Optimal Joint Power Research Articles

Joint resource allocation and power optimization in space division multiplexed elastic optical networks: a new algorithm and comparative analysis

Joint resource allocation and power optimization in space division multiplexed elastic optical networks: a new algorithm and comparative analysis

Joint Power Optimization and Subcarrier Allocation for URLLC in Cell-Free Massive MIMO System

Joint Power Optimization and Subcarrier Allocation for URLLC in Cell-Free Massive MIMO System

Joint Trajectory and Power Optimization for Energy-Efficient UAV Redeployment Against an Eavesdropper Under Consistent Fairness Constraint

Joint Trajectory and Power Optimization for Energy-Efficient UAV Redeployment Against an Eavesdropper Under Consistent Fairness Constraint

DNN-based algorithm for joint SIC ordering and power allocation in downlink NOMA-enabled heterogeneous networks

In the heterogeneous network (HetNet) employing downlink non-orthogonal multiple access (NOMA), we focus on the non-convex optimization problem to optimize the spectral efficiency (SE) while the users satisfy the quality-of-service (QoS) requirement. In the previous work, the optimal joint successive interference cancellation and power allocation (JSPA) algorithm for maximizing SE is proposed to solve the mixed-integer non-linear programming (MINLP) problem in NOMA-enabled HetNet. However, the optimal solution requires exponential complexity by the number of base stations (BSs). Therefore, we present a deep neural network (DNN)-based algorithm for JSPA to reduce the complexity. In particular, to deal with the MINLP-based JSPA problem, we reformulate it into an equivalently simple problem that optimizes only the power consumption of BSs. Then, we introduce the unsupervised DNN-based method for JSPA to handle the simplified problem. The presented scheme yields improved SE and outage performance compared with traditional DNN-based methods. Additionally, we propose a user selection scheme with low complexity to enhance the SE of the proposed DNN-based power allocation. Through simulations, we illustrate that the suggested DNN-based scheme can attain SE performance similar to that of the optimal scheme.

Aggregate Throughput Maximization for UAV-Enabled Relay Networks With Wireless Power Transfer: Joint Trajectory and Power Optimization

Aggregate Throughput Maximization for UAV-Enabled Relay Networks With Wireless Power Transfer: Joint Trajectory and Power Optimization

Dynamic UAV Swarm Collaboration for Multi-Targets Tracking Under Malicious Jamming: Joint Power, Path and Target Association Optimization

Dynamic UAV Swarm Collaboration for Multi-Targets Tracking Under Malicious Jamming: Joint Power, Path and Target Association Optimization

Joint trajectory, transmission time and power optimization for multi-UAV data collecting system

Unmanned aerial vehicles (UAVs) have been generally applied in the field of communication due to their small size, flexible mobility, and convenient deployment. As a mobile base station, the UAV node can quickly establish a line-of-sight link with the ground node, thereby improving communication performance. In this paper, we study a multi-UAV assisted data collecting system. Specifically, in the case of limited system energy consumption, UAV flight energy consumption and ground node data transmission energy consumption are considered as an general limitation, and considering the channel interference between nodes, a multi-UAV assisted data collection model is studied. An non-convex problem that maximizes the minimum amount of data collected from ground nodes is further formulated. Since the original optimization problem is non-convex that difficult to solve directly, the problem is first decomposed into four sub-problems, and then the solution of each sub-problem is obtained by using successive convex approximation and block coordinate descent method. Finally, based on the solution of the four subproblems, an iterative algorithm for joint optimization of data transmission planning, transmission power, UAV trajectory and mission time is proposed. Simulation experiments show that the proposed algorithm can obtain more transmission data than the baseline algorithms.

Joint beam scheduling and power optimization for beam hopping LEO satellite systems

Joint beam scheduling and power optimization for beam hopping LEO satellite systems

Full-Duplex Enabled Integrating Communication and Computation: Joint Beamforming and Power Optimization With Imperfect CSI

Full-Duplex Enabled Integrating Communication and Computation: Joint Beamforming and Power Optimization With Imperfect CSI

When Asymmetry Helps: Joint Power and Blocklength Optimization for Non-Orthogonal Multiple Access in Downlink Communications

When Asymmetry Helps: Joint Power and Blocklength Optimization for Non-Orthogonal Multiple Access in Downlink Communications

Machine Learning Assisted Cross-Layer Joint Optimal Subcarrier and Power Allocation for Device-to-Device Video Transmission

Machine Learning Assisted Cross-Layer Joint Optimal Subcarrier and Power Allocation for Device-to-Device Video Transmission

Learning-Aided Joint Beam Divergence Angle and Power Optimization for Seamless and Energy-Efficient Underwater Optical Communication

Learning-Aided Joint Beam Divergence Angle and Power Optimization for Seamless and Energy-Efficient Underwater Optical Communication

