Mixed-integer Non-convex Optimization Problem Research Articles

UAV-Assisted Data Collection for Ocean Monitoring Networks

Ocean monitoring network (OMN) is a remote oceanic data collection system, which integrates communication and networking technologies. As underwater acoustic communication has been widely used for data transmission between battery- powered underwater sensor nodes (USNs) and sink nodes (SNs), an oceanic data collection system requires energy efficient deployment to prolong the lifetime of the whole network. This work aims to propose an unmanned aerial vehicle (UAV) assisted OMN architecture, in which sensing data are transmitted first from USNs to sea surface SNs in each data collection cycle using underwater acoustic communication, and then a UAV hovering above the SNs collects and relays all the data to a ground base station via wireless communication links. To extend the network lifetime, we model SNs and UAV deployment and resource allocation as a mixed integer non-convex optimization problem. To solve the problem efficiently in a heuristic way, we design a SNs deployment scheme with the help of time division USN-to-SN access and NOMA based SN-to-UAV access schemes. Computer simulations validate the superiority of the proposed deployment and access schemes on OMN lifetime performance. in particular, increasing the number of time slots in the USN-to-SN access process can improve the performance significantly.

Joint Resource Allocation and UAV Trajectory Optimization for Space–Air–Ground Internet of Remote Things Networks

Given the limited transmission power of smart devices in Internet of Remote Things (IoRT), unmanned aerial vehicle (UAV) aided space-air-ground (SAG) networks become a beneficial remedy for uplink data transmission in IoRT networks. In this article, we propose a SAG–IoRT framework, where drones act as relays to upload the data from smart devices to low earth orbit satellites. Considering the large number of smart devices, we maximize the system capacity by jointly optimizing smart devices connection scheduling, power control, and UAV trajectory, where the joint optimization is a nonconvex optimization problem. The formulated problem is a mixed integer nonconvex optimization problem, which is challenging to solve directly. Hence, an efficient iterative algorithm is proposed for solving the above-mentioned nonconvex optimization problem by applying variable substitution, successive convex optimization techniques, and the block coordinate decent algorithm. In particular, we alternately iterate smart device connection scheduling, power control, and UAV trajectory design to obtain the maximum system capacity. Numerical simulation results show that our proposed algorithm substantially improves the system capacity in comparison to the static UAV scheme, and achieves a gain of at least 22.3% in terms of the capacity against dynamic UAV scheme with a circular trajectory.

UAV-Enabled Uplink Non-Orthogonal Multiple Access System: Joint Deployment and Power Control

In order to overcome the inherent latency in multi-user orthogonal multiple access (OMA) unmanned aerial vehicle (UAV) networks. In this paper, we investigate a UAV-enabled uplink non-orthogonal multiple access (NOMA) network, where a UAV is deployed to collect the messages transmitted by the ground users. To maximize the users' sum-rate, we formulate a optimization problem, in which the UAV deployment position and the power control are jointly optimized subject to the transmission power constraints and the quality of service (QoS) constraints. However, this problem is a mixed integer non-convex optimization problem and thus it is difficult to solve directly. Therefore, we first transform the non-convex optimization problem into a convex one based on the first-order approximation technique and the penalty function method. Then, a successive convex approximate (SCA) technique based iterative algorithm as well as its initialization scheme are developed to find a suboptimal solution to the original problem. Afterwards, a low-complexity approximate algorithm is proposed to reduce the high computational complexity of the iterative algorithm. Numerical results show that our proposed iterative algorithm and low-complexity approximate algorithm can achieve a similar performance, and they can efficiently improve the sum-rate compared with OMA scheme and conventional NOMA scheme.

Joint Cache Placement, Flight Trajectory, and Transmission Power Optimization for Multi-UAV Assisted Wireless Networks

It is well known that unmanned aerial vehicles (UAVs) can help terrestrial base stations (BSs) offload data traffic from crowded areas to improve coverage and boost throughput. However, the limited backhaul capacity cannot cope with the ever-increasing data demands, for which caching is introduced to relieve the backhaul bottleneck. In this paper, we focus on a multi-UAV assisted wireless network, and target to fully utilize the benefits of wireless caching and UAV mobility for multiuser content delivery. By taking into account the limited storage, our goal is to maximize the minimum throughput among UAV-served users by jointly optimizing cache placement, UAV trajectory, and transmission power in a finite period. The resultant problem is a mixed-integer non-convex optimization problem. To facilitate solving this problem, an alternating iterative algorithm is proposed by adopting the block alternating descent and successive convex approximation methods. Specifically, this problem is split into three subproblems, namely cache placement optimization, trajectory optimization, and power allocation optimization. Then these subproblems are solved alternately in an iterative manner. We show that the proposed algorithm can converge to the set of stationary solutions of this problem. Besides, we further analyze the computational complexity of this algorithm. Numerical results show that great throughput enhancement is achieved by applying our proposed joint design in comparison with other benchmarks without trajectory design and power control.

