Downlink network-coded multiple access with diverse power

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.

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.

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.

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.

Energy Efficient Power Allocation for Relay-Aided D2D Communications in 5G Networks

As one of the key technologies for the fifth generation(5G) wireless networks,device-to-device(D2D) communications allow user equipment(UE) in close proximity to communicate with each other directly.Forwarded by a relay,the relay-aided D2D(RA-D2D) communications can not only be applied to communications in much longer distance but also achieve a high quality of service(Qo S) .In this paper,we first propose a two-layer system model allowing RA-D2 D links to underlay traditional cellular uplinks.Then we maximize the energy efficiency of the RA-D2 D link while satisfying the minimum data-rate of the cellular link.The optimal transmit power at both D2 D transmitter and D2 D relay sides is obtained by transforming the nonlinear fractional programming into a nonlinear parameter programming.Simulation results show that our proposed power allocation method is more energy efficient than the existing works,and the proposed RA-D2 D scheme outperformed direct D2 D scheme when the distance between two D2 D users is longer.

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.

Spectral Efficiency and Power Allocation for Mixed-ADC Massive MIMO System

This paper investigates the achievable uplink spectral efficiency(SE) of a massive multi-input multi-output(MIMO) system with a mixed analog-to-digital converter(ADC) receiver architecture, in which some antennas are equipped with full-resolution ADCs while others are deployed with low-resolution ADCs. We derive the theoretical results and corresponding approximate expressions of the achievable SE in multi-cell systems with maximum ratio combining(MRC) detector and in single-cell systems with zero-forcing(ZF) detector. Based on approximated results, the effects of physical parameters, including the transmit power, the number of antennas, the proportion of full-resolution ADCs and the quantization precision of the low-resolution ADCs on the achievable SE are revealed. Furthermore, we propose the power allocation algorithms based on the lower bound and upper bound of approximate achievable SE. Our results show that the total achievable SE improves by increasing the number of BS antennas, the signal-to-noise ratio(SNR), and the quantization precision. Results showcase that proposed power allocation algorithms remarkably improve the total achievable SE comparing to the equal power allocation algorithm, which verifies the effectiveness of our proposed schemes.

Power Allocation for NOMA in D2D Relay Communications

Non-orthogonal multiple access(NOMA)is considered as one of the key technologies for the fifth generation(5G)wireless communications.The integration of NOMA and device-to-device(D2D)communications has recently attracted wide attention.In this paper,a relaying D2D communications assisted with cooperative relaying systems using NOMA(DRC-NOMA)is considered.We analyze the ergodic sum-rate for the proposed system and then derive the closed-form expressions.In addition,an optimal power allocation strategy maximizing the ergodic sum-rate is proposed based on these analysis results.Numerical results show the good agreement between the results of analysis and Monte Carlo method.The proposed DRC-NOMA has a great improvement of the ergodic sum-rate in the small regime of average channel gain of D2D pair.

User Association and Power Allocation for UAV-Assisted Networks: A Distributed Reinforcement Learning Approach

Unmanned aerial vehicles(UAVs)can be employed as aerial base stations(BSs)due to their high mobility and flexible deployment.This paper focuses on a UAV-assisted wireless network,where users can be scheduled to get access to either an aerial BS or a terrestrial BS for uplink transmission.In contrast to state-of-the-art designs focusing on the instantaneous cost of the network,this paper aims at minimizing the long-term average transmit power consumed by the users by dynamically optimizing user association and power allocation in each time slot.Such a joint user association scheduling and power allocation problem can be formulated as a Markov decision process(MDP).Unfortunately,solving such an MDP problem with the conventional relative value iteration(RVI)can suffer from the curses of dimensionality,in the presence of a large number of users.As a countermeasure,we propose a distributed RVI algorithm to reduce the dimension of the MDP problem,such that the original problem can be decoupled into multiple solvable small-scale MDP problems.Simulation results reveal that the proposed algorithm can yield lower longterm average transmit power consumption than both the conventional RVI algorithm and a baseline algorithm with myopic policies.

