Energy Efficiency Fairness Research Articles

Fair Energy-Efficient Resource Optimization for Green Multi-NOMA-UAV Assisted Internet of Things

Owing to the advantages of better air-ground channel and higher flexibility, unmanned aerial vehicle (UAV) has been widely applied in the field of Internet of Things (IoT) to increase the communication coverage. However, the UAV with limited energy is facing severe energy shortage when serving more and more IoT devices. In this paper, we propose a non-orthogonal multiple access (NOMA) based green multi-UAV assisted IoT system to increase user capacity while improving the energy utilization of each UAV. Considering the limited energy budget of each UAV, we formulate a fair energy-efficient resource optimization problem under the constraints of maximum transmit power of each UAV, minimum communication rate requirement of each user and UAV mobility. By alternately optimizing the communication scheduling, transmit power allocation and UAV trajectory with the Dinkelbach method and the successive convex approximation (SCA), the energy-efficient fairness can be achieved between the UAVs by maximizing the minimum energy efficiency of the UAVs. The simulation results indicate the fair energy efficiency between the UAVs can be obtained through the alternative resource optimization, and the proposed multi-NOMA-UAV assisted IoT can get higher energy efficiency than the traditional orthogonal multiple access (OMA)-UAV assisted IoT.

Energy Efficiency Fairness of Active Reconfigurable Intelligent Surfaces-Aided Cell-Free Network

Reconfigurable intelligent surface (RIS) has been widely recognized as a promising technique for future wireless communications. Benefiting from its ability to improve channel capacity with low cost and power consumption, the concept of RIS-aided cell-free network is proposed to break the bottleneck of current network architecture. However, the multiplicative fading effect (MFE) introduced by RIS prevents RIS-aided cell-free network from higher network capacity. To further increase the capacity of the RIS-aided cell-free network, in this paper, we propose the concept of active RIS-aided cell-free network, where the RIS is replaced by active RIS in traditional RIS-aided cell-free network. Moreover, we analyze the proposed network by investigating its energy-efficiency fairness (EEF). To elaborate, we formulate the EEF maximization problem in the active RIS-aided cell-free network, and propose a joint beamforming and resource allocation (JBRA) algorithm by exploiting alternating optimization (AO) and fractional programming (FP). Simulations results verify the proposed active RIS-aided cell-free network as well as the JBRA algorithm as an energy-efficient design for cell-free network with large network capacity.

User Energy Efficiency Fairness Algorithms for Task Offload in Cloud Edge Networks

With the emergence of several new services such as driverless vehicles and virtual reality, mobile communication networks face problems such as heavy load and insufficient computing resources. The development of cloud, edge, and mobile edge network computing provides a good solution to this problem. This paper proposes the development of a user energy efficiency fairness task unloading algorithm for cloud-side networks. First, a cloud-side network cooperation model is constructed. The model ensures the efficient use of user energy and addresses the task offloading decision and resource allocation optimization problem jointly. Using the generalized fraction theory, the optimization problem is transformed into an equivalent convex problem by introducing relaxation as well as auxiliary variables. Next, the centralized energy efficiency fairness (CEEF) and alternating direction method of multiplier (ADMM)-based energy efficiency fairness algorithms are implemented to obtain an optimal solution for the optimization problem. Finally, through experimental simulation, the convergence of the CEEF- and ADMM-based energy efficiency fairness algorithms is verified. Compared with noncooperative algorithms, the performance of our proposed method increased by 30.76%. The proposed algorithm has been verified to ensure the fairness of user energy efficiency.

Achieving Secrecy Energy Efficiency Fairness in UAV-Enabled Multi-User Communication Systems

This letter investigates the secrecy energy efficiency (SEE) fairness of unmanned aerial vehicle (UAV)-enabled multi-user communication systems where a UAV disseminates data to multiple ground users (GUs) in the presence of an eavesdropper (EVE). Taking into account the security threats from EVE, the limited on-board energy of UAV and the fairness among GUs, we formulate a user-oriented SEE maximization problem to maximize the minimum GU’s SEE by jointly optimizing the UAV trajectory and resource allocation subject to the UAV motion constraints and resource budgets. Due to the difficulty in solving this problem that arises from the fractional structure of SEE and the coupling characteristic of optimization variables, we first exploit the successive convex approximation to transform the original problem to a convex one, which is further converted into a second order cone program to reduce the computational complexity. Then, a convergent iterative algorithm is designed to find the suboptimal solution. Simulation results show that the proposed scheme significantly improves SEE fairness of GUs as compared to some benchmark schemes.

