Energy Consumption Of User Equipment Research Articles

Distributed and Collective Intelligence for Computation Offloading in Aerial Edge Networks

Unmanned aerial vehicles (UAVs) with integrated computing platforms can be used to provide computing offloading services for ground user equipments (UEs) with limited local computing capabilities, especially in remote areas. In this paper, we focus on the task offloading in an aerial edge network (AEN) assisted by a UAV. We aim at minimizing the sum energy consumption of all UEs by the joint optimization of the task offloading decisions and the UAV position under the constraints of the latency and the total energy of UAV. The formulated optimization problem is a mixed-integer nonconvex problem and involves coupling of many optimization variables. To address this challenge, we first transform the original optimization problem into a linear convex optimization problem via reformulation linearization technology, and then the alternating direction method of multipliers (ADMM) algorithm is proposed to achieve the approximate optimal solution. Numerical results confirm that the proposed ADMM algorithm can effectively reduce the total of energy consumption of UEs and ensure the continuous operation of the UEs.

Edge-Assisted Virtual Viewpoint Generation for Immersive Light Field

Light Field (LF), which describes the light rays that emanate at each point in a scene, can be used as a six-Degrees-of-Freedom (6DOF) immersive media. Similar to traditional multiview video, LF is also captured by an array of cameras, leading to a large data volume that needs to be streamed from a server to users. When a user wishes to watch the scene from a viewpoint that no camera has captured directly, a virtual viewpoint must be rendered in real time from the directly captured viewpoints. This places high requirements on both the computing and caching capabilities of the infrastructure. Edge computing (EC), which brings computation resources closer to users, can be a promising enabler for real-time LF viewpoint rendering. In this paper, we present a novel EC-assisted mobile LF delivery framework that is able to cache parts of LF viewpoints in advance and render the requested virtual viewpoints on demand at the edge node or user's device. Numerical results demonstrate that the proposed framework can reduce the average service response latency by 45% and the energy consumption of user equipment by 60% at the cost of 55% additional caching consumption of edge nodes.

Stochastic modeling for energy efficiency in modified directional discontinuous reception for LTE‐5G networks

SummaryFifth‐generation (5G) networks deal with high‐frequency data rates, ultra‐low latency, more reliability, massive network capacity, more availability, and a more uniform user experience. To validate the high‐frequency rates, 5G networks engage beam searching operation. By adopting a beam searching state between the short and long sleep, one can reduce the system's delay. The energy consumption of user equipment (UE) in 5G networks is much higher than in the 4G networks. To reduce the energy consumption and increase the energy saving in UE, Long‐Term Evolution (LTE)‐5G networks adopt the discontinuous reception (DRX) scheme with a fixed number of short sleep. LTE‐DRX without beam search operation (i.e., beam alignment) cannot work in 5G networks. Hence, keeping this scenario in mind, we have modeled a new modified directional discontinuous reception (MD‐DRX) mechanism for LTE‐5G networks. The MD‐DRX mechanism captures the behavior of a beam searching, an inactive, an active, a long sleep, an ON, and a short sleep states. The short sleep state consists of a maximum short sleep. To get the optimal energy saving and energy consumption (i.e., energy efficiency) from the MD‐DRX mechanism, it is required to check the system's throughput. The trade‐off between energy saving/energy consumption and throughput will provide the system's optimal energy saving and optimal energy consumption. In this paper, we have obtained the system's optimal energy saving and throughput by optimizing the maximum short sleep and short sleep duration. To get the energy efficiency for LTE‐5G networks, the trade‐off between average energy consumption/energy saving and throughput is shown.

