Power Allocation Solution Research Articles

Joint Energy-Efficient Power Allocation and Beamforming for Uplink mmWave-NOMA System

In this paper, we study the joint power allocation (PA) and beamforming (BF) design problem to maximize the energy efficiency (EE) for an uplink millimeter-wave (mmWave) system with non-orthogonal multiple access (NOMA). To track this non-convex optimization problem, we decompose it into the PA problem and BF design problem. For any feasible fixed BF, the condition of the optimal decoding order is firstly presented to reduce the decoding complexity, and then the optimal and suboptimal PA solutions with closed-form expressions are derived, respectively. On this basis, two suboptimal joint design schemes of PA and BF are proposed based on the one-dimensional search method. Furthermore, the extensions of the proposed schemes to mmWave-NOMA system with imperfect channel information and more users are also presented. Simulation results verify the effectiveness of the proposed two joint schemes, and they can achieve superior EE performance over the existing schemes and the conventional time division multiple access scheme.

Service-Oriented Fair Resource Allocation and Auction for Civil Aircrafts Augmented Space-Air-Ground Integrated Networks

In this paper, we present a civil aircrafts augmented space-air-ground integrated networks (CAA-SAGIN) architecture, where each mobile user can access multiple platforms simultaneously. With the target of ensuring fairness, we put forward the fair optimization strategies including resource allocation and resource auction. In the resource allocation, we formulate a service-oriented fair (SOF) resource allocation problem in order to minimize the difference between the allocated and required data rate. After introducing convex-relaxation and logarithmic approximation, the joint optimal solutions of subchannel and power allocation can be obtained by SOF iteration algorithm. Aiming at managing the terrestrial network overload, we present a user update mechanism. After applying SOF resource allocation for selected original users in sky access platforms, we propose a resource auction scheme so as to maximize the utility for both sides. First, we decompose the original problem into several subproblems and transform them into their lower bound by first-order approximation. Then, we can obtain the joint optimal solution with an alternating descent iterative method. From simulation results, we can find that CAA-SAGIN can bring great benefits to traditional terrestrial networks. In addition, the proposed resource allocation and auction schemes are significant in terms of telecommunication and economic parameters.

Novel Resource Allocation Techniques for Downlink Non-Orthogonal Multiple Access Systems

Non-orthogonal multiple access (NOMA) plays an important role in achieving high capacity for fifth-generation (5G) networks. Efficient resource allocation is vital for NOMA system performance to maximize the sum rate and energy efficiency. In this context, this paper proposes optimal solutions for user pairing and power allocation to maximize the system sum rate and energy efficiency performance. We identify the power allocation problem as a nonconvex constrained problem for energy efficiency maximization. The closed-form solutions are derived using Karush–Kuhn–Tucker (KKT) conditions for maximizing the system sum rate and the Dinkelbach (DKL) algorithm for maximizing system energy efficiency. Moreover, the Hungarian (HNG) algorithm is utilized for pairing two users with different channel condition circumstances. The results show that with 20 users, the sum rate of the proposed NOMA with optimal power allocation using KKT conditions and HNG (NOMA-PKKT-HNG) is 6.7% higher than that of NOMA with difference of convex programming (NOMA-DC). The energy efficiency with optimal power allocation using DKL and HNG (NOMA-PDKL-HNG) is 66% higher than when using NOMA-DC.

Resource Allocation for Wireless Cooperative IoT Network With Energy Harvesting

In this paper, resource allocation is studied for a fully sustainable cooperative IoT network, in which a relay powered by renewable energy forwards data to a destination while charging multiple IoT nodes by radio-frequency (RF) signals. An optimal joint time and power allocation problem is formulated to maximize the long-term sum-throughput of IoT nodes, taking into consideration the bounded transmit power of IoT nodes, the stochastic characteristic of energy harvesting (EH) process, and dynamic wireless channel conditions. To solve the formulated problem, we analyze the time allocation for cooperative communications with EH, considering both data and energy dependency of the two hop transmissions in three cases with different network settings. Based on the analysis, we derive the closed-form solutions of optimal time and power allocation in a network with symmetric links. Then, we extend our solution to a general network with asymmetric links. By employing Lyapunov optimization, an online stochastic resource allocation algorithm is proposed to obtain the maximum network throughput. It has been shown that the proposed algorithm can achieve close-to-optimal network throughput while maintaining the stability of the system. Finally, extensive simulations validate the analysis and demonstrate the effectiveness of the proposed algorithm.

