation, including power allocation, is considered the crucial factor and is identified as a single‐objective problem. The suggested work focuses on user pairing and allocation on paired users following processing. The user grouping is designed to connect near‐ and far‐users with high C3 to increase the fairness of NOMA. As a result, this work considers multiple goals, such as C3 maximization, spectrum efficiency, and energy efficiency. This paper aims to present a novel hybrid optimization model with the combination of coati and bald eagle optimization algorithms, to solve the defined optimization problem. The experimental results show that the suggested system outperforms the conventional algorithms by evaluating different performance measures such as channel correlation, spectrum efficiency, energy efficiency, and power allocation. [ABSTRACT FROM AUTHOR]

Summary: Device‐to‐device (D2D) communication offers a low‐cost paradigm where two devices in close proximity can communicate without needing a base station (BS). It significantly improves radio resource allocation, channel gain, communication latency, and energy efficiency and offers cooperative communication to enhance the weak user's network coverage. The cellular mobile users (CMUs) share the spectral resources (e.g., power, channel, and spectrum) with D2D mobile users (DMUs), improving spectral efficiency. However, the reuse of radio resources causes various interferences, such as intercell and intracell interference, that degrade the performance of overall D2D communication. To overcome the aforementioned issues, this paper presents a fusion of AI and coalition game for secure resource allocation in non‐orthogonal multiple access (NOMA)‐based cooperative D2D communication. Here, NOMA uses the successive interference cancellation (SIC) technique to reduce the severe impact of interference from the D2D systems. Further, we utilized a coalition game theoretic model that efficiently and securely allocates the resources between CMUs and DMUs. However, in the coalition game, all DMUs participate in obtaining resources from CMUs, which increases the computational overhead of the overall system. For that, we employ artificial intelligence (AI) classifiers that bifurcate the DMUs based on their channel quality parameters, such as reference signal received power (RSRP), received signal strength indicator (RSSI), signal‐to‐noise ratio (SNR), and channel quality indicator (CQI). It only forwards the DMUs that have better channel quality parameters into the coalition game, thus reducing the computational overhead of the overall D2D communication. The performance of the proposed scheme is evaluated using various statistical metrics, for example, precision score, accuracy, recall, F1 score, overall sum rate, and secrecy capacity, where an accuracy of 99.38% is achieved while selecting DMUs for D2D communication. [ABSTRACT FROM AUTHOR]

Summary: By incorporating on‐demand resources, software and data for collaborative services through the Internet, the conventional Information Technology enterprise has been transformed by cloud computing. Based on the pay‐per‐use approach, Infrastructure, platform or Software resources and servers located across data centres are among the several types of resources offered to consumers in cloud computing. Data centres handle these resources. These resources are constantly provisioned to users based on their availability, demand, and quality requirements. Cloud computing systems are known as one of the largest utilisers of energy resources all over the world. Also, power consumption has become a crucial aspect as most cloud computing systems work on traditional nonrenewable resources of energy. In order to make data centres environment‐friendly, there is a need for optimal approaches to reduce energy consumption and their hazardous effects on the environment. To analyse different available strategies for building and maintaining an energy‐efficient cloud is the main objective of this paper. The paper will comprehensively review several energy‐efficient resource provisioning methods and provide a graphical comparative study of Quality of Service (QoS) Metrics in cloud computing. Moreover, the present study identifies the areas of study that need to be further improved to increase the energy efficiency of cloud computing systems. [ABSTRACT FROM AUTHOR]

Summary: Device‐to‐device (D2D) is a fifth‐generation (5G) network's key technology which allows devices in proximity to communicate one‐to‐one without a base station (BS). Moreover, it ameliorates channel gain, communication latency, energy efficiency, and spectral efficiency. However, mitigating interference (D2D and cellular mobile users [CMUs]) and eavesdropper effect (at D2D links) is a prime concern in D2D communication. These issues can be resolved with an efficient allocation of radio resources. Cognitive radio (CR) and non‐orthogonal multiple access (NOMA) are viable solutions to resolve the aforementioned issues. Motivated by the aforementioned discussion, in this paper, we propose a joint CR and NOMA‐based scheme, that is, SARAS for secure resource allocation in D2D communication. It aims to maximize the overall sum rate and secrecy capacity of D2D users with assured quality of service (QoS) and quality of experience (QoE). CR is used for pairing between strong and weak D2D users (i.e., D2D pair) and NOMA's successive interference cancelation (SIC) technique helps to reduce the interference effect between cellular and D2D users. We used a coalition game for efficient resource utilization, which transfers D2D users from one coalition to another based on the preference order and channel conditions. This landed D2D users in the best coalition having favorable channel conditions. We have executed the proposed SARAS scheme over the MATLAB simulation tool, considering a varying number of D2D and CMUs. Simulation results show that the proposed scheme outperforms the traditional OFDMA, random, and nearest approaches concerning parameters such as sum rate and secrecy capacity. [ABSTRACT FROM AUTHOR]

