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.

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.

Low complexity hybrid power distribution combined with subcarrier allocation algorithm for OFDMA

To improve the total throughput of the uplink orthogonal frequency division multiple access system,a low complexity hybrid power distribution（HPD） combined with subcarrier allocation scheme is proposed.For the fairness mechanism for the subcarrier,the inter-cell interference is first analyzed to calculate the capacity of the multi-cell.The user selects the subcarrier with the largest channel gain.Based on the above subcarrier allocation scheme,a new kind of HPD scheme is proposed,which adopts the waterfilling-power-distributed scheme and the equal-power-distributed scheme in the cell-boundary and the cellcenter,respectively.Simulation results show that compared with the waterfilling-power-distributed scheme in the whole cell,the proposed HPD scheme decreases the system complexity significantly,meanwhile its capacity is 2% higher than that of the equal-powerdistributed scheme over the same subcarrier allocation.

Framework for Random Power Allocation of Wireless Sensor Networks in Fading Channels

In naturally deaf wireless sensor networks or generally when there is no feedback channel, the fixed-level transmit power of all nodes is the conventional and practical power allocation method. Using random power allocation for the broadcasting nodes has been recently proposed to overcome the limitations and problems of the fixed power allocation. However, the previous work discussed only the performance analysis when uniform power allocation is used for quasi-static channels. This paper gives a general framework to evaluate the performance (in terms of outage and average transmit power) of any truncated probability density function of the random allocated power. Furthermore, dynamic Rayleigh fading channel is considered during the performance analysis which gives more realistic results that the AWGN channels assumed in the previous work. The main objective of this paper is to evaluate the communication performance when general random power allocation is used. Furthermore, the truncated inverse exponential probability distribution of the random power allocation is proposed and compared with the fixed and the uniform power allocations. The performance analysis for the proposed schemes are given mathematically and evaluated via intensive simulations.

POWER ALLOCATION OF DATA DEPENDENT SUPERIMPOSED TRAINING

Data Dependent Superimposed Training(DDST) scheme outperforms the traditional su-perimposed training by fully canceling the effects of unknown data in channel estimator.In DDST,however,the channel estimation accuracy and the data detection or channel equalization performance are affected significantly by the amount of power allocated to data and superimposed training sequence,which is the motivation of this research.In general,for DDST,there is a tradeoff between the channel estimation accuracy and the data detection reliability,i.e.,the more accurate the channel estimation,the more reliable the data detection;on the other hand,the more accurate the channel estimation,the more demanding on the power consumption of training sequence,which in turn leads to the less reliable data detection.In this paper,the relationship between the Signal-to-Noise Ratio(SNR) of the data detector and the training sequence power is analyzed.The optimal power allocation of the training sequence is derived based on the criterion of maximizing SNR of the detector.Analysis and simulation results show that for a fixed transmit power,the SNR and the Symbol Error Rate(SER) of detector vary nonlinearly with the increasing of training sequence power,and there exists an optimal power ratio,which accords with the derived optimal power ratio,among the data and training sequence.

基于Dinkelbach-Quadratic算法的高速铁路通信能效优化功率分配研究

为了提高高速铁路通信能量效率,提出一种基于Dinkelbach-Quadratic变换的迭代算法来解决高速铁路通信下行链路的功率分配问题。通过在小区内部署远程天线单元构建高速铁路通信分布式天线系统模型,基于完美信道状态信息(CSI)和列车内接入点反馈的最小速率要求建立能效优化模型;基于所建立的非凸优化模型,采用Dinkelbach变换将分数形式的非线性规划问题转换为求差形式;利用Quadratic变换特性对目标函数进一步展开;采用交替迭代的方法完成能效最优的功率分配。仿真结果表明:所提算法在满足最小传输速率和最大发射功率的同时,可以兼顾系统能量效率和频谱效率;随着发射天线数量和接收中继数量的增加,系统性能也有明显提升,相比传统算法系统的能效和频谱效率至少提升5%。

Optimal Power Allocation for Multiuser Underlay Cognitive Radio Networks under QoS and Interference Temperature Constraints

Power allocation is an important issue for Cognitive Radio Networks(CRNs),since it needs to consider the Quality of Service(QoS) for Secondary Users(SUs) while maintaining the interference power to Primary User(PU) below the Interference Temperature(IT) threshold. In this paper, based on Euclidean projection, we propose a distributed power control algorithm with QoS requirements to minimise the total power consumption of SUs under the time-varying channel scenario. Considering the maximum transmit power constraints and the minimum signal to interference plus noise constraints for each SU, together with the IT constraints for each PU, the power allocation problem is transformed into a convex optimization problem without auxiliary variables, and is solved by the Lagrangian dual method with less information exchange.Simulation results demonstrate that the proposed scheme is superior to the Iterative Water-Filling Algorithm(IWFA).

