In this paper,we optimize the spectrum efficiency(SE)of uplink massive multiple-input multiple-output(MIMO)system with imperfect channel state information(CSI)over Rayleigh fading channel.The SE optimization problem i...In this paper,we optimize the spectrum efficiency(SE)of uplink massive multiple-input multiple-output(MIMO)system with imperfect channel state information(CSI)over Rayleigh fading channel.The SE optimization problem is formulated under the constraints of maximum power and minimum rate of each user.Then,we develop a near-optimal power allocation(PA)scheme by using the successive convex approximation(SCA)method,Lagrange multiplier method,and block coordinate descent(BCD)method,and it can obtain almost the same SE as the benchmark scheme with lower complexity.Since this scheme needs three-layer iteration,a suboptimal PA scheme is developed to further reduce the complexity,where the characteristic of massive MIMO(i.e.,numerous receive antennas)is utilized for convex reformulation,and the rate constraint is converted to linear constraints.This suboptimal scheme only needs single-layer iteration,thus has lower complexity than the near-optimal scheme.Finally,we joint design the pilot power and data power to further improve the performance,and propose an two-stage algorithm to obtain joint PA.Simulation results verify the effectiveness of the proposed schemes,and superior SE performance is achieved.展开更多
In Beyond the Fifth Generation(B5G)heterogeneous edge networks,numerous users are multiplexed on a channel or served on the same frequency resource block,in which case the transmitter applies coding and the receiver u...In Beyond the Fifth Generation(B5G)heterogeneous edge networks,numerous users are multiplexed on a channel or served on the same frequency resource block,in which case the transmitter applies coding and the receiver uses interference cancellation.Unfortunately,uncoordinated radio resource allocation can reduce system throughput and lead to user inequity,for this reason,in this paper,channel allocation and power allocation problems are formulated to maximize the system sum rate and minimum user achievable rate.Since the construction model is non-convex and the response variables are high-dimensional,a distributed Deep Reinforcement Learning(DRL)framework called distributed Proximal Policy Optimization(PPO)is proposed to allocate or assign resources.Specifically,several simulated agents are trained in a heterogeneous environment to find robust behaviors that perform well in channel assignment and power allocation.Moreover,agents in the collection stage slow down,which hinders the learning of other agents.Therefore,a preemption strategy is further proposed in this paper to optimize the distributed PPO,form DP-PPO and successfully mitigate the straggler problem.The experimental results show that our mechanism named DP-PPO improves the performance over other DRL methods.展开更多
In this paper,we investigate IRS-aided user cooperation(UC)scheme in millimeter wave(mmWave)wirelesspowered sensor networks(WPSN),where two single-antenna users are wireless powered in the wireless energy transfer(WET...In this paper,we investigate IRS-aided user cooperation(UC)scheme in millimeter wave(mmWave)wirelesspowered sensor networks(WPSN),where two single-antenna users are wireless powered in the wireless energy transfer(WET)phase first and then cooperatively transmit information to a hybrid access point(AP)in the wireless information transmission(WIT)phase,following which the IRS is deployed to enhance the system performance of theWET andWIT.We maximized the weighted sum-rate problem by jointly optimizing the transmit time slots,power allocations,and the phase shifts of the IRS.Due to the non-convexity of the original problem,a semidefinite programming relaxation-based approach is proposed to convert the formulated problem to a convex optimization framework,which can obtain the optimal global solution.Simulation results demonstrate that the weighted sum throughput of the proposed UC scheme outperforms the non-UC scheme whether equipped with IRS or not.展开更多
In this paper,the channel capacity of the multiple-input multiple-output(MIMO)visible light communication(VLC)system is investigated under the peak,average optical and electrical power constraints.Finding the channel ...In this paper,the channel capacity of the multiple-input multiple-output(MIMO)visible light communication(VLC)system is investigated under the peak,average optical and electrical power constraints.Finding the channel capacity of MIMO VLC is shown to be a mixed integer programming problem.To address this open problem,we propose an inexact gradient projection method to find the channel capacity-achieving discrete input distribution and the channel capacity of MIMO VLC.Also we derive both upper and lower bounds of the capacity of MIMO VLC with the closed-form expressions.Furthermore,by considering practical discrete constellation inputs,we develop the optimal power allocation scheme to maximize transmission rate of MIMO VLC system.Simulation results show that more discrete points are needed to achieve the channel capacity as SNR increases.Both the upper and lower bounds of channel capacity are tight at low SNR region.In addition,comparing the equal power allocation,the proposed power allocation scheme can significantly increase the rate for the low-order modulation inputs.展开更多
Formany years,researchers have explored power allocation(PA)algorithms driven bymodels in wireless networks where multiple-user communications with interference are present.Nowadays,data-driven machine learning method...Formany years,researchers have explored power allocation(PA)algorithms driven bymodels in wireless networks where multiple-user communications with interference are present.Nowadays,data-driven machine learning methods have become quite popular in analyzing wireless communication systems,which among them deep reinforcement learning(DRL)has a significant role in solving optimization issues under certain constraints.To this purpose,in this paper,we investigate the PA problem in a k-user multiple access channels(MAC),where k transmitters(e.g.,mobile users)aim to send an independent message to a common receiver(e.g.,base station)through wireless channels.To this end,we first train the deep Q network(DQN)with a deep Q learning(DQL)algorithm over the simulation environment,utilizing offline learning.Then,the DQN will be used with the real data in the online training method for the PA issue by maximizing the sumrate subjected to the source power.Finally,the simulation results indicate that our proposedDQNmethod provides better performance in terms of the sumrate compared with the available DQL training approaches such as fractional programming(FP)and weighted minimum mean squared error(WMMSE).Additionally,by considering different user densities,we show that our proposed DQN outperforms benchmark algorithms,thereby,a good generalization ability is verified over wireless multi-user communication systems.展开更多
