Optimal Power Control Algorithm Research Articles

Energy-efficient resource allocation for D2D communication underlaying cellular networks with incomplete CSI

Device-to-device (D2D) communication supports direct communications between nearby devices, which has potential to improve network capacity, spectrum efficiency and energy efficiency. Considering the high overhead to obtain the complete channel state information (CSI), we investigate resource allocation problems of D2D communication underlaying cellular networks with incomplete CSI to minimize the total power consumption. To deal with the challenge brought by incomplete CSI in estimating the instantaneous rates of D2D pairs (DPs), we consider two QoS metrics in terms of the expected rate and outage probability using statistical CSI. Based on that, we first investigate the energy-efficient power control problem for single cellular user (CU) and single DP sharing the same spectrum. With rigorous theoretical analysis of the intrinsic properties of CU rate and two QoS metrics, we design an optimal energy-efficient power control (EPO) algorithm for single CU and single DP. Using EPO as a building block, we then propose an energy-efficient resource allocation algorithm for multiple CUs and multiple DPs with incomplete CSI. Simulation results show that our algorithms consume the lowest powers compared with two baseline algorithms.

Joint Video Packet Assignment, Power Control and User Scheduling Over Cognitive Multi-Homing Heterogeneous NOMA Networks

Non-orthogonal multiple access (NOMA)-based cognitive heterogeneous multi-homing networks is a very important scenario in the future wireless networks. In this work, we formulate a joint video packet assignment, power control and user scheduling problem as a mixed integer non-linear programming (MINLP) to maximize the total video transmission quality for cognitive multi-homing heterogeneous NOMA networks, which is subject to the maximum accessed number of secondary users at each subchannel, video encoding characteristics, maximum interference power constraint and total available power constraint. For the joint video packet assignment, power control and user scheduling problem, we divide it into a video packet assignment subproblem, a power control subproblem and a secondary user scheduling subproblem for cognitive multi-homing heterogeneous NOMA networks. Firstly, the secondary user scheduling algorithm is proposed using the greedy method. Then, we utilize successive convex approximation (SCA) method to transform the power control subproblem into a convex programming problem, and an approximated optimal power control algorithm is proposed with the dual decomposition method. Finally, a heuristic video packet assignment algorithm is designed, which utilizes the auction theory. Numerical simulation results demonstrate that the proposed algorithms not only improve the video transmission quality, but also enhance the total throughput of cognitive multi-homing heterogeneous NOMA networks.

Energy efficient robust scheduling of periodic sensor packets for discrete rate based wireless networked control systems

Wireless networked control systems (WNCSs) require the design of a robust scheduling algorithm that meets the stringent timing and reliability requirements of control systems, despite the limited battery resources of sensor nodes and adverse properties of wireless communication for delay and packet errors. In this article, we propose a robust delay and energy constrained scheduling algorithm based on the exploitation of the mostly pre-known periodic data generation nature of sensor nodes in control systems. We first formulate the joint optimization of scheduling, power control and rate adaptation for discrete rate transmission model, in which only a finite set of transmission rates are supported, as a Mixed-Integer Non-linear Programming problem and prove its NP-hardness. Next, we propose an optimal polynomial-time power control and rate adaptation algorithm for minimizing the transmission time of a node subset. We then design a novel polynomial-time heuristic scheduling algorithm based on first determining the concurrently transmitting node subsets and then distributing them uniformly over time by a modified Karmarkar-Karp algorithm. We demonstrate the superior performance of the proposed scheduling algorithm in terms of robustness, delay and runtime on the Low-Rate Wireless Personal Area Network (LR-WPAN) simulation platform, which we developed in network simulator-3 (ns3).

Throughput Analysis of Low-Latency IoT Systems With QoS Constraints and Finite Blocklength Codes

Internet of Things (IoT) is a promising paradigm to connect massive number of devices in future wireless communications while satisfying various quality of service (QoS) requirements. In this paper, we consider a QoS-constrained IoT system operating with finite blocklength (FBL) codes to support low latency communications. Two data arrival models are considered, namely, constant-rate arrival and ON-OFF discrete-time Markov arrival. The throughput performance is studied for both arrival models under statistical queuing constraints and deadline limits. For the scenario with instantaneous channel state information (CSI), we derive the QoS-constrained throughput expressions for both arrival models. Subsequently, an instantaneous-CSI-driven optimal power control algorithm is proposed to maximize the throughput, while guaranteeing a certain reliability target. In addition, we consider a scenario with only average CSI being available at the transmitter and propose to apply hybrid automatic repeat request (HARQ) schemes to improve the FBL performance. The decoding error probability and the outage probability are first characterized, following which the distribution of transmission period is derived. Furthermore, the throughput expressions are provided for both types of arrivals. Via numerical analysis, the impact of error probability, fixed transmission rate, coding blocklength, and QoS constraints on the throughput is studied.