Multiobjective Oriented Task Scheduling in Heterogeneous Mobile Edge Computing Networks

6G wireless networks have raised increasing attention with computation-sensitive services such as AI Internet of things (AIoT) and mobile augmented reality/virtual reality (AR/VR) applications. Mobile edge computing (MEC) provides rich computation resources for user equipments (UE) at the edge of networks. Aided by MEC servers, computation-intensive applications that are commonly modeled as Directed Acyclic Graphs (DAG) can be performed locally and offloaded to MEC servers to enhance execution efficiency. However, it is a key issue to efficiently provide low latency with limited energy. In this paper, we investigate a multiobjective task scheduling problem in MEC-aided 6G network. Then, an improved multiobjective cuckoo search (IMOCS) algorithm is proposed to deal with a DAG-based task scheduling problem, which aims to reduce the execution latency and energy consumption of UE. Particularly, the proposed IMOCS algorithm is based on the single-objective cuckoo search algorithm and Pareto dominance. An external archive is used to record nondominated solutions, whose update strategy improves the quality of solutions by the aid of fast nondominated sorting and crowding distance sorting. Simulation results demonstrate that IMOCS algorithm outperforms other four benchmark algorithms, which can provide optimal task scheduling policy for MEC severs in 6G networks.

DRX in NR Unlicensed for B5G Wireless: Modeling and Analysis

The proliferation in the demand of high data rate and improved performance has urged wireless vendors to think of extending 5G New Radio (NR) in unlicensed bands. The unlicensed band is mainly used by Wi-Fi and Wi-Gig resulting in the reduced probability of channel availability. The User Equipment (UE) has to remain active and wait for the availability of unlicensed channel. This process escalates the energy expense of battery-constrained mobile devices. Discontinuous Reception (DRX) introduced in 3GPP can also be used in NR-Unlicensed (NR-U) to reduce UEs energy consumption. This article introduces a DRX mechanism over NR-U networks for both Standalone (SA) and Non-Standalone (NSA) deployment modes. We have used two different flavors, Beam-Search and Beam-Aware, to model DRX mechanism. Hence, the proposed DRX mechanisms are: Standalone DRX with Beam-Search (SBS-DRX), Standalone DRX with Beam-Aware (SBA-DRX), Non-Standalone DRX with Beam-Search (NBS-DRX), and Non-Standalone DRX with Beam-Aware (NBA-DRX). Semi-Markov based modeling is used to show the estimation of power-saving, average delay, and resource utilization. The power-saving achieved with SBA-DRX is 21.13% and 24.84% higher than 5G NR DRX for both Trace 1 and Trace 2, respectively. Moreover, delay achieved with NBA-DRX is 19.16% less than NBS-DRX with 18% less resource utilization.

Service Provisioning for UAV-Enabled Mobile Edge Computing

Unmanned aerial vehicle (UAV)-enabled mobile edge computing has been recognized as a promising technology to flexibly and efficiently handle computation-intensive and latency-sensitive tasks in the era of fifth generation (5G) and beyond. In this paper, we study the problem of Service Provisioning for UAV-enabled mobile edge computiNg (SPUN). Specifically, under task latency requirements and various resource constraints, we jointly optimize the service placement, UAV movement trajectory, task scheduling, and computation resource allocation, to minimize the overall energy consumption of all terrestrial user equipments (UEs). Due to the non-convexity of the SPUN problem as well as complex coupling among mixed integer variables, it is a non-convex mixed integer nonlinear programming (MINLP) problem. To solve this challenging problem, we propose two alternating optimization-based suboptimal solutions with different time complexities. In the first solution with relatively high complexity in the worst case, the joint service placement and task scheduling subproblem, and UAV trajectory subproblem are iteratively solved by the Branch and Bound (BnB) method and successive convex approximation (SCA), respectively, while the optimal solution to the computation resource allocation subproblem is efficiently obtained in the closed form. To avoid the high complexity caused by BnB, in the second solution, we propose a novel approximation algorithm based on relaxation and randomized rounding techniques for the joint service placement and task scheduling subproblem, while the other two subproblems are solved in the same way as that of the first solution. Extensive simulations demonstrate that the proposed solutions achieve significantly lower energy consumption of UEs compared to three benchmarks.

Computational Offloading in Mobile Edge with Comprehensive and Energy Efficient Cost Function: A Deep Learning Approach.

