Energy Efficiency Optimization Problem Research Articles

Energy-Efficient Buffer-Aided Relaying Systems With Opportunistic Spectrum Access

In this paper, an energy-efficient cross-layer design framework is proposed for cooperative relaying networks, which takes into account the influence of spectrum utilization probability. Specifically, random arrival traffic is considered and an adaptive modulation and coding (AMC) scheme is adopted in the cooperative transmission system to improve the system performance. The average packet dropping rate of the relay-buffer is studied at first. With the packet dropping rate and stationary distribution of the system state, the closed-form expression of the delay is derived. Then the energy efficiency for relay-assisted transmission is investigated, which takes into account the queueing process of the relay and the source. In this context, an energy efficiency optimization problem is formulated to determine the optimum strategy of power and time allocation for the relay-assisted cooperative system. Finally, the energy efficient switching strategy between the relay assisted transmission and the direct transmission is obtained, where packet transmissions have different delay requirements. In addition, energy efficient transmission policy with AMC is obtained. Numerical results demonstrate the effectiveness of the proposed design improving the energy efficiency.

Energy Efficient Resource Allocation in Millimeter-Wave D2D Enabled 5G Cellular Networks

The current trend has seen the data capacity and traffic density increase due to the increased demand for multimedia services. Since this cannot be handled successfully by the current 4G networks, there is a need to integrate the mmWave and the Device-to-Device (D2D) communication 5G technologies to meet this increased demand and traffic density. However, there is the challenge of increased interference between dense D2D users and cellular users if D2D users are allowed to reuse the resources allocated to cellular users. This degrades the performance of the D2D users in terms of achievable data rate and Energy Efficiency (EE). The paper formulates a match theoretic resource allocation scheme to maximize the achievable D2D sum rate. In addition, an EE optimization problem is formulated for D2D users by considering the rate and power constraints. The EE optimization problem is solved by the Lagrangian dual decomposition method. The algorithms were simulated in MATLAB and the results were compared to Hungarian and heuristic optimization algorithms. The results showed that the match theoretic resource allocation is on average 1.82 times better than the Hungarian algorithm. At the same time, the match theoretic resource allocation algorithm increases fairness in resource allocation as it maintains a higher sum rate for low and high-density number of users. The proposed EE optimization algorithm improved the D2D performance by 8.2% compared to the heuristic algorithm.

Content-Centric Heterogeneous Fog Networks Relying on Energy Efficiency Optimization

Next-generation wireless communications are expected to provide reliable high-rate connectivity. The recent concept of fog-enabled architecture comes to rescue by invoking a substantial amount of data storage close to end-user devices for meeting these challenges. Thus, caching popular contents at heterogeneous devices, e.g., fog nodes (FNs) or fog access points (FAPs), constitutes a promising technique of reducing both the traffic and the energy consumption of the backhaul links. Therefore, in this paper, we propose an energy-efficient caching and node association algorithm for cache-aided fog networks. First, we solve the problem of energy-efficient content caching and delivery in the FNs/FAPs in conjunction with a fixed node association strategy, where the FNs communicate either with other FNs by node-to-node (N2N) communications or with the FAPs in their proximity. In both caching scenarios, we investigate the relationship between the caching probability of the file and the energy-efficient content delivery by formulating the associated energy efficiency (EE) optimization problem under caching memory constraints. Then, we derive a joint modulation mode allocation strategy and caching policy for each content caching node and conceive a joint node association and caching algorithm for maximizing the EE. Finally, we quantify both the overall EE and throughput for demonstrating that the proposed caching and transmission strategy achieves significant performance improvements.

Cooperative and efficient content caching and distribution mechanism in 5G network

This paper considers encoding caching and collaborative distribution issues jointly, and proposes a collaborative and efficient content caching and distribution mechanism; and based on the proposed scheme, an energy consumption model is established from the perspective of content caching and content distribution to optimize Energy efficiency of content caching and content distribution. In addition, this paper proposes a cache placement algorithm based on heuristic greedy algorithm to solve this energy efficiency optimization problem by optimizing cache placement. Finally, the simulation results show the performance of the scheme. This paper proposes a content clustering distribution scheme for the problems of random user distribution and limited cache. This solution uses the user’s media cloud and communication technology to complete the distribution of video content. In addition, the use of an efficient adaptive media cloud clustering mechanism and a minimized hops algorithm has enabled the reasonable deployment of 5G network caches based on the popularity of video content and user distribution. The simulation results show that the system content request response time is shortened by 50%, which effectively reduce the impact of content popularity changes on system performance. This paper analyses the important performance indicators such as cache hit rate, optimal communication radius, and maximum link access in 5G networks for multi-layer popular multimedia content distribution in response to the heterogeneity and transmission interference problems for 5G networks in 5G communications. Under the premise of noise ratio and signal-to-interference ratio threshold, related service link selection and scheduling algorithms is further proposed obtaining simulation results that approximate theoretical analysis, which effectively reduces the number of interference by 40% and improves the system throughput by 10%.