Energy-Efficient TDMA Scheduling for UVS Tactical MANET

In a tactical mobile ad-hoc network (MANET), unmanned vehicles are deployed for surveillance and reconnaissance. They send multimedia data to a center node in real time. In this letter, we propose a centralized TDMA slot and power scheduling schemes which maximize energy efficiency (EE) considering Quality-of-Service (QoS) for the tactical MANET. We formulate this problem as a non-concave ratio optimization, and propose the optimal slot allocation and power control algorithms based on the Dinkelbach method and the concave-convex procedure. The performance of the proposed algorithm is verified by numerical results, which show a fairly good energy efficiency.

Cross Layer Power Control and User Pairing for DL Multi-antenna NOMA

In a previous study, Kim et al. proposed a suboptimal user pairing and optimal power control algorithm for maximizing sum capacity in downlink multi-antenna non-orthogonal multiple access (NOMA) systems. First, Kim et al. calculate the correlation between all users. If the channel correlation is greater than the threshold, the user’s channel gain difference is stored in the channel gain-difference set. Then, the first and second large channel gain-differences of the user pairs are selected from the set. Based on the user pairing of Kim et al. in the physical layer, we propose novel iterative user substitution algorithm where a user is substituted if the peak signal-to-noise ratio (PSNR), the video quality, in the application layer increases after substitution. And we propose an optimal power control that minimizes the sum video distortion to maximize PSNR in the application layer. The numerical results show that the proposed cross layer optimal power control and user substitution outperforms Kim et al. by 1.4 dB in PSNR and only 1 dB away from exhaustive upper bound, when SNR = 15 dB and the number of users is 16.

Modeling and Analysis of Point-to-Multipoint Millimeter Wave Backhaul Networks

A tractable stochastic geometry model is proposed to characterize the performance of novel point-to-multipoint (P2MP) assisted backhaul networks with millimeter-wave (mm-wave) capability. The novel performance analysis is studied based on the general backhaul network (GBN) and the simplified backhaul network (SBN) models. To analyze the signal-to-interference-plus-noise ratio (SINR) coverage probability of the backhaul networks, a range of the exact- and closed-form expressions are derived for both the GBN and SBN models. With the aid of the tractable model, the optimal power control algorithm is proposed for maximizing the trade-off between energy-efficiency (EE) and area spectral-efficiency (ASE) for the mm-wave backhaul networks. The analytical results of the SINR coverage probability are validated, and they match those obtained from Monte-Carlo experiments. The numerical results of the ASE performance demonstrate the significant effectiveness of our P2MP architecture over the traditional point-to-point setup. Moreover, our P2MP mm-wave backhaul networks are able to achieve dramatically higher rate performance than that obtained by the ultra-high-frequency networks. Furthermore, to achieve optimal EE and ASE tradeoff, the mm-wave backhaul networks should be designed to limit the link distances and line-of-sight interferences while optimizing the transmission power.

Massive MIMO Configurations for Multi-Cell Multi-User Relay Networks

The performance of massive multiple-input multiple-output (MIMO) configurations for multi-cell multi-user amplify-and-forward relay networks is investigated. Two massive MIMO signal processing techniques (SPT-1 and SPT-2) are proposed, and their computational complexity and performance gains are compared with multi-pair massive MIMO relaying (SPT-3) by deriving the partial and full achievable asymptotic sum rates for perfect and imperfect channel state information. Thereby, the cumulative effects of channel estimation errors, co-channel interference (CCI), and pilot contamination are investigated. The best relay selection for the multi-branch relaying is investigated. An optimal user power control algorithm based on max–min fairness is formulated, and thereby, the power allocation co-efficients and optimal common achievable user rate are derived in closed-form. Our analysis reveals that the effects of pilot contamination for the multi-cell massive MIMO relaying become more severe than that for the single-hop counterpart due to the multiple sources of CCI and propagation of residual interference in dual-hop transmissions.

Optimal Power Control in Wireless Powered Sensor Networks: A Dynamic Game-Based Approach.

In wireless powered sensor networks (WPSN), it is essential to research uplink transmit power control in order to achieve throughput performance balancing and energy scheduling. Each sensor should have an optimal transmit power level for revenue maximization. In this paper, we discuss a dynamic game-based algorithm for optimal power control in WPSN. The main idea is to use the non-cooperative differential game to control the uplink transmit power of wireless sensors in WPSN, to extend their working hours and to meet QoS (Quality of Services) requirements. Subsequently, the Nash equilibrium solutions are obtained through Bellman dynamic programming. At the same time, an uplink power control algorithm is proposed in a distributed manner. Through numerical simulations, we demonstrate that our algorithm can obtain optimal power control and reach convergence for an infinite horizon.