In mobile edge computing (MEC), partial computational offloading can be intelligently investigated to reduce the energy consumption and service delay of user equipment (UE) by dividing a single task into different components. Some of the components execute locally on the UE while the remaining are offloaded to a mobile edge server (MES). In this paper, we investigate the partial offloading technique in MEC using a supervised deep learning approach. The proposed technique, comprehensive and energy efficient deep learning-based offloading technique (CEDOT), intelligently selects the partial offloading policy and also the size of each component of a task to reduce the service delay and energy consumption of UEs. We use deep learning to find, simultaneously, the best partitioning of a single task with the best offloading policy. The deep neural network (DNN) is trained through a comprehensive dataset, generated from our mathematical model, which reduces the time delay and energy consumption of the overall process. Due to the complexity and computation of the mathematical model in the algorithm being high, due to trained DNN the complexity and computation are minimized in the proposed work. We propose a comprehensive cost function, which depends on various delays, energy consumption, radio resources, and computation resources. Furthermore, the cost function also depends on energy consumption and delay due to the task-division-process in partial offloading. None of the literature work considers the partitioning along with the computational offloading policy, and hence, the time and energy consumption due to task-division-process are ignored in the cost function. The proposed work considers all the important parameters in the cost function and generates a comprehensive training dataset with high computation and complexity. Once we get the training dataset, then the complexity is minimized through trained DNN which gives faster decision making with low energy consumptions. Simulation results demonstrate the superior performance of the proposed technique with high accuracy of the DNN in deciding offloading policy and partitioning of a task with minimum delay and energy consumption for UE. More than 70% accuracy of the trained DNN is achieved through a comprehensive training dataset. The simulation results also show the constant accuracy of the DNN when the UEs are moving which means the decision making of the offloading policy and partitioning are not affected by the mobility of UEs.

Trajectory Optimization for UAV Emergency Communication With Limited User Equipment Energy: A Safe-DQN Approach

In post-disaster scenarios, it is challenging to provide reliable and flexible emergency communications, especially when the mobile infrastructure is seriously damaged. This article investigates the unmanned aerial vehicle (UAV)-based emergency communication networks, in which UAV is used as a mobile aerial base station for collecting information from ground users in affected areas. Due to the breakdown of ground power system after disasters, the available energy of affected user equipment (UE) is limited. Meanwhile, with the complex geographical conditions after disasters, there are obstacles affecting the flight of UAV. Aiming at maximizing the uplink throughput of UAV networks during the flying time, we formulate the UAV trajectory optimization problem considering UE energy limitation and location of obstacles. Since the constraint on UE energy is dynamic and long-term cumulative, it is hard to be solved directly. We transform the problem into a constrained Markov decision-making process (CMDP) with UAV as agent. To tackle the CMDP, we propose a safe-deep-Q-network (safe-DQN)-based UAV trajectory design algorithm, where the UAV learns to selects the optimal action in reasonable policy sets. Simulation results reveal that: 1) the uplink throughput of the proposed algorithm converges within multiple iterations and 2) compared with the benchmark algorithms, the proposed algorithm performs better in terms of uplink throughput and UE energy efficiency, achieving a good trade-off between UE energy consumption and uplink throughput.

Energy-Efficient Device Discovery Mechanism for Device-to-Device Communications in 5G Networks

The conventional direct device discovery scheme which uses the random access protocol and encounters contentions or collisions is highly energy and time-consuming. To reduce the energy consumption of user equipments (UEs), this work proposes a two-phase hybrid device discovery mechanism for device-to-device (D2D) communications. In the first phase, the evolved packet core (EPC) or base station (BS) uses the location information of UEs to judge whether two UEs are able to establish a D2D link. In the second phase, UEs use Wi-Fi Direct to discover their target UEs. The BS directly assigns UEs’ states and allocates appropriate Wi-Fi channels to UEs. UEs no longer have to search or listen to all channels, thus reducing the discovery delay. The proposed mechanism saves cellular spectrum resources because it uses unlicensed bands for D2D discovery and communications. The performance of the proposed hybrid D2D discovery mechanism is also theoretically analyzed in this paper. Evaluation results show that the proposed D2D discovery mechanism has better performance in terms of energy consumption, discovery delay and discovery success rate, compared with the conventional direct D2D discovery scheme, especially in the network scenarios with smaller cells such as 5G networks. Additionally, the analytic results coincide with simulation results, demonstrating that our theoretic analysis is accurate.

