Power Allocation for SE Maximization in Uplink Massive MIMO System Under Minimum Rate Constraint

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.

Channel assignment and power allocation for throughput improvement with PPO in B5G heterogeneous edge networks

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.

Resource Allocation for IRS Assistedmm Wave Wireless Powered Sensor Networks with User Cooperation

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.

A Deep Reinforcement Learning-Based Technique for Optimal Power Allocation in Multiple Access Communications

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.

Performance Analysis of AF Relaying Based on Water-filling Power Allocation over Nakagami-m Fading

This paper investigates exact performance of an amplify-and-forward(AF) relay system based on water-filling power allocation in Nakagami-m fading environment, where m is a nonnegative integer plus one half.We first offer the cumulative distribution function(CDF) and probability density function(PDF) of the received signal-to-noise ratio(SNR) at a destination. Then outage probability, moments of SNR, higher-order statistics of the capacity are explicitly conducted. Especially, average symbol error rate(SER) under an additive white generalized Gaussian noise(AWGGN) is developed for water-filling power allocation scheme. While the average SER subjected by an additive white Gaussian noise(AWGN) can be regarded as a special case. Finally, all theoretical formulas are truly attested by various simulation results.

Learning-based user association and dynamic resource allocation in multi-connectivity enabled unmanned aerial vehicle networks

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.

Channel Capacity and Power Allocation of MIMO Visible Light Communication System

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.

Joint target assignment and power allocation in the netted C-MIMO radar when tracking multi-targets in the presence of self-defense blanket jamming

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.

Power Allocation and Antenna Selection for Heterogeneous Cellular Networks

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.

PSO-BP-Based Optimal Allocation Model for Complementary Generation Capacity of the Photovoltaic Power Station

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.

An RVT-based power allocation method for dynamic LEO-MEO links

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.

Joint UAV 3D deployment and sensor power allocation for energy-efficient and secure data collection

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.

Cooperative Relaying in a Three User Downlink NOMA System Using Dynamic Power Allocation

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.

WORST SUBCARRIER AVOIDING WATER-FILLING SUBCARRIER ALLOCATION SCHEME FOR OFDM-BASED CRN

Efficient and reliable subcarrier power joint allocation is served as a promising problem in cognitive OFDM-based Cognitive Radio Networks (CRN). This paper focuses on optimal subcarrier allocation for OFDM-based CRN. We mainly propose subcarrier allocation scheme denoted as Worst Subcarrier Avoiding Water-filling (WSAW), which is based on Rate Adaptive (RA) criterion and three constraints are considered in CRN. The algorithm divides the assignment procedure into two phases. The first phase is an initial subcarrier allocation based on the idea of avoiding selecting the worst subcarrier in order to maximize the transmission rate; while the second phase is an iterative adjustment process which is realized by swapping pairs of subcarriers between arbitrary users. The proposed scheme could assign subcarriers in accordance with channel coherence time. Hence, real time subcarrier allocation could be implemented. Simulation results show that, comparing with the similar existing algorithms, the proposed scheme could achieve larger capacity and a near-optimal BER performance.

Dynamic software allocation algorithm for saving power in pervasive computing

A novel dynamic software allocation algorithm suitable for pervasive computing environments is proposed to minimize power consumption of mobile devices. Considering the power cost incurred by the computation, communication and migration of software components, a power consumption model of component assignments between a mobile device and a server is set up. Also, the mobility of components and the mobility relationships between components are taken into account in software allocation. By using network flow theory, the optimization problem of power conservation is transformed into the optimal bipartition problem of a flow network which can be partitioned by the max-flow rain-cut algorithm. Simulation results show that the proposed algorithm can save si^nificantlv more energy than existing algorithms.

Antenna Selection and Power Allocation Design for 5G Massive MIMO Uplink Networks

Massive MIMO is one of the key technologies in future 5G communications which can satisfy the requirement of high speed and large capacity. This paper considers antenna selection and power allocation design to promote energy conservation then provide good quality of service(QoS) for the whole massive MIMO uplink network. Unlike previous related works, hardware impairment, transmission efficiency, and energy consumption at the circuit and antennas are involved in massive MIMO networks. In order to ensure the QoS, we consider the minimum rate constraint for each user and the system, which increases the complexity of power allocation problem for maximizing energy and spectral efficiency in massive MIMO system. To this end, a quantum-inspired social emotional optimization(QSEO) algorithm is proposed to obtain the optimal power control strategy in massive MIMO uplink networks. Simulation results assess the great advantages of QSEO which previous strategies do not have.