A novel low-complexity power allocation algorithm based on the NOMA system in a low-speed environment

For future wireless communication systems,Power Domain Non-Orthogonal Multiple Access(PD-NOMA)using an advanced receiver has been considered as a promising radio access technology candidate.Power allocation plays an important role in the PD-NOMA system because it considerably affects the total throughput and Geometric Mean User Throughput(GMUT)performance.However,most existing studies have not completely accounted for the computational complexity of the power allocation process when the User Terminals(UTs)move in a slow fading channel environment.To resolve such problems,a power allocation method is proposed to considerably reduce the search space of a Full Search Power(FSP)allocation algorithm.The initial power reallocation coefficients will be set to start with former optimal values by the proposed Lemma before searching for optimal power reallocation coefficients based on total throughput performance.Step size and correction granularity will be adjusted within a much narrower power search range while invalid power combinations may be reasonably discarded during the search process.The simulation results show that the proposed power reallocation scheme can greatly reduce computational complexity while the total throughput and GMUT performance loss are not greater than 1.5%compared with the FSP algorithm.

An Energy Sharing EH-DAS Model and its Power Allocation Among RAUs

For the energy sharing problem of distributed antenna system(DAS)with energy harvesting(EH),a distributed antenna system model capable of sharing collected energy among the components in system is proposed.Compared with the existing model in literatures,the proposed model connects with smart grid through a unified interface and facilitates energy management and scheduling.Based on the proposed model,three kinds of energy sharing methods including the partial energy sharing method,the complete energy sharing method and the self-sustaining energy sharing method are analyzed.Under various energy sharing methods,the corresponding optimization problems of power allocation among the remote antenna units(RAUs)are described,formed and solved.As a result,the corresponding power allocation algorithm to each method has been concluded.Simulation results show that the proposed model is more efficient in terms of the channel capacity and energy efficiency,compared to the existing model.

Energy Efficient Relay Positioning and Power Allocation for One-Way N-Relay Channel

Two end-users which have symmetric traffic requirements in terms of data rate are considered. They exchange information in Rayleigh flat-fading channels and multiple serial half-duplex relay nodes are employed to extend the communication coverage and assist the bidirectional communication between them using the analog network coding( ANC) protocol. With the objective of minimizing the sum transmit energy at the required data rate c,the optimal relay positioning and power allocation problem is firstly investigated and then the sub-optimal solutions for a two-relay channel are proposed,due to no close-form optimal solution. Furthermore,a sub-optimal scheme of relay positioning and power allocation,called equal-distance equal-transmit-power( EDEP) for an arbitrary Nrelay channel,N > 1 is proposed. Simulation results demonstrate a consistence with our proposed scheme.

Power allocation and mode selection methods for cooperative communication in the rectangular tunnel

For the multipath fading on electromagnetic waves of wireless communication in the confined areas,the rectangular tunnel cooperative communication system was established based on the multimode channel model and the channel capacity formula derivation was obtained.On the optimal criterion of the channel capacity,the power allocation methods of both amplifying and forwarding(AF) and decoding and forwarding(DF) cooperative communication systems were proposed in the limitation of the total power to maximize the channel capacity.The mode selection methods of single input single output(SISO) and single input multiple output(SIMO) models in the rectangular tunnel,through which the higher channel capacity can be obtained,were put forward as well.The theoretical analysis and simulation comparison show that,channel capacity of the wireless communication system in the rectangular tunnel can be effectively enhanced through the cooperative technology;channel capacity of the rectangular tunnel under complicated conditions is maximized through the proposed power allocation methods,and the optimal cooperative mode of the channel capacity can be chosen according to the cooperative mode selection methods given in the paper.

Activation Distance and Capacity Analysis for Ambient Backscatter Communications with Sensitivity Constraint and Beamforming

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.

Communication Resources Allocation for Time Delay Reduction of Frequency Regulation Service in High Renewable Penetrated Power System

The high renewable penetrated power system has severe frequency regulation problems.Distributed resources can provide frequency regulation services but are limited by com-munication time delay.This paper proposes a communication resources allocation model to reduce communication time delay in frequency regulation service.Communication device resources and wireless spectrum resources are allocated to distributed resources when they participate in frequency regulation.We reveal impact of communication resources allocation on time delay reduction and frequency regulation performance.Besides,we study communication resources allocation solution in high renewable energy penetrated power systems.We provide a case study based on the HRP-38 system.Results show communication time delay decreases distributed resources'ability to provide frequency regulation service.On the other hand,allocating more communication resources to distributed resources'communica-tion services improves their frequency regulation performance.For power systems with renewable energy penetration above 70%,required communications resources are about five times as many as 30%renewable energy penetrated power systems to keep frequency performance the same.Index Terms-Communication resources allocation,commun-ication time delay,distributed resource,frequency regulation,high renewable energy penetrated power system.

Fast Convergence Resource Allocation in IEEE 802.16 OFDMA Systems with Minimum Rate Guarantee

Resource allocation in the context of OFDMA-based systems is challenging, given a combinatorial nature of the problem. In the context of IEEE 802.16 systems this problem is further exacerbated by additional constraints that are faced with its two dimensional frame nature. The main challenges associated with resource allocation in these systems are: mapping the allocated bandwidth resources to users in this two dimensional frame, power and frequency allocation, and Qo S guarantee. This optimization problem can usually be solved by an iterative algorithm. The solutions proposed have a constant step size in iterations which causes a long convergence time. For this reason, the solutions proposed are not applicable in IEEE 802.16 systems. In this paper we propose a novel resource allocation algorithm in IEEE 802.16 systems which has an adaptive step size in iterations while taking into account the minimum rate guarantee for users.

