Total Energy Consumption Of System Research Articles

Study of an Impedance Function for Mixed Traffic Flows Considering the Travel Time–Cost Characteristics of Long-Distance Electric Vehicle Trips

To quantify the travel time and cost characteristics of mixed traffic involving electric vehicles (EVs) and fuel-powered vehicles on roads, in this paper, we comprehensively consider three factors affecting road impedance: queue length, waiting time, and service rate. Initially, a time characteristic function and a cost characteristic function for mixed traffic impedance are constructed. From the perspective of travel time, we consider the impact of EV penetration on the actual road capacity and introduce a capacity coefficient to modify the BPR (Bureau of Public Roads) road impedance function. Given that different types of vehicles might need to wait at charging stations, we employ queuing theory to calculate the queuing time at these stations and construct an impedance model that considers travel time. From the perspective of travel costs, we account for the energy consumption costs and road usage fees for different types of vehicles. The energy consumption cost for travel mileage is obtained by multiplying the unit mileage energy consumption cost of mixed traffic by the travel mileage. For road usage fees, we adopt the conventional method of multiplying the per-kilometer rate for each vehicle type by the travel mileage, thus constructing an impedance model that incorporates travel costs. Finally, in the numerical analysis section, based on the vehicle travel mileage, we categorize travel into short-, medium-, and long-distance trips for analysis. With the constructed mixed traffic impedance model, we conduct a detailed analysis of the travel time and cost characteristics of mixed traffic over different travel distances. We explore the specific impacts of the electric vehicle penetration rate, traffic flow volume, and travel mileage on road impedance. The results indicate that as the penetration rate of electric vehicles increases, the total energy consumption of the transportation system significantly decreases. Moreover, at high electric vehicle penetration rates, although an increase in traffic flow leads to higher traffic impedance and longer travel times, the overall travel costs are reduced. This demonstrates that increasing the penetration rate of electric vehicles positively contributes to reducing the energy consumption and costs of transportation systems.

Combined solar and ground source heat pump heating system with a latent heat storage tank as a sustainable system to replace an oilfield hot water station

Present study focuses on a clean energy replacement for an oilfield hot water station and develops a combined solar and ground source heat pump (GSHP) heating system with a latent heat storage tank (LHST). The effects of LHST and heat replenishment on energy and coefficient of performance (COP) are quantitatively analyzed during short-term or long-term operation of the system. The suitability of three different phase change materials for the system is compared, and the optimal one is identified. Results show that the LHST with 70 °C melting point can increase the collector efficiency to 53.91 % and reduce the total energy consumption of the system by 52344 kWh. Heat replenishment could effectively mitigate the ground temperature drop to 0.19 °C/year, reducing the annual energy consumption of GSHP by more than 4230 kWh and providing an additional thermal energy of 72 GJ. Compared to the initial heating system, the novel system efficiency increases to 51.25 %. Gas consumption and total system energy consumption decreased by 81.7 % and 75.2 %, respectively. Annual reduction of CO2 emission is about 1150 tons, showing satisfactory performance. Outcomes of this study prove the application of clean energy in oil field industrial processes, and help in achieving the goals of emission reduction and carbon neutrality.

Deep reinforcement learning based controller placement and optimal edge selection in SDN-based multi-access edge computing environments

Multi-Access Edge Computing (MEC) can provide computility close to the clients to decrease response time and enhance Quality of Service (QoS). However, the complex wireless network consists of various network hardware facilities with different communication protocols and Application Programming Interface (API), which result in the MEC system's high running costs and low running efficiency. To this end, Software-defined networking (SDN) is applied to MEC, which can support access to massive network devices and provide flexible and efficient management. The reasonable SDN controller scheme is crucial to enhance the performance of SDN-assisted MEC. At First, we used the Convolutional Neural Networks (CNN)-Long Short-Term Memory (LSTM) model to predict the network traffic to calculate the load. Then, the optimization objective is formulated by ensuring the load balance and minimizing the system cost. Finally, the Deep Reinforcement Learning (DRL) algorithm is used to obtain the optimal value. Based on the controller placement algorithm ensuring the load balancing, the dynamical edge selection method based on the Channel State Information (CSI) is proposed to optimize the task offloading, and according to CSI, the strategy of task queue execution is designed. Then, the task offloading problem is modeled by using queuing theory. Finally, dynamical edge selection based on Lyapunov's optimization is introduced to get the model solution. In the experiment studies, the assessment method evaluated the performance of two sets of baseline algorithms, including SAPKM, the PSO, the K-means, the LADMA, the LATA, and the OAOP. Compared to the baseline algorithms, the proposed algorithms can effectively reduce the average communication delay and total system energy consumption and improve the utilization of the SDN controller.