Summary: In heterogeneous networks (HetNets), sharply increasing data traffic exacerbates the rapid growth in the network energy consumption and at the same time brings tremendous pressure on the backhaul links. Aiming at improving the network energy efficiency (EE) and relieving backhaul data volume traffic pressure, in this paper, we investigate energy efficient resource allocation which restricts small cell data traffic by imposing different kinds of backhaul capacity constraints (BCCs) on small cells, including small cell BCC (SBCC) and macrocell BCC (MBCC). We formulate the joint resource block (RB) and power allocation problem as a fractional programming problem. By using variable relaxation and replacement, we convert this nonconvex optimization problem into a concave–convex fractional programming problem, which can further be converted into a parametric convex problem and solved by convex optimization theory. Based on the Dinkelbach and dual decomposition methods, we propose iterative resource allocation algorithms under different BCCs, where RB and power are jointly allocated among users in each cell. Numerical results reveal that the proposed algorithms have good convergence performance and also evaluates the impact of both SBCC and MBCC on network performance. [ABSTRACT FROM AUTHOR]

Summary: The combination of non‐orthogonal multiple access in millimeter‐wave communication called as mmWave‐NOMA is capable of supporting more data streams with a larger number of users to cope up with the increasing traffic demand. In this paper, an energy‐efficient scheme for downlink mmWave‐NOMA systems with joint user grouping, scheduling, and optimal power allocation with the objective of energy efficiency maximization is proposed. Enhanced k‐means clustering for user grouping which can handle the possible simultaneous access of network to enhance the sum throughput is proposed. Weighted sum algorithm‐based scheduling where weights are calculated based on the service requirement of the users to maximize the sum rate and a proportional‐based scheme of power allocation modeled as a resource allocation problem considering the beam gain and discrimination weight of each user for energy‐efficient power allocation is discussed. The simulation results show that the proposed scheme is energy efficient than the conventional mmWave orthogonal multiple access systems. [ABSTRACT FROM AUTHOR]

Summary: This paper investigates the energy‐efficient resource allocation problem in orthogonal frequency division multiplexing (OFDM)‐based cognitive radio (CR) networks with multiple decode‐and‐forward relays. In order to maximize the system energy efficiency (EE), we jointly optimize the relay selection, subcarrier pairing, and power allocation subject to the transmit power constraints, the interference thresholds at the primary system, and the quality of service (QoS) provisioning for the CR system. Using the fractional programming and the Lagrangian dual decomposition method, the optimization problem which is a mixed‐integer nonlinear program (MINLP) can be efficiently solved to obtain an asymptotically optimal solution. We also propose a suboptimal scheme to reduce the computational complexity at the expense of a little performance loss. Simulation results demonstrate the performance of proposed schemes and the effect of the constraints values. [ABSTRACT FROM AUTHOR]

This paper proposed an energy-aware cross-layer mobile cloud resource allocation approach. In this paper, a hybrid cloud architecture is adopted for provisioning mobile service to mobile device users, which include nearby local cloud and remote public cloud. The computation-intensive tasks can be processed by the remote public cloud, while the delay-sensitive computation can be processed by the nearby local cloud. On the basis of the system context and mobile user preferences, the energy-aware cross-layer mobile cloud resource allocation approach can optimize the consumption of cloud resource and system performance. The cooperation and collaboration among local cloud agent, public cloud supplier, and mobile cloud user are regulated through the economic approach. The energy-aware cross-layer mobile cloud resource allocation is performed on the local cloud level and the public cloud level, which comprehensively considers the benefits of all participants. The energy-aware cross-layer mobile cloud resource allocation algorithm is proposed, which is evaluated in the experiment environment, and comparison results and analysis are discussed. [ABSTRACT FROM AUTHOR]