Generative Adversarial Network Assisted Power Allocation for Cooperative Cognitive Covert Communication System

This letter investigates a power allocation problem for a cooperative cognitive covert communication system, where the relay secondary transmitter (ST) covertly transmits private information under the supervision of the primary transmitter (PT). Aiming to achieve the tradeoff between the covert rate and the probability of detection errors, a novel generative adversarial network based power allocation algorithm (GAN-PA) is proposed to perform power allocation at the relay ST for covert communication. Under the proposed GAN-PA, the generator adaptively generates the power allocation solution for covert communication, while the discriminator determines whether transmitting covert message or not. In particular, by utilizing the proposed deep neural network (DNN), the discriminator and the generator are alternately trained in a competitive manner. Numerical results show that the proposed GAN-PA can attain near-optimal power allocation solution for the covert communication and achieve rapid convergence.

Energy-efficient user selection and resource allocation in mobile edge computing

Mobile edge computing (MEC) as a new type of computing model can expand the computing power of cloud computing to the edge of radio access network (RAN), which brings a large number of applications close for end user. Compared to traditional cloud computing, computation tasks being offloaded to edge clouds nearby to execute can reduce transmission delay and energy consumption. However, how to select the best edge cloud in a dense cell to execute tasks remains challenging. To address this challenge, in this paper we propose joint user selection and resource allocation algorithm in MEC to maximize the user’s energy efficiency, defined as the ratio of user throughput to its energy consumption. We formulate the energy efficiency maximization problem as a mixed integer fractional nonlinear optimization problem, which involves both users’ offloading selection and uplink transmission power. To solve this non-convex optimization problem, we transform it into an equivalent subtractive convex optimization problem by relaxation transformation method, and furthermore provide the corresponding optimal solution of user selection and power allocation. Numerical results show that compared with other selection schemes, the proposed optimal scheme has a significant improvement in energy efficiency.

Secure mm‐Wave communications with imperfect hardware and uncertain eavesdropper location

AbstractThis article examines the secrecy performance of millimeter‐wave (mm‐Wave) communications with imperfect hardware and uncertain eavesdropper (Eve) location. We consider a multiple‐antenna source communicating with a single‐antenna destination using masked beamforming to transmit the information signals with artificial noise (AN) in the presence of a passive Eve. For this system, we derive new expressions for the secrecy outage probability (SOP) and secrecy throughput with mm‐Wave multipath propagation under slow‐fading channel conditions and hardware imperfections. Based on this, optimal power allocation (OPA) solutions are derived for the information and AN signals aimed at minimizing the SOP and maximizing the secrecy throughput. Our results reveal that it is non‐trivial to achieve an OPA solution for the general scenario of imperfect hardware. We also highlight that our proposed masked beamforming with OPA scheme significantly enhances the secrecy throughput compared with the benchmark schemes of maximal‐ratio transmission and equal power allocation.

Optimal power allocation for non‐orthogonal multiple access in wireless backhaul networks

In this study, the authors adopt the non-orthogonal multiple access (NOMA) technique to improve the spectrum efficiency in the wireless backhaul networks, whereby the downlink and uplink NOMA techniques are applied for the backhaul and access links, respectively. Due to the coupling between the backhaul and access transmission stages, the transmission power should be carefully allocated in both stages so that the overall throughput can be maximised. They start with the single user equipment (UE) case and consider different scenarios and analyse the tradeoff between the access and backhaul links, and the optimal power allocation solutions are obtained accordingly. They then extend the analysis to the multi-UE case and formulate the optimal power allocation problem, which is solved using the Lagrangian dual decomposition algorithm. Simulation results demonstrate that the proposed schemes are effective in improving the throughput and outperforms the conventional orthogonal multiple access technique under different network settings.

Energy Efficiency Optimization for Distributed Antenna Systems With D2D Communications Under Channel Uncertainty

In this paper, we investigate the energy efficiency (EE) maximization problem in distributed antenna systems (DAS) with underlaid device-to-device (D2D) communications. The EE optimization relies on the channel state information (CSI), and the interference from D2D pairs to the cellular user equipment (CUE) also closely depends on CSI. In this paper, we consider the case that the CSI in the systems is uncertain, which is more realistic and reasonable. We aim to obtain the robust power allocation (PA) solution that can achieve the maximum EE of the D2D systems. The optimization problem can be formulated as a non-convex and non-linear problem with infinite interference constraint. In order to solve it, the interference constraint is treated as chance constraint and handled by Bernstein approximation. Moreover, an equivalent objective function with a sub-tractive form is transformed by exploiting fractional programming theory. After that, the non-convex objective function can be transformed by using the difference of convex (D.C.) programming. The concave convex procedure (CCCP) algorithm is used to tackle the problem and obtain the optimal power allocation for D2D users. Simulation results show the robustness of the system and the effectiveness of the proposed algorithm.