Risk and Energy Based Optimization for Fire Monitoring System in Utility Tunnel Using Cellular Automata

Fire is one of the biggest threats to the safety of utility tunnels, and establishing camera-based monitoring systems is conducive to early fire finding and better understanding of the evolution of tunnel fires. However, conventional monitoring systems are being faced with the challenge of high energy consumption. In this paper, the camera operation in a utility tunnel was optimized considering both fire risk and energy consumption. Three design variables were investigated, namely the camera sight, the number of cameras in simultaneous operation, and the duration of camera operation. Cellular automata were used as a simple but effective method to simulate the spread of fire in a utility tunnel. Results show that as the number of cameras in simultaneous operation increases, the probability of fire capture also increases, but the energy consumption decreases. A shorter duration of camera operation can lead to a higher probability of fire capture, and meanwhile, lower energy consumption. For the duration of camera operation shorter than or equal to the allowable time, the probability of fire capture is significantly higher than that for the duration longer than the allowable time. Increasing the camera sight will significantly increase the probability of fire capture and lower the total energy consumption when a blind monitoring area exists. The total energy consumption of a camera-based monitoring system roughly satisfies hyperbolic correlation with the duration of camera operation, while the probability of fire capture can be predicted based on the number of cameras in simultaneous operation through a power model. The optimal design for the modeled tunnel section is two cameras in simultaneous operation with a tangent monitoring area. The duration of camera operation should be as short as possible, at least shorter than the allowable time. The study is expected to provide a reference for the sustainable design of energy-saving utility tunnels with lower fire risk.

Energy performance of buildings with multi-ribbed composite wall structural system

Multifunctional multi-ribbed composite wall (MRCW) structural system has been increasingly used due to its dual load bearing and energy saving capacities. Most of existing studies were focused on its mechanical properties. This paper aims to evaluate its energy saving effect. To this end, two models are constructed using EnergyPlus for the same residential building in Beijing, China, but with different systems of MRCW and shear wall. The dynamic simulation of hourly energy consumption throughout one year is carried out at the building operation stage. The results show that the heating energy consumption is higher than the cooling energy consumption. The total energy consumption of the MRCW system can be reduced by 11.4 % compared with that of the shear wall system. Further, the annual energy consumption of the residential building models in five typical climate regions in China with different orientations is simulated. The results show that the energy saving of MRCW system range from 5 % to 13 % in the five regions when compared with shear wall system, and the absolute value of energy saving is the largest in severe cold and cold regions. The north-south orientation is the best energy-saving orientation. Based on this study, it can be concluded that, other than carrying load, MRCW system can also save energy compared with shear wall system, especially in severe cold and cold regions.

QoS-aware task offloading and resource allocation optimization in vehicular edge computing networks via MADDPG

To meet the stringent requirements for real-time performance of computing tasks on the Internet of Vehicles (IoV) scenario, the Mobile Edge Computing (MEC) technique is introduced to combine edge servers with vehicles that have storage and computing resources, thereby reducing latency. However, successfully accessing the channel and completing the offloading computation within the specified time remains a big challenge in scenarios with multitasking vehicles and multibase stations equipped with multiple channels. To address this problem, we propose the Multi-Agent Deep Deterministic Policy Gradient-based Offloading and Resource Allocation (MADDPG-RA) algorithm. First, a sub-optimal offloading strategy is determined using the MADDPG algorithm to minimize the sum of system latency and energy consumption. Specifically, this strategy determines whether each vehicle should perform local or offload computation, which MEC server to choose, and which channel to access if offloaded. Based on the above, a closed form expression for the optimal computational resource allocation for MEC is derived using Lagrange multipliers. The simulation results demonstrate that the proposed MADDPG-RA algorithm can effectively reduce the total system latency and energy consumption compared to the existing algorithms.