Coalitional Game Based Resource Allocation in D2D-Enabled V2V Communication

The joint resource block (RB) allocation and power optimization problem is studied to maximize the sum-rate of the vehicle-to-vehicle (V2V) links in the device-to-device (D2D)-enabled V2V communication system, where one feasible cellular user (FCU) can share its RB with multiple V2V pairs. The problem is first formulated as a nonconvex mixed-integer nonlinear programming (MINLP) problem with constraint of the maximum interference power in the FCU links. Using the game theory, two coalition formation algorithms are proposed to accomplish V2V link partitioning and FCU selection, where the transferable utility functions are introduced to minimize the interference among the V2V links and the FCU links for the optimal RB allocation. The successive convex approximation (SCA) is used to transform the original problem into a convex one and the Lagrangian dual method is further applied to obtain the optimal transmit power of the V2V links. Finally, numerical results demonstrate the efficiency of the proposed resource allocation algorithm in terms of the system sum-rate.

Energy efficiency maximization for UAV-enabled amplify-and-forward relaying via joint power and trajectory optimization

This paper investigates an optimization problem corresponding to energy efficiency maximization of an unmanned aerial vehicle (UAV)-enabled relaying system, where a fixed-wing UAV acts as an amplify-and-forward mobile relay to assist data transmission between a source node and a destination node. On the premise of satisfying the speed and acceleration constraints of the UAV, the energy efficiency (EE) of the relaying system is maximized by jointly optimizing the UAV’s trajectory and the individual transmit power levels of the source and the UAV relay. The initial joint optimization problem is non-convex and cannot be solved directly. Therefore, the joint optimization problem is decomposed into two sub-problems which are solved by applying the successive convex approximation technique and the Dinkelbach’s algorithm. On this basis, an efficient iterative algorithm is proposed to tackle the joint optimization problem through the block coordinate descent technique. Simulation results demonstrated that by conducting the proposed algorithm, the flight trajectory of the UAV and the individual transmit power levels of the nodes can be flexibly adjusted according to the system conditions, and the proposed algorithm contributes to the higher EE compared with the benchmark schemes.

Learning and optimizing charging behavior at PEV charging stations: Randomized pricing experiments, and joint power and price optimization

In this paper, we introduce, implement, and assess a framework for jointly optimizing the pricing policy and the charging schedule of electric vehicles (EVs) by learning and shaping human behavior with pricing. The proposed methodology uses time-based pricing to incentivize user behavior at charging stations towards actions that achieve the station operator's objectives. The optimization framework incorporates endogenous human behaviors by explicitly accounting for the willingness to delay charging, as well as the plug-in duration of each session, as a function of the hourly prices. The approach also addresses the issue of overstay at EV charging stations by casting the problem as a trade-off between occupying resources and giving more flexibility to the station operator. We discuss the design and analysis of the behavioral experiments used to model the charging behavior of participants at the charging stations, and demonstrate the effectiveness of those learned behavioral models in an optimization framework aimed at minimizing the total cost of providing the charging service without sacrificing the user experience. Our simulations show that our framework increases total revenue, reduces utility cost, and increases net profit for the station operator, while maintaining a high level of service and consumer utility.

Energy-Efficient Hierarchical Resource Allocation in Uplink–Downlink Decoupled NOMA HetNets

The dense deployment of small cell networks is a key feature of next-generation mobile networks aimed at providing the necessary capacity increase. It is noteworthy that small cell networks employ high-capacity backhaul links on millimeter-wave bands to develop multi-hop topologies in order to mitigate data transmission costs. The current static backhaul infrastructures cannot control severe fluctuating network traffic. To resolve this problem, this paper proposed a novel adaptive backhaul topology with the ability to adapt to different traffic patterns. Based on the graph theory, the adaptive system dynamically allows changes to the hybrid millimeter-wave backhaul architecture, and it also provides the possibility of effective channel allocation to each backhaul link to meet capacity and QoS demands. Also, regarding the importance of green networking in integrated-access-and-backhaul networks we proposed a dynamic optimization model which minimizes the overall energy consumption of UL/DL Decoupled NOMA heterogeneous networks in addition to providing the essential coverage and capacity. The proposed model optimizes user association/power utilization and presents an effective modular and scalable framework for analytical technology-oriented modeling of integrated multi-hop backhauls. The numerical results proved that the joint power optimization and hybrid backhaul architecture can increase the total network throughput by 18 percent compared to the current optimized static architectures. It can also reduce the energy consumption level by 30 percent, and enhance users’ quality satisfaction by 24.5 percent with respect to user distribution patterns.