Multi-Agent Deep Reinforcement Learning-Based Trajectory Planning for Multi-UAV Assisted Mobile Edge Computing

An unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) framework is proposed, where several UAVs having different trajectories fly over the target area and support the user equipments (UEs) on the ground. We aim to jointly optimize the geographical fairness among all the UEs, the fairness of each UAV’ UE-load and the overall energy consumption of UEs. The above optimization problem includes both integer and continues variables and it is challenging to solve. To address the above problem, a multi-agent deep reinforcement learning based trajectory control algorithm is proposed for managing the trajectory of each UAV independently, where the popular Multi-Agent Deep Deterministic Policy Gradient (MADDPG) method is applied. Given the UAVs’ trajectories, a low-complexity approach is introduced for optimizing the offloading decisions of UEs. We show that our proposed solution has considerable performance over other traditional algorithms, both in terms of the fairness for serving UEs, fairness of UE-load at each UAV and energy consumption for all the UEs.

Energy-Efficient Joint Computation Offloading and Resource Allocation in Multi-User MEC Systems

Mobile edge computing (MEC) is envisioned as an emerging paradigm to enable energy-constrained and computation-limited user equipments (UEs) to offload various computation tasks to the edge of mobile networks in order to save energy consumption and prolong the battery life of UEs. In this paper, we consider a multi-user MEC system where each UE has a computation task to be processed locally or offloaded for remote processing. Specifically, we investigate the task offloading problem with the aim of minimizing the energy consumption of UEs via jointly optimizing the task admission decision, the transmission power, local computing and edge computing capacities. To solve this nonconvex problem, we transform it into four subproblems and propose a low-complexity algorithm by solving subproblems in an iterative manner. Simulation results demonstrate that the proposed algorithm requires lower energy consumption than the existing benchmark schemes.

Federated Learning Over Wireless Networks: Convergence Analysis and Resource Allocation

There is an increasing interest in a fast-growing machine learning technique called Federated Learning, in which the model training is distributed over mobile user equipments (UEs), exploiting UEs' local computation and training data. Despite its advantages in data privacy-preserving, Federated Learning (FL) still has challenges in heterogeneity across UEs' data and physical resources. We first propose a FL algorithm which can handle the heterogeneous UEs' data challenge without further assumptions except strongly convex and smooth loss functions. We provide the convergence rate characterizing the trade-off between local computation rounds of UE to update its local model and global communication rounds to update the FL global model. We then employ the proposed FL algorithm in wireless networks as a resource allocation optimization problem that captures the trade-off between the FL convergence wall clock time and energy consumption of UEs with heterogeneous computing and power resources. Even though the wireless resource allocation problem of FL is non-convex, we exploit this problem's structure to decompose it into three sub-problems and analyze their closed-form solutions as well as insights to problem design. Finally, we illustrate the theoretical analysis for the new algorithm with Tensorflow experiments and extensive numerical results for the wireless resource allocation sub-problems. The experiment results not only verify the theoretical convergence but also show that our proposed algorithm outperforms the vanilla FedAvg algorithm in terms of convergence rate and testing accuracy.

