Dynamic Power Allocation Algorithm Research Articles

Energy-Efficient Power and Subcarrier Allocation in Downlink OFDMA Systems with Channel Estimation Errors

In this paper, we investigate the joint optimization of power and subcarrier allocation for maximizing the energy efficiency (EE) in downlink OFDMA systems. The problem of EE maximization is formulated as a stochastic optimization to determine the optimal power and subcarrier allocation, in which many practical factors including channel estimation errors, delay requirements, and time-varying channel are considered. We first propose a dynamic power and subcarrier allocation (DPSA) algorithm to solve the formulated problem. Then, we derive the two bounds of EE and delay and reveal the tradeoff between them. The theoretical analysis and simulation results demonstrate the variations of EE and delay with channel estimation errors.

Energy-Efficient Optimization of mm-Wave Communication Using a Novel Approach of Beamspace MIMO-NOMA

The new concept of Beamspace Multiple Input Multiple Output system (BS-MIMO) was developed to address the issue of reduced Energy Efficiency (EE) of the traditional MIMO in millimeter-Wave (mm-Wave) wireless communication systems by decreases the large number of radio frequency chains (RF-chains) while maintaining the same number of antennas in those systems. On the other hand, a fewer RF-Chains leads to a smaller number of users that the system can serve as a result of the number of users that can be served must be equal to/or less than the RF-chains’ number. To overcome the above issue, the BS-MIMO is proposed to be integrated with Non-Orthogonal Multiple-Access (NOMA) scheme to produce the novel approach of BS-MIMO-NOMA. As a result, the novel scheme is capable of serving a group of users with correlated channels via a single RF-chain. In this paper, we will address the issue of EE of the above-mentioned communication systems. Specifically, we propose and develop an iterative algorithm with a low complexity that achieves near-perfect performance. The proposed scheme, named MSE-DPA (for Mean-Square-Error-Based Dynamic Power Allocation Algorithm), is checked to ensure this validity of Figure-of-Merit. The simulation results indicate that the EE is approximately 85% greater than that of traditional (fully-digital) MIMO systems for a certain fair system and environment scenario.

Dynamic power allocation algorithm for non-orthogonal multiple access visible light communication system based on polar codes

Visible light communication (VLC) with non-orthogonal multiple access (NOMA) is one of the most promising technologies in wireless optical communication. To improve the system’s performance, we first proposed a NOMA-VLC system based on polar codes. Second, to evaluate the effect of power allocation algorithm on the transmission quality of the NOMA-VLC system based on polar codes, the performance of the NOMA-VLC system under a fixed power allocation algorithm was studied. Finally, considering the time-varying characteristics of wireless channels and users’ service quality, a quality of service (QoS)-based dynamic power allocation algorithm was proposed. In the dynamic power allocation algorithm, we prioritized the QoS of user1 (the user with a lower channel gain) by ensuring that the transmission rate of user1 was always greater than or equal to the minimum transmission rate, and the remaining power was allocated to users with higher channel gains to maximize the total transmission rate. Compared with the NOMA-VLC system without polar codes, simulation results show that user1 obtained 4.1 dB gain for the polar-coded NOMA-VLC system, when the bit error rate was 10 − 4 and α = 1 / 9. And the total transmission rate of the proposed dynamic power allocation algorithm improved by 38.2% compared with the fixed power allocation algorithm with α = 1 / 9 and improved by 48.1% compared with the gain ration power allocation (GRPA) algorithm, when the signal-to-noise ratio was 10 dB. In addition, compared with the fixed power allocation algorithm with α = 1 / 9 and the GRPA algorithm, the outage probability performance of user2 (the user with a higher channel gain) was significantly improved, whereas the outage probability of user1 was basically unchanged under the dynamic power allocation algorithm.

Dynamic power optimization of pilot and data for downlink OFDMA systems

In this paper, we investigate the power allocation problem in downlink orthogonal frequency-division multiple access (OFDMA) networks. Different from previous researches on power allocation, we take into account various practical factors, such as the stochastic traffic arrival, the time-varying channel, the queue stability requirements of all users, the channel estimation cost and the corresponding effect of imperfect channel state information (CSI) on data transmission rate. The power allocation problem is formulated as maximizing the time-averaged data transmission rate by optimizing pilot and data power allocation subject to the queue stability and the maximum transmit power constraints. The data transmission rate is defined in terms of the pilot transmit power, the data transmit power and the channel estimation error, which is non-concave. To solve the non-concave and stochastic optimization problem, a dynamic pilot and data power allocation (DPDPA) algorithm is proposed with the aids of approximate transformation, Lyapunov optimization and Lagrange dual formulation. Moreover, we derive the bounds of performances, in terms of the time-averaged data transmission rate and queue length.