Efficient Resource Allocation in SCMA-Enabled Device-to-Device Communication for 5G Networks

According to advanced wireless network standards, Device-to-device based communication underlaid conventional cellular network is considered a promising technology to improve the network performance. Precisely, this hybrid architecture provides an efficient resource allocation for cellular and D2D users while increasing the flexible utilization of the spectrum resources. Recently, the sparse code multiple access (SCMA) has been proposed as an efficient non-orthogonal multiple access technology for the 5G network paradigm. The SCMA scheme enhances the spectral efficiency, supports a massive connectivity, and diverses applications by enabling system overloading. Thus, in this paper, SCMA technology is applied to a D2D enabled cellular network, targeted at utilizing the overloading feature of the SCMA scheme to support a massive device connectivity while enhancing the overall network performance. The SCMA scheme is implemented to jointly optimize the codebook and power allocation in the downlink D2D enabled cellular network, with the aim to maximize the system data rate. This joint optimization problem is solved by decomposing the original problem into two sub-problems: codebook allocation and power allocation. For the codebook allocation, the rate aware codebook selection scheme for D2D system (RACBS-D2D) is proposed using conflict graph. For the power allocation solution, a geometric water-filling (GWF) method is utilized to propose the iterative GWF-based power allocation (IGWFPA) scheme. The performance of the proposed schemes is evaluated through simulations that reveal the benefits of the proposed solutions under different scenarios.

An Efficient Resource Allocation for Massive MTC in NOMA-OFDMA Based Cellular Networks

To alleviate random access congestion and support massive-connections with less energy consumption for machine-type communications (MTC) in the 5G cellular network, we propose an efficient resource allocation for massive MTC (mMTC) with hybrid non-orthogonal multiple access (NOMA)-orthogonal frequency division multiple access (OFDMA). First, a hybrid multiple access scheme, including the NOMA-based congestion-alleviating access scheme (NCAS) and OFDMA-based congestion-alleviating access scheme (OCAS), is proposed, in which the NOMA based devices coexist with OFDMA based ones. Then, aiming at maximizing the system access capacity, a traffic-aware resource blocks (RBs) allocation is investigated to optimize RBs allocation for preamble transmission and data packets transmission, as well as to optimize the RBs allocation between NCAS and OCAS for the RBs usage efficiency improvement. Next, aiming at the problem of high computational complexity and improving energy efficiency in hybrid NOMA-OFDMA based cellular M2M communications, this paper proposes an improved low complexity power allocation algorithm. The feasibility conditions of power allocation solution under the maximum transmit power constraints and quality of service (QoS) requirements of the devices is investigated. The original non-convex optimization problem is solved under the feasibility conditions by two iterative algorithms. Finally, a device clustering scheme is proposed based on the channel gain difference and feasible condition of power allocation solution, by which NOMA based devices and OFDMA based devices can be determined. Simulation results show that compared with non-orthogonal random access and transmission (NORA-DT), the proposed resource allocation scheme for hybrid NOMA-OFDMA systems can efficiently improve the performance of access capacity and energy efficiency.

Joint backhaul bandwidth and power allocation in heterogeneous cellular networks: A two‐level approach

SummaryWe investigate the problem of joint downlink wireless backhaul bandwidth (WBB) and power allocation in heterogeneous cellular networks (HCNs). A WBB partitioning scheme is considered, which allocates the whole bandwidth between the macrocell and small cells for data transmission and backhauling. We formulate an optimization problem to maximize the weighted sum logarithmic utility function by jointly optimizing WBB portion and fronthaul power allocation of each base station with consideration of the backhaul capacity limitation on each small cell. In order to solve this joint optimization problem, we propose a hierarchical two‐level approach and decompose the original problem into two independent subproblems: the WBB allocation at the macrocell base station (MBS) and the power allocation at both the MBS and small cell base stations (SBSs). Accordingly, the optimal WBB portion and power allocation solutions are obtained, respectively. Furthermore, we develop a distributed algorithm to implement the joint WBB and power allocation. Numerical results verify the effectiveness of the proposed approach and analyze the impact of the weighted coefficient and backhaul capacity limitation on the network performance. In addition, significant performance gains can be achieved by the proposed approach over the benchmark.

Joint Resource Optimization for Orthogonal Frequency Division Multiplexing Based Cognitive Amplify and Forward Relaying Networks.