Energy efficiency optimization in MIMO heterogeneous wireless powered communication networks

This paper studies the energy efficiency (EE) optimization problem in multiple-user multiple-input–multiple-output heterogeneous wireless powered communication network (HWPCN) in which the users in small cells can harvest energy from the small cell base stations in downlink and use harvested energy for uplink transmission. We consider the joint design problem of wireless energy transmission and wireless information transmission precoding matrices subject to the transmit power constraints at each BS and each user. Since the optimization problem under consideration is highly nonlinear non-convex fractional programming in design variables, it is mathematically challenging to obtain the optimal solutions. To tackle with this difficulty, we employ the difference of convex programming to obtain the concave lower bound of the achievable sum rates. Then, we apply the Dinkelbach approach to develop an efficient iterative algorithm in which the convex optimization problems are solved. Simulation results are provided to investigate the EE of the proposed algorithm in HWPCNs.

Energy efficiency maximization in a wireless powered IoT sensor network for water quality monitoring

This study presents novel approaches to the allocation of resources in Internet-of-Things sensor network (IoTSN) systems applied to water-quality monitoring for optimal and more sustainable utilization of resources. To tackle the long-standing energy scarcity issue that currently plagues sensor network (SN) systems, energy harvesting is explored and exploited to maximize its untapped potential to develop asuccessive wireless power sensor network (WPSN) system embedded with a scheduling algorithm, and operate as a non-orthogonal multiple access (NOMA) system. Similarly, quality of service parameters are crucial design considerations for network efficiency,and energy efficiency (EE) is considered here. Consequently, an EE optimization problem is formulated for the successiveWPSN system and solved by exploiting the problem structure and through a meta-heuristic algorithm. The new system is validated through the numerical simulation results presented in this work by thoroughly analyzing, evaluating and comparing the proposed meta-heuristic based WPSN system with the baseline state-of-the-art WPSN systems that combined a meta-heuristic algorithm, two additional meta-heuristic algorithms including genetic algorithm (GA) and ant-colony optimization (ACO) algorithm as well as a non-meta-heuristic algorithm – specifically an iterative based Dinkelbach algorithm.The experimental outcomes show that the proposed system significantly outperforms the contemporary WPSN systems in terms of EE performance gains.

Energy-efficient 5G cloud RAN with virtual BBU server consolidation and base station sleeping

Heterogeneous network (HetNet) deployment where macro cells are overlaid by small cells is considered a de-facto solution for meeting the ever increasing mobile traffic demand in fifth generation (5G) networks. However, deployment of a large number of small cell base stations (BSs) result in considerable increase in HetNet energy consumption. Cloud radio access networks (C-RAN) has been proposed as an energy-efficient architecture that leverages cloud computing technology where baseband processing is performed in virtual baseband units (vBBU) in the BS cloud. In this paper, we address the energy efficiency (EE) optimization problem in the downlink for two-tier heterogeneous C-RAN (H-CRAN) comprising of macro and pico-cells. At the radio side of H-CRAN, a dynamic pico BS switching OFF algorithm based on a utility function is proposed while maintaining coverage and quality of service (QoS). In the cloud side, heuristic approximation algorithms are proposed including simulated annealing (H-CRAN SA) and genetic algorithm (H-CRAN GA) to minimize energy consumption by reducing the number of BBU servers used through vBBU placement. The proposed scheme is compared with distributed long term evolution advanced (LTE-A) Hetnet system and simulation results show that the proposed H-CRAN SA and H-CRAN GA schemes save 48% and 45% of energy on a daily average, respectively while maintaining the required QoS.

