Optimal Power Splitting Ratio Research Articles

Transmit Power Optimization in Multihop Amplify-and-Forward Relay Systems with Simultaneous Wireless Information and Power Transfer

In this paper, we study a multi-hop amplify-and-forward (AF) simultaneous wireless information and power transmission (SWIPT) relay system. Each relay node harvests power using a power split (PS) method from a portion of the received signal, amplifies the remaining received signal, and passes it to the next relay. Based on this system model and signal flow, we derived and solved the convex power minimization problem with the optimal PS ratio. In this case, it was found that using the optimal PS ratio consumed a lower amount of power than when using a fixed PS ratio (0.5). We then investigated the impact of processing cost on the AF-SWIPT system using decoding and forwarding SWIPT as benchmarks, and found that AF-SWIPT was superior.

Sum-rate maximization for a distributed space-time block code-aided cooperative NOMA with energy harvesting

AbstractIn this paper, we exploit the spatial and transmission diversities in cooperative non-orthogonal multiple access (C-NOMA) networks to improve the system sum-rate. To achieve this, we propose a user-pairing scheme where near-field user pairs serve as relays for user pairs in the far-field region. Based on this pairing scheme, we incorporate a space-time block code transmission technique at the near-field user pairs to maximize the transmission diversity in the cooperative phase. Moreover, we consider a non-linear energy harvesting model at the near-field user pair to alleviate the problem of energy consumption during the cooperative transmission phase. Further to this, we formulate a sum-rate maximization problem that is addressed from the viewpoint of joint power allocation factor and power splitting ratio optimization. We develop a low-computational iterative algorithm based on the concepts of the Stackelberg game and the Nash bargaining solution. We benchmark our findings with different scenarios, such as energy harvesting C-NOMA with a fixed power allocation factor and power splitting ratio, energy harvesting C-NOMA without STBC, non-cooperative NOMA, and orthogonal multiple access. The results obtained via simulations outperform the benchmark schemes.

Joint beamforming and power splitting design for MISO downlink communication with SWIPT: a comparison between cell-free massive MIMO and small-cell deployments

Simultaneous wireless information and power transfer (SWIPT) has been advocated as a highly promising technology for enhancing the capabilities of 5G and 6G devices. However, the challenge of dealing with large propagation path loss poses a significant hurdle. To address this issue, massive multiple-input multiple-output (MIMO) is employed to enhance the efficiency of SWIPT in cellular-based networks with multiple small cells, and especially increase the energy for cell-edge users. In addition, by leveraging a large set of spatially distributed base stations to collaboratively serve SWIPT-enabled user equipment, the cell-free massive MIMO has the potential to provide even better performance than the conventional small-cell systems. In this work, we extend the investigation to include the application of SWIPT technology with alternating current (AC) logic in the cell-free networks and the small-cell networks and propose joint beamforming and power splitting optimization frameworks to maximize the system sum-rate, subject to the constraints on harvested energy, AC logic energy supply, and total transmit power. The optimization problem is shown to be non-convex, posing a significant challenge. To address this challenge, we resort to a two-stage decomposition approach. Specifically, we first introduce quadratic transform-based fractional programming (FP) algorithms to iteratively solve the non-convex optimization problems in the first stage, achieving near-optimal solutions with low time complexities. To further reduce the complexities, we also incorporate conventional schemes such as zero forcing, maximum ratio transmission, and signal-to-leakage-and-noise ratio for the design of beamforming vectors. Second, to determine the optimal power splitting ratio within the framework, we develop a one-dimensional (1-D) search algorithm to tackle the single variable optimization problem reduced in the second stage. These algorithms are then evaluated in the context of cell-free MIMO and small-cell networks with numerical experiments. The results show that the FP-based algorithms can consistently outperform those utilizing the conventional beamforming schemes, and the solutions of this work can achieve up to fivefold improvement in the system sum-rate than the small-cell counterpart while providing different but comparable performance trends in energy harvesting (EH).