Effects of connected automated vehicle on stability and energy consumption of heterogeneous traffic flow system

With the development of intelligent and interconnected traffic system, a convergence of traffic stream is anticipated in the foreseeable future, where both connected automated vehicle (CAV) and human driven vehicle (HDV) will coexist. In order to examine the effect of CAV on the overall stability and energy consumption of such a heterogeneous traffic system, we first take into account the interrelated perception of distance and speed by CAV to establish a macroscopic dynamic model through utilizing the full velocity difference (FVD) model. Subsequently, adopting the linear stability theory, we propose the linear stability condition for the model through using the small perturbation method, and the validity of the heterogeneous model is verified by comparing with the FVD model. Through nonlinear theoretical analysis, we further derive the KdV-Burgers equation, which captures the propagation characteristics of traffic density waves. Finally, by numerical simulation experiments through utilizing a macroscopic model of heterogeneous traffic flow, the effect of CAV permeability on the stability of density wave in heterogeneous traffic flow and the energy consumption of the traffic system is investigated. Subsequent analysis reveals emergent traffic phenomena. The experimental findings demonstrate that as CAV permeability increases, the ability to dampen the propagation of fluctuations in heterogeneous traffic flow gradually intensifies when giving system perturbation, leading to enhanced stability of the traffic system. Furthermore, higher initial traffic density renders the traffic system more susceptible to congestion, resulting in local clustering effect and stop-and-go traffic phenomenon. Remarkably, the total energy consumption of the heterogeneous traffic system exhibits a gradual decline with CAV permeability increasing. Further evidence has demonstrated the positive influence of CAV on heterogeneous traffic flow. This research contributes to providing theoretical guidance for future CAV applications, aiming to enhance urban road traffic efficiency and alleviate congestion.

An energy consumption minimization resource allocation strategy in satellite‐terrestrial communication networks

AbstractIn this letter, a joint route selection and time‐slot allocation problem is formulated in a satellite‐terrestrial communication system. An optimization problem that minimizes the total system energy consumption is proposed to solve the formulated problem. Two algorithms, including a multi‐time‐expanding graph‐based maximum transmission rate route (MTEG‐R) selection algorithm and a minimum system energy consumption ‐ Dijkstra (MSEC‐D) algorithm, are proposed to find the optimal route and time‐slot for data transmission, and simulation results demonstrate the effectiveness of the proposed scheme.

Investigation on the control strategy of new spray cooling system based on PV/T and heat recovery in sow houses: As a case study in Nanchang, China

The uncertain cooling effect and additional huge primary energy consumption caused by humidity regulation are currently the main problems of traditional air-conditioning systems in sow houses. In order to address the above problems, a new spray cooling system is proposed based on PV/T and heat recovery in sow houses in this paper. Compared with traditional spray cooling systems, the cooling efficiency of new system can be improved and its energy consumption can be reduced due to solar dehumidification and heat recovery. Meanwhile, the mathematical models are established for its key equipment and their reliability is verified according to literature data. The results show that the maximum error is less than 7.2% for the models. The main operating parameters affecting the system characteristics are determined through simulation. The results show that with the changes in the set inlet temperature of second heat exchanger (IHET), the set subcooled temperature (SCT) and the set superheated temperature (SHT) of sensible heat exchanger, the total energy consumption and gain of new system are changed by 2%–11.9%, while they are changed by 54.5%–86% with the changes in the set inlet relative humidity of rotary dehumidifier (IRH) and the set inlet temperature of spray device (ISDT). In other words, the total energy consumption and gain of new system are slightly affected by IHET, SCT and SHT, but significantly affected by IRH and ISDT. The annual control strategy is further proposed for the new system. In cooling season, supply air of sow house will be dehumidified, heat exchanged with exhaust air and cooled, and the optimal setting ranges of operating parameters are IRH = 70%, SCT = 18 °C, SHT = 26 °C and ISDT = 22–24 °C. In heating season, it will be heat exchanged with exhaust air and heated, and the optimal setting ranges of operating parameters are SCT = 19 or 20 °C, SHT = 26–28 °C and IHET = 22–24 °C. The above research will provide guidance for the improvement of traditional spray cooling systems and the operating control of new system.