To improve the operation efficiency of the photovoltaic power station complementary power generation system,an optimal allocation model of the photovoltaic power station complementary power generation capacity based o...To improve the operation efficiency of the photovoltaic power station complementary power generation system,an optimal allocation model of the photovoltaic power station complementary power generation capacity based on PSO-BP is proposed.Particle Swarm Optimization and BP neural network are used to establish the forecasting model,the Markov chain model is used to correct the forecasting error of the model,and the weighted fitting method is used to forecast the annual load curve,to complete the optimal allocation of complementary generating capacity of photovoltaic power stations.The experimental results show that thismethod reduces the average loss of photovoltaic output prediction,improves the prediction accuracy and recall rate of photovoltaic output prediction,and ensures the effective operation of the power system.展开更多
The netted radar system(NRS)has been proved to possess unique advantages in anti-jamming and improving target tracking performance.Effective resource management can greatly ensure the combat capability of the NRS.In t...The netted radar system(NRS)has been proved to possess unique advantages in anti-jamming and improving target tracking performance.Effective resource management can greatly ensure the combat capability of the NRS.In this paper,based on the netted collocated multiple input multiple output(CMIMO)radar,an effective joint target assignment and power allocation(JTAPA)strategy for tracking multi-targets under self-defense blanket jamming is proposed.An architecture based on the distributed fusion is used in the radar network to estimate target state parameters.By deriving the predicted conditional Cramer-Rao lower bound(PC-CRLB)based on the obtained state estimation information,the objective function is formulated.To maximize the worst case tracking accuracy,the proposed JTAPA strategy implements an online target assignment and power allocation of all active nodes,subject to some resource constraints.Since the formulated JTAPA is non-convex,we propose an efficient two-step solution strategy.In terms of the simulation results,the proposed algorithm can effectively improve tracking performance in the worst case.展开更多
In this paper,we investigate the system performance of a heterogeneous cellular network consisting of a macro cell and a small cell,where each cell has one user and one base station with multiple antennas.The macro ba...In this paper,we investigate the system performance of a heterogeneous cellular network consisting of a macro cell and a small cell,where each cell has one user and one base station with multiple antennas.The macro base station(MBS)and the small base station(SBS)transmit their confidential messages to the macro user(MU)and the small user(SU)over their shared spectrum respectively.To enhance the system sum rate(SSR)of MBS-MU and SBS-SU transmission,we propose joint antenna selection combined with optimal power allocation(JAS-OPA)scheme and independent antenna selection combined with optimal power allocation(IAS-OPA)scheme.The JAS-OPA scheme requires to know the channel state information(CSI)of transmission channels and interference channels,while the IAS-OPA scheme only needs to know the CSI of transmission channels.In addition,we carry out the analysis for conventional round-robin antenna selection combined with optimal power allocation(RR-OPA)as a benchmark scheme.We formulate the SSR maximization problem through the power allocation between MBS and SBS and propose iterative OPA algorithms for JAS-OPA,IAS-OPA and RR-OPA schemes,respectively.The results show that the OPA schemes outperform the equal power allocation in terms of SSR.Moreover,we provide the closed-form expression of the system outage probability(SOP)for IAS scheme and RR scheme,it shows the SOP performance can be significantly improved by our proposed IAS scheme compared with RR scheme.展开更多
As the sixth generation network(6G)emerges,the Internet of remote things(IoRT)has become a critical issue.However,conventional terrestrial networks cannot meet the delay-sensitive data collection needs of IoRT network...As the sixth generation network(6G)emerges,the Internet of remote things(IoRT)has become a critical issue.However,conventional terrestrial networks cannot meet the delay-sensitive data collection needs of IoRT networks,and the Space-Air-Ground integrated network(SAGIN)holds promise.We propose a novel setup that integrates non-orthogonal multiple access(NOMA)and wireless power transfer(WPT)to collect latency-sensitive data from IoRT networks.To extend the lifetime of devices,we aim to minimize the maximum energy consumption among all IoRT devices.Due to the coupling between variables,the resulting problem is non-convex.We first decouple the variables and split the original problem into four subproblems.Then,we propose an iterative algorithm to solve the corresponding subproblems based on successive convex approximation(SCA)techniques and slack variables.Finally,simulation results show that the NOMA strategy has a tremendous advantage over the OMA scheme in terms of network lifetime and energy efficiency,providing valuable insights.展开更多
An efficient spaee-time-frequency （STF） coding strategy for multi-input multi-output orthogonal frequency division multiplexing （MIMO-OFDM） systems is presented for high bit rate data transmission over frequency s...An efficient spaee-time-frequency （STF） coding strategy for multi-input multi-output orthogonal frequency division multiplexing （MIMO-OFDM） systems is presented for high bit rate data transmission over frequency selective fading channels. The proposed scheme is a new approach to space-time-frequency coded OFDM （ODFDM） that combines OFDM with space-time coding, linear precoding and adaptive power allocation to provide higher quality of transmission in terms of the bit error rate performance and power efficiency. In addition to exploiting the maximux diversity gain in frequency, time and space, the proposed scheme enjoys high coding advantages and low-complexity decoding. The significant performance improvement of our design is confirned by corroborating numerical simulations.展开更多