Queue-Aware Dynamic Resource Allocation for the Joint Communication-Radar System

The joint communication-radar system (JCRS) is promising in the future wireless network which can provide great advantages, such as structure simplification, interference mitigation and less resource occupation, compared with the traditional separate deployment of communication and radar systems. However, with the large number of users accessing the network both for communication and radar purposes, massive user data packets may arrive at the base station equipped with the JCRS in a short period of time, which can easily cause the backlog of user data packets and affect network stability. To solve this problem, we introduce orthogonal frequency division multiple access (OFDMA) technique into the JCRS to support communication function for massive users. Then, we design a queue scheduling model of user data packets to perceive the status of current queue backlog and schedule the queue dynamically. Different from the existing works, we formulate a transmit power minimization problem, while taking network stability and radar detection performance into account. Since the problem is a non-convex problem, we transform it into the upper-bound minimization of drift-plus-function by using the Lyapunov optimization technique. Further, the successive convex approximation method and parameter transformation method are exploited to convert this problem into a convex problem. Finally, we derive an optimal closed-form power allocation expression by using the Lagrangian dual decomposition method and propose a dynamic subcarrier and power allocation algorithm. Simulation results show that the proposed algorithm can achieve an $[{\rm {O}}(1/\eta),{\rm {O}}(\eta)]$ power-backlog tradeoff and demonstrate the efficacy of the proposed algorithm in reducing transmit power while significantly enhancing network stability.

Research on PDMA communication system based on complete complementary sequence

Pattern division multiple access (PDMA) is a new non-orthogonal multiple access (NOMA) technology. It is proposed to meet the challenge of 5G large-scale connectivity and high-frequency spectral efficiency. Compared with traditional orthogonal multiple access (OMA), PDMA can support more users through the allocation of non-orthogonal resources. Due to perfect aperiodic correlation, complete complementary sequence (CCS) greatly improves the spectrum efficiency of the system. It has been widely used in wireless communication and radar sensing, and it still has research value in 5G. In order to apply the advantages of CCS to NOMA communication system, this paper proposes a system model of CCS spread spectrum coding based on PDMA. CCS is used as spread spectrum code to improve the performance of PDMA communication system. At the same time, on the basis of spread spectrum technology, this paper analyzes the average power allocation algorithm and water-filling power allocation algorithm, and a dynamic power allocation algorithm based on the transmission rate and practical application is proposed. The simulation results show that the system model can effectively improve the performance of the system.

Energy-efficient resource allocation for multi-RAT networks under time average QoS constraint

In this paper, we propose an energy-efficient resource allocation algorithm for multiple radio access technologies (multi-RAT) networks, where the user equipments (UEs) transmit data over multiple radio interfaces for exploiting the complementary advantages of different RATs. In this scenario, the resource allocation is formulated as a stochastic energy efficiency (EE) maximization problem. More specifically, the time average quality of service constraint is considered to provide the flexible resource allocation over the time-varying fading channels. The virtual queue is introduced for each UE to deal with the time average transmission requirement. By adopting Lyapunov optimization technique and fractional programming theory, the non-concave EE maximization is converted into a mixed integer nonlinear optimization (MINO) problem. After that, the continuity relaxation and Lagrange dual methods are used to find the solution of the MINO problem. Then, we develop an EE-based dynamic joint subcarrier and power allocation algorithm, which does not require any prior knowledge of the channel state information. In addition, the performance bounds of the EE and virtual queue are provided. Our simulation results show that the performance of the proposed algorithm is better than other general algorithms.

An online power allocation algorithm based on deep reinforcement learning in multibeam satellite systems

SummaryDynamic power allocation (DPA) is the key technique to improve the system throughput by matching the offered capacity with that required among distributed beams in multibeam satellite systems. Existing power allocation studies tend to adopt the metaheuristic optimization algorithms such as the genetic algorithm. The achieved DPA cannot adapt to the dynamic environments due to the varying traffic demands and the channel conditions. To solve this problem, an online algorithm named deep reinforcement learning‐based dynamic power allocation (DRL‐DPA) algorithm is proposed in this paper. The key idea of the proposed DRL‐DPA lies in the online power allocation decision making other than the offline way of the traditional metaheuristic methods. Simulation results show that the proposed DRL‐DPA algorithm can improve the system performance in terms of system throughput and power consumption in multibeam satellite systems.