Multi-agent deep reinforcement learning based resource management in SWIPT enabled cellular networks with H2H/M2M co-existence

Machine-to-Machine (M2M) communication is crucial in developing Internet of Things (IoT). As it is well known that cellular networks have been considered as the primary infrastructure for M2M communications, there are several key issues to be addressed in order to deploy M2M communications over cellular networks. Notably, the rapid growth of M2M traffic dramatically increases energy consumption, as well as degrades the performance of existing Human-to-Human (H2H) traffic. Sustainable operation technology and resource management are efficacious ways for solving these issues. In this paper, we investigate a resource management problem in cellular networks with H2H/M2M coexistence. First, considering the energy-constrained nature of machine type communication devices (MTCDs), we propose a novel network model enabled by simultaneous wireless information and power transfer (SWIPT), which empowers MTCDs with the ability to simultaneously perform energy harvesting (EH) and information decoding. Given the diverse characteristics of IoT devices, we subdivide MTCDs into critical and tolerable types, further formulating the resource management problem as an energy efficiency (EE) maximization problem under divers Quality-of-Service (QoS) constraints. Then, we develop a multi-agent deep reinforcement learning (DRL) based scheme to solve this problem. It provides optimal spectrum, transmit power and power splitting (PS) ratio allocation policies, along with efficient model training under designed behavior-tracking based state space and common reward function. Finally, we verify that with a reasonable training mechanism, multiple M2M agents successfully work cooperatively in a distributed way, resulting in network performance that outperforms other intelligence approaches in terms of convergence speed and meeting the EE and QoS requirements.

Multi-Dimensional Resource Allocation for Throughput Maximization in CRIoT with SWIPT

To solve the power supply problem of battery-limited Internet of Things devices (IoDs) and the spectrum scarcity problem, simultaneous wireless information and power transfer (SWIPT) and cognitive radio (CR) technology were integrated into the Internet of Things (IoT) network to build a cognitive radio IoT (CRIoT) with SWIPT. In this network, secondary users (SUs) could adaptively switch between spectrum sensing, SWIPT, and information transmission to improve the total throughput. To solve the complicated multi-dimensional resource allocation problem in CRIoT with SWIPT, we propose a multi-dimensional resource allocation algorithm for maximizing the total throughput. Three-dimensional resources were jointly optimized, which are time resource (the duration of each process), power resource (the transmit power and the power splitting ratio of each node), and spectrum resource, under some constraints, such as maximum transmit power constraint and maximum permissible interference constraint. To solve this intractable mixed-integer nonlinear program (MINLP) problem, firstly, the sensing task assignment for cooperative spectrum sensing (CSS) was obtained by using a greedy sensing algorithm. Secondly, the original problem was transformed into a convex problem via some transformations with fixed-power splitting ratio and time switching. The Lagrange dual method and subgradient method were adopted to obtain the optimal power and channel allocation. Then, a one-dimensional search algorithm was used to obtain the optimal power splitting ratio and the time switching ratio. Finally, a heuristic algorithm was adopted to obtain the optimal sensing duration. The simulation results show that the proposed algorithm can achieve higher total system throughput than other benchmark algorithms, such as a greedy algorithm, an average algorithm, and the Kuhn–Munkres (KM) algorithm.

Intelligent Reflecting Surface-Assisted Over-the-Air Computation for Backscatter Sensor Networks

For achieving efficient data aggregation and energy supply in Internet of Things, this paper investigates the optimal design for an intelligent reflecting surface (IRS)-aided backscatter sensor network with over-the-air computation (AirComp). To ensure the accuracy of data aggregation, this paper presents a mean-squared error (MSE) minimization problem via jointly optimizing the normalization factor, power splitting ratios of backscatter devices (BDs), and phase shifts of IRS. Inspired by the block coordinate descent technique, we propose an alternating optimization method to solve the formulated non-convex problem. Specifically, we derive the optimal normalization factor and power splitting ratios in closed-form expressions, and exploit the variable substitution technique and semi-definite relaxation method to address the phase shift optimization subproblem. Simulation results demonstrate the significant MSE reduction achieved by the proposed method, in comparison with other benchmark methods.

Joint Data Transmission and Energy Harvesting for MISO Downlink Transmission Coordination in Wireless IoT Networks.