Massive Multiple-Input-Multiple-Output(MIMO)is a promising technology to meet the demand for the connection of massive devices and high data capacity for mobile networks in the next generation communication system.How...Massive Multiple-Input-Multiple-Output(MIMO)is a promising technology to meet the demand for the connection of massive devices and high data capacity for mobile networks in the next generation communication system.However,due to the massive connectivity of mobile devices,the pilot contamination problem will severely degrade the communication quality and spectrum efficiency of the massive MIMO system.We propose a deep Monte Carlo Tree Search(MCTS)-based intelligent Pilot-power Allocation Scheme(iPAS)to address this issue.The core of iPAS is a multi-task deep reinforcement learning algorithm that can automatically learn the radio environment and make decisions on the pilot sequence and power allocation to maximize the spectrum efficiency with self-play training.To accelerate the searching convergence,we introduce a Deep Neural Network(DNN)to predict the pilot sequence and power allocation actions.The DNN is trained in a self-supervised learning manner,where the training data is generated from the searching process of the MCTS algorithm.Numerical results show that our proposed iPAS achieves a better Cumulative Distribution Function(CDF)of the ergodic spectral efficiency compared with the previous suboptimal algorithms.展开更多
Optimizing the power resources allocation method of low earth orbit(LEO)satellites to medium earth orbit(MEO)satellite'links is a significant way to construct efficient satellite constellations for satellite commu...Optimizing the power resources allocation method of low earth orbit(LEO)satellites to medium earth orbit(MEO)satellite'links is a significant way to construct efficient satellite constellations for satellite communication.A game theory power allocation method based on remaining visible time(RVT)of LEO-MEO satellites is proposed.Firstly,one LEO-MEO satellite network is classified as a cluster in which the RVT of LEO satellites is modeled.Secondly,the cost function of RVT concerning the character of orbit and throughput in each LEO satellite is mainly designed,which gives greater punishment of utility value to LEO satellites with less RVT and is an essential part of the reasonable utility function applied in diverse motion scenes.Meanwhile,the existence of Nash equilibrium for the proposed utility function in game theory area is proved.Thirdly,an off-cluster scheme for LEO satellites through the proposed threshold is raised to ensure the overall utility value of the whole LEO satellites in cluster.Finally,the performance improvement of the proposed algorithm to the baseline algorithm is verified through simulations in different scenarios.展开更多
We investigate the optimal joint power allocation in Heterogeneous Networks （HetNets） to maximise its capacity. Consider- ing frequency reuse in the network, we study two power-constraint cases, i.e., per-cell po- w...We investigate the optimal joint power allocation in Heterogeneous Networks （HetNets） to maximise its capacity. Consider- ing frequency reuse in the network, we study two power-constraint cases, i.e., per-cell po- wer constraint case and per-tier power con- straint case. We formulate the capacity maxi- mization problem by allowing each subcarrier of Marco eNodeB （MeNB） to be shared by users from multiple Picos. We mathematically demonstrate that the optimal power allocation in the per-cell power constraint case has a re- markably simple nature： each Pico transmits to its user with maximum power, while MeNB either selects only one user to jointly transmit with maximum power or does not transmit to any user. In the per-tier power constraint case, the difference is that the power allocation be- tween two Picos takes the form of water-fill- ing. Numerical results verify that our proposed schemes outperform the conventional interfe- rence coordination schemes.展开更多
With the obvious throughput shortage in traditional cellular radio networks,Device-to-Device(D2D)communications has gained a lot of attention to improve the utilization,capacity and channel performance of nextgenerati...With the obvious throughput shortage in traditional cellular radio networks,Device-to-Device(D2D)communications has gained a lot of attention to improve the utilization,capacity and channel performance of nextgeneration networks.In this paper,we study a joint consideration of power and channel allocation based on genetic algorithm as a promising direction to expand the overall network capacity for D2D underlaied cellular networks.The genetic based algorithm targets allocating more suitable channels to D2D users and finding the optimal transmit powers for all D2D links and cellular users efficiently,aiming to maximize the overall system throughput of D2D underlaied cellular network with minimum interference level,while satisfying the required quality of service QoS of each user.The simulation results show that our proposed approach has an advantage in terms of maximizing the overall system utilization than fixed,random,BAT algorithm(BA)and Particle Swarm Optimization(PSO)based power allocation schemes.展开更多
Unmanned aerial vehicles(UAVs) are advantageous for data collection in wireless sensor networks(WSNs) due to its low cost of use,flexible deployment,controllable mobility,etc. However,how to cope with the inherent iss...Unmanned aerial vehicles(UAVs) are advantageous for data collection in wireless sensor networks(WSNs) due to its low cost of use,flexible deployment,controllable mobility,etc. However,how to cope with the inherent issues of energy limitation and data security in the WSNs is challenging in such an application paradigm. To this end,based on the framework of physical layer security,an optimization problem for maximizing secrecy energy efficiency(EE) of data collection is formulated,which focuses on optimizing the UAV’s positions and the sensors’ transmit power. To overcome the difficulties in solving the optimization problem,the methods of fractional programming and successive convex approximation are then adopted to gradually transform the original problem into a series of tractable subproblems which are solved in an iterative manner. As shown in simulation results,by the joint designs in the spatial domain of UAV and the power domain of sensors,the proposed algorithm achieves a significant improvement of secrecy EE and rate.展开更多