Energy Efficient Mode Selection, Base Station Selection and Resource Allocation Algorithm in D2D Heterogeneous Networks

The mode selection and resource allocation are important issues in Device-to-Device (D2D) communication, which have been investigated in single Base Station (BS) cellular network. However, the joint problem of the base station selection, mode selection and resource allocation is a challenging issue. Because the mode selection, base station and resource allocation are inter-coupled with each other. The joint problem of D2D User Equipments (DUEs) mode selection, base station selection, channel allocation and power allocation remains an open problem. In this paper, the scenario with multiple BSs in D2D heterogeneous networks is considered, and the joint problem of DUEs mode selection, base station selection, channel allocation and power allocation is studied. The objective is to maximize the system energy efficiency. We consider the DUEs multiplex the cellular user uplink resource. Meanwhile, the constraint of the total transmission power of the DUEs, and the constraint of the load balancing of the BSs are considered. The joint problem of mode selection, base station selection and resource allocation is formulated. The formulated problem is a non-convex mixed-integer optimization problem. In order to handle the formulated problem, a joint mode selection, base station selection and resource allocation algorithm based on particle swarm optimization is proposed. Numerical results demonstrate that the proposed algorithm can improve the system energy efficiency and obtain the desired target.

A Two-Timescale Approach for Network Slicing in C-RAN

Network slicing is a promising technique for cloud radio access networks (C-RANs). It enables multiple tenants (i.e., service providers) to reserve resources from an infrastructure provider. However, users' mobility and traffic variation result in resource demand uncertainty for resource reservation. Meanwhile, the inaccurate channel state information (CSI) estimation may lead to difficulties in guaranteeing the quality of service (QoS). To this end, we propose a two-timescale resource management scheme for network slicing in C-RAN, aiming at maximizing the profit of a tenant, which is the difference between the revenue from its subscribers and the resource reservation cost. The proposed scheme is under a hierarchical control architecture, which includes long timescale resource reservation for a slice and short timescale intra-slice resource allocation. To handle traffic variation, we utilize the statistics of users' traffic. Moreover, to guarantee the QoS under CSI uncertainty, we apply the uncertainty set of CSI for resource allocation among users. We formulate the profit maximization as a two-stage stochastic programming problem. In this problem, long timescale resource reservation for a slice is performed in the first stage with only the statistical knowledge of users' traffic. Given the decision in the first stage, short timescale intra-slice resource allocation is performed in the second stage, which is adaptive to real-time user arrival and departure. To solve the problem, we first transform the stochastic programming problem into a deterministic optimization problem. We further apply semidefinite relaxation to transform the problem into a mixed integer nonconvex optimization problem, which can be solved by combining branch-and-bound and primal-relaxed dual techniques. Simulation results show that our proposed scheme can well adapt to traffic variation and CSI uncertainty. It obtains a higher profit when compared with several baseline schemes.

Resource Allocation for Secure Multi-UAV Communication Systems With Multi-Eavesdropper

In this paper, we study the resource allocation and trajectory design for secure unmanned aerial vehicle (UAV)-enabled communication systems, where multiple multi-purpose UAV base stations are dispatched to provide secure communications to multiple legitimate ground users (GUs) in the existence of multiple eavesdroppers (Eves). Specifically, by leveraging orthogonal frequency division multiple access (OFDMA), active UAV base stations can communicate to their desired ground users via the assigned subcarriers while idle UAV base stations can serve as jammer simultaneously for communication security provisioning. To achieve fairness in secure communication, we maximize the average minimum secrecy rate per user by jointly optimizing the communication/jamming subcarrier allocation policy and the trajectory of UAVs, while taking into account the constraints on the minimum safety distance among multiple UAVs, the maximum cruising speed, the initial/final locations, and the existence of cylindrical no-fly zones (NFZs). The design is formulated as a mixed integer non-convex optimization problem which is generally intractable. Subsequently, a computationally-efficient iterative algorithm is proposed to obtain a suboptimal solution. Simulation results illustrate that the performance of the proposed iterative algorithm can significantly improve the average minimum secrecy rate compared to various baseline schemes.

