Joint Power Allocation Problem Research Articles

Joint QoS Aware Admission Control and Power Allocation in NOMA Downlink Networks

In this work, we talk about the problem of joint power allocation and user association based on quality-of-service for non-orthogonal multiple access (NOMA) to downlink networks. The problem is especially difficult due to its non-convex form and the large number of optimization variables, which are solved using two different nature-inspired algorithms with low complexity. We investigate the effect of different network parameters on increasing users. Numerical results show that, for a growing number of users, the problem is becoming increasingly difficult, which indicates the increasing network resources required to solve it. The results of the simulations show that using evolutionary algorithms is a fast and effective way to solve this kind of problem. Moreover, the NOMA advantage over OMA becomes clear as the number of users increases. Evolutionary techniques outperform randomly generated solutions, as expected.

NOMA-Based Hybrid Satellite-UAV-Terrestrial Networks for 6G Maritime Coverage

Current fifth-generation (5G) networks do not cover maritime areas, causing difficulties in developing maritime Internet of Things (IoT). To tackle this problem, we establish a nearshore network by collaboratively using on-shore terrestrial base stations (TBSs) and tethered unmanned aerial vehicles (UAVs). These TBSs and UAVs form virtual clusters in a user-centric manner. Within each virtual cluster, non-orthogonal multiple access (NOMA) is adopted for agilely including various maritime IoT devices, which are sparsely distributed over the vast ocean. The nearshore network also shares the spectrum with marine satellites. In such a NOMA-based hybrid satellite-UAV-terrestrial network, interference among different network segments, different clusters, and different users occurs. We thereby formulate a joint power allocation problem to maximize the sum rate of the network. Different from existing studies, we use large-scale channel state information (CSI) only for optimization to reduce system overhead. The large-scale CSI is obtained by using the position information of maritime IoT devices. The problem is non-convex with intractable non-linear constraints. We tackle these difficulties by adopting max-min optimization, the auxiliary function method, and the successive convex approximation technique. An iterative power allocation algorithm is accordingly proposed, which is shown to be effective for coverage enhancement by simulations. This shows the potential of NOMA-based hybrid satellite-UAV-terrestrial networks for maritime on-demand coverage.

A Joint Scheme of Antenna Placement and Power Allocation in a Compressive-Sensing-Based Colocated MIMO Radar

The spatial sparsity of targets in the radar scene is widely used in multiple-input multiple-output (MIMO) radar signal processing, either to improve the detection/estimation performance of the radar or to reduce the cost of the conventional MIMO radars (e.g., by reducing the number of antennas). While sparse target estimation is the main challenge in such an approach, here, we address the design of a compressive-sensing-based MIMO radar, which facilitates such estimations. In particular, we propose an efficient solution for the problem of joint power allocation and antenna placement based on minimizing the number of transmit antennas while constraining the coherence of the sensing matrix. Numerical results confirm the superiority of the proposed method over the existing ones.

Fairness-aware resource allocation for D2D-enabled IoT in NOMA-based cellular networks with mutual successive interference cancellation

Device-to-device (D2D) communications and non-orthogonal multiple access (NOMA) are promising technologies to meet the growing demand for IoT-connected devices. However, they bring about new challenges including the co-channel interference, that can limit the performance improvement. To manage the co-channel interference, we address the problem of joint power allocation and sub-channel assignment for D2D-enabled IoT devices (IoTDs) underlaying a NOMA-based cellular network, in which the successive interference cancellation (SIC) decoding is enabled at the level of IoTDs and cellular user equipment (CUE)to increase the number of connected devices and the capacity. This problem is modeled as a mixed-integer nonconvex optimization problem which includes the concept of fairness with respect to the data rates of IoTDs. To solve the problem, a semi-distributed algorithm is developed, which is of polynomial time complexity. The proposed algorithm leverages the successive convex approximation and a heuristic approach. Evaluation results demonstrate the efficiency of the proposed scheme with respect to the sum rate, fairness, access rate and computational complexity.