Fairness-Based Energy-Efficient 3-D Path Planning of a Portable Access Point: A Deep Reinforcement Learning Approach

In this work, we optimize the 3D trajectory of an unmanned aerial vehicle (UAV)-based portable access point (PAP) that provides wireless services to a set of ground nodes (GNs). Moreover, as per the Peukert effect, we consider pragmatic non-linear battery discharge for UAV’s battery. Thus, we formulate the problem in a novel manner that represents the maximization of a fairness-based energy efficiency metric and is named fair energy efficiency (FEE). The FEE metric defines a system that lays importance on both the per-user service fairness and the PAP’s energy efficiency. The formulated problem takes the form of a non-convex problem with non-tractable constraints. To obtain a solution we represent the problem as a Markov Decision Process (MDP) with continuous state and action spaces. Considering the complexity of the solution space, we use the twin delayed deep deterministic policy gradient (TD3) actor-critic deep reinforcement learning (DRL) framework to learn a policy that maximizes the FEE of the system. We perform two types of RL training to exhibit the effectiveness of our approach: the first (offline) approach keeps the positions of the GNs the same throughout the training phase; the second approach generalizes the learned policy to any arrangement of GNs by changing the positions of GNs after each training episode. Numerical evaluations show that neglecting the Peukert effect overestimates the air-time of the PAP and can be addressed by optimally selecting the PAP’s flying speed. Moreover, the user fairness, energy efficiency, and hence the FEE value of the system can be improved by efficiently moving the PAP above the GNs. As such, we notice massive FEE improvements over baseline scenarios of up to 88.31%, 272.34%, and 318.13% for suburban, urban, and dense urban environments, respectively

Towards Energy-Fairness in LoRa Networks

LoRa has become one of the most promising networking technologies for Internet-of-Things applications. Distant end devices have to use a low data rate to reach a LoRa gateway, causing long in-the-air transmission time and high energy consumption. Compared with the end devices using high data rates, they will drain the batteries much earlier and the network may be broken early. Such an energy unfairness can be mitigated by deploying more gateways. However, with more gateways, more end devices may choose small spreading factors to reach closer gateways, increasing the collision probability. In this paper, we propose a networking solution for LoRa networks, EF-LoRa, that can achieve energy fairness among end devices by carefully allocating network resources, including frequency channels, spreading factors and transmission power. We develop a LoRa network model to study the energy consumption of the end devices, considering the unique features of LoRa networks such as LoRaWAN MAC protocol and the capacity limitation of a gateway. We formulate the energy fairness allocation as an optimization problem, and propose a greedy allocation algorithm to achieve max-min fairness of energy efficiency. Simulation results show that EF-LoRa can improve the energy fairness of the state-of-the-art works by 177.8%.

Computational EE Fairness in Backscatter-Assisted Wireless Powered MEC Networks

In this letter, we study the computational energy efficiency (EE) fairness in a backscatter-assisted wireless powered mobile edge computing (MEC) network, where multiple edge users (EUs) can offload tasks to the MEC server via passive backscatter communications (BackComs) and active transmissions (ATs) under the guidance of the harvest-then-transfer protocol. Specifically, with the practical non-linear energy harvesting (EH) model and the partial offloading scheme considered at each EU, we propose a max-min computational EE-based resource allocation scheme to ensure the fairness among multiple EUs by jointly optimizing the reflection coefficient, transmit power, local computing frequency and execution time of each EU, as well as EUs' time sharing between BackComs and ATs, and then develop a Dinkelbach-based iterative algorithm to obtain the optimal solutions. We further employ the Lagrange duality method to obtain instrumental insights on the max-min computational EE-based resource allocation scheme. Simulation results verify the superiority of the proposed scheme over benchmark schemes in terms of computational EE fairness.

Energy-Efficiency Fairness of Interference Multi-Relay Networks for Multi-User Communications

We investigate a single-antenna multi-user multi-relay interference network, where multiple source nodes simultaneously communicate with their respective destination nodes via half-duplex decode-and-forward relays. To ensure fairness among users, we consider a power allocation strategy to maximize the worst-case energy efficiency (EE) of all users for a fixed relay assignment. The resulting optimization problem turns out to be non-convex. Different from those in the literature, our method here is an iterative algorithm where two geometric programs (GPs) are solved in each iteration, one producing an upper-bound to the solution of the original problem and the other providing a feasible lower-bound. Moreover, the upper-bound GP approaches the original problem asymptotically. Our algorithm also works for the problem arising in the non-interfering (orthogonal) transmission, which was previously solved as a fractional program. Numerical results reveal that non-orthogonal transmission outperforms orthogonal transmission in terms of the worst-case EE at low and medium signal-to-noise ratios.