Mobile-Edge Computing in the Sky: Energy Optimization for Air–Ground Integrated Networks

Unmanned aerial vehicles (UAVs) are expected to be deployed as aerial base stations (BSs) in future wireless networks to provide extensive coverage and additional computational capabilities for user equipments (UEs). In this article, we study mobile-edge computing (MEC) in air–ground integrated wireless networks, including ground computational access points (GCAPs), UAVs, and UEs, where UAVs and GCAPs cooperatively provide computing resources for UEs. Our goal is to minimize the total energy consumption of UEs by jointly optimizing users’ association, uplink power control, channel allocation, computation capacity allocation, and UAV 3-D placement, subject to the constraints on deterministic binary offloading, UEs’ latency requirements, computation capacity, UAV power consumption, and available bandwidth. Due to the nonconvexity of the primary problem and the coupling of variables, we introduce a coordinate descent algorithm that decomposes the UEs’ energy consumption minimization problem into several subproblems which can be efficiently solved. The simulation results demonstrate the advantages of the proposed algorithm in terms of the reduced total energy consumption of UEs.

Intelligent dynamic service pricing strategy for multi-user vehicle-aided MEC networks

With the increasing popularity of smart user equipments (UEs), numerous exciting wireless big data services are coming into life. However, due to the constraints of physical size, UEs are struggling with limited battery energy and computation capacity. In this paper, we propose a multi-user vehicle-aided multi-access edge computing (MEC) network architecture by deploying parked vehicles as the temporary computation service providers. A dynamic pricing strategy is proposed to minimize the energy consumption of UEs under the constraints on quality of service while maximize mobile service providers (MSPs)’ revenue. A differential evolution (DE) algorithm is proposed to determine UE’s fine-grained offloading decision. Moreover, a Q-learning algorithm is utilized to assist the DE algorithm in selecting the proper unit service price. The results show that the proposed dynamic pricing strategy can achieve better performance than the fixed pricing strategy and the differential pricing strategy regarding the overall cumulative cost. In addition, the proposed approach promises higher resource efficiency in comparison with all local execution and random offloading algorithms. The results obtained in this paper can be applied to design the future healthcare service pricing scheme of the IoT-based intelligent system.

A Fast Algorithm for Energy-Saving Offloading With Reliability and Latency Requirements in Multi-Access Edge Computing

Multi-Access Edge Computing (MEC) is a promising paradigm that providing cloud-like service for handling the high-complexity and latency-sensitive applications on user equipment (UE) via computation offloading. However, the execution reliability is rarely considered in current MEC studies, which is an important factor to guarantee the quality of service (QoS). For that, this paper considers an energy-saving offloading to satisfy the reliability and latency requirements of the application. Specifically, we formulate an optimization problem to minimize the UE's energy consumption with reliability and latency constraints. To tackle this NP-hard problem, we first divide the entire application into multiple directed-acyclic-graph-(DAG)-based subtasks, where the subtask can be executed on the UE locally or MEC server remotely. Then, we decompose the overall reliability and latency requirements into multiple constraints for each subtask. Finally, we propose a fast heuristic algorithm to find a solution satisfying the constraints. Simulation results demonstrate the proposed algorithm obtains lower energy consumption compared with the local execution and random assignment and costs less runtime compared with the greedy algorithm.

Task Popularity-Based Energy Minimized Computation Offloading for Fog Computing Wireless Networks

Fog computing is a new systematic paradigm which provides low latency enabled cloud services to mobile and Internet of Things (IoT) networks by provisioning the computation capability within the radio access network (RAN) assignable to mobile end users. In this letter, an energy optimal offloading scheme based on a probabilistic priority model of cloud tasks is investigated over fog computing networks. The optimization problem jointly minimizes the energy consumption of user equipment (UE) and the fog server. Simulation results show that the proposed joint UE and fog server energy optimization (JUFO) scheme provides a better performance compared to the conventional offloading scheme, which operates strictly within the determined delay bound.