Dynamic Wireless Information and Power Transfer Scheme for Nano-Empowered Vehicular Networks

In this article, we investigate the wireless power transfer and energy-efficiency (EE) optimization problem for nano-empowered vehicular networks operating over the terahertz band. The nano-sensors in air can harvest energy from a power station and then can transmit the trace information to the micro-device under reconnaissance vehicular scenarios. Hence, by considering the properties of the terahertz band, we develop a long-term EE optimization problem. Furthermore, with the help of the equivalent transformation method, we converted the EE optimization problem into a series of energy-efficient resource allocation problems over the time slots. Each reformulated optimization problem becomes a mixed integer nonlinear programming (MINLP) over a time slot. Hence, to obtain the sub-optimal solution of the reformulated optimization problem, we developed a Quantum-behaved Particle swarm-based EE Optimization (QPEEO) algorithm. Furthermore, by exploiting the special structure of the reformulated problem, we propose an Improved Discrete Particle swarm-based EE Optimization (IDPEEO) algorithm. The proposed IDPEEO algorithm handles the problem's constraints effectively, and greatly reduces the search space and the convergence time. Our simulation results validate the theoretical analysis of the proposed scheme.

Adaptive mesh size (AMS) algorithm for energy efficiency maximisation in underlay D2D communication

The need to maximise energy efficiency (EE) for device-to-device (D2D) communication has become an urgent necessity for the fifth-generation wireless networks. In this study, the power allocation problem is considered for the cellular system underlaid with D2D users (DUs) to maximise the EE for the whole network. Firstly, the random distribution of DUs and cellular users (CUs) are described by utilising the homogeneous Poisson point process based on stochastic geometry. Secondly, successful transmission probabilities are deduced. Finally, the optimisation problem for maximising EE is formulated to determine the optimal power values for both D2D and CUs. An adaptive mesh size algorithm is suggested to solve the non-convexity of the EE optimisation problem. Simulation results prove that the suggested technique has the ability to accomplish the near-optimal EE. Furthermore, it has superior performance when compared with the related algorithms.

Secure Energy Efficiency Maximization in Cognitive Satellite-Terrestrial Networks

This article investigates the secure energy efficiency (EE) optimization problem in a cognitive satellite-terrestrial network with a capable eavesdropper. The objective is to maximize the secure EE for the primary satellite network while satisfying the allowable signal-to-interference-plus-noise ratio requirements of the secondary and primary users along within the transmit power limitation of both satellite and the terrestrial base station. Owing to the nonconvexity and intractability of the original optimization problem, a beamforming scheme and associated transformation algorithms are proposed by jointly applying the Taylor approximation, fraction programming, and alternating search to cope with the implementation difficulty. The key is to convert the original optimization problem into a simple convex framework and obtain the optimal solution step by step. Finally, numerical simulations are given to verify the feasibility and practicability of the proposed optimization algorithms.

Energy Efficiency Optimization for Downlink Massive MIMO With Statistical CSIT

We investigate energy efficiency (EE) optimization for single-cell massive multiple-input multiple-output (MIMO) downlink transmission with only statistical channel state information (CSI) available at the base station. We first show that beam domain transmission is favorable for energy efficiency in the massive MIMO downlink, by deriving a closed-form solution for the eigenvectors of the optimal transmit covariance matrix. With this conclusion, the EE optimization problem is reduced to a real-valued power allocation problem, which is much easier to tackle than the original large-dimensional complex matrix-valued precoding design problem. We further propose an iterative water-filling-structured beam domain power allocation algorithm with low complexity and guaranteed convergence, exploiting the techniques from sequential optimization, fractional optimization, and random matrix theory. Numerical results demonstrate the near-optimal performance of our proposed statistical CSI aided EE optimization approach.

Hybrid PSO-GSA for energy efficient spectrum sensing in cognitive radio network

The Cognitive Radio Network (CRN) in 5G heterogeneous network has trade-off between energy efficiency and spectrum sensing efficiency. Energy Efficiency is important for designing the battery-powered cognitive radio network. Existing methodologies are mainly focused on solving the optimization problem of energy efficiency in spectrum sensing using convex optimization. However, The real-time spectrum sensing is a non-convex optimization problem. In this paper, energy efficiency for various spectrum sensing scenarios is modeled as non-convex optimization problem. To detect spectrum holes with improved energy utilization, this paper proposes a novel hybridization of Particle Swarm Optimization (PSO) with Gravitational Search Algorithm (GSA) called as Hybrid PSO-GSA. With the proposed novel hybridization of PSO and GSA, it is possible to achieve balanced trade-off between exploration and exploitation abilities of PSO-GSA algorithm. In addition to that, with the incorporation of mutation and crossover factor in the PSO-GSA, the proposed algorithm is efficiently able to detect the spectrum holes with the optimized values of transmission power, sensing bandwidth and power spectral density. Thus, improving the energy efficiency of the spectrum sensing. Simulation results substantiate the efficiency of PSO-GSA in optimizing the energy efficiency for spectrum sensing in terms of transmission power, spectrum sensing bandwidth and power spectral density compared with existing PSO and Artificial Bee Colony (ABC) algorithms.