Efficient resource allocation algorithms for high energy efficiency with fairness among users in OFDMA networks

In this article, we devise two efficient radio resource allocation algorithms in order to increase fair energy efficiency among users of OFDMA networks. The objective function is considered to be the well-known max-min function in order for allocating radio resources fairly in terms of energy efficiency, by taking the maximum transmit power, minimum rate requirements, and subchannel assignment constraints into account. The resulting problem is a nonconvex mixed-integer nonlinear problem for which two algorithms are proposed. In the first proposed algorithm, by reallocating the transmit power on subchannels, we obtain a suitable subchannel assignment with high fair energy efficiency. In the second proposed algorithm, by applying generalized fractional programming, mathematically modifying the objective function and constraints to a continuous and convex form and employing sequential convex programming, we allocate power and subchannel jointly. The proposed algorithms efficiently utilize radio resources and therefore, yield a far better performance than available solutions.

Fairness-Driven Energy Efficient Resource Allocation in Uplink MIMO Enabled HetNets

Heterogeneous network (HetNet) is a promising solution to meeting the unprecedented demand for higher data rate in the next generation mobile networks. Compared with the traditional single-layer cellular networks, the resource management (e.g., user association and power control problems) is more challenging in the multi-tier HetNets. In addition, due to the new application scenarios and battery-limited nature of the user equipment (UE), improving energy efficiency (EE) of individual while ensuring EE fairness among UEs is one of the key design issues in the uplink HetNets. In this paper, by considering the fairness for EE, joint user association and power control problems are studied in the uplink multiple input multiple output (MIMO) enabled HetNets. Under the quality of service (QoS) and transmit power constraints of UE, two kinds of fairness, namely, proportional fairness and max-min fairness, are investigated. The optimization problems of maximizing the EE are formulated by considering the fairness among UEs. Considering the non-convex characteristic of the problem, the formulated problem is divided into two subproblems, i.e., user association and power control subproblems, then an iterative algorithm is proposed to achieve the fair EE resource allocation for each fairness criterion until convergence. For the proportional fairness problem, the dual decomposition and Newton methods are applied. Then, by using successive convex approximation (SCA) and dual decomposition, the problem of max-min fairness is solved. Through both theoretical analysis and simulations, the convergence and the effectiveness of the proposed algorithms are proved and evaluated. Simulation results show that the proposed algorithms can significantly improve the average EE of users.

Fair Energy-Efficient Resource Allocation for Downlink NOMA Heterogeneous Networks

The increasing in energy consumptions of the current wireless networks, leads towards designing energy-efficient 5G networks. The application of non-orthogonal multiple access (NOMA) in the heterogeneous networks (HetNets) improves the spectrum utilization with the cost of efficient resource allocation. Hence, this article proposes optimal user-pairing and power allocation solutions towards achieving fair energy-efficient resource allocation in downlink femtocell NOMA-HetNets. In the proposed optimization process, the considered constraints are the user's transmission rate, transmit power budget at the base station (BS), and the interference. The energy consumption of both the transmitter and the receiver are considered to simulate the real system design. The Greedy Algorithm (GA) is used to achieve a low-complex optimal solution during the user-pairing process. Simultaneously, the max-min energy efficiency optimization approach is employed to maximize the minimum energy efficiency of the femtocell users to achieve the optimal power allocation solution. The mathematical formulation of the max-min energy efficiency is a non-convex fractional programming problem and is intractable. Thus, the fractional programming theory is adopted to transform the problem into a sequence of subtractive form, followed by the Sequential Convex Programming (SCP) approach to determine the optimal solution. Simulation results show that the proposed NOMA with optimal power allocation method using SCP and GA (NOMA-SCP-GA) achieves fair energy efficiency performance with lower complexity compared to the benchmark methods. Moreover, the minimum energy efficiency of the femtocell user is 38.22% higher than NOMA with Difference of Convex programming (NOMA-DC). The NOMA-SCP-GA method can assure 5G capability demands.

An Offloading Mechanism Towards SE–EE Experiences Tradeoff in Heterogeneous Cellular Networks

To achieve a tradeoff between spectral efficiency (SE) and energy efficiency (EE) experiences, we design an offloading mechanism to maximize the sum of weighted logarithmic utilities with respect to SE and EE for heterogeneous cellular networks, where the weighting parameters are used for adjusting the SE–EE experiences. Unlike the most existing offloading mechanisms that often try to find a tradeoff between SE and EC (energy consumption), we concentrate on a tradeoff between SE–EE experiences in our mechanism. That is to say, our mechanism directly optimizes EE and is weighted in favour of SE–EE fairness. To solve the finally formulated problem in a mixed-integer and nonlinear form, we firstly make some relaxation for the optimization objective to obtain its an upper bound. Then, we perform a decoupling operation to achieve a decomposable form of dual problem. Finally, we develop a feasible algorithm that can be well implemented in a distributed manner. Numerical results show that the designed mechanism can definitely reach a tradeoff between SE–EE experiences.