Multicasting in NOMA‐based UAV networks: Path design and throughput maximisation

In this paper, a new resource allocation framework for unmanned aerial vehicle (UAV) assisted multicast wireless networks is proposed in which the network users according to their request are divided into several multicast groups. Power domain non-orthogonal multiple access is adopted as the transmission technology using which the dedicated signals of multicast groups are superimposed and transmitted simultaneously as the UAV passes over the communication area for fixed and mobile users. The proposed scenarios are discussed from two perspectives, offline and online modes. In offline mode, the problem is implemented for fixed and mobile users whose locations are predictable (the location of users, over the communication time, is known at the beginning of the communication time) and in online mode for mobile users whose locations are unpredictable (the location of users, over the communication time, is unknown at the beginning of the communication time). Also, a scenario is proposed in which the online mode the number of mobile users can grow in each time slot. The problem of joint power allocation and UAV trajectory design as an optimisation problem that is non-linear and non-convex for two proposed scenarios is formulated. To solve the problem, an alternate search method, successive convex approximation, and geometric programming are adopted. Using simulation results, the performance of the proposed scheme is evaluated for different values of the network parameters.

Network sum-rate maximization via joint power allocation and antenna selection for clustered downlink/uplink NOMA networks

This paper considers the problem of joint power allocation and antenna selection (J-PA-AS) for downlink (DL) and uplink (UL) clustered non-orthogonal multiple-access (NOMA) networks. In particular, the goal is to perform antenna selection for each user cluster and allocate transmit power to its users so as to maximize the network sum-rate in the DL and UL directions, while satisfying quality-of-service (QoS) requirements. The formulated problem happens to be non-convex and NP-hard, and thus, there is no systematic or computationally-efficient approach to solve it directly. In turn, a low-complexity two-stage algorithm is proposed. Specifically, the first stage optimally solves the sum-rate maximizing power allocation for each (antenna, user cluster) pair. After that, antenna selection is optimally solved in polynomial-time complexity via the Kuhn–Munkres with backtracking (KMB) algorithm. Extensive simulation results are provided to validate the proposed algorithm, which is shown to efficiently yield the optimal network sum-rate in each link direction, in comparison to the optimal J-PA-AS scheme (solved via a global optimization package), and superior to other benchmark schemes. Light is also shed on the impact of spatial-diversity on the network sum-rate, where it is shown that the greater the number of base-station antennas is, the higher the network sum-rate, and the lower the outage events. Additionally, the significance of decoupling antenna selection in each link direction on the network sum-rate is highlighted. Lastly, the cases of imperfect channel state information (CSI) and imperfect successive interference cancellation (SIC) have been investigated, where it is demonstrated that spatial-diversity gains reduce the adverse effects of imperfect CSI and SIC on the network sum-rate.

Joint power allocation and collaborative beamforming for physical layer security in underlay CR NOMA relay systems

In this paper, we investigate the problem of joint power allocation and collaborative beamforming for physical layer security in underlay cognitive radio non-orthogonal multiple access relay systems. We consider two relaying schemes, i.e., amplify-and-forward and decode-and-forward, with the goal of maximizing the secrecy rate for the central user subjective to the transmit power constraint at the source node, individual transmit power constraints at relays, rate requirements at the cognitive users and the interference power constraint at the primary receiver. The optimization problems are nonconvex and hard to solve. We propose an efficient iterative algorithm by the constrained concave–convex procedure to solve the nonconvex optimization problems. For a comparison, we provide another method, namely alternating optimization (AO) algorithm. In addition, simulations are provided to show the superiority of our proposed methods with comparison to the AO algorithm and traditional orthogonal multiple access schemes.

