Optimal Power Allocation Scheme Research Articles

Distinctive analysis of power and frequency allocation for Device to Device (D2D) communication

Wireless communication makes an impeccable advancement from 1 G to 5 G and beyond. Simultaneously, modern technologies like Internet of Things (IoT) and Vehicular Communications also emerged. Improved connectivity between many different devices is provided by 5 G, which maximises data throughput and reduces interference. Non-Orthogonal Multiple Access (NOMA) schemes or Orthogonal Multiple Access techniques (OMA) suit 5 G. It is a NOMA-based device-to-device communication by correlating it with the Orthogonal Frequency Division Multiple Access (OFDMA). NOMA is also utilized to propose an optimal power allocation scheme and duly evaluated. The aim of such a scheme is to achieve a minimized outage and a maximized sum rate. A NOMA based Greedy Interference Algorithm is proposed for interference management by exquisite frequency allocation. The results generated from a comprehensive evaluation justify the algorithm utilization. Simulation results infer that the algorithms proposed improves system throughput and enhance energy efficiency.

Optimal resource allocation method for energy harvesting based underlay Cognitive Radio networks.

This paper proposes an optimal resource allocation method. The method is to maximize the Energy Efficiency (EE) for an Energy Harvesting (EH) enabled underlay Cognitive Radio (CR) network. First, we assumed the Secondary Users (SUs) can harvest energy from the surrounding Radio Frequency (RF) signals. Then, we modelled the EE maximisation problem as a joint time and power optimization model. Next, the optimal EH time allocation factor can be calculated. After that the optimal power allocation strategy can be obtain by the fractional programming and Lagrange multiplier method. Finally simulation results show that the proposed iterative method can be better performance advantages compared with the exhaustive method and genetic algorithm. And the EE of this system model is significantly improved compared to the EE model without considering EH.

Energy-efficient resource allocation in NOMA-integrated V2X networks

In Vehicle-to-Everything (V2X) communication networks, cellular Device-to-Device (D2D) communication can improve the spectrum efficiency, and non-orthogonal multiple access (NOMA) can further enhance the connection density. However, for the cellular D2D communications, the involved execution condition for NOMA may be affected by the additional interference introduced by cellular links in V2X. In this paper, we study the energy-efficient resource allocation problem in cellular D2D-aided V2X networks with NOMA. To efficiently meet the quality of service (QoS) requirements while taking into account the maximum performance from the user’s perspective, we are committed to maximizing the minimum energy efficiency (EE) of each matching link, by jointly optimizing power allocation and spectrum reusing at Vehicle-to-Infrastructure (V2I) links and cellular D2D-based Vehicle-to-Vehicle (V2V) links. Since this problem is mathematically mixed-integer and non-convex, we first develop a two-layer block coordinate descent (BCD) scheme to solve power allocation subproblem. Specifically, the optimal intra-group power allocation strategy is derived in the inner-layer. Then, Dinkelbach and concave–convex procedure (CCCP) are employed to tighten the lower bound of original problem in the outer-layer. Followed by this, we address the remaining spectrum sharing subproblem based on the principle of minimum EE maximization. Simulation results show that the proposed algorithm can achieve excellent performance and outperform the other benchmark schemes significantly.

Online Utility Optimization in Multi-User Interference Networks Under a Long-Term Budget Constraint

In this paper, we consider a multi-user interference network where a central controller allocates power resources to multiple base stations for maximizing the entire network utility under a long-term power budget constraint. The optimal power allocation strategy depends on accurate and instant channel state information (CSI). However, due to users’ mobility and the existence of channel fading and interference, timely channel estimation is unavailable. To overcome the difficulty of unknown channel states, we resort to the Lyapunov drift analysis framework and design an online power allocation algorithm based on historical observations. The algorithm can be proven to achieve sub-linear performance for both cumulative regret and power budget violation. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sub-linear regret</i> indicates the proposed algorithm can asymptotically achieve the optimal static power allocation performance in hindsight. Moreover, we use a general doubling trick to construct a parameter-free algorithm based on the aforementioned online algorithm without the knowledge of time horizon <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$T$</tex-math></inline-formula> , which can be proven to preserve the order of the regret and violation upper bound. Simulation results are provided to validate the performance of the proposed online algorithms compared with the offline cave-filling algorithms, as well as its robustness in the presence of adversarial interference.