Power allocation scheme for downlink and uplink NOMA networks

Resource allocation problem is a key issue in multi-carrier non-orthogonal multiple access (NOMA) networks. Maximum access problem and power allocation problem are two important problems in resource allocation problem. In this study, two algorithms are proposed to solve the two problems for multi-carrier NOMA networks, namely mixed integer programming algorithm (MIPA) and dynamic power allocation algorithm (DPAA). In order to solve the maximum access problem, a MIPA is proposed in uplink multi-carrier NOMA networks. For the power allocation problem, a DPAA is proposed in downlink multi-carrier NOMA networks. Simulation results show that MIPA can increase the number of supported users compared to other schemes, DPAA can improve user data rate by increasing mean channel quality indicator and make power allocation more reasonable. According to DPAA, the gains of weighted sum-rate utility per subcarrier can be improved with the increase in the number of subcarriers. Simulation results also show that the proposed scheme is superior to reservation channel technique in terms of the probability of call dropping and probability of call blocking.

A novel solution to energy efficiency in multi-user multi-carrier decode-and-forward wireless relay networks

In the last few years, energy efficient wireless communication systems are attracting great research attention due to increase in global warming. Current challenge is to meet the rising demand for channel capacity and energy efficiency without compromising on quality of service (QoS). Relay aided cellular network is one of the solutions that can mitigate the problem of energy consumption. This work proposes a new solution for maximizing energy efficiency in relay assisted multi-carrier multiuser wireless networks. In this context, a new dynamic (optimal) power allocation algorithm for Decode-and-Forward relay is proposed here. The energy efficiency is optimized in terms of consumed energy per bit subjected to constraint of total power. From numerical results, it is observed that energy efficiency performance improves with proposed dynamic power allocation algorithm.

Efficient User Clustering, Receive Antenna Selection, and Power Allocation Algorithms for Massive MIMO-NOMA Systems

Massive multiple-input multiple-output (MIMO) and nonorthogonal multiple access (NOMA)-based technologies are considered as essential parts in the 5G systems to fulfill the escalating demands of higher connectivity and data rates for emerging wireless applications. In this paper, a new approach of massive MIMO-NOMA with receive antenna selection (RAS) is considered for the uplink channel to significantly increase the number of connected devices and overall sum rate capacity with improved user-fairness and less complexity. The proposed scheme is designed from two multiuser MIMO (MU-MIMO) clusters, based on the available number of radio frequency chains (RFCs) at the base station and channel conditions, followed by power-domain NOMA for the simultaneous signal transmission. We derive the sum rate and capacity region expressions for MIMO-NOMA with RAS over Rayleigh fading channels. Then, an optimal and three highly efficient sub-optimal dynamic user clustering, RAS, and power allocation algorithms are proposed for sum rate maximization under received power constraints and minimum rate requirements of the allowed users. The effectiveness of designed algorithms is verified through extensive analysis and numerical simulations compared to the reference MU-MIMO and MIMO-NOMA systems. The achieved results show a substantial increase in connectivity, up to two-fold for the accessible number of RFCs, and overall sum rate capacity while satisfying the minimum users’ rates. Besides, important tradeoffs can be realized between system performances, hardware and computational complexities, and desired user-fairness in terms of serving more users with equal/unequal rates.

Bio-inspired energy conserving adaptive power and rate control in MANET

Maximizing network lifetime of unreliable and resource constrained wireless networks such as mobile ad hoc networks involves optimal allocation of power and shared channels in the network. Existing optimal power and channel allocation mechanisms mainly focus on throughput maximization and energy efficiency but fail to obtain a trade-off between delay and energy consumption in the network. This paper proposes a game theoretic energy efficient approach that mimics a fair non-cooperative game among nodes competing for shared channels and power level. Unlike existing power and rate control mechanisms, the proposed energy conserving adaptive power and rate control (ECAPRC) mechanism takes into consideration the constraints of outage probability and total average delay to deal with stochastic changes in the channel that improves the network performance in terms of throughput, delay and total energy consumption. Proposed super-modular game has a unique convergence point called “Nash Equilibrium” and its existence and uniqueness is proved in the paper. To enhance the convergence rate of proposed ECAPRC approach, an adaptive grey wolf optimizer is employed to deal with delay and energy constraints. Simulation results and their analysis show that proposed ECAPRC approach outperforms existing approaches such as dynamic rate and power allocation algorithm, rate-effective network utility maximization and energy conserving power and rate control in terms of above mentioned network parameters and total utility of the system.