Energy-Efficient Power Allocation and User Association in Heterogeneous Networks with Deep Reinforcement Learning

This paper studies the problem of joint power allocation and user association in wireless heterogeneous networks (HetNets) with a deep reinforcement learning (DRL)-based approach. This is a challenging problem since the action space is hybrid, consisting of continuous actions (power allocation) and discrete actions (device association). Instead of quantizing the continuous space (i.e., possible values of powers) into a set of discrete alternatives and applying traditional deep reinforcement approaches such as deep Q learning, we propose working on the hybrid space directly by using the novel parameterized deep Q-network (P-DQN) to update the learning policy and maximize the average cumulative reward. Furthermore, we incorporate the constraints of limited wireless backhaul capacity and the quality-of-service (QoS) of each user equipment (UE) into the learning process. Simulation results show that the proposed P-DQN outperforms the traditional approaches, such as the DQN and distance-based association, in terms of energy efficiency while satisfying the QoS and backhaul capacity constraints. The improvement in the energy efficiency of the proposed P-DQN on average may reach 77.6% and 140.6% over the traditional DQN and distance-based association approaches, respectively, in a HetNet with three SBS and five UEs.

Resource Allocation in Virtualized CoMP-NOMA HetNets: Multi-Connectivity for Joint Transmission

In this work, we design a generalized joint transmission coordinated multi-point (JT-CoMP)-non-orthogonal multiple access (NOMA) model for a virtualized multi-infrastructure network. In this model, all users benefit from multiple joint transmissions of CoMP thanks to the multi-connectivity opportunity provided by wireless network virtualization (WNV) in multi-infrastructure networks. The NOMA protocol in CoMP results in an unlimited NOMA clustering (UNC) scheme, where the order of each NOMA cluster is the maximum possible value. We show that UNC results in maximum successful interference cancellation (SIC) complexity at users. In this regard, we propose a limited NOMA clustering (LNC) scheme, where the SIC is performed to only a subset of users. We formulate the problem of joint power allocation and user association for the UNC and LNC schemes. Then, one globally and one locally optimal solution are proposed for each problem based on mixed-integer monotonic optimization and sequential programming, respectively. Numerical assessments reveal that WNV and LNC improves users sum-rate and reduces users SIC complexity by up to 35% and 46% compared to the non-virtualized CoMP-NOMA system and UNC model, respectively. Therefore, the proposed algorithms are suitable candidates for the implementation on open and intelligent radio access networks.

Power-Optimal Distributed Beamforming for Multi-Carrier Asynchronous Bidirectional Relay Networks

We consider the problem of joint power allocation and distributed beamforming design for asynchronous two-way multi-relay networks, where two transceivers rely on an orthogonal frequency division multiplexing (OFDM) scheme to exchange information through multiple amplify-and-forward relay nodes. The network is assumed to be asynchronous in the sense that different relaying paths are subject to different propagation delays. This assumption renders the end-to-end channel between the two transceivers frequency-selective. The impulse response of such a channel can be modeled as a finite impulse response (FIR) system with multiple taps. We present a simple semi-closed-form solution for the problem of minimization of the total transmission power consumed in such networks subject to two quality of service constraints on the transceivers rates. We show that at the optimum, the end-to-end channel must be frequency-flat, which means only one tap of the end-to-end channel impulse response is non-zero. As a result, our semi-closed-form solution leads to a relay selection scheme, in which only those relays associated with the non-zero tap of the end-to-end channel impulse response are switched on, and the rest of the relays are kept off. The simulation results demonstrate that using the proposed semi-closed-form algorithm significantly outperforms the traditional best relay selection method.

Joint Power Allocation for NOMA-Based Diamond Relay Networks With and Without Cooperation

Non-orthogonal multiple access (NOMA)-based diamond relaying (NDR) is an efficient approach for combining NOMA and relaying techniques in such a way as to enhance the achievable rate from the source to the destination in the network. This paper examines the problem of joint power allocation among all the transmission phases during the operation of such networks. Based on the Karush-Kuhn-Tucker (KKT) condition and the second-order sufficient condition (SOSC), at least the local optimal solution is derived and analyzed. In addition, a new protocol for NDR networks based on cooperative communications is introduced and the associated joint power-allocation problem is examined. It is shown that this cooperative NDR (C-NDR) network further improves the achievable rate in some typical placements of the relays. Simulation results verify the correctness of the deviation and confirm the effectiveness of the proposed joint power-allocation method for both NDR and C-NDR networks.