A New Store-Then-Amplify-and-Forward Protocol for UAV Mobile Relaying

In this letter, we consider the use of an unmanned aerial vehicle (UAV) as a mobile relay to assist the communication between two ground users without a direct link. We propose a novel store-then-amplify-and-forward (SAF) relaying protocol for the UAV to exploit its mobility jointly with the low-complexity AF relaying. Specifically, the received signal from the source is first stored in a buffer at the UAV, then amplified and forwarded to the destination when the UAV flies closer to the destination. With this new SAF protocol, we aim to maximize the throughput of the UAV-enabled relaying system by jointly optimizing the source/UAV transmit power and the UAV trajectory, as well as the time-slot pairing for each data packet received and forwarded by the UAV. As this problem is a non-convex mixed integer optimization problem that is difficult to solve, we propose an efficient algorithm for obtaining a suboptimal solution for it by applying the techniques of Hungary algorithm, alternating optimization and successive convex approximation. Numerical results show that the proposed mobile SAF relaying outperforms the conventional AF relaying without signal storing.

Beamforming Optimization for Intelligent Reflecting Surface Assisted MIMO: A Sum-Path-Gain Maximization Approach

Recently, intelligent reflecting surface (IRS) has emerged as an appealing technique that enables wireless communications with low hardware cost and low power consumption. In this letter, we consider an IRS-assisted point-to-point multi-input multi-output (MIMO) system, where a source communicates with its destination with the help of an IRS. Our goal is to maximize the spectral efficiency of this system by jointly optimizing the (active) precoding at the source and the (passive) phase shifters (PSs) at the IRS. However, this turns out to be an intractable mixed integer non-convex optimization problem. To circumvent the intractability, we propose a new sum-path-gain maximization (SPGM) criterion to obtain a high-quality and efficient suboptimal solution to this problem. Specifically, the PSs are first designed based on a simplified optimization problem, which aims to maximize the sum-gains of the spatial paths between the source and the destination. Then, a low-complexity alternating direction method of multipliers (ADMM) algorithm is utilized to solve this simplified problem. Finally, with the above obtained PSs, the source precoding is derived by performing the singular value decomposition (SVD) on the effective channel between the source and the destination. Numerical results demonstrate that the proposed scheme can achieve near-optimal performance.

Admission Control and Power Allocation for NOMA-Based Satellite Multi-Beam Network

This paper investigates the admission control problem on the satellite multi-beam networks with non-orthogonal multiple access (NOMA). The goal is to maximize the number of supported users on the premise of ensuring the quality of service (QoS) by optimizing the subchannel and power allocation. We provide the system model and then formulate the admission control problem as a mixed integer non-convex optimization problem. The non-convexity and existence of integer variable make the optimal solution difficult to get. Therefore, we propose a joint subchannel matching and power allocation algorithm to obtain the suboptimal solution so as to reduce the computation complexity. The proposed algorithm can be used for both NOMA and orthogonal frequency division multiplexing access (OFDMA). Specifically, the subchannel matching problem is solved by a two-stage matching process where users are accessed to subchannel dynamically. The power allocation problem is modeled as a super-modular game where the existence and uniqueness of Nash equilibrium (NE) are analyzed. Moreover, an iterative power allocation algorithm is proposed based on the NE searching method. Finally, simulation results are provided for demonstrating the effectiveness and feasibility of the proposed algorithm.

Joint Trajectory and Resource Optimization for UAV-Enabled Relaying Systems

Unmanned aerial vehicles (UAVs) have attracted attentions due to their mobility and high possibility of the line of sight (LoS) channel. We can fully use these two properties by carefully optimizing the UAVs' trajectories and cooperating with some facilities. A UAV working as a mobile relay now attracts many interests due to its low cost and reliable performance. In this paper, we study a relaying system, where a UAV works as an aerial mobile relay to help some ground base stations send information to ground users periodically by using time division multiple access (TDMA). We aim to maximize the minimum average user rate through solving a non-convex and information causality constraints involved problem by jointly optimizing the UAV trajectory, nodes scheduling, and power allocation to ensure the fairness among all users. Finally, we propose an efficient iterative algorithm to solve a derived mixed-integer non-convex optimization problem to achieve this target by using block coordinate descent (BCD) and successive convex approximation (SCA) methods and prove the convergence of our algorithm. Simulations show the effectiveness of our proposed algorithm, some useful trade-offs and insights about the structure of our optimized trajectory, and the influence of two widely used trajectory initialization methods.