A cross-layer design(CLD)scheme with combination of power allocation,adaptive modulation(AM)and automatic repeat request(ARQ)is presented for space-time coded MIMO system under imperfect feedback,and the corresponding...A cross-layer design(CLD)scheme with combination of power allocation,adaptive modulation(AM)and automatic repeat request(ARQ)is presented for space-time coded MIMO system under imperfect feedback,and the corresponding system performance is investigated in a Rayleigh fading channel.Based on imperfect feedback information,a suboptimal power allocation(PA)scheme is derived to maximize the average spectral efficiency(SE)of the system.The scheme is based on a so-called compressed SNR criterion,and has a closed-form expression for positive power allocation,thus being computationally efficient.Moreover,it can improve SE of the presented CLD.Besides,due to better approximation,it obtains the performance close to the existing optimal approach which requires numerical search.Simulation results show that the proposed CLD with PA can achieve higher SE than the conventional CLD with equal power allocation scheme,and has almost the same performance as CLD with optimal PA.However,it has lower calculation complexity.展开更多
Circuit sensitivity of sensors or tags without battery is one practical constraint for ambient backscatter communication systems.This letter considers using beamforming to reduce the sensitivity constraint and evaluat...Circuit sensitivity of sensors or tags without battery is one practical constraint for ambient backscatter communication systems.This letter considers using beamforming to reduce the sensitivity constraint and evaluates the corresponding performance in terms of the tag activation distance and the system capacity.Specifically,we derive the activation probabilities of the tag in the case of single-antenna and multi-antenna transmitters.Besides,we obtain the capacity expressions for the ambient backscatter communication system with beamforming and illustrate the power allocation that maximizes the system capacity when the tag is activated.Finally,simulation results are provided to corroborate our proposed studies.展开更多
Unmanned Aerial Vehicles(UAvs)as aerial base stations to provide communication services for ground users is a flexible and cost-effective paradigm in B5G.Besides,dynamic resource allocation and multi-connectivity can ...Unmanned Aerial Vehicles(UAvs)as aerial base stations to provide communication services for ground users is a flexible and cost-effective paradigm in B5G.Besides,dynamic resource allocation and multi-connectivity can be adopted to further harness the potentials of UAVs in improving communication capacity,in such situations such that the interference among users becomes a pivotal disincentive requiring effective solutions.To this end,we investigate the Joint UAV-User Association,Channel Allocation,and transmission Power Control(J-UACAPC)problem in a multi-connectivity-enabled UAV network with constrained backhaul links,where each UAV can determine the reusable channels and transmission power to serve the selected ground users.The goal was to mitigate co-channel interference while maximizing long-term system utility.The problem was modeled as a cooperative stochastic game with hybrid discrete-continuous action space.A Multi-Agent Hybrid Deep Reinforcement Learning(MAHDRL)algorithm was proposed to address this problem.Extensive simulation results demonstrated the effectiveness of the proposed algorithm and showed that it has a higher system utility than the baseline methods.展开更多
Normally,in the downlink Network-Coded Multiple Access(NCMA)system,the same power is allocated to different users.However,equal power allocation is unsuitable for some scenarios,such as when user devices have differen...Normally,in the downlink Network-Coded Multiple Access(NCMA)system,the same power is allocated to different users.However,equal power allocation is unsuitable for some scenarios,such as when user devices have different Quality of Service(QoS)requirements.Hence,we study the power allocation in the downlink NCMA system in this paper,and propose a downlink Network-Coded Multiple Access with Diverse Power(NCMA-DP),wherein different amounts of power are allocated to different users.In terms of the Bit Error Rate(BER)of the multi-user decoder,and the number of packets required to correctly decode the message,the performance of the user with more allocated power is greatly improved compared to the Conventional NCMA(NCMA-C).Meanwhile,the performance of the user with less allocated power is still much better than NCMA-C.Furthermore,the overall throughput of NCMA-DP is greatly improved compared to that of NCMA-C.The simulation results demonstrate the remarkable performance of the proposed NCMA-DP.展开更多
Non-orthogonal multiple access(NOMA)represents the latest addition to the array of multiple access techniques,enabling simultaneous servicing of multiple users within a singular resource block in terms of time,frequen...Non-orthogonal multiple access(NOMA)represents the latest addition to the array of multiple access techniques,enabling simultaneous servicing of multiple users within a singular resource block in terms of time,frequency,and code.A typical NOMA configuration comprises a base station along with proximate and distant users.The proximity users experience more favorable channel conditions in contrast to distant users,resulting in a compromised performance for the latter due to the less favorable channel conditions.When cooperative communication is integrated with NOMA,the overall system performance,including spectral efficiency and capacity,is further elevated.This study introduces a cooperative NOMA setup in the downlink,involving three users,and employs dynamic power allocation(DPA).Within this framework,User 2 acts as a relay,functioning under the decode-and-forward protocol,forwarding signals to both User 1 and User 3.This arrangement aims to bolster the performance of the user positioned farthest from the base station,who is adversely affected by weaker channel conditions.Theoretical and simulation outcomes reveal enhancements within the system’s performance.展开更多
基金supported by the Fundamental Research Funds for the Central Universities of NUAA(No.kfjj20200414)Natural Science Foundation of Jiangsu Province in China(No.BK20181289).