Power Allocation and Performance Analysis of the Collaborative NOMA Assisted Relaying Systems in 5G

Serving multiple cell-edge mobile terminals poses multifaceted challenges due to the increased transmission power and interferences, which could be overcome by relay communications. With the recent advancement of 5G technologies, non-orthogonal multiple access(NOMA) has been used at relay node to transmit multiple messages simultaneously to multiple cell-edge users. In this paper, a Collaborative NOMA Assisted Relaying(CNAR) system for 5G is proposed by enabling the collaboration of source-relay(S-R) and relay-destination(R-D) NOMA links. The relay node of the CNAR decodes the message for itself from S-R NOMA signal and transmits the remaining messages to the multiple cell-edge users in R-D link. A simplified-CNAR(S-CNAR) system is then developed to reduce the relay complexity. The outage probabilities for both systems are analyzed by considering outage behaviors in S-R and R-D links separately. To guarantee the data rate, the optimal power allocation among NOMA users is achieved by minimizing the outage probability. The ergodic sum capacity in high SNR regime is also approximated. Our mathematical analysis and simulation results show that CNAR system outperforms existing transmission strategies and S-CNAR reaches similar performance with much lower complexity.

Joint Subcarrier and Power Allocation for Multi-UAV Systems

This paper investigates subcarrier and power allocation in a multi-UAV OFDM system.The study considers a practical scenario,where certain subcarriers are unavailable for dynamic subcarrier allocation,on account of pre-allocation for burst transmissions.We first propose a novel iterative algorithm to jointly optimize subcarrier and power allocation,so as to maximize the sum rate of the uplink transmission in the multiUAV OFDM system.The key idea behind our solution is converting the nontrivial allocation problem into a weighted mean square error(MSE) problem.By this means,the allocation problem can be solved by the alternating optimization method.Besides,aiming at a lower-complexity solution,we propose a heuristic allocation scheme,where subcarrier allocation and transmit power allocation are separately optimized.In the heuristic scheme,closedform solution can be obtained for power allocation.Simulation results demonstrate that in the presence of stretched subcarrier resource,the proposed iterative joint optimization algorithm can significantly outperform the heuristic scheme,offering a higher sum rate.

Optimal Power Allocation for Multiuser OFDM-Based Cognitive Heterogeneous Networks

Heterogeneous network(HetNet) as a promising technology to improve spectrum efficiency and system capacity has been concerned by many scholars, which brings huge challenges for power allocation and interference management in multicell network structures. Although some works have been done for power allocation in heterogeneous femtocell networks, most of them focus centralized schemes for single-cell network under interference constraint of macrocell user. In this paper, a sum-rate maximization based power allocation algorithm is proposed for a downlink cognitive Het Net with one macrocell network and multiple microcell networks. The original power allocation optimization problem with the consideration of cross-tier interference constraint, maximum transmit power constraint of microcell base station and inter-cell interference of microcell networks is converted into a geometric programming problem which can be solved by Lagrange dual method in a distributed way. Simulation results demonstrate the performance and effectiveness of the proposed algorithm by comparing with the equal power allocation scheme.

Optimal Power Allocation with Limited Feedback of Channel State Information in Multi-User MIMO Systems

In this paper,an expression for the user’s achievable data rate in the multi-user multiple-input multiple-output(MU-MIMO)system with limited feedback(LF)of channel state information(CSI)is derived.The energy efficiency(EE)is optimized through power allocation under quality of service(QoS)constraints.Based on mathematical equivalence and Lagrange multiplier approach,an energy-efficient unequal power allocation(EEUPA)with LF of CSI scheme is proposed.The simulation results show that as the number of transmitting antennas increases,the EE also increases which is promising for the next generation wireless communication networks.Moreover,it can be seen that the QoS requirement has an effect on the EE of the system.Ultimately,the proposed EEUPA with LF of CSI algorithm performs better than the existing energy-efficient equal power allocation(EEEPA)with LF of CSI schemes.