Joint Texture/Depth Power Allocation for 3-D Video SoftCast

Recently, a novel uncoded (pseudoanalog) scheme called SoftCast is proposed for wireless video transmission, which eliminates the cliff effect of the state-of-the-art source-channel coding based schemes and achieves linear quality transition within a wide range of channel signal-to-noise ratio. Therefore, SoftCast-like uncoded and hybrid transmission has become an attractive research issue for natural 2-D video. However, very few studies focus on the SoftCast-based wireless transmission of the 3-D video (3DV) currently. One critical issue of 3DV SoftCast is how to allocate the limited power budget of the transmitter to the texture videos and depth maps of the 3DV to achieve the optimal overall quality on the receiver side, including the transmission quality of the reference views and the synthesis quality of the virtual views. This paper attempts to solve the optimal joint power allocation problem in an efficient way. First, we formulate the target problem as a constrained power-distortion optimization (PDO) problem mathematically. Then, each part of the distortion is analyzed and formulated in a closed form. Finally, the PDO problem is mapped to an unconstrained convex optimization problem and solved by the Lagrangian multiplier method. Simulation results demonstrate that the performance of the proposed method is close to that of the full search method, which can provide the best performance theoretically. Nevertheless, the complexity of the proposed method is negligible compared with that of the full search method. In addition, as compared with the fixed ratio (e.g., 1:1) power allocation between texture and depth, the proposed method can achieve a PNSR gain up to 1.8 dB.

Joint power allocation and user association in non-orthogonal multiple access networks: An evolutionary approach

In this paper, the problem of joint power allocation and user association is studied for non-orthogonal multiple access (NOMA) downlink networks with multiple base stations (BSs). We consider that users are grouped into orthogonal clusters to allocate into different physical resource blocks (PRBs). The problem is formulated using two different utility functions. The first is the maximization of the weighted sum rate and the other is the maximization of the minimum achievable user rate. We apply two different evolutionary algorithms in order to solve this problem. Namely, the recently introduced Salp Swarm Algorithm (SSA) and the popular Particle Swarm Optimization (PSO). The simulation results show that the above-described problem can be effectively solved by both algorithms. SSA is more efficient in average than PSO. The effect of increasing the number of user is also studied. In this case the problem becomes more difficult to solve, which indicates that more network resources are required.

Joint Power Allocation for Coordinated Multi-Point Diversity Transmission in Rayleigh Fading Channels

We consider the problem of joint power allocation (JPA) in a coordinated multi-point (CoMP) joint diversity transmission (JDT) network with a total coordination point power (TCPP) constraint, aimed at maximizing the ergodic cooperative capacity (ECC) in Rayleigh fading channels. In this paper, we first extend the JPA problem in the coordinated two-point (Co2P) JDT networkto the case of a non-unity TCPP constraint. Furthermore, we introduce more accurate log-quadratic approximated (LQA) expressions to obtain the coordinated transmission point (CTP) powers. Next, we extend our study to a coordinated three-point (Co3P) JDT network. Given the mean branch gain-to-noise ratios, we first obtain a log-linear approximated (LLA) expression for obtaining the optimum power of the third CTP (i.e., the worst quality-providing CTP). After obtaining the third-CTP power, we obtain the CTP powers of two better quality-providing CTPs by invoking the LLA CTP power expressions for Co2P JDT power allocation, under the remaining power given by the TCPP minus the third-CTP power. The numerical results demonstrate that the LQA and LLA CTP power expressions for Co2P JDT and the LLA CTP power expressions for Co3P JDT are very efficient in terms of the simplicity for JPA and CoMP set selection, as well as the resulting ECC.