Summary: Wireless network with high data rate applications has seen a rapid growth in recent years. This improved quality of service (QoS) leads to huge energy consumption in wireless network. Therefore, in order to have an energy‐efficient resource allocation in cellular system, a device‐to‐device (D2D) communication is the key component to improve the QoS. In this paper, we propose a noncooperative game (NCG) theory approach for resource allocation to improve energy efficiency (EE) of D2D pair. A three‐tier network with macrocell base station (MBS), femtocell base station (FBS), and D2D pair is considered, which shares the uplink resource block. A resource allocation strategy with constraints is arrived, which maintains minimum throughput for each user in the network. The proposed resource allocation strategy optimizes the EE of D2D pair in the three‐tier network, which achieves Nash equilibrium (NE) and Pareto optimality (PO). Simulation results validate that EE is uniform and optimum for all D2D pair, which converges to NE when channel is static and it converges to PO when the channel is dynamic. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a low‐complexity optimal power allocation (PA) scheme is developed to maximize energy efficiency (EE) in a distributed antenna system (DAS) under maximum power constraint and target bit error rate (BER) requirement. Composite Rayleigh fading, multiple receive antennas, and dynamic circuit power consumption are all considered in the system. Unlike conventional schemes, the presented scheme provides a closed‐form expression of PA. Firstly, the optimization problem is formulated according to the definition of EE. Using the Karush‐Kuhn‐Tucker conditions, a general form of the optimal PA, in which the number of active antennas and corresponding power allocation are required only, is then proposed. With this general form, an effective algorithm is presented to yield the closed‐form PA. The proposed scheme can be applied to the system with static circuit power consumption and/or without target BER constraint to obtain optimal PA. Simulation results corroborate the effectiveness of the developed scheme, and the scheme can achieve the same EE performance as the existing optimal schemes with lower complexity. Moreover, the distributed antenna system with multiple receive antennas has higher EE than that with single receive antenna. [ABSTRACT FROM AUTHOR]

Because energy efficiency (EE) is inevitable in future wireless cellular networks, in this paper, we focus on improving the number of bits delivered to users for each unit energy consumption in the downlink of orthogonal frequency-division multiple access cellular networks with base stations (BSs) coordination. Specifically, each BS shares the channel qualities of users with others and jointly choose the set of co-channel users and the transmit power allocated to maximize the EE of the system subject to the transmit power ceiling of each BS and minimum required data rate. We formulate the problem as a nonlinear fractional optimization problem, using nonlinear fractional programming, the original hard-to-solve problem is transferred to a new one that has the same optimal solution and is easier to solve, this enables two iterative algorithms that achieve nearly the same maximum EE. Numerical results are provided to show the convergence and superiority of the two proposed. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A new opportunistic cross-layer MAC protocol involving channel allocation and packet scheduling for cognitive radio networks is proposed. Cognitive radio allows secondary users (SUs) to exploit the available portions of the licensed spectrum bands without interfering with primary users. In particular, we consider a cognitive radio system, where SUs are equipped with two transceivers: a control transceiver and a software-defined radio transceiver. Data traffic characteristics of SUs are considered to ameliorate system performance. So, we propose a mechanism of resource reservation to improve QoS requirements that favors successful SUs to transmit data during x time slots without interfering with primary users. The key novelty of this paper is giving priority for SUs with important data traffic and which frequently solicits data channels to transmit for the remaining time of the ongoing time slot and for the next time slots directly after checking the channel availability. We develop a new analytical model to evaluate delay parameter for two scenarios with and without resource reservation and we then investigate the impact of those scenarios on the energy consumption. We show through simulations that cognitive radio performances increase noticeably with the proposed scheme. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY In this paper, we study the adaptive resource allocation in multiuser orthogonal frequency division multiplexing (OFDM) systems. We try to maximize the sum capacity of an OFDM system with given transmission power budget, while meeting users' minimal rate requirements. Unlike other resource allocation schemes, which generally separate subchannel allocation and power distribution into independent procedures, our proposed algorithm implements joint subchannel and power allocation. Given a set of subchannels, the required power to satisfy a user's minimal rate constraint is calculated by water-filling policy. Then, the user who requires the maximum power to meet the rate requirement has a priority to obtain an additional subchannel. The procedure continues until all subchannels are consumed, by which time the consumed power to meet all users' rate requirements is also worked out. Finally, the margin power is allocated among all subchannels in an optimal manner to maximize the sum capacity of the OFDM system. Simulation results show that our proposed algorithm performs better than other existing ones. The solution produced by our proposed algorithm is close to the upper bound, while its complexity is relatively lower compared with other methods, which makes it attractive for applications. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Summary: In recent years, cooperative communications have been widely used to improve the spectrum efficiency in cognitive radio networks. In this paper, we propose a new cooperative model involving primary and secondary users, where a primary transmitter may select a number of the secondary users to act as relays in order to maximize its data rate and to transmit at lower energy level, thereby saving energy and reducing interference at the secondary base station. The cooperative transmission is a multiple two‐hop relaying scheme, which guarantees an achievable data rate exceeding that in the direct transmission. In the proposed approach, the problem of joint relay selection and power allocation is formulated as a Stackelberg game, which converges to a unique optimal Nash equilibrium. Performance evaluation shows that this model offers benefit to both sides, where the primary users achieve higher data rate at lower energy consumption and the signal to interference plus noise ratio at the secondary base station is increased significantly. In addition, the results show that the proposed solution outperforms the investigated models in terms of achievable data rate. In this paper, we propose a new cooperative model involving primary and secondary users, where a primary transmitter may select a number of the secondary users to act as relays in order to maximize its data rate and to transmit at lower energy level, thereby saving energy and reducing interference at the secondary base station. The cooperative transmission is a multiple two‐hop relaying scheme which guarantees an achievable data rate exceeding that in the direct transmission. [ABSTRACT FROM AUTHOR]

