Proportional Fair Trajectory Design and Resource Allocation for UAV-Assisted SWIPT System

In Long Term Evolution Advanced networks with Type I in‐band half‐duplex decode‐and‐forward relay nodes, proportional fair (PF) resource allocation is aiming at guaranteeing two‐hop match and optimising global proportional fairness. The two‐hop match is defined as equal data rates in the access links and the corresponding backhaul links. The global proportional fairness is between all the user equipments served by the evolved nodes B and the relay nodes. Existing centralised schemes achieve these targets at the cost of enormous channel state information (CSI) exchange. Existing distributed schemes focus on resource partitioning and employ a traditional single‐hop PF scheduling algorithm in access links, with less CSI exchange. The traditional PF scheduling algorithm maximises single‐hop proportional fairness between the data rates in the access links rather than two‐hop proportional fairness between the end‐to‐end data rates in the two hops. In order to reduce CSI exchange and at the same time to maximise the two‐hop proportional fairness, a distributed two‐hop PF resource allocation scheme is proposed. The proposed scheme includes two‐hop PF resource scheduling algorithms and adaptive resource partitioning algorithms, applied in different two‐hop transmission protocols. Simulation results demonstrate the proposed scheme is better than the existing distributed schemes in obtaining better proportional fairness and larger cell‐edge user equipment throughputs. Copyright © 2014 John Wiley & Sons, Ltd.

In this study, we investigate the maximization of the available energy for an unmanned aerial vehicle (UAV)-aided simultaneous wireless information and power transfer (SWIPT) system, in which the ground terminals (GTs) decode information and collect energy simultaneously from the downlink signal sent by the UAV based on a power splitting (PS) policy. To guarantee that each GT has a fair amount of available energy, our aim is to optimize the trajectory and transmit power of the UAV and the PS ratio of the GTs to maximize the minimum average available energy among all GTs while ensuring the average spectral efficiency requirement. To address the nonconvexity of the formulated optimization problem, we apply a successive convex optimization technique and propose an iterative algorithm to derive the optimal strategies of the UAV and GTs. Through performance evaluations, we show that the proposed scheme outperforms the existing baseline schemes in terms of the max-min available energy by adaptively controlling the optimization variables according to the situation.

Energy-efficient trajectory design for secure SWIPT systems assisted by UAV-IRS

This article studies a joint resource allocation for an unmanned aerial vehicle (UAV) enabled simultaneous wireless information and power transfer (SWIPT) system in a multicasting scenario, where a UAV is dispatched to simultaneously send common information and wireless power to multiple users on the ground while the users decode information and harvest energy based on the power splitting (PS) receiver architecture. In this system, we aim to maximize the minimum achievable rate among all users under their harvested energy constraints by optimizing trajectory and transmit power of the UAV jointly with PS ratios of the users. To solve this challenging nonconvex problem, we first convert it into a more tractable form by exploiting the quadratic transform technique. Then, we propose an efficient iterative algorithm for the joint optimization. Numerical results show that the proposed scheme outperforms baseline schemes.

Dynamic particle swarm optimization (PSO) problems are generally characterized by the exhaustively examined issues of the changing location of optima, the changing fitness of optima, and measurement noise/errors. However, the challenging issue of continuously changing problem dimensionality has not been similarly examined. Given that in anytime dynamic resource allocation it is necessary to maintain a high quality solution, we argue that, rather than restarting the PSO algorithm, a more appropriate approach is to design an algorithm that robustly handles changing problem dimensionality. Specifically, we propose an indirect particle encoding scheme specifically designed for a dynamic multi-dimensional PSO algorithm for proportional fair constrained resource allocation. This PSO algorithm is implemented for the proportional fair allocation of power and users to channels within a simulation of an Orthogonal Frequency-Division Multiple Access (OFDMA) wireless network with mobile users switching cells as they traverse the simulation environment. The proposed PSO algorithm is evaluated using simulations, which demonstrate the ability of the proposed indirect encoding scheme to maximize the overall proportional fair optimization goal, without unfairly penalizing the individual components of the solution related to newly introduced problem dimensions.