文摘In this paper,we optimize the spectrum efficiency(SE)of uplink massive multiple-input multiple-output(MIMO)system with imperfect channel state information(CSI)over Rayleigh fading channel.The SE optimization problem is formulated under the constraints of maximum power and minimum rate of each user.Then,we develop a near-optimal power allocation(PA)scheme by using the successive convex approximation(SCA)method,Lagrange multiplier method,and block coordinate descent(BCD)method,and it can obtain almost the same SE as the benchmark scheme with lower complexity.Since this scheme needs three-layer iteration,a suboptimal PA scheme is developed to further reduce the complexity,where the characteristic of massive MIMO(i.e.,numerous receive antennas)is utilized for convex reformulation,and the rate constraint is converted to linear constraints.This suboptimal scheme only needs single-layer iteration,thus has lower complexity than the near-optimal scheme.Finally,we joint design the pilot power and data power to further improve the performance,and propose an two-stage algorithm to obtain joint PA.Simulation results verify the effectiveness of the proposed schemes,and superior SE performance is achieved.
基金supported by the Key Research and Development Program of China(No.2022YFC3005401)Key Research and Development Program of China,Yunnan Province(No.202203AA080009,202202AF080003)Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX21_0482).
文摘In Beyond the Fifth Generation(B5G)heterogeneous edge networks,numerous users are multiplexed on a channel or served on the same frequency resource block,in which case the transmitter applies coding and the receiver uses interference cancellation.Unfortunately,uncoordinated radio resource allocation can reduce system throughput and lead to user inequity,for this reason,in this paper,channel allocation and power allocation problems are formulated to maximize the system sum rate and minimum user achievable rate.Since the construction model is non-convex and the response variables are high-dimensional,a distributed Deep Reinforcement Learning(DRL)framework called distributed Proximal Policy Optimization(PPO)is proposed to allocate or assign resources.Specifically,several simulated agents are trained in a heterogeneous environment to find robust behaviors that perform well in channel assignment and power allocation.Moreover,agents in the collection stage slow down,which hinders the learning of other agents.Therefore,a preemption strategy is further proposed in this paper to optimize the distributed PPO,form DP-PPO and successfully mitigate the straggler problem.The experimental results show that our mechanism named DP-PPO improves the performance over other DRL methods.
基金This work was supported in part by the open research fund of National Mobile Communications Research Laboratory,Southeast University(No.2023D11)in part by Sponsored by program for Science&Technology Innovation Talents in Universities of Henan Province(23HASTIT019)+2 种基金in part by Natural Science Foundation of Henan Province(20232300421097)in part by the project funded by China Postdoctoral Science Foundation(2020M682345)in part by the Henan Postdoctoral Foundation(202001015).
文摘In this paper,we investigate IRS-aided user cooperation(UC)scheme in millimeter wave(mmWave)wirelesspowered sensor networks(WPSN),where two single-antenna users are wireless powered in the wireless energy transfer(WET)phase first and then cooperatively transmit information to a hybrid access point(AP)in the wireless information transmission(WIT)phase,following which the IRS is deployed to enhance the system performance of theWET andWIT.We maximized the weighted sum-rate problem by jointly optimizing the transmit time slots,power allocations,and the phase shifts of the IRS.Due to the non-convexity of the original problem,a semidefinite programming relaxation-based approach is proposed to convert the formulated problem to a convex optimization framework,which can obtain the optimal global solution.Simulation results demonstrate that the weighted sum throughput of the proposed UC scheme outperforms the non-UC scheme whether equipped with IRS or not.
基金supported by the Graduate Innovation Program of China University of Mining and Technology (2022WLKXJ016)in part by the Postgraduate Research&Practice Innovation Program of Jiangsu Province (KYCX222549)+3 种基金supported by Shaanxi Provincial Natural Science Foundation of China (2023-JC-YB-510)the Fundamental Research Funds for the Central Universities,CHD (300102322103)supported in part by Natural Science Foundation of Jiangsu Province (BK20200488)supported in part by Challenge Cup National Student Curricular Academic Science and Technology Works Competition (DCXM202212)。
文摘In this paper,the channel capacity of the multiple-input multiple-output(MIMO)visible light communication(VLC)system is investigated under the peak,average optical and electrical power constraints.Finding the channel capacity of MIMO VLC is shown to be a mixed integer programming problem.To address this open problem,we propose an inexact gradient projection method to find the channel capacity-achieving discrete input distribution and the channel capacity of MIMO VLC.Also we derive both upper and lower bounds of the capacity of MIMO VLC with the closed-form expressions.Furthermore,by considering practical discrete constellation inputs,we develop the optimal power allocation scheme to maximize transmission rate of MIMO VLC system.Simulation results show that more discrete points are needed to achieve the channel capacity as SNR increases.Both the upper and lower bounds of channel capacity are tight at low SNR region.In addition,comparing the equal power allocation,the proposed power allocation scheme can significantly increase the rate for the low-order modulation inputs.