The advent of simultaneous wireless information and power (SWIPT) has been regarded as a promising technique to provide power supplies for an energy sustainable Internet of Things (IoT), which is of paramount importance due to the proliferation of high data communication demands of low-power network devices. In such networks, a multi-antenna base station (BS) in each cell can be utilized to concurrently transmit messages and energies to its intended IoT user equipment (IoT-UE) with a single antenna under a common broadcast frequency band, resulting in a multi-cell multi-input single-output (MISO) interference channel (IC). In this work, we aim to find the trade-off between the spectrum efficiency (SE) and energy harvesting (EH) in SWIPT-enabled networks with MISO ICs. For this, we derive a multi-objective optimization (MOO) formulation to obtain the optimal beamforming pattern (BP) and power splitting ratio (PR), and we propose a fractional programming (FP) model to find the solution. To tackle the nonconvexity of FP, an evolutionary algorithm (EA)-aided quadratic transform technique is proposed, which recasts the nonconvex problem as a sequence of convex problems to be solved iteratively. To further reduce the communication overhead and computational complexity, a distributed multi-agent learning-based approach is proposed that requires only partial observations of the channel state information (CSI). In this approach, each BS is equipped with a double deep Q network (DDQN) to determine the BP and PR for its UE with lower computational complexity based on the observations through a limited information exchange process. Finally, with the simulation experiments, we verify the trade-off between SE and EH, and we demonstrate that, apart from the FP algorithm introduced to provide superior solutions, the proposed DDQN algorithm also shows its performance gain in terms of utility to be up to 1.23-, 1.87-, and 3.45-times larger than the Advantage Actor Critic (A2C), greedy, and random algorithms, respectively, in comparison in the simulated environment.

Dynamic NOMA-HARQ in the Finite Blocklength Regime: Performance Analysis and Optimization

In this letter, we propose a dynamic re-transmission strategy for the two-user uplink non-orthogonal multiple access scenario (NOMA) with hybrid automatic repeat request (HARQ) and more than one allowed re-transmissions. In the proposed scheme, whenever both users simultaneously receive negative acknowledgment (NACK) from the base station (BS), the high-power user is asked to re-transmit its previous packet, while the low-power user is requested to send a new packet in the next time slot. The low-power user is allowed to retransmit its previous packets when the other user stops the re-transmission. We propose a Markov model to analyze the dynamics of the proposed scheme and the conventional scheme, where the users always send the re-transmission when receiving a NACK from the BS. We find the optimal power splitting ratios to minimize the maximum packet error rate (PER) amongst users. Simulation results show that the proposed dynamic approach significantly improves the PER of the low-power user at the expense of a negligible increase in the PER of the high-power user.

On the Performance of Coded Cooperative Communication with Multiple Energy-Harvesting Relays and Error-Prone Forwarding

In this paper, we consider a coded cooperative communication network with multiple energy-harvesting (EH) relays. In order to adequately address the problem of error propagation due to the erroneous decoding at the relays, as in the case of conventional decode and forward (DF) relaying protocol, we propose coded cooperative schemes with hard information relaying (HIR) and soft information relaying (SIR) strategies. The performance of the relayed communication with EH relay depends crucially on the channel decoding capability at the relay, channel gains at the source–relay and relay–destination links, and ultimately on the power-splitting ratio of the relay EH receiver. The exact closed-form expression for the outage probability performance of the coded cooperative scheme with HIR strategy and relay selection (CC-HIR-RS) is derived for both cases, namely for constant and optimal power-splitting ratios. Concerning the coded cooperative scheme with SIR strategy, a Rayleigh Gaussian log likelihood ratio-based model is used to describe the soft estimated symbols at the output of the relay soft encoder. Directives are provided to determine the model parameters, and, accordingly, the signal-to-noise ratio (SNR) of the equivalent one-hop relaying channel is derived. A closed-form expression for the outage probability performance of the proposed coded cooperative scheme with SIR and relay selection (CC-SIR-RS) is derived. In addition, a fuzzy logic-based power-splitting scheme in EH relay applying SIR is proposed. The fading coefficients of the source–relay and relay–destination links and distance between source and relay node are considered as input parameters of the fuzzy logic system to obtain an appropriate power-splitting ratio that leads to a quasi-optimal SNR of the equivalent end-to-end channel. Monte Carlo simulations are presented to demonstrate the validity of the analytical results, and a comparison between the performance of the CC-HIR-RS scheme with constant and optimized power-splitting ratios and that of the CC-SIR-RS scheme with constant and fuzzy logic-based power-splitting ratios is provided.