Field energy performance of cold storage in East China: A case study

The energy consumption cold storage is rapidly increasing due to fast development of cold chain. However, the energy consumption composition, energy efficiency, and cooling load composition of cold storage are not clear, which become a barrier for increasing energy efficiency in cold storage. In the previous research, an accurate and convenient field cooling capacity test method is lacking. In this study, an extended compressor energy conservation –compressor volumetric efficiency method was proposed to calculate the cooling capacity of each cold room in centralized cold storage. A cold storage system in Jiangsu Province was selected for field testing. The operational characteristics and composition of electricity consumption were investigated firstly. The field test results show that the refrigeration system accounts for 80% of the total energy consumption of cold storage. Statistical analysis revealed that the valley electricity price interval and compressors accounted for 64.0% and 67.3% of the total energy consumption of the refrigeration system, in time and space, respectively. Furthermore, during the transition season (4/1–5/31), with a cold storage temperature of −18 ℃, the average daily energy efficiency ratio of the refrigeration system in the cold storage is 1.33 kWh·kWh−1. An analysis of the composition of the refrigeration load was also conducted, and the results indicated that for the overall cooling load of the cold storage, the envelope cooling load, door leakage & personnel operating cooling load, income cargo cooling load, and electrical equipment cooling load accounted for 26%, 27%, 32%, and 15%, respectively.

Humidity grade-matched fresh air dehumidification system with adjustable treating stages

Air conditioning systems using liquid desiccants are efficient in dehumidification, but their performance can be further improved under both design and different partial load conditions. In this study, a humidity grade-matched system with adjustable treatment stages is designed based on the use of appropriate humidity sinks to handle loads. The method to determine reasonable treatment stage numbers and packing tower areas is given by optimizing the total system energy consumption and economic performance under different working conditions, based on validated numerical system performance models and device prices from the market or literature. The performance of the proposed system is compared with traditional systems in a case office building in Nanjing. The results are as follows: 1. By optimizing the total system power consumption and economic performance under different conditions, the humidity difference for one-stage and mass transfer humidity difference should be set at 3–5 and 2–3 g/kg to determine the stage number and packing tower area, respectively. 2. The proposed fresh air dehumidification system as applied to an office building without exhaust air in Nanjing reduces 16.6% and 13.5% of the seasonal energy consumption for the entire system relative to single-stage and three-stage systems, respectively. 3. When the system selectively uses exhaust air or outdoor air, 11.0% of additional seasonal energy consumption can be saved for the entire system relative to a system using only outdoor air.

MSEC-D based energy consumption optimization strategy in satellite communication systems

In this paper, we consider a satellite communication system that consists of a source low earth orbit (SLEO) satellite, several relay low earth orbit (RLEO) satellites, and a destination ground station (GS). Under the assumption that the data can be split into different sizes of data flow while transmitted through the system in different time slots, a joint route selection and time-slot allocation problem are formulated. Considering the constraints on the availability of transmission links, conservation of data flow, and system resources, an optimization problem that minimizes the total system energy consumption is formulated. As the original optimization problem is NP-hard, which cannot be solved conveniently, we transform the problem to the maximum transmission rate route selection subproblem and energy-efficient resource allocation subproblem. To solve these subproblems, we propose two algorithms, namely the multi-time-expanding graph-based maximum transmission rate route selection (MTEG-R) algorithm and the minimum system energy consumption - Dijkstra (MSEC-D) algorithm. The proposed algorithms enable the determination of routes selection and time slots allocation for data transmission. Simulation results demonstrate the effectiveness of the proposed scheme.