In the current 4G era, the dual connectivity technique utilizes radio resources scheduled by two distinct base stations for a single user equipment to enhance the data throughput. Multi- connectivity, as a natural evolution of dual connectivity, is one of the key 5G techniques to improve both the user performance and overall resource utilization, allowing dynamic user traffic steering across multiple connections of one or more radio access technologies (RATs). However, one of the main challenge in multi-connectivity is to efficiently allocate resources across multiple connections under heterogeneous quality of service (QoS) requirements. In this paper, we examine a resource allocation problem under multi- connectivity in an evolved LTE network and propose a utility proportional fairness (UPF) resource allocation that supports QoS in terms of requested rates. We evaluate the proposed policy with the proportional fairness (PF) resource allocation through extensive simulations and characterize performance gain from both the user and network perspectives under different conditions.

Multi-connectivity is considered as a 5G key technique to improve both the user performance and the overall resource utilization. In this paper, we examine a resource allocation problem under multi-connectivity in evolved LTE and propose a utility proportional fair (UPF) resource allocation that preserves users quality-of-service (QoS) considering backhaul capacity limitations. The proposed policy is compared with proportional fair (PF) resource allocation through extensive simulations. Presented results show that multi-connectivity outperforms single-connectivity in terms of network aggregated rate and users QoS satisfaction in different network case studies, i.e., empty and loaded cell scenarios with fixed and variable backhaul capacity.

Due to the development of the 5th generation network, the high power wireless devices, such as Internet of things network devices, have become part and parcel of some new domain. The simultaneous wireless information and power transfer (SWIPT) system can transmit information while harvesting energy. Although introducing massive multiple-input multiple-output (MIMO) technologies into SWIPT systems can reduce propagation loss, there still exists the bottleneck about a trade-off between energy and information, that is, the information decoding users suppress interference while the energy harvesting users take advantage of it. In this paper, we propose a time-division (TD) transmission scheme and a power-splitting (PS) one, which all can increase Rate-Energy (R-E) region, a novel method to evaluate the system performance. We find the optimal allocation factors for schemes, which are crucial in balancing data rate and harvest power. Simulation results demonstrate that the PS scheme has larger R-E region for massive MIMO enabled SWIPT system, which represents a better energy efficiency.

In the recent era, unmanned aerial vehicle (UAV) plays an important role in numerous application fields related to the wireless communication system. Due to its precise control, efficient deployment, and affordable cost, UAV-assisted communication attracts significant attention to all the sectors including the defense sector, agriculture sector, and security purpose, and so on. Though UAVassisted relaying has enormous advantages but there are potential challenges while UAV deploys as a relay. For example, deploying UAV in the wireless communication field, its battery life is the main concern due to its limited battery size and storage capacity. To get significant benefits from UAV while deployed in the cooperative communication network, the battery status of the UAV is an unavoidable issue. To minimize the aforementioned problem, energy harvesting (EH) techniques can be an efficient solution. The UAV can harvest energy from the transmitted power by the source and with the help of this harvested energy UAV can retransmit the signal to the destination. However, there are several parameters that also significantly influence the UAV-based cooperative system performance such as UAV’s position, time allocation factor and power allocation factor, and UAV’s height. Considering the importance of the aforementioned parameters, in this paper, we have considered simultaneous wireless information and power transfer (SWIPT) enabled UAV-assisted relaying network and evaluate the system outage performance with different parameters aspects. We have provided some insight about the parameters such as the UAV’s position, the power allocation factor and the time allocation factor and the UAV’s height by providing simulation results such as the outage probability versus transmit power in the different urban scenario, the outage probability versus time allocation factor and power allocation factor and the outage probability versus UAV’s height. These simulation results clearly show the significance of the abovementioned parameters in wireless-powered UAVassisted cooperative communication. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.

This letter studies a multiuser multiple-input single-output (MISO) intelligent reflecting surface (IRS)-aided simultaneous wireless information and power transfer (SWIPT) system. Specifically, a multi-antenna base station (BS) transmits data along with energy to a set of users to decode data and harvest energy by adopting the power splitting (PS) simultaneously. The energy efficiency indicator (EEI) is introduced to trade off between data rate and harvested energy, which is maximized by jointly optimizing beamforming vectors at the BS, PS ratio at each user, and phase shifts at the IRS. To solve this non-convex optimization problem, we first adopt the majorization-minimization (MM) approach to construct a concave-convex fractional function which can be handled via the Dinkelbach algorithm and then propose an efficient algorithm for solving two subproblems based on the alternating optimization (AO). For the first subproblem, semi-definite relaxation (SDR), MM approach, and Dinkelbach algorithm are adopted, while for the second sub-problem, a new manifold approach is proposed to handle the unit-modulus constraints due to IRS passive reflection. Simulation results demonstrate the superior performance of our proposed algorithm compared to other baseline schemes.