Energy-Efficient Resource Allocation in SWIPT Cooperative Wireless Networks

In this article, energy-efficient resource allocation in simultaneous wireless information and power transfer (SWIPT) cooperative wireless networks is studied. With the transferred power, the SWIPT relays are adopted to improve the energy efficiency at the destination node. Two typical relay structures, decode-and-forward (DF) and amplify-and-forward (AF), are exploited for the optimal relay selection and power allocation with a power splitting SWIPT architecture. Nonconvex energy efficiency optimization problems are formulated for both DF and AF relay types. Based on the SNRs at the destination node, closed-form expressions of the optimal power splitting ratios are provided for DF and AF relays, respectively. With the optimal power splitting ratio, relay selection schemes based on full and partial knowledge of the channel state information are derived. Moreover, a novel power allocation scheme is proposed and illustrated based on the property of the simplified optimization problem with the power and quality of service constraints. Simulation results demonstrate that the proposed resource allocation scheme achieves the maximum energy efficiency with low computational complexity, in which the proposed relay selection outperforms the typical relay selection schemes in terms of energy efficiency.

Secure Green-Oriented Multiuser Scheduling for Wireless-Powered Internet of Things

In this paper, we consider the secure green-oriented multiuser scheduling for the wireless-powered Internet of Things (IoT) scenario, in which multiple source sensors communicate with a controller assisted by an intermediate sensor with the existence of a passive tapping device. Due to the limited energy, all sensors must acquire energy from external power beacons (PBs). Specifically, for the security improvement, we introduce two multiuser scheduling schemes possessing the optimal PB chosen by the relay, i.e., the best source sensor is scheduled in a random way (BSR), while the best source sensor is decided by the best PB (BSBP). Furthermore, for every scheme, we derive the analytical expressions for the secrecy outage probability (SOP) and investigate the secure energy efficiency (SEE) optimization problem with constricted transmission power in PBs. Simulation results reveal that the BSBP scheme provides better secrecy performance, and elevating the PBs quantity or reducing both the ratio of distance from PBs to source users and the total communication distance to some extent is helpful for improving SEE. In addition, the time-switching factor shows an important effect upon secrecy performance of the considered system.

An energy-efficient torque distribution strategy for in-wheel-motored EVs based on model predictive control

In order to improve the energy efficiency (EE) for in-wheel-motored electric vehicles (IWM EVs), an optimal torque distribution algorithm is investigated based on model predictive control (MPC). Firstly, an optimisation structure is developed by considering energy efficiency characteristics of the motor and energy loss of tyre slip. To achieve the driving requirements, an upper layer controller determines a desired motor torque, considering the maximum capacity of the motor drive and regenerative brake. Then, an MPC-based torque distribution algorithm in lower layer deals with this energy efficiency optimisation problem with system dynamic and torque saturation constraints. Then to obtain the optimal motor torques fast, an efficient solution approach is given by combining constraint conversion and numerical methods. Finally, simulation results in different driving cycles indicate that overall energy efficiency and computational efficiency of vehicle can be improved. Also, the real-time control performance is guaranteed in the hardware-in-the-loop (HiL) simulation.

Distributed Energy Efficiency Optimization for Multi-User Cognitive Radio Networks Over MIMO Interference Channels: A Non-Cooperative Game Approach

Energy efficiency (EE) optimization is investigated for a multi-user cognitive radio network (CRN) over multiple-input-multiple-output (MIMO) interference channels (ICs). To reduce the system overhead due to information exchange among the secondary CR uses (SUs), the EE optimization problem is formulated as a non-cooperative game, where each SU transmitter competes against the other SU pairs by optimizing its transmit covariance matrix. Specifically, each multi-antenna SU maximizes locally its energy efficiency in terms of the number of bits transmitted per unit energy consumption, subject to the per-SU transmit power constraint and the primary user (PU) perceived total interference constraint. It is proved that the formulated non-cooperative game admits at least one Nash equilibrium (NE), and the sufficient condition for a unique NE is derived subsequently. Primal decomposition is employed in the local EE optimization problem to relax the coupling PU perceived interference constraint such that fully distributed operation is allowed. A distributed iterative EE optimization algorithm (DIEEOA) is then proposed to obtain the unique NE, which is shown to converge to the global optimum. Linear precoding techniques are employed to mitigate the impacts of multi-user interference and imperfect channel state information (CSI). Through numerical simulations, effectiveness of the proposed scheme is validated and the system setting parameters’ impacts on the performance are studied.