DRX over LAA-LTE-A New Design and Analysis Based on Semi-Markov Model

Ever-increasing demand of data rate and subscriber penetration are resulting in continuous growth of mobile data traffic. In order to meet this exponential data traffic growth 3GPP Release 13, we have decided to enable the operation of Long Term Evolution (LTE) in the unlicensed band, termed as Licensed-Assisted Access (LAA). As the unlicensed spectrum is mainly used by WiFi, LTE evolved NodeB (eNB) has to perform Listen Before Talk (LBT) procedure to access the unlicensed channel. If the unlicensed spectrum is occupied by other services, User Equipment (UE) should remain active and wait for unlicensed channel to become idle. UE consumes a high power by remaining active, when unlicensed channel is not available. The UE's energy consumption could be saved by configuring Discontinuous Reception (DRX) in the unlicensed band, similar to DRX in LTE. In this article, we introduce and analyze a new Licensed-Assisted Access DRX mechanism (LAA-DRX) over LTE networks. Using our novel four state semi-Markov model, we show the probabilistic estimation of power saving and wake up latency associated with our proposed LAA-DRX process. Mathematical analysis and simulation results, using real wireless trace, point out that comparing to existing LTE DRX process, our LAA-DRX can achieve almost 4 percent higher power savings and up to 58 percent reduction in resource usage.

A Deep Learning Approach for Energy Efficient Computational Offloading in Mobile Edge Computing

Mobile edge computing (MEC) has shown tremendous potential as a means for computationally intensive mobile applications by partially or entirely offloading computations to a nearby server to minimize the energy consumption of user equipment (UE). However, the task of selecting an optimal set of components to offload considering the amount of data transfer as well as the latency in communication is a complex problem. In this paper, we propose a novel energy-efficient deep learning based offloading scheme (EEDOS) to train a deep learning based smart decision-making algorithm that selects an optimal set of application components based on remaining energy of UEs, energy consumption by application components, network conditions, computational load, amount of data transfer, and delays in communication. We formulate the cost function involving all aforementioned factors, obtain the cost for all possible combinations of component offloading policies, select the optimal policies over an exhaustive dataset, and train a deep learning network as an alternative for the extensive computations involved. Simulation results show that our proposed model is promising in terms of accuracy and energy consumption of UEs.

Energy and spectrum efficient mobility-aware resource management for D2D multicasting

The rapid growth of cellular traffic which is mostly due to increasing demands for multimedia and social network applications, has led to spectrum deficiency in cellular networks. Device to Device (D2D) communication is an excellent technology for offloading cellular traffic via direct communication between user equipment reusing cellular radio resources or unlicensed bands. Moreover, using D2D communication, there is a chance of reducing the energy consumption of user equipment. Multicasting is a known scenario of D2D communication which is suitable for some content sharing scenarios such as public safety services and TV broadcasting in ultra-dense networks. Efficient resource management is a challenging problem of D2D multicasting which has not been addressed adequately. This paper proposes an energy and spectrum efficient resource management method for D2D multicast communications which takes user requirements into account. In contrast to previous methods, this work also considers the mobility of user equipment in terms of the stability of possible D2D links using a mobility correlation measure. The multicast group formation and channel/power allocations have been formulated as a multi-objective optimization technique and NSGA-II has been exploited to solve the problem. Simulation results demonstrate the superior performance of the proposed method compared to previous methods in terms of energy and spectrum efficiency.

A hybrid game method for interference management with energy constraint in 5G ultra-dense HetNets

Massive deployment of small access points is emerging as a promising technology to cope with the increasing communication demand in future 5G networks. However, it brings severe interference problems. In this paper, we analyze the uplink interference problem in ultra-dense heterogeneous networks (HetNets), where remote radio heads (RRHs) are overlaid on a primary macrocell. A hybrid game method is proposed to manage the interference between user equipments (UEs) of macrocell and RRHs, then the solution is analyzed and its Nash equilibrium is proven. Different from other interference mitigation approaches merely considering spectrum efficiency, our proposed method can maintain both the spectrum efficiency and energy consumption of UEs, so as to improve the energy efficiency of the whole network. Moreover, the proposed method ensures that UEs utilize only local information and limited interaction to maximize their utility. Thus, it is not necessary to exchange the power information among UEs, which is more suitable for the complex 5G scenarios. Simulation results validate the feasibility and demonstrate the superiority of our approach, compared with other two existing schemes.