Radio frequency (RF) energy transfer techniques have been regarded as the key enabling solutions to supply continuous and stable energy for the energy-constrained wireless devices. Simultaneous wireless information and power transfer (SWIPT) has been developed as a more promising RF energy transfer technique since it enables wireless information and wireless energy to access users from a same transmitted signal. Therefore, SWIPT has received remarkable attention. This thesis provides an investigation on applications and security issues of this emerging technology in various wireless communication scenarios. First, this thesis examines the application of SWIPT to a multi-user cooperative network in which the amplify-and-forward (AF) relay protocol is employed at the multi-antenna relay. A power splitting (PS) receiver architecture is utilized at each destination node to implement energy harvesting (EH) and information decoding (ID) simultaneously. The aim of this chapter is to minimize the relay transmit power by jointly designing relay beamforming vectors and PS ratios based on channel uncertainty models. The non-convex problem is converted into a semidefinite programming (SDP) problem by using the semidefinite relaxation (SDR) approach. In addition, a rank-one proof presents that the solution generated by the relaxed problem is optimal to the original problem. Second, a security issue about the SWIPT system is investigated in a cooperative network in the presence of potential eavesdroppers. The AF relay protocol and a PS receiver architecture are adopted at the multi-antenna relay and the desired destination node, respectively. Based on the system setup and the assumption of perfect channel state information (CSI), a transmit power minimization problem combined with the secrecy rate and harvested energy constraints is proposed to jointly optimize the beamforming vector and the PS ratio. The proposed optimization problem is non-convex and hard to tackle due to the issues of the quadratic terms and the coupled variables. To deal with this non-convex problem, two algorithms are proposed. In the first algorithm case, the proposed problem can be globally solved by using a two-level optimization approach which involves the SDR method and the one-dimensional (1-D) line search method. In addition, a rank reduction theorem is introduced to guarantee the tightness of the relaxation of the proposed scheme. In the second algorithm case, the proposed problem can be locally solved by exploiting a low complexity iterative algorithm which is embedded in the sequential parametric convex approximation (SPCA) method. Furthermore, the proposed optimization problem is extended to the imperfect CSI case. Third, a secure communication case is studied in an underlay multiple-input multiple-output (MIMO) cognitive radio (CR) network where the secondary transmitter (ST) provides SWIPT to receivers. In this chapter, two uncertainty channel models are proposed. One is based on the assumption that the ST has the perfect channel knowledge of the secondary information receiver (SIR) and the imperfect channel knowledge of secondary energy receivers (SERs) and primary receivers (PUs). The other one assumes that the ST only has the imperfect channel knowledge of all receivers. In each uncertainty channel model, an outage-constrained secrecy rate maximization (OC-SRM) problem combined with probability constraints is proposed to jointly optimizing the transmit covariance matrix and the artificial noise (AN)- aided covariance matrix. The designed OC-SRM problem for both models is non-convex due to the unsolvable probabilistic constraints. To solve this non-convex problem, the log determinant functions are first approximated to the easy handle the functions that the channel error terms are included in the trace function. Then, the probability constraints are converted into the deterministic constraints by exploiting the Bernstein-type inequality (BTI) approach. Finally, the reformulated problem for both models is solvable by using the existing convex tools. Last, a novel security issue is investigated in a MIMO-SWIPT downlink network where nonlinear energy receivers (ERs) are considered as the potential eavesdroppers. In this chapter, two uncertainty channel models, namely partial channel uncertainty (PCU) and full channel uncertainty (FCU), are proposed. An OC-SRM problem of each model is proposed to design the transmit signal covariance matrix while satisfying probabilistic constraints of the secrecy rate and the harvested energy. To surmount the non-convexity of the proposed OC-SRM problem in each model, several transformations and approximations are utilized. In the PCU model, the OC-SRM problem is first converted into two subproblems by introducing auxiliary variables. Then, three conservative approaches are adopted to obtain the safe approximation expressions of the probabilistic constraints, which are deterministic constraints. Moreover, an alternating optimization (AO) algorithm is proposed to iteratively solve two convex conic subproblems. In the FCU model, log determinant functions are first approximated to the trace functions. Then, the three approaches aforementioned are employed to convert probabilistic constraints into deterministic ones. The bisection method is utilized to solve the reformulated problem. Finally, the computational complexity of the proposed three approaches based on the PCU and FCU model is analyzed.