Multi-Channel Resource Allocation Toward Ergodic Rate Maximization for Underlay Device-to-Device Communications

In underlay device-to-device (D2D) communications, a D2D pair reuses the cellular spectrum causing interference to regular cellular users. Maximizing the performance of underlay D2D communications requires joint consideration for the achieved D2D rate and the interference to cellular users. In this paper, we consider the D2D power allocation optimization over multiple resource blocks (RBs), aimed at maximizing either the ergodic D2D rate or the ergodic sum rate of D2D and cellular users, under the long-term sum-power constraint of the D2D users and per-RB probabilistic signal-to-interference-and-noise (SINR) requirements for all cellular users. We formulate stochastic optimization problems for D2D power allocation over time. The proposed optimization framework is applicable to both uplink and downlink cellular spectrum sharing. To solve the proposed stochastic optimization problems, we first convexify the problems by introducing a family of convex constraints as a replacement for the non-convex probabilistic SINR constraints. We then present two dynamic power allocation algorithms: a Lagrange dual-based algorithm that is optimal but with a high computational complexity and a low-complexity heuristic algorithm based on dynamic time averaging. Through simulation, we show that the performance gap between the optimal and heuristic algorithms is small, and the effective long-term stochastic D2D power optimization over the shared RBs can lead to substantial gains in the ergodic D2D rate and the ergodic sum rate.

CDMA-Based Dynamic Power and Bandwidth Allocation (DPBA) Scheme for Multiclass EPON: A Weighted Fair Queuing Approach

Multiclass systems with different quality of service (QoS) requirements are essential in today's Ethernet passive optical networks (EPONs). In this paper, we propose a code division multiple-access-enabled dynamic power and bandwidth allocation (DPBA) algorithm for a multiclass system. The novelty of the proposed algorithm is the resource allocation components: power control and bandwidth allocation. Both resources are related and optimized through the weights of the weighted round-robin scheduler in a way to meet the physical layer signal to interference ratio and the network layer packet delay requirements for every class of users. Our objective is to offer a differentiated class of services for all optical network units while optimizing network performance and guaranteeing fairness between different classes. A closed-form solution for the optimal power and bandwidth allocation using the DPBA algorithm is analytically derived. It is shown that the proposed algorithm can radically enhance the network performance in terms of packet delay, throughput, queue size management, transmission cycle time, and class of service fairness while guaranteeing the QoS requirements for all classes.

Dynamic power allocation for a multiuser transmitter with hybrid energy sources

In this paper, we investigate the problem of dynamic power allocation for a multiuser transmitter supplied by hybrid energy sources in details. Specifically, we focus on the hybrid energy sources which include both the traditional power grid and various renewable sources whereby there are a few issues in considerations: (1) The energy harvested jointly from various renewable sources is time-varying and possibly unpredictable and is stored in a limited capacity buffer with battery leakage. (2) At the meantime, the data arrives randomly to the transmitter and queues according to the individual receivers to wait to be transmitted. (3) In addition, the wireless channels fluctuate randomly due to fading. Taking into account the time variant and dynamic features of this system, we develop a dynamic power allocation algorithm for the transmitter with the aim of minimizing the average amount of energy consumption from the power grid over an infinite horizon, subject to all data in queues cannot exceed a given deadline of receivers. The research question is formulated as a stochastic optimization problem, then we utilize Lyapunov optimization to exploit an online algorithm with low complexity, and it does not require prior statistical knowledge of the stochastic processes. Performance analysis of the proposed algorithm is carried out in theory, which shows that the proposed algorithm performs arbitrarily close to the optimal objective value; meanwhile, the algorithm ensures that the maximum delay of all data queues cannot exceed a given value. Finally, performance comparison shows that our proposed algorithm provides not only better performance but also less time delay than other two algorithms.

Worst-Case Energy Efficiency Maximization in a 5G Massive MIMO-NOMA System.

In this paper, we examine the robust beamforming design to tackle the energy efficiency (EE) maximization problem in a 5G massive multiple-input multiple-output (MIMO)-non-orthogonal multiple access (NOMA) downlink system with imperfect channel state information (CSI) at the base station. A novel joint user pairing and dynamic power allocation (JUPDPA) algorithm is proposed to minimize the inter user interference and also to enhance the fairness between the users. This work assumes imperfect CSI by adding uncertainties to channel matrices with worst-case model, i.e., ellipsoidal uncertainty model (EUM). A fractional non-convex optimization problem is formulated to maximize the EE subject to the transmit power constraints and the minimum rate requirement for the cell edge user. The designed problem is difficult to solve due to its nonlinear fractional objective function. We firstly employ the properties of fractional programming to transform the non-convex problem into its equivalent parametric form. Then, an efficient iterative algorithm is proposed established on the constrained concave-convex procedure (CCCP) that solves and achieves convergence to a stationary point of the above problem. Finally, Dinkelbach’s algorithm is employed to determine the maximum energy efficiency. Comprehensive numerical results illustrate that the proposed scheme attains higher worst-case energy efficiency as compared with the existing NOMA schemes and the conventional orthogonal multiple access (OMA) scheme.