Joint Trajectory and Communication Design for Buffer-Aided Multi-UAV Relaying Networks

With the rapid development and evolvement of unmanned aerial vehicle (UAV) technology, UAV aided wireless communication technology has been widely studied recently. In this paper, a buffer aided multi-UAV relaying network is investigated to assist blocked ground communication. According to the mobility and implementation flexibility of UAV relays, it is assumed that the communication link between air-to-ground is the Rician fading channel. On the basis of information causality, we derive the state change of the information in the buffer of UAV relays and maximize the end-to-end average throughput by join the relay selection, UAV transmit power, and UAV trajectory optimization. However, the considered problem is a mixed integer non-convex optimization problem, and therefore, it is difficult to solve directly with general optimization methods. In order to make the problem tractable, an efficient iterative algorithm based on the block coordinate descent and the successive convex optimization techniques is proposed. The convergence of the proposed algorithm will be verified analytically at the end of this paper. The simulation results show that by alternately optimizing the relay selection, UAV transmit power, and UAV trajectory, the proposed algorithm is able to achieve convergence quickly and significantly improve the average throughput, as compared to other benchmark schemes.

Energy-Efficient Data Collection and Device Positioning in UAV-Assisted IoT

The Internet of Things (IoT) will significantly change both industrial manufacturing and our daily lives. Data collection and 3-D positioning of IoT devices are two indispensable services of such networks. However, in conventional networks, only terrestrial base stations (BSs) are used to provide these two services. On the one hand, this leads to high energy consumption for devices transmitting at cell edges. On the other hand, terrestrial BSs are relatively close in height, resulting in poor performance of device positioning in elevation. Due to their high maneuverability and flexible deployment, unmanned aerial vehicles (UAVs) could be a promising technology to overcome the above shortcomings. In this article, we propose a novel UAV-assisted IoT network, in which a low-altitude UAV platform is employed as both a mobile data collector and an aerial anchor node to assist terrestrial BSs in data collection and device positioning. We aim to minimize the maximum energy consumption of all devices by jointly optimizing the UAV trajectory and devices’ transmission schedule over time, while ensuring the reliability of data collection and required 3-D positioning performance. This formulation is a mixed-integer nonconvex optimization problem, and an efficient differential evolution (DE)-based method is proposed for solving it. Numerical results demonstrate that the proposed network and the optimization method achieve significant performance gains in both energy-efficient data collection and 3-D device positioning, as compared with a conventional terrestrial IoT network.

Joint User Association and UAV Location Optimization for UAV-Aided Communications

This letter is concerned with investigating a joint user association and unmanned aerial vehicle (UAV) location optimization problem for UAV-aided communications. Different from the majority of existing studies, this letter aims at deriving solutions to the joint optimization problem that is modeled as a mixed-integer non-convex optimization problem (MINCOP) with a goal of maximizing users’ total achievable data rates. A novel iterative optimization scheme is proposed to handle the MINCOP. Specifically, the MINCOP is first decomposed into an integer optimization subproblem and a non-convex optimization subproblem. An iterative algorithm is then developed to optimize these two separated subproblems alternatively. Besides, the non-convexity of the non-convex subproblem is handled via successive convex approximation. Based on approximate results, Karush-Kuhn-Tucker (KKT) conditions are leveraged to obtain the evolutionary-form solution to the joint optimization problem. The comparison with the state-of-the-art verifies that the developed algorithm is convergent and is able to achieve good performance.

Resonant Beam Charging-Powered UAV-Assisted Sensing Data Collection

Wireless power transfer (WPT) technology represented by the resonant beam charging (RBC) enables convenient charging for unmanned aerial vehicles (UAVs). This letter studies a UAV communication network where a short-endurance UAV is deployed to collect data reliably from a group of buoy sensors far off the coast in a three-dimensional (3D) coordinate system. We jointly optimize the UAV's trajectory and the power of the charging station to maximize the overall power transmission efficiency. To attain this goal, we formulate a mixed-integer non-convex optimization problem and then decouple the problem into three sub-problems, which can be iteratively solved by utilizing the inexact block coordinate descent. Simulation results validate the efficiency of our proposed scheme.

Secrecy Energy Efficiency Maximization for UAV-Enabled Mobile Relaying

This paper investigates the secrecy energy efficiency (SEE) maximization problem for unmanned aerial vehicle enabled mobile relaying system, where a high-mobility UAV is exploited to assist delivering the confidential information from a ground source to a legitimate ground destination with the direct link blocked, in the presence of a potential eavesdropper. We aim to maximize SEE of the UAV by jointly optimizing the communication scheduling, power allocation, and UAV trajectory over a finite time horizon. The formulated problem is a mixed-integer non-convex optimization problem that is challenging to be solved optimally. To make the problem tractable, we decompose the problem into three subproblems, and propose an efficient iterative algorithm that alternately optimizes the subproblems. In addition, two special cases are considered as benchmarks in this paper. Simulation results show that the proposed design significantly improves the SEE of the UAV, as compared to the benchmarks