Downlink Power Allocation for CoMP-NOMA in Multi-Cell Networks

This paper considers the problem of dynamic power allocation in the downlink of multi-cell networks, where each cell utilizes non-orthogonal multiple access (NOMA)-based resource allocation. Also, coordinated multi-point (CoMP) transmission is utilized among multiple cells to serve users experiencing severe inter-cell interference (ICI). Under this CoMP- NOMA framework, CoMP transmission is applied to a user experiencing less distinctive channel gain with multiple base stations (BSs)/cells (i.e., severe ICI-prone user) and non-CoMP transmission (i.e., transmission without any coordination among multiple BSs) is applied to a user experiencing dominating channel gain with only one BS/cell, while NOMA is utilized at each BS to schedule CoMP and non-CoMP users over the same transmission resources, i.e., time, spectrum and space. After discussing various CoMP- NOMA models for downlink power allocation in multi-cell networks, we focus on a joint transmission CoMP- NOMA (JT-CoMP-NOMA) model. For the JT-CoMP-NOMA model, an optimal joint power allocation problem is formulated and the solution is derived for each CoMP- set consisting of multiple cooperating BSs (i.e., CoMP BSs). To avoid the huge computational complexity of the joint power optimization approach, we propose a distributed power optimization approach at each cooperating BS whose optimal solution is independent of the solution of other coordinating BSs. The distributed solution for the joint power optimization problem is validated and numerical performance evaluation is carried out for the proposed CoMP- NOMA models including JT-CoMP-NOMA and coordinated scheduling CoMP- NOMA (CS-CoMP-NOMA). The obtained results reveal significant gains in spectral and energy efficiency in comparison with conventional CoMP- orthogonal multiple access (CoMP-OMA) systems.

Ergodic optimization of stochastic differential systems in wireless networks

The present work is the second article in a couple of intertwined papers. They form complementary items on the same subject. They both address the problem of joint power allocation and time slot scheduling in a wireless communication system with time varying traffic. The system is handled by a single base station transmitting over time varying channels. The operating time horizon is divided into time slots, and a fixed amount of power is available at each time slot. The mobile users share each time slot and the power available at this time slot. Since many wireless network applications have stringent delay requirements, designing high-performance resource allocation algorithms to achieve minimum possible delay is of great importance, and this is the main objective of the work presented in this paper. The delay performance of a resource allocation algorithm can be characterized by the average delay experienced by the data transmitted in the network. We propose a heavy traffic analysis for the physical system on hand, i.e., appropriate re-scaling that leads to a diffusion approximation of the system in the sense of weak convergence. The approximate diffusion is constrained or bounded in the K-dimensional positive orthant. We establish the convergence result of the heavy traffic analysis, and then a closed form solution to the resource optimization problem is provided. Here the solution relies on the ergodicity of the approximate diffusion.

Joint Source and Relay Secure Beamforming for Nonregenerative MIMO Relay Systems With Wireless Information and Power Transfer

Simultaneous wireless information and power transfer (SWIPT) is able to prolong the lifetime of energy-constrained wireless networks. In this paper, we study the security issue of a nonregenerative multiple-input multiple-out (MIMO) relay system with SWIPT. When the full channel state information (CSI) of the eavesdropper is available, we aim to maximize the secrecy rate under the source and relay transmit power constraints and energy-harvesting (EH) constraints at the multiple EH receivers by jointly designing the source and relay beamforming matrices, which is a nonconvex optimization and is still an open problem. In order to enhance the secrecy rate effectively, we first transform the optimization into a difference of convex programming and propose a sequential parametric convex approximation-based iterative algorithm which is able to achieve a local optimum. To reduce the complexity, we also propose a singular value decomposition (SVD)-generalized SVD-based method to simplify the optimization into a joint source and relay power allocation problem. The alternating optimization is employed to find a suboptimal solution of the joint power allocation problem. When the CSI of the eavesdropper is unknown, we propose an artificial-noise (AN) aided joint source and relay beamforming scheme, which can maximize the AN power to jam the eavesdropper under the limited source and relay transmit power and satisfy the quality of service of the receiver. Simulation results have shown the effectiveness of our proposed schemes.