Joint channel and power allocation scheme for cognitive wireless networks

Cognitive radio （CR） is a promising technology deemed to improve the efficiency of spectrum utilization. This paper considers a spectrum underlay cognitive radio network, in which the cognitive users （CUs） are allowed to use the radio spectrum concurrently with the primary users （PUs） under the interference temperature constraint. We investigate the system performance by using the proposed joint channel and power allocation scheme under two transmit strategies to achieve higher data rates and performance diversity gain respectively. Simulation results show that the proposed scheme provides a significant improvement on the bit error rate （BER） performance and spectrum efficiency of a cognitive wireless network.

Distributed power allocation over indoor multi-pico stations

A low-complexity distributed power allocation algorithm is proposed to reduce the interference and improve the transmitting rate of edge users. Different scenarios are considered and user experience of indoor communication is promoted. The simulation results prove the effectiveness of our algorithm. The proposed power control scheme ensures that more users can achieve their required rate and the fairness of different users is improved. Besides, more than 5096 energy can be saved without loss in outage ability, and energy efficiency is also promoted. In addition, the proposed algorithm can be extended to scenarios that the required rates of pico stations can be changed periodically.

Unequal decoding power allocation for efficient video transmission

We present an unequal decoding power allocation （UDPA） approach for minimization of the receiver power consumption subject to a given quality of service （QoS）, by exploiting data partitioning and turbo decoding. We assign unequal decoding power of forward error correction （FEC） to data partitions with different priority by jointly considering the source coding, channel coding and receiver power consumption. The proposed scheme is applied to H.264 video over additive white Gaussion noise （AWGN） channel, and achieves excellent tradeoff between video delivery quality and power consumption, and yields significant power saving compared with the conventional equal decoding power allocation （EDPA） approach in wireless video transmission.

Power Allocation Optimization for Spectrum-Efficient Multi-Pair Two-Way Massive MIMO Full-Duplex Relay over Ricean Channels

In this paper we investigate the power allocation optimization for spectrum efficient multi-pair two-way massive MIMO (TWMM) amplify-and-forward (AF) full-duplex (FD) relay over Ricean fading channels, where multiple user-pairs exchange information within pair through a AF-FD relay with very large number of antennas, while each user equipped with a single antenna. First, the zeroforcing reception/zeroforcing transmission and maximum-ratio combining/maximum ratio transmission processing matrices with imperfect channel state information at the relay are presented. Then, the unified asymptotic signal-to-interference-plus-noise ratio (SINR) expression of the system at general power scaling schemes are investigates. Finally, the joint user-relay power allocation (JURPA) scheme is proposed to improve the spectral efficiency of TWMM-AF-FD relay system. Simulation results show that the proposed JURPA scheme outperforms traditional user-side only power allocation scheme.

Joint channel allocation and power control with fairness in multi-hop cognitive radio networks

This paper investigates channel allocation and cognitive radio networks. The color-sensitive graph power control schemes in OFDM-based multi-hop coloring （CSGC） model is viewed as an efficient solution to the spectrum assignment problem. The model is extended to combine with the power con- trol strategy to avoid interference among secondary users and adapt dynamic topology. The optimiza- tion problem is formulated encompassing the channel allocation and power control with the interfer- ence constrained below a tolerable limit. Meanwhile, the proposed resource allocation scheme takes the fairness of secondary users into account in obtaining the solution of optimization. Numerical re- suits show that the proposed strategy outperforms the existing spectrum assignment algorithms on the performance of both the network throughput and minimum route bandwidth of all routes, as well as the number of connected multi-hop routes which implies the fairness among secondary users.

A joint optimization scheme of resource allocation in downlink NOMA with statistical channel state information

In a real communication scenario,it is very difficult to obtain the real-time channel state infor-mation(CSI)accurately,so the non-orthogonal multiple access(NOMA)system with statistical CSI has been researched.Aiming at the problem that the maximization of system sum rate cannot be solved directly,a step-by-step resource allocation optimization scheme based on machine learning is proposed.First,in order to achieve a trade-off between the system sum rate and user fairness,the system throughput formula is derived.Then,according to the combinatorial characteristics of the system throughput maximization problem,the original optimization problem is divided into two sub-problems,that are power allocation and user grouping.Finally,genetic algorithm is introduced to solve the sub-problem of power allocation,and hungarian algorithm is introduced to solve the sub-problem of user grouping.By comparing the ergodic data rate of NOMA users with statistical CSI and perfect CSI,the effectiveness of the statistical CSI sorting is verified.Compared with the orthogonal multiple access(OMA)scheme,the NOMA scheme with the fixed user grouping scheme and the random user grouping scheme,the system throughput performance of the proposed scheme is signifi-cantly improved.