Deep Reinforcement Learning-Based Power Allocation for Rate-Splitting Multiple Access in 6G LEO Satellite Communication System

Rate-splitting multiple access (RSMA) softly reconciles and decodes the extreme interference by non-orthogonal transmission, which can remarkably solve the spectrum scarcity for future six-generation (6G) low earth orbits (LEO) satellite communication system. In this letter, we investigate the power allocation problem in LEO satellite networks with RSMA mechanism based on the deep reinforcement learning (DRL) technique. Specifically, in order to achieve better RSMA performance, the LEO satellite base station (SBS) has to effectively allocate transmit power to common and private streams, which is very challenging due to the uncertain and limited information of the channel distribution. To solve this problem, a highly-effective proximal policy optimization (PPO) based scheme is further proposed, which enables the LEO SBS to learn an optimal power allocation strategy to maximize the sum rate of the system without knowing any prior information. Simulation results prove that the proposed scheme significantly outperforms the other three baseline schemes in terms of the sum rate metric with low computation complexity.

Deep Learning Based Power Allocation Schemes for NOMA System with Imperfect SIC

Non-orthogonal multiple access (NOMA) has been a promising technology for 5G communication system. NOMA allows more than one user to utilize the same resource at the same time, which can lead to high performance in terms of spectral efficiency, energy efficiency or fairness. NOMA enables power domain multiplexing and separates the multiplexed signal by using successive interference cancellation (SIC). Therefore, the full benefit of NOMA depends on power allocation. However, in practical system, residual interference caused by SIC process can severely degrade a system performance. In this paper, we propose two power allocation schemes based on deep learning to alleviate the effect of imperfect SIC for downlink NOMA system, including a deep learning-based sum rate power allocation scheme (DL-SRPAS) and a deep learning-based energy-efficient power allocation scheme (DL-EEPAS). our two proposed schemes learn two optimal power allocation schemes provided through exhaustive search. We search the solution of the optimization problems, where two optimization problems are formulated to maximize the sum rate and the energy efficiency subject to a minimum user data rate requirement. The simulation results verify that our two proposed schemes can alleviate the effect of imperfect SIC and outperform two conventional power allocation schemes which maximize the sum rate and the energy efficiency without considering imperfect SIC. In addition, our proposed schemes achieve near-optimal performance with very low computational time.

Hybrid 3DMA for multi-user MIMO-VLC

In this paper, we propose and investigate a hybrid three-dimensional multiple access (3DMA) scheme for multi-user multiple-input multiple-output visible light communication (MU-MIMO-VLC) systems that can fully exploit the 3D resources of the system including frequency, space, and power. Particularly, all users in the MU-MIMO-VLC system applying hybrid 3DMA are first divided into multiple user groups (UGs) in the spatial domain, and users within each UG are further divided into multiple user pairs (UPs) in the frequency domain. In each UP, two users are multiplexed in the power domain via superposition coding. Due to the efficient 3D resource allocation in the MU-MIMO-VLC system applying hybrid 3DMA, the available bandwidth of each user can be substantially increased. Moreover, the impact of error propagation caused by imperfect successive interference cancellation (SIC) is considered, and the optimal power allocation (OPA) strategy is also derived to maximize the achievable sum rate of each UP. The feasibility and superiority of the proposed hybrid 3DMA with OPA have been successfully verified by the obtained analysis and simulation results. It is shown that the achievable average sum rate of an indoor 4 × 4 MU-MIMO-VLC system can be substantially improved by applying the proposed hybrid 3DMA with OPA, in comparison to benchmark schemes such as orthogonal frequency division multiple access and space division multiple access. Moreover, it is further revealed that hybrid 3DMA with OPA exhibits high tolerance against imperfect SIC induced error propagation, which also shows excellent robustness and performance consistency to support multiple randomly located users.