文摘Formany years,researchers have explored power allocation(PA)algorithms driven bymodels in wireless networks where multiple-user communications with interference are present.Nowadays,data-driven machine learning methods have become quite popular in analyzing wireless communication systems,which among them deep reinforcement learning(DRL)has a significant role in solving optimization issues under certain constraints.To this purpose,in this paper,we investigate the PA problem in a k-user multiple access channels(MAC),where k transmitters(e.g.,mobile users)aim to send an independent message to a common receiver(e.g.,base station)through wireless channels.To this end,we first train the deep Q network(DQN)with a deep Q learning(DQL)algorithm over the simulation environment,utilizing offline learning.Then,the DQN will be used with the real data in the online training method for the PA issue by maximizing the sumrate subjected to the source power.Finally,the simulation results indicate that our proposedDQNmethod provides better performance in terms of the sumrate compared with the available DQL training approaches such as fractional programming(FP)and weighted minimum mean squared error(WMMSE).Additionally,by considering different user densities,we show that our proposed DQN outperforms benchmark algorithms,thereby,a good generalization ability is verified over wireless multi-user communication systems.
文摘To improve the operation efficiency of the photovoltaic power station complementary power generation system,an optimal allocation model of the photovoltaic power station complementary power generation capacity based on PSO-BP is proposed.Particle Swarm Optimization and BP neural network are used to establish the forecasting model,the Markov chain model is used to correct the forecasting error of the model,and the weighted fitting method is used to forecast the annual load curve,to complete the optimal allocation of complementary generating capacity of photovoltaic power stations.The experimental results show that thismethod reduces the average loss of photovoltaic output prediction,improves the prediction accuracy and recall rate of photovoltaic output prediction,and ensures the effective operation of the power system.
基金National Natural Science Foundation of China(Grant No.62001506)to provide fund for conducting experiments。
文摘The netted radar system(NRS)has been proved to possess unique advantages in anti-jamming and improving target tracking performance.Effective resource management can greatly ensure the combat capability of the NRS.In this paper,based on the netted collocated multiple input multiple output(CMIMO)radar,an effective joint target assignment and power allocation(JTAPA)strategy for tracking multi-targets under self-defense blanket jamming is proposed.An architecture based on the distributed fusion is used in the radar network to estimate target state parameters.By deriving the predicted conditional Cramer-Rao lower bound(PC-CRLB)based on the obtained state estimation information,the objective function is formulated.To maximize the worst case tracking accuracy,the proposed JTAPA strategy implements an online target assignment and power allocation of all active nodes,subject to some resource constraints.Since the formulated JTAPA is non-convex,we propose an efficient two-step solution strategy.In terms of the simulation results,the proposed algorithm can effectively improve tracking performance in the worst case.
基金supported by National Natural Science Foundation of China(No.62071253)Postgraduate Research and Practice Innovation Program of Jiangsu Province(KYCX210747).
文摘In this paper,we investigate the system performance of a heterogeneous cellular network consisting of a macro cell and a small cell,where each cell has one user and one base station with multiple antennas.The macro base station(MBS)and the small base station(SBS)transmit their confidential messages to the macro user(MU)and the small user(SU)over their shared spectrum respectively.To enhance the system sum rate(SSR)of MBS-MU and SBS-SU transmission,we propose joint antenna selection combined with optimal power allocation(JAS-OPA)scheme and independent antenna selection combined with optimal power allocation(IAS-OPA)scheme.The JAS-OPA scheme requires to know the channel state information(CSI)of transmission channels and interference channels,while the IAS-OPA scheme only needs to know the CSI of transmission channels.In addition,we carry out the analysis for conventional round-robin antenna selection combined with optimal power allocation(RR-OPA)as a benchmark scheme.We formulate the SSR maximization problem through the power allocation between MBS and SBS and propose iterative OPA algorithms for JAS-OPA,IAS-OPA and RR-OPA schemes,respectively.The results show that the OPA schemes outperform the equal power allocation in terms of SSR.Moreover,we provide the closed-form expression of the system outage probability(SOP)for IAS scheme and RR scheme,it shows the SOP performance can be significantly improved by our proposed IAS scheme compared with RR scheme.
基金supported by National Natural Science Foundation of China(No.62171158)the project“The Major Key Project of PCL(PCL2021A03-1)”from Peng Cheng Laboratorysupported by the Science and the Research Fund Program of Guangdong Key Laboratory of Aerospace Communication and Networking Technology(2018B030322004).
文摘As the sixth generation network(6G)emerges,the Internet of remote things(IoRT)has become a critical issue.However,conventional terrestrial networks cannot meet the delay-sensitive data collection needs of IoRT networks,and the Space-Air-Ground integrated network(SAGIN)holds promise.We propose a novel setup that integrates non-orthogonal multiple access(NOMA)and wireless power transfer(WPT)to collect latency-sensitive data from IoRT networks.To extend the lifetime of devices,we aim to minimize the maximum energy consumption among all IoRT devices.Due to the coupling between variables,the resulting problem is non-convex.We first decouple the variables and split the original problem into four subproblems.Then,we propose an iterative algorithm to solve the corresponding subproblems based on successive convex approximation(SCA)techniques and slack variables.Finally,simulation results show that the NOMA strategy has a tremendous advantage over the OMA scheme in terms of network lifetime and energy efficiency,providing valuable insights.