Dynamic Link Selection and Power Allocation With Reliability Guarantees for Hybrid FSO/RF Systems

In this paper, we study transmission strategies for hybrid free space optical (FSO)/radio frequency (RF) systems by jointly considering link selection, power allocation, and reliability guarantees. Specifically, under an explicit long-term average reliability requirement, the transmitter in the hybrid FSO/RF system makes decisions about which links should be selected and how much power should be allocated to the corresponding active link. The problem of minimizing the power consumption cost while guaranteeing packet success-probability requirements and peak and average power constraints is considered and formulated as a stochastic problem. Using the Lyapunov optimization techniques, we solve this problem and derive closed-form power allocation solutions for different link selection modes. Furthermore, we design a dynamic link selection and power allocation algorithm that can arbitrarily push the consumed power approach to the optimum at the expense of a tradeoff over reliability queue occupancy. Simulation results verify the theoretical analysis and validate the performance superiority of our proposed scheme.

Energy Efficiency and Delay Tradeoff for Time-Varying and Interference-Free Wireless Networks

In this paper, we investigate the fundamental tradeoff between energy efficiency (EE) and delay for time-varying and interference-free wireless networks. We formulate the problem as a stochastic optimization model, which optimizes the system EE subject to network stability and the average and peak transmit power constraints. By adopting the fractional programming theory and Lyapunov optimization technique, a general and effective algorithm, referred to as the EE-based dynamic power allocation algorithm (EE-DPAA), is proposed. The EE-DPAA does not require any prior knowledge of traffic arrival rates and channel statistics, yet yields an EE that can arbitrarily approach the theoretical optimum achieved by a system with complete knowledge of future events. Most importantly, we quantitatively derive the EE-delay tradeoff as [O(1/V),O(V)] with V as a control parameter for the first time. This result provides an important method for controlling the EE-delay performance on demand. Simulation results validate the theoretical analysis on the EE-delay tradeoff, as well as show the adaptiveness of the EE-DPAA.

Optimization of Power Allocation for a Multibeam Satellite Communication System with Interbeam Interference

In multibeam satellite communication systems it is important to improve the utilization efficiency of the power resources, due to the scarcity of satellite power resources. The interbeam interference between the beams must be considered in the power allocation; therefore, it is important to optimize the power allocated to each beam in order to improve the total system performance. Initially the power allocation problem is formulated as a nonlinear optimization, considering a compromise between the maximization of total system capacity and the fairness of the power allocation amongst the beams. A dynamic power allocation algorithm based on duality theory is then proposed to obtain a locally optimal solution for the optimization problem. Compared with traditional power allocation algorithms, this proposed dynamic power allocation algorithm improves the fairness of the power allocation amongst the beams, and, in addition, the proposed algorithm also increases the total system capacity in certain scenarios.

IDMA-based MAC protocol for satellite networks with consideration on channel quality.

In order to overcome the shortcomings of existing medium access control (MAC) protocols based on TDMA or CDMA in satellite networks, interleave division multiple access (IDMA) technique is introduced into satellite communication networks. Therefore, a novel wide-band IDMA MAC protocol based on channel quality is proposed in this paper, consisting of a dynamic power allocation algorithm, a rate adaptation algorithm, and a call admission control (CAC) scheme. Firstly, the power allocation algorithm combining the technique of IDMA SINR-evolution and channel quality prediction is developed to guarantee high power efficiency even in terrible channel conditions. Secondly, the effective rate adaptation algorithm, based on accurate channel information per timeslot and by the means of rate degradation, can be realized. What is more, based on channel quality prediction, the CAC scheme, combining the new power allocation algorithm, rate scheduling, and buffering strategies together, is proposed for the emerging IDMA systems, which can support a variety of traffic types, and offering quality of service (QoS) requirements corresponding to different priority levels. Simulation results show that the new wide-band IDMA MAC protocol can make accurate estimation of available resource considering the effect of multiuser detection (MUD) and QoS requirements of multimedia traffic, leading to low outage probability as well as high overall system throughput.