A Robust Power Allocation Algorithm for Cognitive Radio Networks Based on Hybrid PSO.

The use of a cognitive radio power allocation algorithm is an effective method to improve spectral utilization. However, there are three problems with traditional cognitive radio power allocation algorithms: (1) based on the ideal channel model analysis, channel fluctuation is not considered; (2) they do not consider fairness among cognitive users; and (3) some algorithms are complex and locating the optimal power allocation scheme is not an easy task. For the above problems, this study establishes a robust model which adds the cognitive user transmission rate variance constraint to solve the maximum channel capacity time power allocation scheme by considering the worst-case channel transmission model, and finally solves this complex non-convex optimization problem by using the hybrid particle swarm algorithm. Simulation results show that the algorithm has good robustness, improves the fairness among the cognitive users, makes full use of the channel resources under the constraints, and has a simple algorithm, fast convergence, and good optimization results.

Energy management and nonlinear control strategy of hybrid energy storage system for electric vehicle

The energy management of hybrid energy storage system (HESS) and the nonlinear control strategy of the interface circuit are studied in this paper. In order to realize the reasonable distribution of load power between the battery and the supercapacitor, the energy management strategies (EMS) are used to decompose the load power required for electric vehicles under different driving cycle conditions, aiming at choosing an optimal power allocation strategy. Based on the local modeling, the global mathematical model of two bidirectional DC/DC converters under the HESS with active control topology is established, and the nonlinear control strategies are studied for the interface circuit and then the target controller is designed to realize the EMS. The current inner loop controller based on synergetic control theory is to track the battery current and supercapacitor current. The voltage outer loop controller takes power conservation as the criterion and adds the DC bus voltage PI controller to construct the Lyapunov function, where the connection between the DC bus voltage stability and the supercapacitor reference current is established. The supercapacitor replaces the battery to overcome the fluctuation of the DC bus voltage caused by the sudden change of the load power, maintaining the stability of the entire control system, and at the same time the case that the power allocated by the supercapacitor is consistent with the EMS is ensured. Two typical vehicle driving cycle conditions, IM240 and ECE-15, are selected to test the effectiveness of the current controller and the voltage controller. The experimental platform of a small-capacity HESS is built to conduct experimental verification. The simulation and experimental results show that the nonlinear controllers are feasible and have strong robustness and good adaptability under different driving conditions.

Adaptive optimal power allocation scheme based on intelligent user association for NOMA-VLC systems

In this paper, the joint problem of user association and power allocation for non-orthogonal multiple access (NOMA)-visible light communication (VLC) systems is investigated. First, a novel intelligent user association (UA) method for multi-user NOMA-VLC is proposed to improve the multiplexing efficiency. Second, a new adaptive optimal power allocation scheme based on the UA method is presented and three corresponding mathematical optimization problems are established, aiming to optimize the system throughput while each user's illumination requirement, quality of service demand, eye safety, and transmission power limit are satisfied simultaneously. Third, for the error propagation at the NOMA receiver, the perfect and imperfect interference cancellations are considered in the established power allocation models. Finally, the numerical results demonstrate the superiority of the proposed scheme on the VLC network capacity under perfect and imperfect interference cancellation. Furthermore, it is also found that the proposed scheme could achieve higher system throughput by modifying users' field-of-views (FOVs).