Operation Mode and Energy Consumption Analysis of a New Energy Tower and Ground Source-Coupled Heat Pump System

In order to solve the problems of performance degradation in energy tower heat pump (ETHP) systems under low temperature conditions and soil heat imbalances in ground source heat pump (GSHP) systems in cold regions, a new coupled system of ETHP and GSHP systems (the ET–GSHP system) and its operating mode were proposed. The mathematical model of the system was constructed along with the system’s form and operation scheme. The COP (coefficient of performance) and total energy consumption of the coupled system were then simulated and studied under a number of common operating situations. The heating season is divided into four periods based on varying outdoor ambient temperatures: the first period operates in series mode and has an average outdoor temperature of 2.38 °C; the second period operates in parallel mode and has an average outdoor temperature of −8.56 °C; the third period uses soil source heat pumps to operate separately; and the fourth period operates in series mode and has an average outdoor temperature of −11.32 °C. Operation of the coupled system in four periods was simulated and analyzed, and the operational efficiency and energy saving of the system were analyzed using an actual commercial building in a cold region as an example. The results demonstrate that the ET–GSHP system’s overall energy consumption during the heating period is reduced by 4.34% when compared to the traditional GSHP systems; the system’s COP can maintain a high level throughout the heating period, with an average COP of 3.315; and the soil temperature at the conclusion of the heating period is 25 °C, which is 8.89 °C higher than that of the traditional GSHP system, providing a guarantee of summer heat return. The new ET–GSHP system significantly boosts the efficiency of the system’s operation, achieves effective coupling between various heat sources through multi-stage control, and offers improved energy-saving advantages.

Energy-Efficient Hybrid NOMA-FDMA Assisted Distributed Two-Tier Edge-Cloudlet Multi-Access Computation Offloading

Driven by the increasing interests of multi-tier computing architecture, this paper considers a hybrid non-orthogonal multiple access (NOMA) and frequency division multiple access (FDMA) assisted two-tier edge-cloudlet multi-access computation offloading. In particular, part of the computation tasks of the edge-computing user (EU) can be offloaded to different edge servers (ESs) via NOMA, and part of the computing tasks received by the ESs can be further offloaded to the cloudlet server (CS) via FDMA. We formulate an optimization problem to minimize the total energy consumption of the two-tier offloading system by jointly optimizing the multi-access offloading decision, NOMA-transmission duration, FDMA-transmission duration and bandwidth allocations. Although the problem is non-convex, we identify the structural feature of the problem and propose a decomposition-based algorithm to attain the optimal two-tier offloading decisions and the associated allocations of the computing/communication resources. Specifically, by leveraging the hidden convexity after decomposition, we propose a distributed algorithm, which is based on the block coordinated descent (BCD) method and Lagrange duality, for solving the subproblem. In our distributed algorithm, the EU optimizes its Tier-I offloading towards different ESs, each ES individually optimizes its Tier-II offloading towards the CS, and moreover, the CS optimizes its bandwidth allocations to the ESs. Furthermore, based on the proposed algorithm for the singe-EU case, the multi-EU scenario is further analyzed. To improve the energy-efficiency and fairness for the multi-EU scheduling, we propose a gradient projection (GP) based multi-EU scheduling algorithm, which leverages our single-EU algorithm as a subroutine, to optimize all EUs’ energy-efficiency in computation offloading over a long-term period. Numerical results verify the performance of our proposed algorithms. For the single-EU scenario, our simulation results demonstrate that the decomposition-based algorithm can outperform some benchmark algorithms. In the case of multi-EU scenario, our proposed GP-based multi-EU scheduling can effectively improve all EUs’ energy-efficiency compared to the round-robin scheduling.