Achievable rate optimization for space–time block code-aided cooperative NOMA with energy harvesting

Cooperative non-orthogonal multiple access (C-NOMA) is one of the promising techniques that support the massive connectivity of users and improve the fair allocation of network resources. However, the performance of C-NOMA networks is restricted due to the complexity introduced by multiple user-assisted relays coupled with their energy consumption bottleneck. In this paper, we develop a low-complexity network framework with the main goal of achieving a fair and optimal achievable rate. For the network model, we adopt a Space–time block code transmission scheme to increase the spatial diversity of the network with a minimum number of relays. Further to this, we consider a radio frequency-based energy harvesting technique known as simultaneous wireless information and power transfer in order to compensate for energy expended by the relays over the cooperating transmission links. Based on the proposed network model, we solve the achievable rate maximization problem from the perspective of joint optimal power allocation factor and power splitting ratio. Due to the non-convex nature of the problem and to also ensure fair resource allocation, we propose a low-complexity solution based on the concept of Nash bargaining solution. Further to this, we evaluate the achievable rate and energy efficiency of the system. Simulation results show that our proposed solution outperforms the baseline approaches.

Stochastic Hybrid Model Predictive Control: Application to Parallel Hybrid Electric Vehicles

Hybrid electric vehicles (HEVs) are attracting attention for their high fuel efficiency and low emissions compared to diesel-powered vehicles. The fuel efficiency of the HEV is highly dependent on the optimal power split ratio. The problem of finding the optimal power split ratio trajectory is difficult to solve with linear and/or deterministic controllers because it has nonlinear and stochastic characteristics. In this study, stochastic hybrid model predictive control (SHMPC) was applied to the HEV system. Piecewise affine (PWA) modeling of HEV systems was performed to model nonlinear systems with smaller model-plant mismatch compared to linear models. Furthermore, to account for the driver's stochastic behavior, a scenario-based stochastic model predictive control (SMPC) approach was employed. Controllers of scenario-based linear SMPC and scenario-based SHMPC are built. The controllers were tested in MATLAB/Simulink HEVP2 Reference Application simulation environment.

SWIPT-Enabled NOMA in Distributed Antenna System With Imperfect CSI for Max-Sum-Rate and Max–Min Fairness

Motivated by the fact that the energy harvesting (EH) of simultaneous wireless information and power transfer (SWIPT) and the system throughput of nonorthogonal multiple access (NOMA) can be greatly increased with the help of the distributed antenna system (DAS), we present a framework for applying the DAS to SWIPT-enabled NOMA. In this article, we jointly optimize power splitting (PS) for SWIPT and power allocation for NOMA signaling in SWIPT-enabled NOMA helped by the DAS for two different following goals: 1) sum-rate maximization; and 2) achieving max–min fairness. Also, we analyze the impact of imperfect channel state information (CSI) on power splitting and power allocation of SWIPT-NOMA with the DAS in both problems. Due to the nonconvexity of the joint optimization problem, we first obtain the optimal PS ratios and use them to convert the problem to a single-variable problem. The joint optimal solution is then obtained in the closed-form by finding and comparing all local extrema of the objective function using the first-order derivative. Numerical results were used to validate the theoretical findings and demonstrate that the proposed framework of using SWIPT-enabled NOMA in DAS achieves higher data rates than the existing SWIPT-enabled NOMA while guaranteeing minimum harvested energy and data rate requirements.

Exploiting Hybrid SWIPT in Ambient Backscatter Communication-Enabled Relay Networks: Optimize Power Allocation and Time Scheduling

Ambient backscatter communication (ABC) has become an innovative technique for energy-efficient and short-range communication. In this article, we propose a hybrid simultaneously wireless information and power transfer (SWIPT)-assisted relay transmission scheme to extend the communication range of the ABC mode. That is, SWIPT with time-switching (TS) and power-splitting (PS) approaches is adopted into the relay for harvesting energy and processing information. Moreover, after harvesting energy, all the relays simultaneously transmit data according to power-domain nonorthogonal multiple access (NOMA) technology. Therefore, in the ABC-enabled hybrid SWIPT relay networks with power-domain NOMA, there is a problem of how to maximize the network throughput by allocating available power and time resources. We first formulate an optimization problem to achieve the maximum network throughput by finding the optimal PS ratio of relays as well as the optimal time allocation among users and relays. In order to reduce the computational complexity of the problem, we decompose it into two subproblems: 1) PS ratio optimization and 2) time allocation optimization. For PS ratio optimization, we derive the closed form of the optimal PS ratio for relays by the Lagrange dual theory and subgradient method. For time allocation optimization, the Lagrange dual theory and subgradient method are also adopted to obtain the closed form of the optimal time allocation among users and relays. Then, we propose a joint optimization of the PS ratio and time allocation-based iterative algorithm to solve the problem. Finally, simulation results show that our proposed scheme has an obvious throughput superiority over other two benchmark schemes.