Improving Energy Efficiency Fairness of Wireless Networks: A Deep Learning Approach

Achieving energy efficiency (EE) fairness among heterogeneous mobile devices will become a crucial issue in future wireless networks. This paper investigates a deep learning (DL) approach for improving EE fairness performance in interference channels (IFCs) where multiple transmitters simultaneously convey data to their corresponding receivers. To improve the EE fairness, we aim to maximize the minimum EE among multiple transmitter–receiver pairs by optimizing the transmit power levels. Due to fractional and max-min formulation, the problem is shown to be non-convex, and, thus, it is difficult to identify the optimal power control policy. Although the EE fairness maximization problem has been recently addressed by the successive convex approximation framework, it requires intensive computations for iterative optimizations and suffers from the sub-optimality incurred by the non-convexity. To tackle these issues, we propose a deep neural network (DNN) where the procedure of optimal solution calculation, which is unknown in general, is accurately approximated by well-designed DNNs. The target of the DNN is to yield an efficient power control solution for the EE fairness maximization problem by accepting the channel state information as an input feature. An unsupervised training algorithm is presented where the DNN learns an effective mapping from the channel to the EE maximizing power control strategy by itself. Numerical results demonstrate that the proposed DNN-based power control method performs better than a conventional optimization approach with much-reduced execution time. This work opens a new possibility of using DL as an alternative optimization tool for the EE maximizing design of the next-generation wireless networks.

Energy efficient resource allocation for D2D multicast communications

The resource allocation for device-to-device (D2D) multicast communications is investigated. To achieve fair energy efficiency (EE) among different multicast groups, the max-min fairness criterion is used as the optimization criterion and the EE of D2D multicast groups are taken as the optimization objective function. The aim is to maximize the minimum EE for different D2D multicast groups under the constraints of the maximum transmit power and minimum transmit rate, which is modeled as a non-convex and mixed-integer fractional programming problem. Here, suboptimal resource allocation algorithms are proposed to solve this problem. First, channel assignment scheme is performed to assign channel to D2D multicast groups. Second, for a given channel assignment, iterative power allocation schemes with and without loss of cellular users’ rate are completed, respectively. Simulation results corroborate the convergence performance of the proposed algorithms. In addition, compared with the traditional throughput maximization algorithm, the proposed algorithms can improve the energy efficiency of the system and the fairness achieved among different multicast groups.

Optimal Energy-Efficient Beamforming Designs for Cloud-RANs With Rate-Dependent Fronthaul Power

We study the downlink of a limited fronthaul capacity cloud-radio access networks (C-RANs). Three energy efficiency metrics, namely, global energy efficiency (GEE), weighted sum energy efficiency (WSEE), and energy efficiency fairness (EEF) are maximized by jointly designing transmit beamforming, remote radio head (RRH) selection, and RRH-user association. Furthermore, we incorporate a rate-dependent fronthaul power model, in which the fronthaul power consumption is proportional to the user sum rate. The formulated problems are difficult to solve. Our first contribution is to customize a branch and reduce and bound (BRB) method based on monotonic optimization to find globally optimal solutions for the three energy efficiency maximization problems. Subsequently, for a more practical approach, we propose a unified framework based on successive convex approximation (SCA) method that can be applied to all the considered problems. Our novelty lies in the equivalent transformations leading to more tractable problems that are amenable to the SCA. Specifically, appropriate continuous relaxation and convex approximation techniques are employed to arrive at a sequence of second-order cone programs (SOCPs) for which dedicated solvers are available. Then, a post-processing algorithm is devised to obtain a high-performance feasible solution from the continuous relaxation. The numerical results demonstrate that the proposed SCA-based algorithms converge rapidly and achieve near-optimal performance as well as outperform the known methods. They also highlight the importance of the rate dependent fronthaul power model in designing the energy efficient C-RANs.

Energy Efficiency Fairness Beamforming Designs for MISO NOMA Systems

In this paper, we propose two beamforming designs for a multiple-input single-output non-orthogonal multiple access system considering the energy efficiency (EE) fairness between users. In particular, two quantitative fairness-based designs are developed to maintain fairness between the users in terms of achieved EE: max-min energy efficiency (MMEE) and proportional fairness (PF) designs. While the MMEE-based design aims to maximize the minimum EE of the users in the system, the PF-based design aims to seek a good balance between the global energy efficiency of the system and the EE fairness between the users. Detailed simulation results indicate that our proposed designs offer many-fold EE improvements over the existing energy-efficient beamforming designs.