Improved Analysis of Borehole Heat Exchanger Performance

This paper provides recommendations for improved analyses of the performance of ground-coupled heat pumps. Most research on ground-coupled heat pumps focuses on improving the performance of borehole heat exchangers (BHE) and reducing system costs. However, the potential improvements are mainly assessed at the BHE level rather than considering the entire system incorporating a heat pump, circulation pump, and building needs. This paper shows that such an approach can be misleading, and improvements in BHE are significantly overestimated if the operation of the entire system is not simulated. For instance, improvements in pipe thermal conductivity (from 0.4 to 3 W/(m K)) result in a 7.57% improvement in BHE performance when simulating only a BHE (constant inlet temperature assumed). However, a more realistic simulation of the entire system shows that improvements at a system level are only 0.15%. Other important simulation aspects are also investigated, focusing on different choices regarding the sensitivity analysis method, flow condition type, and optimization strategy. The results suggest that modifications to individual BHE parameters have a limited impact on the overall system performance, while modifying all parameters simultaneously can lead to more significant reductions in total system energy consumption (6% in this study). Furthermore, the research also shows that the potential savings in investment costs (by reducing the borehole depth) outweigh potential savings in operational costs.

The analysis of time-sharing economic exergy efficiency of the CCHP system using planetary search algorithms

CCHPs (Combined Cooling, Heating, and Power Systems) are capable of providing cold energy, heat, and electricity to users, allowing cascading utilization of energy and improving energy efficiency. The imbalance between cooling, heating, and electrical energy makes it difficult to accurately evaluate the performance of a CCHP system. Existing indices for evaluating the performance of the CCHP system do not account for the influence of time-sharing tariffs; therefore, the quantitative index of time-sharing is added and the time-sharing economic exergy efficiency of the radiator is established. Given that the electrical and thermal characteristics of the advanced absolutely hot compressed airheat storage system (AA-CAES) can complement the CCHP system, a model of the CCHP system with AA-CAES is established, which can be used to validate the validity of the quantitative evaluation index of time-sharing. A planetary search algorithm is proposed for solving the CCHP system model to address the multi-parameter solving characteristics of the CCHP system model and the disadvantages of the existing multi-objective optimization algorithms, which are prone to local optimality and poor optimization accuracy. Simulation validation demonstrates that the time-sharing economic exergy efficiency proposed in this paper can more accurately reflect the total energy consumption of the CCHP system than the existing evaluation indices. The performance of the CCHP system can be improved by using AA-CAES as a heat storage device.

Optimization Control Strategy for a Central Air Conditioning System Based on AFUCB-DQN

The central air conditioning system accounts for 50% of the building energy consumption, and the cold source system accounts for more than 60% of the total energy consumption of the central air conditioning system. Therefore, it is crucial to solve the optimal control strategy of the cold source system according to the cooling load demand, and adjust the operating parameters in time to achieve low energy consumption and high efficiency. Due to the complex and changeable characteristics of the central air conditioning system, it is often difficult to achieve ideal results using traditional control methods. In order to solve this problem, this study first coupled the building cooling load simulation environment and the cold source system simulation environment to build a central air conditioning system simulation environment. Secondly, noise interference was introduced to reduce the gap between the simulated environment and the actual environment, and improve the robustness of the environment. Finally, combined with deep reinforcement learning, an optimal control strategy for the central air conditioning system is proposed. Aiming at the simulation environment of the central air conditioning system, a new model-free algorithm is proposed, called the dominant function upper confidence bound deep Q-network (AFUCB-DQN). The algorithm combines the advantages of an advantage function and an upper confidence bound algorithm to balance the relationship between exploration and exploitation, so as to achieve a better control strategy search. Compared with the traditional deep Q-network (DQN) algorithm, double deep Q-network (DDQN) algorithm, and the distributed double deep Q-network (D3QN) algorithm, the AFUCB-DQN algorithm has more stable convergence, faster convergence speed, and higher reward. In this study, significant energy savings of 21.5%, 21.4%, and 22.3% were obtained by conducting experiments at indoor thermal comfort levels of 24 °C, 25 °C, and 26 °C in the summer.