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.

Fair subcarrier-power allocation scheme for multiuser multicarrier systems

The main objective of multiuser orthogonal frequency division multiple access(MU-OFDM) is to maximize the total system capacity in wireless communication systems. Thus, the problem in MU-OFDM system is the adaptive allocation of the resources(subcarriers, bits and power) to different users subject to several restrictions to maximize the total system capacity. In this work, a proposed subcarrier allocation algorithm was presented to assign the subcarriers with highest channel gain to the users. After the subcarrier allocation, subcarrier gain-based power allocation(SGPA) was employed for power and bit loading. The simulation results show that the proposed subcarrier-power allocation scheme can achieve high total system capacity and good fairness in allocating the resources to the users with slightly high computational complexity compared to the existing subcarrier allocation algorithms.

Joint Power Allocation and Mode Selection for D2D Communications with Imperfect CSI

Device-to-device(D2D) communications can be underlaid with a cellular infrastructure to increase resource utilization, improve user throughput and save battery energy. In such networks, power allocation and mode selection are crucial problems. To address the joint optimization of power and mode selection under imperfect CSI, we propose an optimal, energy-aware joint power allocation and mode selection(JPAMS) scheme. First, we derive the closed-form solution for the power minimization for both D2 D and cellular links while satisfying different quality of service(Qo S) constraints. Second, we address the mode selection problem in presence of imperfect CSI, based on the derived power allocation. Moreover, the theoretical analysis and simulation results are presented to evaluate the proposed scheme for the D2 D communications.

Joint Optimal Sensing Threshold and Subcarrier Power Allocation in Wideband Cognitive Radio for Minimising Interference to Primary User

Cognitive Radio （CR） can use the fre- quency band allocated to a Primary User （PU） on the premise that it will prevent significant of avoiding causing great interference to the PU. In this paper, we consider a wideband CR system where the Secondary User （SU） mini- raises its interference to the PU by jointly al- locating the optimal sensing threshold and sub- carrier power. A multi-parameter optimization problem is formulated to obtain the joint opt- imal allocation by alternating direction opti- mization, which minimises the total interfer- ence to the PU over all of the subcarriers sub- ject to the constraints on the throughput, Bit Error Rate （BER） and maximal total power of the SU, the subcarrier rate and interference power of the PU, and the false alarm and mis- detection probabilities of each subcarrier. The simulation results show that the proposed joint allocation algorithm can achieve the desired mitigation on the interference to the PU at the different subcarrier gains.

An Energy-Efficient UAV Deployment Scheme for Emergency Communications in Air-Ground Networks with Joint Trajectory and Power Optimization

The space-air-ground integrated network(SAGIN)has gained widespread attention from academia and industry in recent years.It is widely applied in many practical fields such as global observation and mapping,intelligent transportation systems,and military missions.As an information carrier of air platforms,the deployment strategy of unmanned aerial vehicles(UAVs)is essential for communication systems’performance.In this paper,we discuss a UAV broadcast coverage strategy that can maximize energy efficiency(EE)under terrestrial users’requirements.Due to the non-convexity of this issue,conventional approaches often solve with heuristics algorithms or alternate optimization.To this end,we propose an iterative algorithm by optimizing trajectory and power allocation jointly.Firstly,we discrete the UAV trajectory into several stop points and propose a user grouping strategy based on the traveling salesman problem(TSP)to acquire the number of stop points and the optimization range.Then,we use the Dinkelbach method to dispose of the fractional form and transform the original problem into an iteratively solvable convex optimization problem by variable substitution and Taylor approximation.Numerical results validate our proposed solution and outperform the benchmark schemes in EE and mission completion time.