This paper investigates two resource allocation problems in cognitive relaying networks where both secondary network and primary network coexist in the same frequency band and adopt orthogonal frequency division multiplexing (OFDM) technology. The first one is the sum rate maximization problem of a secondary network under total power budget of a secondary network and tolerable interference constraint of a primary network. The second one is the sum rate maximization problem of a secondary network under separate power budgets of a secondary network and tolerable interference constraint of a primary network. These two optimization problems are completely different from those in traditional cooperative communication due to interference management constraint condition. A joint optimization algorithm is proposed, where power allocation and subcarrier pairing are decomposed into two subproblems with reasonable cost. The first one is a closed form solution of power allocation of the secondary network while managing the interference to a primary network under a constraint condition. The other is optimal subcarrier pairing at given power allocation. Simulation results reveal aspects of average signal to noise ratio (SNR), interference level, relay position, and power ratio on the sum rate of a secondary network.

Integrated Resource Management for Terrestrial-Satellite Systems

As data traffic in terrestrial-satellite systems surges, the integration of power allocation for caching, computing, and communication (3C) has attracted much research attention. However, previous works on 3C power allocation in terrestrial-satellite systems mostly focus on maximizing the overall system throughput. In this paper, we aim to guarantee both throughput fairness and data security in terrestrial-satellite systems. Specifically, we first divide the system implementation into three steps, i.e., data accumulation, blockchain computing, and wireless transmission. Then, we model and analyze the delay and power consumption in each step by proposing several theorems and lemmas regarding 3C power allocation. Based on the theorems and lemmas, we further formulate the problem of 3C power allocation as a Nash bargaining game and construct an optimization model for the game. Last, we solve the optimization problem using dual decomposition and obtain the optimal period of the satellite serving the ground stations as well as the optimal 3C power allocation solution. The optimal solution can provide guidelines for parameter configuration in terrestrial-satellite systems. The performance of the proposed terrestrial-satellite architecture is verified by extensive simulations.

Proactive eavesdropping in UAV-aided mobile relay systems

This paper studies a new wireless surveillance scenario, where a legitimate monitor attempts to eavesdrop on the dubious messages forwarded by an unmanned aerial vehicle (UAV)-aided suspicious relay. Specifically, we consider two relaying protocols commonly used by the UAV-aided relay, namely, amplify-and-forward (AF) and decode-and-forward (DF). For each relaying protocol, we maximize the average effective eavesdropping rate by optimizing the legitimate monitor’s jamming power allocation, subject to the average power constraint. The optimal jamming power allocation solutions are derived in closed-form for each of the two protocols, respectively. Numerical results show that the proposed proactive eavesdropping approach can noticeably improve the eavesdropping rate compared to the reference schemes.

Energy Efficient Power Domain Non-Orthogonal Multiple Access Based Cellular Device-to-Device Communication for 5G Networks

This paper examines the resource block and power allocation in the power domain non-orthogonal multiple access (PD-NOMA) based cellular device-to-device (D2D) systems. To improve the energy efficiency of the D2D systems and to manage the mutual interference level as well as the quality of service (QoS) requirement of cellular users, different power level is applied to the D2D users sharing the same resource blocks (RBs) to the legacy users. It is essential to design an efficient resource block and power allocation method for PD-NOMA based cellular D2D systems which guarantee the successive interference cancellation (SIC) order in the power allocation solution. In this paper, we propose an iterative algorithm of resource block and power allocation for cellular D2D system which incorporates the SIC aware geometric water filling (GWF) method in the power allocation solution. It is shown that the proposed SIC aware geometric water filling achieves higher energy efficiency compared to iterative water-filling (IWF) power allocation and the GWF based orthogonal multiple access (OMA) method.

Optimal spectral and energy efficiency trade-off for massive MIMO technology: analysis on modified lion and grey wolf optimization

As the technology makes progress towards the era of fifth generation (5G) communication networks, energy efficiency (EE) becomes an significant design criterion, because it guarantees sustainable evolution. In this regard, the massive multiple-input multiple-output (MIMO) technology, where the base stations are outfitted with enormous count of antennas so as to reach multiple orders of spectral and energy efficiency gains, will be a fundamental technology enabler for 5G. This paper plans to implement a massive MIMO model considering the spectral efficiency (SE) and EE. Here, the main goal is to generate the optimal solutions for beam-forming vectors and power allocation. The optimal solution is formed in such a way that both the SE and EE are maximum through resource efficiency metric model. The beam-forming vectors and power allocations are generated by two modified meta-heuristic algorithm to frame a valuable analysis. The first algorithm uses the modified grey wolf optimization (GWO) termed as improved random vector-based GWO (IRV-GWO), and the second algorithm uses the modified lion algorithm (LA) termed as improved random vector-based LA (IRV-LA). Both the algorithms have the ability to solve the complex optimization problems under different applications with respect to better convergence rate, which in turn performs well for pertaining better trade-off between the SE and EE in massive MIMO technology.