基金This project was supported by the National Natural Science Foundation of China (60272079) and the"863"High Tech-nology Research and Development Programof China (2003AA123310)
文摘An efficient spaee-time-frequency （STF） coding strategy for multi-input multi-output orthogonal frequency division multiplexing （MIMO-OFDM） systems is presented for high bit rate data transmission over frequency selective fading channels. The proposed scheme is a new approach to space-time-frequency coded OFDM （ODFDM） that combines OFDM with space-time coding, linear precoding and adaptive power allocation to provide higher quality of transmission in terms of the bit error rate performance and power efficiency. In addition to exploiting the maximux diversity gain in frequency, time and space, the proposed scheme enjoys high coding advantages and low-complexity decoding. The significant performance improvement of our design is confirned by corroborating numerical simulations.
文摘Massive Multiple-Input-Multiple-Output(MIMO)is a promising technology to meet the demand for the connection of massive devices and high data capacity for mobile networks in the next generation communication system.However,due to the massive connectivity of mobile devices,the pilot contamination problem will severely degrade the communication quality and spectrum efficiency of the massive MIMO system.We propose a deep Monte Carlo Tree Search(MCTS)-based intelligent Pilot-power Allocation Scheme(iPAS)to address this issue.The core of iPAS is a multi-task deep reinforcement learning algorithm that can automatically learn the radio environment and make decisions on the pilot sequence and power allocation to maximize the spectrum efficiency with self-play training.To accelerate the searching convergence,we introduce a Deep Neural Network(DNN)to predict the pilot sequence and power allocation actions.The DNN is trained in a self-supervised learning manner,where the training data is generated from the searching process of the MCTS algorithm.Numerical results show that our proposed iPAS achieves a better Cumulative Distribution Function(CDF)of the ergodic spectral efficiency compared with the previous suboptimal algorithms.
基金Supported by the National Key Research and Development Program of China(No.2019YFB1803101)the Natural Science Foundation of Shanghai(No.19ZR1467200).
文摘Optimizing the power resources allocation method of low earth orbit(LEO)satellites to medium earth orbit(MEO)satellite'links is a significant way to construct efficient satellite constellations for satellite communication.A game theory power allocation method based on remaining visible time(RVT)of LEO-MEO satellites is proposed.Firstly,one LEO-MEO satellite network is classified as a cluster in which the RVT of LEO satellites is modeled.Secondly,the cost function of RVT concerning the character of orbit and throughput in each LEO satellite is mainly designed,which gives greater punishment of utility value to LEO satellites with less RVT and is an essential part of the reasonable utility function applied in diverse motion scenes.Meanwhile,the existence of Nash equilibrium for the proposed utility function in game theory area is proved.Thirdly,an off-cluster scheme for LEO satellites through the proposed threshold is raised to ensure the overall utility value of the whole LEO satellites in cluster.Finally,the performance improvement of the proposed algorithm to the baseline algorithm is verified through simulations in different scenarios.
基金supported by the National Major Science and Technology Project under Grant No.2009ZX03003-003-01Huawei Innovation Project under Grant No.YJCB2011060WL
文摘We investigate the optimal joint power allocation in Heterogeneous Networks （HetNets） to maximise its capacity. Consider- ing frequency reuse in the network, we study two power-constraint cases, i.e., per-cell po- wer constraint case and per-tier power con- straint case. We formulate the capacity maxi- mization problem by allowing each subcarrier of Marco eNodeB （MeNB） to be shared by users from multiple Picos. We mathematically demonstrate that the optimal power allocation in the per-cell power constraint case has a re- markably simple nature： each Pico transmits to its user with maximum power, while MeNB either selects only one user to jointly transmit with maximum power or does not transmit to any user. In the per-tier power constraint case, the difference is that the power allocation be- tween two Picos takes the form of water-fill- ing. Numerical results verify that our proposed schemes outperform the conventional interfe- rence coordination schemes.
文摘With the obvious throughput shortage in traditional cellular radio networks,Device-to-Device(D2D)communications has gained a lot of attention to improve the utilization,capacity and channel performance of nextgeneration networks.In this paper,we study a joint consideration of power and channel allocation based on genetic algorithm as a promising direction to expand the overall network capacity for D2D underlaied cellular networks.The genetic based algorithm targets allocating more suitable channels to D2D users and finding the optimal transmit powers for all D2D links and cellular users efficiently,aiming to maximize the overall system throughput of D2D underlaied cellular network with minimum interference level,while satisfying the required quality of service QoS of each user.The simulation results show that our proposed approach has an advantage in terms of maximizing the overall system utilization than fixed,random,BAT algorithm(BA)and Particle Swarm Optimization(PSO)based power allocation schemes.
基金Supported by the National Natural Science Foundation of China(No.61871401).
文摘Unmanned aerial vehicles(UAVs) are advantageous for data collection in wireless sensor networks(WSNs) due to its low cost of use,flexible deployment,controllable mobility,etc. However,how to cope with the inherent issues of energy limitation and data security in the WSNs is challenging in such an application paradigm. To this end,based on the framework of physical layer security,an optimization problem for maximizing secrecy energy efficiency(EE) of data collection is formulated,which focuses on optimizing the UAV’s positions and the sensors’ transmit power. To overcome the difficulties in solving the optimization problem,the methods of fractional programming and successive convex approximation are then adopted to gradually transform the original problem into a series of tractable subproblems which are solved in an iterative manner. As shown in simulation results,by the joint designs in the spatial domain of UAV and the power domain of sensors,the proposed algorithm achieves a significant improvement of secrecy EE and rate.