Thermodynamic analysis of an efficient pressure-swing CO2 capture system based on ionic liquid with residual pressure energy recovery

In this work, an efficient pressure-swing ionic liquid-based CO2 capture system with residual pressure energy recovery is proposed. In this system, the N2–O2 mixture from flue gas which is not absorbed by the ionic liquid in the absorber flows into the turbine to recover the energy for the purpose of reducing energy consumption. 1-butyl-3-methylimidazolium tetrafluoroborate ([Bmim][BF4]) and 1-butyl-3-methylimidazolium hexafluorophosphate ([Bmim][PF6]) are selected as the capture solvent and a comprehensive thermodynamic analysis is performed. The total energy consumption of the new system is 1.770 GJ/tCO2 under design conditions which is 28.45% lower than the conventional system for [Bmim][BF4] and 1.737GJ/tCO2 which is 28.84% lower than the conventional system for [Bmim][PF6]. The effect of absorption pressure, desorption temperature, heat storage fluid flow rate, distribution ratio of heat storage fluid in the intercooler, flue gas composition and type of heat storage fluid on system thermodynamic performance were analyzed. The result shows that relatively lower capture pressure, the lower desorption temperature, the lower heat storage fluid flow rate, the more heat storage fluid distributed in the first intercooler and the higher CO2 fraction in flue gas can improve the thermodynamic performance of the system. Also, the exergy analysis represents that cold tank and the capture module are the two components with maximum exergy destruction. And the comparison results with the conventional system show that the new system has significantly lower energy consumption and capture cost.

Energy-Efficient Collaborative Multi-Access Edge Computing via Deep Reinforcement Learning

The joint problem of task offloading, collaborative computing and resource allocation for Multi-access Edge Computing (MEC) is a challenging issue. In this paper, splitting computing tasks at MEC servers through collaboration among MEC servers and a cloud server, we investigate the joint problem of collaborative task offloading and resource allocation. A collaborative task offloading, computing resource allocation, and subcarrier and power allocation problem in MEC is formulated. The goal is to minimize the total energy consumption of the MEC system while satisfying a delay constraint. The formulated problem is a non-convex mixed-integer optimization problem. In order to solve the problem, we propose a Deep Reinforcement Learning (DRL) based bi-level optimization framework. The task offloading decision, computing collaboration decision, and power and subcarriers allocation subproblems are solved at the upper-level, while the computing resource allocation subproblem is solved at the lower-level. We combine Dueling-Deep-Q-Network (DQN), Double-DQN and add adaptive parameter space Noise (D <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> N-DQN) to improve DRL performance in MEC. Simulation results demonstrate that the proposed algorithm achieves near-optimal performance in Energy Efficiency (EE) and task completion rate compared to other DRL-based approaches and other benchmark schemes under various network parameter settings.

Resource Allocation and Trajectory Optimization in OTFS-Based UAV-Assisted Mobile Edge Computing

Mobile edge computing (MEC) powered by unmanned aerial vehicles (UAVs), with the advantages of flexible deployment and wide coverage, is a promising technology to solve computationally intensive communication problems. In this paper, an orthogonal time frequency space (OTFS)-based UAV-assisted MEC system is studied, in which OTFS technology is used to mitigate the Doppler effect in UAV high-speed mobile communication. The weighted total energy consumption of the system is minimized by jointly optimizing the time division, CPU frequency allocation, transmit power allocation and flight trajectory while considering Doppler compensation. Thus, the resultant problem is a challenging nonconvex problem. We propose a joint algorithm that combines the benefits of the atomic orbital search (AOS) algorithm and convex optimization. Firstly, an improved AOS algorithm is proposed to swiftly obtain the time slot allocation and high-quality solution of the UAV optimal path. Secondly, the optimal solution for the CPU frequency and transmit power allocation is found by using Lagrangian duality and the first-order Taylor formula. Finally, the optimal solution of the original problem is iteratively obtained. The simulation results show that the weighted total energy consumption of the OTFS-based system decreases by 13.6% compared with the orthogonal frequency division multiplexing (OFDM)-based system. The weighted total energy consumption of the proposed algorithm decreases by 11.7% and 26.7% compared with convex optimization and heuristic algorithms, respectively.