Summary: The legacy power grid was becoming inefficient in the last decades, mostly due to a changed behavior in energy consumption and generation. To this aim, a novel approach, called smart grid (SG), has been introduced, enabling a smarter management of the electrical power grid, mostly based on a more efficient monitoring and management of the energy consumption and generation by exploiting the presence of sensing and actuator nodes. To properly work, the SG nodes need to interact among themselves and with the centralized monitoring system; therefore, an integrated SG communication network (SGCN) should be considered in the design. In this paper, wireless communication technologies have been considered for the SGCN due to their advantages with respect to wired connections, by focusing on heterogeneous networks (HetNets). However, because of radio resource shortage, an optimization process for their deployment should be considered. We resort in the following to a cost functions approach for optimizing the allocation of the SG node types to different Radio Access Technologies by considering both communication and energy aspects. The numerical results highlight that, thanks to the proposed allocation approach, it is possible to implement an efficient HetNet for supporting different SG node types by trading off between communication requirements and energy consumption. The conventional power grid is becoming inefficient, mostly due to the changes in energy consumption and generation paradigms, leading to the introduction of a novel approach called smart grid. To work efficiently, the smart grid nodes needs to interact among them through a communication network able to satisfy their requirements. In this paper, a cost function‐based optimization technique for a heterogeneous wireless network is proposed, able to optimize the node allocation to the different radio networks. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we propose an energy‐efficient power control and harvesting time scheduling scheme for resource allocation of the subchannels in a nonorthogonal multiple access (NOMA)–based device‐to‐device (D2D) communications in cellular networks. In these networks, D2D users can communicate by sharing the radio resources assigned to cellular users (CUs). Device‐to‐device users harvest energy from the base station (BS) in the downlink and transmit information to their receivers. Using NOMA, more than one user can access the same frequency‐time resource simultaneously, and the signals of the multiusers can be separated successfully using successive interference cancellation (SIC). In fact, NOMA, unlike orthogonal multiple access (OMA) methods, allows sharing the same frequency resources at the same time by implementing adaptive power allocation. Our aim is to maximize the energy efficiency of the D2D pairs, which is the ratio of the achievable throughput of the D2D pairs to their energy consumption by allocating the proper subchannel of each cell to each device user equipment (DUE), managing their transmission power, and setting the harvesting and transmission time. The constraints of the problem are the quality of service of the CUs, minimum required throughput of the subchannels, and energy harvesting of DUEs. We formulate the problem and propose a low‐complexity iterative algorithm on the basis of the convex optimization method and Karush‐Kuhn‐Tucker conditions to obtain the optimal solution of the problem. Simulation results validate the performance of our proposed algorithm for different values of the system parameters. [ABSTRACT FROM AUTHOR]