CoMP-Assisted NOMA and Cooperative NOMA in Indoor VLC Cellular Systems

In this paper, we investigate the dynamic power allocation for a visible light communication (VLC) cellular system consisting of two coordinating attocells, each equipped with one access-point (AP). The coordinated multipoint (CoMP) between the two cells is introduced to assist users experiencing high inter-cell-interference (ICI). Specifically, the coordinated zero-forcing (ZF) precoding is used to cancel the ICI at the users located near the centers of the cells, whereas the joint transmission (JT) is employed to eliminate the ICI at the users located at the edge of both cells and to improve their receptions as well. Furthermore, two multiple access techniques are invoked within each cell, namely, non-orthogonal-multiple-access (NOMA) and cooperative non-orthogonal-multiple-access (C-NOMA). Hence, two multiple access techniques are proposed for the considered multi-user multi-cell system, namely, the CoMP-assisted NOMA scheme and the CoMP-assisted C-NOMA scheme. For each scheme, two power allocation frameworks are formulated each as an optimization problem, where the objective of the former is maximizing the network sum data rate while guaranteeing a certain quality-of-service (QoS) for each user, whereas the goal of the latter is to maximize the minimum data rate among all coexisting users. The formulated optimization problems are not convex, and hence, difficult to be solved directly unless using heuristic methods, which comes at the expense of high computational complexity. To overcome this issue, optimal and low complexity power allocation schemes are derived. In the simulation results, the performance of the proposed CoMP-assisted NOMA and CoMP-assisted C-NOMA schemes are compared with those of the CoMP-assisted orthogonal-multiple-access (OMA) scheme, the C-NOMA scheme and the NOMA scheme, where the superiority of the proposed schemes are demonstrated. Finally, the performance of the proposed schemes and the considered baselines is evaluated while varying various system parameters.

Lifetime Enhancement of Cooperative Wireless Ad-Hoc Networks using Optimal Power Allocation and Relay Selection Scheme

Cooperative Communication (CC) has greatly reduced the effect of multipath fading, thereby improving the quality of transmitted signals in wireless ad-hoc networks. However, existing works on CC suffers from degradation of network lifetime due to inaccurate selection of the optimal relay terminal. This results in frequent battery recharging due to high energy consumed and battery depletion. Hence, in this paper, lifetime enhancement of cooperative wireless ad-hoc networks is carried out using allocated optimal power and relay selection scheme to ensure an optimal selection of the best relay by balancing and conserving the energy consumption of the nodes. Transmit power optimization problems were formulated for Amplify-and-Forward (AF) and Decode-and-Forward (DF) relaying strategies to obtain the optimal power allocations required to transmit the information. The proposed scheme named Energy-Aware Relay Selection (EARS) was simulated in MATLAB software environment and average network lifetime performance evaluation was performed. The result obtained shows that the proposed EARS improves the network performance by 10.06% and 3.56% for AF and DF relaying strategies, respectively over the existing power-aware relay selection schemes.

Decentralized Power Allocation for MIMO-NOMA Vehicular Edge Computing Based on Deep Reinforcement Learning

Vehicular edge computing (VEC) is envisioned as a promising approach to process the explosive computation tasks of vehicular user (VU). In the VEC system, each VU allocates power to process partial tasks through offloading and the remaining tasks through local execution. During the offloading, each VU adopts the multi-input multi-output and non-orthogonal multiple access (MIMO-NOMA) channel to improve the channel spectrum efficiency and capacity. However, the channel condition is uncertain due to the channel interference among VUs caused by the MIMO-NOMA channel and the time-varying path loss caused by the mobility of each VU. In addition, the task arrival of each VU is stochastic in the real world. The stochastic task arrival and uncertain channel condition affect greatly on the power consumption and latency of tasks for each VU. It is critical to design an optimal power allocation scheme considering the stochastic task arrival and channel variation to optimize the long-term reward, including the power consumption and latency in the MIMO-NOMA VEC. Different from the traditional centralized deep reinforcement learning (DRL)-based scheme, this article constructs a decentralized DRL framework to formulate the power allocation optimization problem, where the local observations are selected as the state. The deep deterministic policy gradient (DDPG) algorithm is adopted to learn the optimal power allocation scheme based on the decentralized DRL framework. Simulation results demonstrate that our proposed power allocation scheme outperforms the existing schemes.