Joint power allocation for MIMO-OFDM full-duplex relaying communications

In this paper, we address the problem of joint power allocation in a two-hop MIMO-OFDM network, where two full-duplex users communicate with each other via an amplify-and-forward relay. We consider a general model in which the full-duplex relay can forward the received message in either one-way or two-way mode. Our aim is to maximize the instantaneous end-to-end total throughput, subject to (i) the separate sum-power constraints at individual nodes or (ii) the joint sum-power constraint of the whole network. The formulated problems are large-scale nonconvex optimization problems, for which efficient and optimal solutions are currently not available. Using the successive convex approximation approach, we develop novel iterative algorithms of extremely low complexity which are especially suitable for large-scale computation. In each iteration, a simple closed-form solution is derived for the approximated convex program. The proposed algorithms guarantee to converge to at least a local optimum of the nonconvex problems. Numerical results verify that the devised solutions converge quickly, and that our optimal power allocation schemes significantly improve the throughput of MIMO-OFDM full-duplex one-way/two-way relaying over the conventional half-duplex relaying strategy.

Joint Power Allocation in Wireless Relay Networks: the Case of Hybrid Digital-Analog Transmission

In conventional digital communication systems, the quality of the received signal does not improve beyond a certain level as the channel quality increases. Such kind of quality saturation effect is caused by the unrecoverable quantization errors produced by source coding. The Hybrid Digital-Analog (HDA) transmission, where the quantization errors are transmitted in an analog mode along with the quantized data in a digital mode, has been recognized as an effective technique to combat the quality saturation effect. In this paper, we introduce HDA transmission in Wireless Relay Networks (WRNs) over Rayleigh slow-fading channels to eliminate the quality saturation effect and achieve graceful improvement for the better channel quality. Our goal is to minimize the end-to-end distortion by optimal power allocation. We note that digital-analog power allocation involved in HDA transmission is coupled with source-relay power allocation in WRNs. Therefore, the joint power allocation problem should be considered. We investigate this problem for two kinds of relays in WRNs, i.e., Amplify-and-Forward (AF) relays and Decode-and-Forward (DF) relays. In the case of AF relays, we find that the joint power problem is concave and thus derive the explicit expressions of the optimal solution. In the case of DF relays, we formulate the joint power allocation problem as a nonlinear fractional programming problem and then propose an efficient algorithm to search the optimal solution. Simulation results show that the proposed joint power allocation schemes outperform existing schemes in terms of end-to-end distortion in both WRNs with AF relays and that with DF relays under various channel conditions.

A Power Allocation Strategy for Multiple Poisson Spectrum-Sharing Networks

This paper develops a power allocation strategy for multiple networks of Poisson-distributed single-antenna nodes that share the available spectrum in a spectrum underlay scenario. This strategy aims to maximize the overall throughput obtained by sharing the spectrum while limiting the degradation of the successful transmission probability of each network. In its original form, this joint power allocation problem is difficult to solve. However, we demonstrate that the problem can be transformed into a convex optimization formulation, which can be efficiently solved. Furthermore, we obtain a quasi-closed-form solution that has a water-filling interpretation by analyzing the optimality conditions. Numerical results indicate that, when a spectrum-sharing scheme employs the proposed optimal strategy of power allocation, the throughput substantially improves over that obtained by exclusively allocating the spectrum to the primary network. Moreover, when the number of spectrum-sharing networks increases, the enhancement is significant, being up to the limit imposed by the maximum allowable degradation in the performance of each network.