Multi-UAV trajectory planning for RIS-assisted SWIPT system under connectivity preservation

Unmanned aerial vehicle (UAV)-assisted simultaneous wireless information and power transfer (SWIPT) systems have recently gained significant attraction in internet-of-things (IoT) applications that have limited or no infrastructure. Specifically, the free mobility of UAVs in three-dimensional (3D) space allows us good-quality channel links, thereby enhancing the communication environment and improving performance in terms of achievable rates, latency, and energy efficiency. Meanwhile, IoT devices can extend their battery life by harvesting the energy following the SWIPT protocol, which leads to an increase in the overall system lifespan. In this paper, we propose a secure UAV-assisted SWIPT system designed to optimize the secrecy energy efficiency (SEE) of a ground network, wherein a base station (BS) transmits confidential messages to an energy-constrained device in the presence of a passive eavesdropper. Here, we employ a UAV acting as a helper node to improve the SEE of the system and to aid in the energy harvesting (EH) of the battery-limited ground device following the SWIPT protocol. To this end, we formulate the SEE maximization problem by jointly optimizing the transmit powers of the BS and UAV, the power-splitting ratio for EH operations, and the UAV’s flight path. The solution is obtained via a proposed algorithm that leverages successive convex approximation (SCA) and Dinkelbach’s method. Through simulations, we corroborate the feasibility and effectiveness of the proposed algorithm compared to conventional partial optimization approaches.

Simultaneous wireless information and power transfer (SWIPT) and interference alignment (IA) are two emerging techniques in energy harvesting and interference management for the next generation wireless networks, respectively. Although many studies have focused on SWIPT and IA, the conjunction of these two techniques is largely ignored, which should be noted to reuse the interference as energy for harvesting. In this paper, we jointly study SWIPT and IA in the multiuser MIMO system to realize energy harvesting and interference management simultaneously and effectively. Specifically, antenna selection (AS) based SWIPT scheme is proposed and analyzed for IA networks. Furthermore, power allocation (PA) for multiple data streams is designed to further improve its performance, and the closed-form solution can be obtained by Lagrange duality method. In addition, given the constrained number of antennas, another scheme called power splitting (PS) based SWIPT is utilized, where PA is also considered and formulated as a joint optimization problem. Simulation results are presented to show the superiority of the proposed schemes.

This paper considers transmit covariance matrix design for secrecy rate maximization problem in a multiple-input single-output (MISO) multicasting simultaneous wireless information and power transfer (SWIPT) system. In order to enhance the performance of the system, artificial noise (AN) is added to the transmit signal in the design for the following purposes: to reduce the received signal-to-noise ratio (SNR) at the eavesdroppers and increase the harvested energy. We assume that all the channel-state-information (CSI) is perfectly known at the transmitter and all legitimate users are capable of simultaneously receiving information and harvesting energy. In addition, all the eavesdroppers are passive and they can harvest energy only when they are not intercepting or eavesdropping the messages intended for the legitimate users. The original secrecy rate maximization problem is not convex in terms of transmit and artificial covariance matrices as well as the power splitting (PS) ratio. In order to circumvent this non-convexity issue, we exploit the Charnes-Cooper Transformation and semidefinite relaxation (SDR) to convert this original problem into a convex one. However, this convex problem does not always yield the rank-one transmit and AN covariance matrices to obtain the solution of the original problem. Therefore, we analyze the optimal conditions and utilize a Gaussian randomization (GR) method to construct the rank-one solutions from the non-rank one results. Simulation results have been provided to demonstrate the performance of the proposed transmit covariance matrices design for MISO multicasting SWIPT system.