基金Supported by the Foundation of Huaian Industrial Projects(HAG2013064)the Foundation of Huaiyin Institute of Technology(HGB1202)the Doctoral Fund of Ministry of Education of China(20093218120021)
文摘A cross-layer design(CLD)scheme with combination of power allocation,adaptive modulation(AM)and automatic repeat request(ARQ)is presented for space-time coded MIMO system under imperfect feedback,and the corresponding system performance is investigated in a Rayleigh fading channel.Based on imperfect feedback information,a suboptimal power allocation(PA)scheme is derived to maximize the average spectral efficiency(SE)of the system.The scheme is based on a so-called compressed SNR criterion,and has a closed-form expression for positive power allocation,thus being computationally efficient.Moreover,it can improve SE of the presented CLD.Besides,due to better approximation,it obtains the performance close to the existing optimal approach which requires numerical search.Simulation results show that the proposed CLD with PA can achieve higher SE than the conventional CLD with equal power allocation scheme,and has almost the same performance as CLD with optimal PA.However,it has lower calculation complexity.
基金supported by National Natural Science Foundation of China(No.62101601)the Fundamental Research Funds for the Central Universities under Grant 2020JBM017Joint Key Project of National Natural Science Foundation of China(No.U22B2004)。
文摘Circuit sensitivity of sensors or tags without battery is one practical constraint for ambient backscatter communication systems.This letter considers using beamforming to reduce the sensitivity constraint and evaluates the corresponding performance in terms of the tag activation distance and the system capacity.Specifically,we derive the activation probabilities of the tag in the case of single-antenna and multi-antenna transmitters.Besides,we obtain the capacity expressions for the ambient backscatter communication system with beamforming and illustrate the power allocation that maximizes the system capacity when the tag is activated.Finally,simulation results are provided to corroborate our proposed studies.
基金supported in part by the National Natural Science Foundation of China(grant nos.61971365,61871339,62171392)Digital Fujian Province Key Laboratory of IoT Communication,Architecture and Safety Technology(grant no.2010499)+1 种基金the State Key Program of the National Natural Science Foundation of China(grant no.61731012)the Natural Science Foundation of Fujian Province of China No.2021J01004.
文摘Unmanned Aerial Vehicles(UAvs)as aerial base stations to provide communication services for ground users is a flexible and cost-effective paradigm in B5G.Besides,dynamic resource allocation and multi-connectivity can be adopted to further harness the potentials of UAVs in improving communication capacity,in such situations such that the interference among users becomes a pivotal disincentive requiring effective solutions.To this end,we investigate the Joint UAV-User Association,Channel Allocation,and transmission Power Control(J-UACAPC)problem in a multi-connectivity-enabled UAV network with constrained backhaul links,where each UAV can determine the reusable channels and transmission power to serve the selected ground users.The goal was to mitigate co-channel interference while maximizing long-term system utility.The problem was modeled as a cooperative stochastic game with hybrid discrete-continuous action space.A Multi-Agent Hybrid Deep Reinforcement Learning(MAHDRL)algorithm was proposed to address this problem.Extensive simulation results demonstrated the effectiveness of the proposed algorithm and showed that it has a higher system utility than the baseline methods.
文摘Normally,in the downlink Network-Coded Multiple Access(NCMA)system,the same power is allocated to different users.However,equal power allocation is unsuitable for some scenarios,such as when user devices have different Quality of Service(QoS)requirements.Hence,we study the power allocation in the downlink NCMA system in this paper,and propose a downlink Network-Coded Multiple Access with Diverse Power(NCMA-DP),wherein different amounts of power are allocated to different users.In terms of the Bit Error Rate(BER)of the multi-user decoder,and the number of packets required to correctly decode the message,the performance of the user with more allocated power is greatly improved compared to the Conventional NCMA(NCMA-C).Meanwhile,the performance of the user with less allocated power is still much better than NCMA-C.Furthermore,the overall throughput of NCMA-DP is greatly improved compared to that of NCMA-C.The simulation results demonstrate the remarkable performance of the proposed NCMA-DP.
文摘Non-orthogonal multiple access(NOMA)represents the latest addition to the array of multiple access techniques,enabling simultaneous servicing of multiple users within a singular resource block in terms of time,frequency,and code.A typical NOMA configuration comprises a base station along with proximate and distant users.The proximity users experience more favorable channel conditions in contrast to distant users,resulting in a compromised performance for the latter due to the less favorable channel conditions.When cooperative communication is integrated with NOMA,the overall system performance,including spectral efficiency and capacity,is further elevated.This study introduces a cooperative NOMA setup in the downlink,involving three users,and employs dynamic power allocation(DPA).Within this framework,User 2 acts as a relay,functioning under the decode-and-forward protocol,forwarding signals to both User 1 and User 3.This arrangement aims to bolster the performance of the user positioned farthest from the base station,who is adversely affected by weaker channel conditions.Theoretical and simulation outcomes reveal enhancements within the system’s performance.