Joint Transmit-and-Receive Antenna Selection System for MIMO-NOMA With Energy Harvesting

In this paper, outage probability (OP) of a joint transmit and receive antenna selection (JTRAS) scheme is analyzed in multiple-input multiple-output non orthogonal multiple access based downlink energy harvesting (EH) relaying networks. In this dual-hop and amplify-and-forward relaying based network, since the first and second hops are types of single-user and multi-user systems, respectively, the optimal JTRAS and suboptimal majority-based JTRAS schemes are employed in the first and second hops. The theoretical OP analysis is carried out over Nakagami-m fading channels in the cases of perfect and imperfect successive interference cancellation. Finally, Monte Carlo simulations are performed to substantiate the accuracy of the theoretical analysis. It is shown that the optimal power splitting ratios at the EH relay are different for users and the users with good channel conditions have minimum optimal ratios.

Throughput Enhancement in FD- and SWIPT-Enabled IoT Networks Over Nonidentical Rayleigh Fading Channels

Simultaneous wireless information and power transfer (SWIPT) and full-duplex (FD) communications have emerged as prominent technologies in overcoming the limited energy resources in Internet of Things (IoT) networks and improving their spectral efficiency (SE). This article investigates the outage and throughput performance for a decode-and-forward (DF) relay SWIPT system, which consists of one source, multiple relays, and one destination. The relay nodes in this system can harvest energy from the source’s signal and operate in the FD mode. A suboptimal, low-complexity, yet efficient relay selection scheme is also proposed. Specifically, a single relay is selected to convey information from a source to a destination so that it achieves the best channel from the source to the relays. An analysis of outage probability (OP) and throughput performed on two relaying strategies, termed static power splitting-based relaying (SPSR) and optimal dynamic power splitting-based relaying (ODPSR), is presented. Notably, we considered independent and nonidentically distributed (i.n.i.d.) Rayleigh fading channels, which pose new challenges in obtaining analytical expressions. In this context, we derived exact closed-form expressions of the OP and throughput of both SPSR and ODPSR schemes. We also obtained the optimal power splitting ratio of ODPSR for maximizing the achievable capacity at the destination. Finally, we present extensive numerical and simulation results to confirm our analytical findings. Both simulation and analytical results show the superiority of ODPSR over SPSR.

Sum-Rate Maximization in Two-Way MIMO Cooperative Networks With an Energy Harvesting Relay

This study considers a two-way amplify-and-forward relay system wherein two users exchange information with the assistance of an energy-harvesting MIMO relay node. With two-time-slot transmission, the relay receives the transmitted signal from both users in the first time-slot and splits it into two parts for information forwarding and energy harvesting, respectively, in accordance with a particular power splitting ratios. The phases of the RF signals used for energy harvesting at each relay antenna are shifted before being combined to achieve the highest power. Meanwhile, the RF signals used for information forwarding are passed through a linear precoder and then broadcast to the users in the second time-slot. We aim to optimize the power splitting ratios, precoding matrix, and RF phase shifting factors such that the sum-rate is maximized. Since joint optimization is intractable, the parameters are partitioned into three sub-optimization problems. For each parameter, a sub-optimal solution is derived in a (semi-)closed form. It shows numerically that the proposed sub-optimal solutions for the optimal phase shift and power split ratio are close to the global optimal solutions. Moreover, the proposed solution for the precoding matrix outperforms the so-called ’General Power Iterative’ algorithm proposed in the literature.

Active RIS Versus Passive RIS: Which is Superior With the Same Power Budget?

This letter theoretically compares the active reconfigurable intelligent surface (RIS)-aided system with the passive RIS-aided system. For a fair comparison, we consider that these two systems have the same overall power budget that can be used at both the base station (BS) and the RIS. For active RIS, we first derive the optimal power splitting between the BS’s transmit signal power and RIS’s output signal power. We also analyze the impact of various system parameters on the optimal power splitting ratio. Then, we theoretically and numerically compare the performance between the active RIS and the passive RIS, which demonstrates that the active RIS would be superior if the power budget is not very small and the number of RIS elements is not very large.