Energy Efficiency Fairness for Multi-Pair Wireless-Powered Relaying Systems

We consider a multi-pair amplify-and-forward relay network where the energy-constrained relays adopting the time-switching protocol harvest energy from the radio-frequency signals transmitted by the users for assisting user data transmission. Both one-way and two-way relaying techniques are investigated. Aiming at energy efficiency (EE) fairness among the user pairs, we construct an energy consumption model incorporating rate-dependent signal processing power, the dependence on output power level of power amplifiers' efficiency, and nonlinear energy harvesting (EH) circuits. Then, we formulate the max-min EE fairness problems in which the data rates, users' transmit power, relays' processing coefficient, and EH time are jointly optimized under the constraints on the quality of service and users' maximum transmit power. To achieve efficient suboptimal solutions to these nonconvex problems, we devise monotonic descent algorithms based on the inner approximation (IA) framework, which solve a second-order-cone program in each iteration. To further simplify the designs, we propose an approach combining IA and zero-forcing beamforming, which eliminates inter-pair interference and reduces the numbers of variables and required iterations. Finally, extensive numerical results are presented to validate the proposed approaches. More specifically, the results demonstrate that ignoring the realistic aspects of power consumption might degrade the performance remarkably, and jointly designing parameters involved could significantly enhance the EE.

FPGA Acceleration for Computationally Efficient Symbol-Level Precoding in Multi-User Multi-Antenna Communication Systems

In this paper, we demonstrate an FPGA-accelerated design of the computationally efficient symbol-level precoding (SLP) for high-throughput communication systems. The SLP technique recalculates the optimal beam-forming vectors by solving a non-negative least squares problem per every set of transmitted symbols. It exploits the advantages of constructive inter-user interference to minimize the total transmitted power and increase service availability. The benefits of using SLP come with a substantially increased computational load at the gateway. The FPGA design enables the SLP technique to perform in real-time operation mode and provide a high symbol throughput for the multiple receive terminals. We define the SLP technique in a closed-form algorithmic expression and translate it to hardware description language (HDL) and build an optimized HDL core for an FPGA. We evaluate the FPGA resource occupation, which is required for the high throughput multiple-input-multiple-output (MIMO) systems with sizeable dimensions. We describe the algorithmic code, the I/O ports mapping, and the functional behavior of the HDL core. We deploy the IP core to an actual FPGA unit and benchmark the energy efficiency performance of the SLP. The synthetic tests demonstrate a fair energy efficiency improvement of the proposed closed-form algorithm compared to the best results obtained through the MATLAB numerical simulations.

Proportional Fair Energy-Efficient Resource Allocation in Energy-Harvesting-Based Wireless Networks

In energy-limited networks, battery-powered nodes suffer from energy famine, which can reduce network lifetime and affect the robustness of networks. To alleviate an energy problem, it is possible to harvest energy from ambient radio frequency signals. In this paper, we consider a proportional fair energy efficiency, which jointly considers energy efficiency and fairness in energy-harvesting-based wireless networks. We formulate a nonconvex optimization problem for solving subchannel and power allocation in order to maximize proportional fair energy efficiency. Using nonlinear fractional programming, we transform the optimization problem into a tractable convex problem. We also derive the solution of the transformed problem and propose a resource allocation algorithm using an iterative method. In addition, we prove the convergence of the proposed algorithm in view of a suboptimal point. Through intensive simulations, we compare the performance of our proposed algorithm with those of conventional algorithms. It is shown that the proposed algorithm improves fairness considerably while maintaining energy efficiency, compared with conventional algorithms.

Optimal Energy Efficiency Fairness of Nodes in Wireless Powered Communication Networks.

In wireless powered communication networks (WPCNs), it is essential to research energy efficiency fairness in order to evaluate the balance of nodes for receiving information and harvesting energy. In this paper, we propose an efficient iterative algorithm for optimal energy efficiency proportional fairness in WPCN. The main idea is to use stochastic geometry to derive the mean proportionally fairness utility function with respect to user association probability and receive threshold. Subsequently, we prove that the relaxed proportionally fairness utility function is a concave function for user association probability and receive threshold, respectively. At the same time, a sub-optimal algorithm by exploiting alternating optimization approach is proposed. Through numerical simulations, we demonstrate that our sub-optimal algorithm can obtain a result close to optimal energy efficiency proportional fairness with significant reduction of computational complexity.