Summary: Aiming at further improving the energy efficiency of system while meeting the data rate requirement of the users, in this paper, a network energy efficiency optimization problem by jointly optimizing the resource allocation of macrocells and small cells is formulated and proven to be an NP‐hard problem, which cannot be worked out by direct methods in polynomial time. Considering the effectiveness of the modified particle swarm optimization (MPSO) algorithm, an MPSO‐based resource allocation and cell association algorithm is employed to solve the proposed joint optimization problem where the frequency resource partitioning of the macrocells, the transmission power, and the cell‐association bias of the small cells are jointly optimized. During the implementation of the optimization algorithm, the cell association alters with the transmission power and cell‐association bias adjustment, and the small cells where there are no active users associating with them will be turned off. The data rate requirement of the users is an indispensable metric in the communication system. Taking no account of the data rate requirement of the users, the frequency resource partitioning and cell association alteration will deteriorate the data rate of a few of users. Under such circumstances, we take the users' data rate requirement as a constraint of the joint optimization problem. The system level simulation results show that, by jointly optimizing the resource allocation of macrocells and small cells while guaranteeing the users' data rate requirement, the energy efficiency and system throughput can be further improved, and energy consumption can be further reduced. [ABSTRACT FROM AUTHOR]

Recently, in order to satisfy the heavy demands of network capacity brought about by the proliferation of wireless devices, service providers are increasingly deploying heterogeneous cellular networks (HetNets) for boosting the network coverage and capacity. In this paper, we present an iterative energy-efficient scheduling scheme for downlink OFDM-based HetNets with QoS consideration. We formulate the problem as a nonlinear fractional programming problem aiming to maximize the QoS-aware energy efficiency (QEE) in HetNets. In order to solve this problem, we first transform it into a parametric programming problem, which takes QEE as an evolved parameter in the iterative procedure of iterative energy-efficient scheduling scheme. In each iteration, for the given value of QEE, subchannel and power assignment subproblem is a nonlinear nondeterministic polynomial time-hard problem. And hence, we adopt dual decomposition method for obtaining the optimal assignment of subchannels and power of the subproblem for the given value of QEE. Simulation results depict that both outer QEE parameter search and inner subgradient search can converge in a few iterations, and the resultant solutions outperform the equal power allocation scheme [1] and capacity maximization scheme [2] in terms of QEE. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper, we present a QoS-driven energy efficient power and subcarrier allocation in green orthogonal frequency division multiple access networks. Our proposed scheme aims at maximizing the effective energy efficiency (EEE), defined as overall effective capacity per total power consumption, subject to given user delay-QoS requirements and an average sum transmit power constraints. We analytically obtain the uniquely global optimal power and subcarrier using fractional programming, which transforms the fractional program into an equivalent parametric convex program that has a tractable solution. Analytical results show that the optimal solution has the same structure as effective capacity maximization and overall power minimization solutions. Simulation results show that there exists a trade-off between EEE and delay. When the delay is more stringent, the EEE decreases. Moreover, when the number of subcarriers increase, EEE increases, where a fixed optimal EEE can be achieved regardless of stringency of delay-QoS requirements. Furthermore, our proposed resource allocation provides significant EEE gains over both the independent resource allocation and overall power minimization schemes. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Because the energy consumption is growing rapidly, green radio, which lays emphasis on the energy efficiency (EE) in wireless networks, is becoming increasingly important. As potential paradigms for future wireless network design, cognitive radio (CR) is a promising technology to solve the spectrum shortage and inefficiency issues, while cooperative relay is capable of improving spectral efficiency by combating severe fading in wireless environment. In this paper, we investigate the energy-efficient power allocation problem in orthogonal frequency division multiplexing (OFDM)-based relaying CR networks. We develop a general framework to maximize the overall EE of the CR system, under the constraints of transmission power budget, traffic demands, and the interference constraints of the primary users. Our problem formulation is a nonconvex optimization task, and it is hard to obtain the optimal solution. We first convert our formulated problem into a convex optimization problem via its hypograph form, which can be solved by the barrier method. Then we further speed up the computation of Newton step during the barrier method, significantly reducing the complexity of the algorithm by exploiting its special structure. Numerical results validate that our method can exploit the overall EE of CR systems, while the algorithm converges efficiently and stably. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]