Achievable Rate Maximization for Multi-Relay AF Cooperative SWIPT Systems with a Nonlinear EH Model.

In this paper, the maximization of the achievable information rate is proposed for the multi-relay amplify-and-forward cooperative simultaneous wireless information and power transfer communication systems, where the nonlinear characteristic of the energy harvesting (EH) circuits is taken into account for the receivers of the relay nodes. The time switching (TS) and power splitting (PS) schemes are considered for the EH receivers and the achievable rate maximization problems are formulated as convex and non-convex optimization problems, respectively. The optimal TS and PS ratios for the relay nodes along with the maximum achievable rates for the system are obtained, respectively, by solving the optimal problems with efficient algorithms. The asymptotic maximum achievable rates at low and high input signal-to-noise ratios (SNRs) for both the PS and TS schemes are also analyzed. It is demonstrated that the PS scheme is more susceptible to the variation of the relays’ location and the channel parameters than TS scheme, whereas the TS scheme is more susceptible to the mismatch of the resource allocation than PS scheme. Specifically, compared to the linear EH model, the nonlinear EH model achieves significant performance gain for the TS scheme, whereas inconspicuous performance improvement is achieved for the PS scheme.

Worst-Case Energy Efficiency in Secure SWIPT Networks With Rate-Splitting ID and Power-Splitting EH Receivers

This paper studies the robust beamforming design for simultaneous wireless information and power transfer (SWIPT)-enabled networks, where the rate-splitting (RS) scheme and the power-splitting (PS) energy harvesting (EH) receiver are adopted for secure information transfer and EH, respectively. In order to explore the worst-case energy efficiency (EE) performance limit of the system, an EE maximization problem is formulated with the elliptically bounded channel state information error model under the constraints of the quality of service (QoS) requirements of information decoding users, the EH requirements of EH users and the power budget at the transmitter. To tackle the formulated non-convex problem, a sequential minimal optimization-based algorithm is first proposed to construct a mapping table and the optimal PS ratios of the PS EH receiver are found by searching the table. Then, a dual-layer iterative algorithm is designed to obtain the maximal EE based on the Dinkelbach’s method in the inner loop and the successive convex approximation method in the outer loop. To accelerate the convergence of the outer loop, an efficient initialization algorithm is also designed. Simulation results show that the RS scheme contributes to the EE enhancement, and the PS EH receiver enlarges the rate-energy region restricted by the non-linear EH circuit. Moreover, traditional sum-rate maximization design and power minimization design may induce a notable worst-case EE performance loss at the high-power region and the low-QoS requirement region, respectively.

Joint Coordinated Beamforming and Power Splitting Ratio Optimization in MU-MISO SWIPT-Enabled HetNets: A Multi-Agent DDQN-Based Approach

This paper proposes a multi-agent double deep Q network (DDQN)-based approach to jointly optimize the beamforming vectors and power splitting (PS) ratio in multi-user multiple-input single-output (MU-MISO) simultaneous wireless information and power transfer (SWIPT)-enabled heterogeneous networks (HetNets), where a macro base station (MBS) and several femto base stations (FBSs) serve multiple macro user equipments (MUEs) and femto user equipments (FUEs). The PS receiver architecture is deployed at FUEs. An optimization problem is formulated to maximize the achievable sum information rate of FUEs under the constraints of the achievable information rate requirements of MUEs and FUEs and the energy harvesting (EH) requirements of FUEs. Since the optimization problem is challenging to handle due to the high dimension and time-varying environment, an efficient multi-agent DDQN-based algorithm is presented, which is trained in a centralized manner and runs in a distributed manner, where two sets of deep neural network parameters are jointly updated and trained to tackle the problem and avoid overestimation. To facilitate the presented multi-agent DDQN-based algorithm, the action space, the state space and the reward function are designed, where the codebook matrix is employed to deal with the complex transmit beamforming vectors. Simulation results validate the proposed algorithm. Notable performance gains are achieved by the proposed algorithm due to considering the beam directions in the action space and the adaptability to the Doppler frequency shifts. Besides, the proposed algorithm is shown to be superior to other benchmark ones numerically.