Joint Topology Construction and Power Adjustment for UAV Networks:A Deep Reinforcement Learning Based Approach

In this paper,we investigate a backhaul framework jointly considering topology construction and power adjustment for self-organizing UAV networks.To enhance the backhaul rate with limited information exchange and avoid malicious power competition,we propose a deep reinforcement learning(DRL)based method to construct the backhaul framework where each UAV distributedly makes decisions.First,we decompose the backhaul framework into three submodules,i.e.,transmission target selection(TS),total power control(PC),and multi-channel power allocation(PA).Then,the three submodules are solved by heterogeneous DRL algorithms with tailored rewards to regulate UAVs’behaviors.In particular,TS is solved by deep-Q learning to construct topology with less relay and guarantee the backhaul rate.PC and PA are solved by deep deterministic policy gradient to match the traffic requirement with proper finegrained transmission power.As a result,the malicious power competition is alleviated,and the backhaul rate is further enhanced.Simulation results show that the proposed framework effectively achieves system-level and all-around performance gain compared with DQL and max-min method,i.e.,higher backhaul rate,lower transmission power,and fewer hop.

Energy-Efficient Power Allocation for IoT Devices in CR-NOMA Networks

Non-orthogonal multiple access is a promising technique to meet the harsh requirements for the internet of things devices in cognitive radio networks.To improve the energy efficiency(EE)of the unlicensed secondary users(SU),a power allocation(PA)algorithm with polynomial complexity is investigated.We first establish the feasible range of power consumption ratio using Karush-Kuhn-Tucker optimality conditions to support each SU’s minimum quality of service and the effectiveness of successive interference cancellation.Then,we formulate the EE optimization problem considering the total transmit power requirements which leads to a non-convex fractional programming problem.To efficiently solve the problem,we divide it into an inner-layer and outer-layer optimization sub-problems.The inner-layer optimization which is formulated to maximize the sub-carrier PA coefficients can be transformed into the difference of convex programming by using the first-order Taylor expansion.Based on the solution of the inner-layer optimization sub-problem,the concave-convex fractional programming problem of the outer-layer optimization sub-problem may be converted into the Lagrangian relaxation model employing the Dinkelbach algorithm.Simulation results demonstrate that the proposed algorithm has a faster convergence speed than the simulated annealing algorithm,while the average system EE loss is only less than 2%.

A New Power Allocation Algorithm in Cognitive Radio Networks

Most resource allocation algorithms are based on interference power constraint in cognitive radio networks.Instead of using conventional primary user interference constraint,we give a new criterion called allowable signal to interference plus noise ratio(SINR) loss constraint in cognitive transmission to protect primary users.Considering power allocation problem for cognitive users over flat fading channels,in order to maximize throughput of cognitive users subject to the allowable SINR loss constraint and maximum transmit power for each cognitive user,we propose a new power allocation algorithm.The comparison of computer simulation between our proposed algorithm and the algorithm based on interference power constraint is provided to show that it gets more throughput and provides stability to cognitive radio networks.

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.

SER analysis and power allocation for hybrid cooperative transmission system

The symbol-error-rate（SER） and power allocation for hybrid cooperative（HC） transmission system are investigated.Closed-form SER expression is derived by using the moment generating function（MGF）-based approach.However,the resultant SER contains an MGF of the harmonic mean of two independent random variables（RVs）,which is not tractable in SER analysis.We present a simple MGF expression of the harmonic mean of two independent RVs which avoids the hypergeometric functions used commonly in previous studies.Using the simple MGF,closed-form SER for HC system with M-ary phase shift keying（M-PSK） signals is provided.Further,an approximation as well as an upper bound of the SER is presented.It is shown that the SER approximation is asymptotically tight.Based on the tight SER approximation,the power allocation of the HC system is investigated.It is shown that the optimal power allocation does not depend on the fading parameters of the source-destination（SD） channel and it only depends on the source-relay（SR） and relay-destination（RD） channels.Moreover,the performance gain of the power allocation depends on the ratio of the channel quality between RD and SR.With the increase of this ratio,more performance gain can be acquired.