Optimal Power Allocation for Achieving Secure Green Cognitive Radio Networks

In cognitive radio networks, wireless nodes adapt to the surrounding radio environment and utilize the spectrum of licensed users. The cognitive radio environment is dynamic, and wireless channels are accessible by both legitimate and illegitimate users. Therefore, maintaining the security of cognitive radio networks is a challenging task, which must be addressed thoroughly. Further, with the recent exponential surge in wireless nodes and associated high data rate requirements, energy consumption is also growing at an unprecedented rate. Hence, energy efficiency becomes an important metric that must be considered in the design of future wireless networks. Accordingly, by considering the great ecological and economic benefits of green wireless networks, this work focus on energy-efficient resource allocation in secure cognitive radio networks. Since physical-layer security is an emerging technique that improves the security of communication devices, in this paper, an ergodic secure energy efficiency problem for a cognitive radio network is formulated with a primary user, a secondary user, and an eavesdropper. As the formulated problem is non-convex, a concave lower bound is applied to transform the original non-convex problem into a convex one. Further, by adopting the fractional programming and dual decomposition techniques, optimal power allocation strategies are obtained with the aim of maximizing the ergodic secure energy efficiency of the secondary user with constraints on the average interference power and average transmit power. Numerical examples are used to demonstrate the effectiveness of the proposed algorithm.

Optimal LED Power Allocation Framework for a Location-Assisted Indoor Visible Light Communication System

The widespread deployment of white light-emitting6 diodes (LEDs) for illumination provides a unique opportunity to create a flexible, accurate, and ubiquitous indoor communication and positioning system. In this paper, we exploit the location information obtained via LEDs to improve the communication performance of an indoor visible light communication (VLC) system. Specifically, we propose an optimal LED power management framework to maximize the average data rate across the room while satisfying the bit error rate (BER) and illumination constraint across the room. The maximum allowed localization error, as a function of the number of blockages and the LED irradiance angle, have been calculated. In addition, the closed-form expression for the BER is derived for the proposed optimal LED power allocation scheme. We have also formulated an optimization problem that will maximize the power savings among the LEDs with respect to the number of blockages and permissible error in localization. It has been shown that, by employing the proposed optimal LED power allocation will results in a significant amount of power-saving, which is approximately 40% for 4 LEDs configuration and 70% 26 for 8 LEDs configuration as compared to equal power allocation. Further, the maximum allowed localization error is found out to be 28 approximately 7 cm and 18 cm with 4 and 8 LEDs, respectively, to 29 achieve the maximum achievable data rate. Finally, it is shown that 30 the dimming range of up to 70% can be achieved for the proposed system.

Achievable Rate and Energy Efficiency Analysis of Multiuser Relay-Aided Massive MIMO Downlink

In this article, the achievable sum rate and the energy efficiency (EE) are investigated for a multiuser relay-aided massive multiple-input–multiple-output (MIMO) downlink. Low-resolution digital-to-analog converters (DACs) are equipped at both the base station (BS) and the relay station (RS), and the amplify-and-forward protocol is adopted at the RS. Under the Rician fading channel, closed-form approximate expressions for the achievable sum rate are derived with perfect and imperfect channel state information. A more general power law is extracted to save transmit power without reducing the achievable sum rate, and a local optimal power allocation scheme is proposed to improve the channel capacity of active users. Then, the tradeoff between the achievable sum rate and the EE is discussed. The numerical results show that, due to the quantization noise, the transmit power has a limited increase in the achievable rate. In addition, it is more valuable to improve the DAC quantization bit at the RS when the number of BS antennas is larger than that of RS antennas. In addition, under strong line-of-sight channels or high pilot transmit power conditions, the channel estimation accuracy is higher, and the best tradeoff between the achievable sum rate and the EE can be obtained when the DAC quantization bit is 4.

Energy Efficiency Maximization in the Wireless-Powered Backscatter Communication Networks with DF Relaying

This paper focuses on the design of an optimal resource allocation scheme to maximize the energy efficiency (EE) in the wireless-powered backscatter communication networks (WPBCN) with decode and forward (DF) relaying. The two different devices are supported to operate in different modes, the harvest-then-transmit (HTT) mode and backscatter communication (BackCom) mode, respectively. In particular, we formulate an optimization problem to maximize system EE by jointly optimizing the transmit power of hybrid access point (H-AP) and the system time resource allocation. To deal with the nonconvex problem, we investigate the characteristics of the EE expression and a variable substitution approach. Then, the optimal power allocation scheme and iterative optimization algorithm were derived for achieving maximum EE. Extensive simulation results have demonstrated that the system EE can be improved about 10% because the proposed scheme provides more flexibility to utilize the resource efficiently by employing the proposed scheme.