Queue-Aware Resource Allocation in Full-Duplex Multi-Cellular Wireless Networks

In this paper, we aim to tackle the challenges of resource block scheduling and power allocation in the context of multi-cell full-duplex wireless networks. This is a more realistic setting than the single cell scenario, and it better envisions how full-duplex wireless communications could eventually be implemented. We propose an optimal queue-aware joint scheduling and power allocation algorithm for full-duplex wireless networks in a multi-cell scenario. Because of its mathematical intractability, we decouple the problem and solve it for scheduling first, and for power allocation second. We consider both indoor and outdoor scenarios and show that the gains of multi-cell full-duplex wireless networks, with respect to their half-duplex counter-parts, are not always prevalent. Furthermore, we highlight the importance of inter-cell cooperation when it comes to scheduling resources and show that depending on the scenario at hand, interference mitigation from inter-cell cooperation can improve the performance of user equipment in terms of throughput and waiting delay. Finally, we show that power allocation can improve user equipment throughput with its efficiency being tied to the deployment scenario at hand.

Full-Duplex Communication for ISAC: Joint Beamforming and Power Optimization

Beamforming design has been widely investigated for integrated sensing and communication (ISAC) systems with full-duplex (FD) sensing and half-duplex (HD) communication, where the base station (BS) transmits and receives radar sensing signals simultaneously while the integrated communication operates in either downlink or uplink. To achieve higher spectral efficiency, in this paper, we extend existing ISAC beamforming design to a general case by considering the FD capability for both radar and communication. Specifically, we consider an FD ISAC system, where the BS performs target detection and communicates with multiple downlink users and uplink users reusing the same time and frequency resources. We jointly optimize the downlink dual-functional transmit signal and the uplink receive beamformers at the BS and the transmit power at the uplink users. The problems are formulated under two criteria: power consumption minimization and sum rate maximization. The downlink and uplink transmissions are tightly coupled due to both the desired target echo and the undesired interference received at the BS, making the problems challenging. To handle these issues in both cases, we first determine the optimal receive beamformers in closed forms with respect to the BS transmit beamforming and the user transmit power. Subsequently, we invoke these results to obtain equivalent optimization problems and propose iterative algorithms to solve them. In addition, we consider a special case under the power minimization criterion and propose an alternative low complexity design. Numerical results demonstrate that the optimized FD communication-based ISAC brings tremendous improvements in terms of both power efficiency and spectral efficiency compared to the conventional ISAC with HD communication.

THz-Enabled UAV Communications Using Non-Orthogonal Multiple Access

The fact that integrating the unmanned aerial vehicles (UAV) newfangled applications in the envisioned next generation (nextG) necessitates a considerable high throughput, makes terahertz (THz) band an emerging key enabler to support stringent requirements in the nextG wireless ecosystem. To fully utilize the potential of a synergy of UAV with THz frequency band, a THz-enabled UAV communication is scrutinized in this paper, where a single antenna UAV is serving the ground users by incorporating non-orthogonal multiple access (NOMA) scheme. Our objective is to maximize the total rate of all ground users by jointly optimizing bandwidth allocation, power allocation, and UAV deployment in THz communication systems. The formulated max-sum rate optimization problem is non-convex, which makes it hard to solve optimally. Therefore, to cope with the aforementioned puzzle, we adopt an inner convex approximation-based path-following approach that considers a Joint Optimization of Power, Bandwidth allocation, and UAV's Deployment (JOPBD) by utilizing NOMA scheme. Furthermore, a comparison of our proposed JOPBD algorithm, with the orthogonal multiple access, and other baseline schemes is conducted. Then, the numerical simulations are carried out to reveal the impact of our proposed approach, and also to confirm its significance for the UAV operating in THz band.

Joint Hovering Height, Power, and Rate Optimization for Air-to-Ground UAV-RSMA Covert Communications

In this paper, we investigate covert communications for an unmanned aerial vehicle (UAV)-aided rate-splitting multiple access (RSMA) system in which the UAV transmits to the covert and public users separately while shielding covert transmissions from a warden. Under the RSMA principles, the messages of the covert and public users are converted to common and private streams for air-to-ground transmissions. Subject to the quality of service (QoS) requirements of the public user and covertness constraint of the UAV-aid RSMA (UAV-RSMA) system, the aim of covert communication design is to maximize the covert rate by jointly optimizing the transmit power allocation, common rate allocation, and UAV hovering height, which each contribute to the uncertainty of the warden’s binary decision and attempts to enhance communication performance. To address the non-convex covert rate maximization problem in addition to the highly coupled system parameters, we decouple the original problem into three subproblems of transmit power allocation, common rate allocation, and UAV hovering height. We derive the optimal solutions for each of the subproblems of the transmit power and rate allocations and formulate a signomial programming problem to tackle UAV hovering height optimization. The simulation results indicate the superior covert rate performance achieved by the proposed AO algorithm and demonstrate that the proposed UAV-RSMA scheme achieves a higher covert rate than the benchmark schemes.