Join trajectory optimization and communication design for UAV-enabled OFDM networks

Due to the advantages of high mobility, flexible maneuverability and fast deployment, Unmanned Aerial Vehicle (UAV), which can be usually served as aerial communication platform, not only supports information transmission in the Internet of things (IoT), but also provides reliable power supplement for low-power wireless devices. Considering an UAV-enabled Orthogonal Frequency Division Multiplexing (OFDM) network where subcarriers are divided into two groups for information transmission and energy harvesting, respectively, a joint UAV trajectory optimization and communication design scheme is proposed based on Simultaneous Wireless Information and Power Transfer (SWIPT) technology. Under the given average harvested energy for users, the objective of the scheme is to maximize the average achievable rate for all users by jointly optimizing UAV trajectory, user scheduling, subcarrier and power allocation. To solve the formulated optimization problem, it is transformed into two subproblems to optimize resource allocation and UAV trajectory, respectively. The former is a mixed integer non-convex optimization problem. To solve it, a three-variable alternative iteration algorithm is proposed to obtain the optimal user scheduling, subcarrier and power allocation. Since the latter is non-convex, it can be transformed into convex optimization problem by relaxing objective function and constraint to obtain the optimal trajectory. Simulation results demonstrate that the proposed algorithm has good convergence and the proposed UAV-enabled OFDM network has better performance.

A randomized two-stage iterative method for switched nonlinear systems identification

This paper addresses the identification of discrete time switched nonlinear systems, which are collections of discrete time nonlinear continuous systems (modes) indexed by a finite-valued variable defining the current mode. In particular, we consider the class of Switched Nonlinear AutoRegressive eXogenous (Switched NARX, or SNARX) models, where the continuous dynamics are represented by NARX models. Given a set of input–output data, the identification of a SNARX model for the underlying system involves the simultaneous identification of the mode sequence and of the NARX model associated to each mode, configuring a mixed integer non-convex optimization problem, hardly solvable in practice due to the large combinatorial complexity. In this paper, we propose a black-box iterative identification method, where each iteration is characterized by two stages. In the first stage the identification problem is addressed assuming that mode switchings can occur only at predefined time instants, while in the second one the candidate mode switching locations are refined. This strategy allows to significantly reduce the combinatorial complexity of the problem, thus allowing an efficient solution of the optimization problem. The combinatorial optimization is addressed using a randomized method, whereby the sample-mode map and the SNARX model structure are characterized by a probability distribution, which is progressively tuned via a sample-and-evaluate strategy, until convergence to a limit distribution concentrated on the best SNARX model of the system generating the observed data.

Robust Joint Optimization of Transmit Power and Decoding Order in Uplink NOMA Systems

We consider a robust design problem for achieving max-min fairness amongst users in an uplink non-orthogonal multiple access system under imperfect channel state information. Contrary to the conventional approach adopted in the literature, we propose an optimal decoding order-based successive interference cancellation technique by introducing new binary variables, which results in a difficult class of mixed-integer nonconvex optimization problem. For a practical application, we devise an efficient suboptimal solution based on the inner convex approximation framework, which solves a second-order-cone program at each iteration. Simulation results are provided to demonstrate its performance gain over state-of-the-art designs. The proposed design also yields data rates close to those obtained by an exhaustive search method.

Partial Offloading Scheduling and Power Allocation for Mobile Edge Computing Systems

Mobile edge computing (MEC) is a promising technique to enhance computation capacity at the edge of mobile networks. The joint problem of partial offloading decision, offloading scheduling, and resource allocation for MEC systems is a challenging issue. In this paper, we investigate the joint problem of partial offloading scheduling and resource allocation for MEC systems with multiple independent tasks. A partial offloading scheduling and power allocation (POSP) problem in single-user MEC systems is formulated. The goal is to minimize the weighted sum of the execution delay and energy consumption while guaranteeing the transmission power constraint of the tasks. The execution delay of tasks running at both MEC and mobile device is considered. The energy consumption of both the task computing and task data transmission is considered as well. The formulated problem is a nonconvex mixed-integer optimization problem. In order to solve the formulated problem, we propose a two-level alternation method framework based on Lagrangian dual decomposition. The task offloading decision and offloading scheduling problem, given the allocated transmission power, is solved in the upper level using flow shop scheduling theory or greedy strategy, and the suboptimal power allocation with the partial offloading decision is obtained in the lower level using convex optimization techniques. We propose iterative algorithms for the joint problem of POSP. Numerical results demonstrate that the proposed algorithms achieve near-optimal delay performance with a large energy consumption reduction.