Spectral and Energy Efficiency of ACO-OFDM in Visible Light Communication Systems

In this paper, we study the spectral efficiency (SE) and energy efficiency (EE) of asymmetrically clipped optical orthogonal frequency division multiplexing (ACO-OFDM) for visible light communication (VLC). Firstly, we derive the achievable rates for Gaussian distributions inputs and practical finite-alphabet inputs. Then, we investigate the SE maximization problems subject to both the total transmit power constraint and the average optical power constraint with the above two inputs, respectively. By exploiting the relationship between the mutual information and the minimum mean-squared error, an optimal power allocation scheme is proposed to maximize the SE with finite-alphabet inputs. To reduce the computational complexity of the power allocation scheme, we derive a closed-form lower bound of the SE. Also, considering the quality of service, we further tackle the non-convex EE maximization problems of ACO-OFDM with the two inputs, respectively. The problems are solved by the proposed Dinkelbach-type iterative algorithm. In each iteration, the interior point algorithm is applied to obtain the optimal power allocation.The performance of the proposed power allocation schemes for the SE and EE maximization are validated through numerical analysis.

Optimal Power Allocation in Downlink Multicarrier NOMA Systems: Theory and Fast Algorithms

In this work, we address the problem of finding globally optimal power allocation strategies to maximize the users sum-rate (SR) as well as system energy efficiency (EE) in the downlink of single-cell multicarrier non-orthogonal multiple access (MC-NOMA) systems. Each NOMA cluster includes a set of users in which the well-known superposition coding (SC) combined with successive interference cancellation (SIC) technique is applied among them. By obtaining the closed-form expression of intra-cluster power allocation, we show that MC-NOMA can be equivalently transformed to a virtual orthogonal multiple access (OMA) system, where the effective channel gain of these virtual OMA users is obtained in closed-form. Then, the SR and EE maximization problems are solved by using very fast water-filling and Dinkelbach algorithms, respectively. The equivalent transformation of MC-NOMA to the virtual OMA system brings new theoretical insights, which are discussed throughout the paper. The extensions of our analysis to other scenarios, such as considering users rate fairness, admission control, long-term performance, and a number of future next-generation multiple access (NGMA) schemes enabling recent advanced technologies, e.g., reconfigurable intelligent surfaces are discussed. Extensive numerical results are provided to show the performance gaps between single-carrier NOMA (SC-NOMA), OMA-NOMA, and OMA.

Power Minimization in Dual-Hop Underlay Cooperative Cognitive Radio Relay Networks for Optimal Resource Allocation

In this paper, we investigate a multi-user one-way dual-hop multi-relay cognitive radio underlay spectrum sharing network. .The joint optimal resource allocation of the bandwidth and power allocation in underlay Cooperative Cognitive Radio Relay Network (CCRRN) was studied and a convex optimization analytical framework is presented. A combined optimal power and bandwidth allocation (COPBA) scheme for the minimization of the total network power and blocking probability of the call admission control stage of the CCRRN is investigated. Our goal is to jointly optimize the bandwidth and power allocation such that the total transmission power in the CCRRN is minimized without compromising the Quality of Service (QoS) demands of the secondary users (SUs) and the interference constraint thresholds of the primary users (PUs) in the primary networks. The mathematical model and convex optimization problem with the aim of minimizing the total transmission power of the CCRRN was formulated. The resulting optimization problem was solved in the standard computer based numerical optimization solver package CONOPT in MATLAB/TOMLAB environment. Simulation results obtained is compared the equal-bandwidth optimal power allocation scheme. Keywords—Radio resource allocations; Cognitive radio Networks; Convex optimization; Cooperative relay networks, Transmit power minimization