Fair Energy-Efficient Resource Allocation for Downlink NOMA Heterogeneous Networks

The increasing in energy consumptions of the current wireless networks, leads towards designing energy-efficient 5G networks. The application of non-orthogonal multiple access (NOMA) in the heterogeneous networks (HetNets) improves the spectrum utilization with the cost of efficient resource allocation. Hence, this article proposes optimal user-pairing and power allocation solutions towards achieving fair energy-efficient resource allocation in downlink femtocell NOMA-HetNets. In the proposed optimization process, the considered constraints are the user's transmission rate, transmit power budget at the base station (BS), and the interference. The energy consumption of both the transmitter and the receiver are considered to simulate the real system design. The Greedy Algorithm (GA) is used to achieve a low-complex optimal solution during the user-pairing process. Simultaneously, the max-min energy efficiency optimization approach is employed to maximize the minimum energy efficiency of the femtocell users to achieve the optimal power allocation solution. The mathematical formulation of the max-min energy efficiency is a non-convex fractional programming problem and is intractable. Thus, the fractional programming theory is adopted to transform the problem into a sequence of subtractive form, followed by the Sequential Convex Programming (SCP) approach to determine the optimal solution. Simulation results show that the proposed NOMA with optimal power allocation method using SCP and GA (NOMA-SCP-GA) achieves fair energy efficiency performance with lower complexity compared to the benchmark methods. Moreover, the minimum energy efficiency of the femtocell user is 38.22% higher than NOMA with Difference of Convex programming (NOMA-DC). The NOMA-SCP-GA method can assure 5G capability demands.

Statistical QoS Aware for Wireless Powered Cooperative Communications in Internet of Things

This article unveils the importance of statistical quality of service (QoS) for resource allocation in a two-hop network. Particularly, an access point (AP) serves multiple IoT devices for information transfer with the assistance of the energy harvesting (EH) relaying. To explore the maximum constant data arrival metric, we aim to maximize the effective capacity (EC) under specified QoS requirements. Also, the statistical QoS inspired resource allocation policies are investigated for half/full duplex (HD/FD) modes, respectively, to jointly optimize power allocation and power splitting (PS) ratio. To solve the formulated problem, we first derive the closed-form solution of the optimal power allocation at the AP and the PS ratio. To gain more insights, we further derive the boundary conditions of optimal power allocation and PS ratio. Finally, numerical results are demonstrated to validate the theoretical derivations, which highlights the proposed scheme in terms of EC performance in comparison to the benchmark scheme.

Optimization of MAC Frame Slots and Power in Hybrid VLC/RF Networks

Hybrid visible light communication and radio frequency (VLC/RF) networks provide combined benefits of both visible light and radio frequency technologies. This is an eminent technology for providing 5G services in an indoor environment. Because of small coverage range, multiple VLC cells co-exist in a close proximity. For efficiently utilizing resources of multiple VLC access points (VLC-APs), a different approach than conventional multi RF-APs based resource allocation schemes is required due to unique characteristics of light. In this paper, we provide an optimized solution for combined power and slot allocation in hybrid VLC/RF networks with the objective of maximizing sum rate for a downlink communication scenario. The resources are optimized on frame by frame basis, instead of per request basis. This frame-based optimization reduces the frequency of optimization and also improves the utilization of the resources. The non-convex problem of joint slot and power allocation is divided into less complex sub problems and solved iteratively. The performance of the joint slot and power allocation (JSPA) scheme has been analyzed for data rate and fairness factor for varying values of network size and minimum quality of service (QoS) requirements. The analysis exhibits on average more than 65% improvement in both fairness factor and average sum rate as compared to single VLC-AP based scheme (SV O RF), in which single VLC-AP is utilized for a user and RF-AP is utilized to offload traffic when VLC resources are not sufficient. Moreover, we propose a hybrid user-centeric and network-centeric (H-Uc/Nc) scheme for slot allocation which entails far less computational complexity. Although, H-Uc/Nc scheme performs inferior to optimized scheme, however, it exhibits much better performance than (SV O RF).