Heterogeneity-Aware Energy Saving and Energy Efficiency Optimization in Dense Small Cell Networks

In dense small cell networks (DSCNs), small cells are heterogeneous due to the unplanned deployment and various traffic loads, resulting in different energy efficiency (EE) preferences. In this paper, taking into account the heterogeneity of small cells, energy saving (ES) and EE are jointly optimized through subchannel allocation, subframe configuration and power allocation. In order to quantify the effects of small cells’ heterogeneous information on EE, an EE preference function is first defined. Then, the joint ES and EE optimization problem is formulated as a multi-objective optimization problem. Due to the coupling of ES and EE, obtaining the solution is non-trivial. Therefore, we propose a heterogeneity-aware ES and EE (HESEE) optimization algorithm, where subchannel allocation is optimized to ensure the fairness of active subframes required by the users in the same small cell base stations (SBSs). Then subframe configuration is conducted via group formation sleep mechanism. Particularly, address the non-concave sum-of-ratios optimization for system EE, the concave-convex procedure (CCCP) method is adopted. Simulation results show that the proposed HESEE algorithm can optimize the SBSs’ EEs according to their EE preferences. In addition, the HESEE algorithm can achieve good performance in reducing energy consumption as well as improving the system EE.

Optimization of Resource Allocation Model With Energy-Efficient Cooperative Sensing in Green Cognitive Radio Networks

Green cognitive radios show promise for high energy efficiency (EE) in the future of wireless communications. Spectrum sensing refers to an energy-consuming procedure that allows cognitive users to independently identify unused radio spectrum segments and prevent interference to primary users, and it should be minimized due to resource limitations. In this paper, we present a wireless multiple-access channel function to establish the primary users’ presence and the mean EE optimization problem of the cognitive radio systems with mathematical structure computation purposes. EE, as the average throughput to the average energy consumption ratio, is used to measure the network’s performance subject to detection constraint. More specifically, since secondary users are generally battery-powered devices, saving on energy is crucial. We aim to decrease the energy consumption of secondary users while maximizing the total EE and preserving the accurate sensing by maximizing the sensing time detection subject to secondary user power constraints and minimum data rate. To address the non-convexity optimization problem, one can consider energy-efficient power allocation based on an iterative method. Simulation results show the optimum of our framework when combined by the multiple-access channel computation-based scheme. Green cognitive radio should consider the tradeoff against its complexity and maximum available EE metric. However, one can observe from the simulation results that the improved EE presented in this work yields much higher when compared with others with the same detection performance.

Hybrid Precoding Design for Energy Efficient Millimeter-Wave Massive MIMO Systems

In this letter, we propose a hybrid precoding design algorithm by utilizing RF chain selection to optimize the number of RF chains to achieve high energy efficiency (EE) with slightly spectral efficiency (SE) performance degradation. In this algorithm, we firstly reformulate the energy efficiency optimization problem, and resolve the hybrid precoding optimization problem with alternating minimization method to seek the approximate optimal analog precoder and digital precoder under the proposed structure. Then, we perform the transmitter RF chain selection to find the optimal RF chain set corresponding to better EE and SE performance. Simulation results show the proposed algorithm achieves the good trade-off between EE and SE performance when the transmit power constraint is no larger than 30 dBm.

Robust THP Design for Energy Efficiency of Multibeam Satellite Systems with Imperfect CSI

In this letter, we investigate a robust zero-forcing (ZF) Tomlinson-Harashima Precoding (THP) scheme for multibeam satellite downlink systems to improve the energy efficiency (EE) when the channel state information (CSI) at the transmitter is imperfect. By using a norm-bounded error (NBE) model with unknown statistical characterization, the worst case EE optimization problem under transmit power constraint is solved with two steps. Firstly, the optimal CSI uncertainty parameter minimizing the EE is obtained, then sequential convex approximation (SCA) is used to obtain the solution via a nested iteration method. The proposed robust algorithm achieves better EE performance in comparison with the nonrobust one in the literature in the presence of CSI error, which can be shown from simulation results.