Optimal distributed generation allocation for reactive power control

One of the main concerns of utilities is to minimise voltage variations along their feeders when they enhance their networks by various distributed generation (DG) systems that have uncertainty in their output. Furthermore, such DG systems can provide reactive power to the grid. Thus, the objective of this study is to optimally allocate locations and capacities of DG systems, particularly photovoltaic systems in this study, on distribution networks that host DG inverters with the capability of reactive power control, in other words, Volt/Var management and control. For this purpose, this study proposes a hybrid method based on optimal reactive power control and genetic algorithms. The proposed method is verified in well-known test feeders.

Design and Field Test of an Optimal Power Control Algorithm for Base Stations in Long Term Evolution Networks

An optimal power control algorithm based on convex optimization is proposed for base stations in long term evolution networks. An objective function was formulated to maximize the proportional fairness of the networks. The optimal value of the objective function was obtained using convex optimization and distributed methods based on the path loss model between the base station and users. Field tests on live networks were conducted to evaluate the performance of the proposed algorithm. The experimental results verified that, in a multi-cell multi-user scenario, the proposed algorithm increases system throughputs, proportional fairness, and energy efficiency by 9, 1.31 and 20.2 %, respectively, compared to the conventional fixed power allocation method.

An optimal power control algorithm for STDMA MAC protocols in multihop wireless networks

Multihop spatial time division multiple access (STDMA) medium access control (MAC) protocols constitute an important building block of wireless networks. There are not many practical power control algorithms that can optimally tradeoff power consumption against transmission rates with a reasonable computational complexity. In this paper, we introduce an energy-efficient distributed power control algorithm for STDMA MAC protocols. The motivation for this study is two fold, namely, maximizing the spatial reuse of the system resources and maximizing power efficiency. We develop a mathematical formulation for maximizing spatial reuse and power efficiency under discrete SINR and rate constrains. After proving that power is a convex function of data rates in our problem, we demonstrate that our problem in simultaneous transmission environments can be reduced to a linear programming (LP) problem. Then, we solve this LP problem using dynamic programming (DP). Finally, based on our proposed solution, we propose a low complexity optimal power control (OPC) algorithm which can be generically embedded within any existing STDMA MAC protocol. Through analytical and experimental studies, we show that our power control algorithm cannot only significantly improve the throughput, power consumption, and delay performance of STDMA MAC protocols compared to their baseline alternatives, but also outperform existing STDMA algorithms.

Distributed control algorithm for optimal reactive power control in power grids

Reactive power generation has been commonly used for power loss minimization and voltage profile improvement in power systems. However, the opportunity cost of reactive power generation should be considered since it affects the frequency control capability of the generator to some degree. This paper proposed a distributed nonlinear control based algorithm to achieve the optimal reactive power generation for multiple generators in a power grid. The reactive power control setting update for each generator only requires local measurement and information exchange with its neighboring buses. It is demonstrated that the proposed algorithm can reduce the non-convex objective function monotonically till convergence and achieve comparable solutions to the centralized technique: particle swarm optimization with faster convergence speed. The proposed algorithm has been tested on the IEEE 9-bus, 39-bus and 162-bus systems to validate its effectiveness and scalability.

Optimal Ad Hoc Power Control Algorithm Based on Outage Probability

Optimal Ad Hoc Power Control Algorithm Based on Outage Probability

Energy Efficiency Tradeoff in Interference Channels

Interference is one of the major obstacles to improving the performance in wireless communication systems. As the ever-growing data traffic is carried over extremely dense networks, how to deal with interference becomes even more relevant. In this paper, we investigate a network with ${N}$ pairs of users transmitting on the same channel simultaneously from the energy efficiency (EE) perspective. For such an interference network, we aim to address two issues: what is the EE tradeoff between users and how to design energy-efficient resource allocation scheme? To answer these two questions, we formulate a non-concave multi-objective optimization problem (MOOP) to investigate the EE tradeoff, taking into account the minimum data rate requirement of each user. The weighted Tchebycheff method is utilized to solve the MOOP by converting it into a single-objective optimization problem, which is then solved by the Dinkelbach method and the concave-convex procedure method. Based on the above, a power control algorithm is developed for the interference network to achieve at least a local optimum. The proposed algorithm is compared with the orthogonal bandwidth sharing, where each user orthogonally shares the whole bandwidth without interfering each other. In this scenario, the weighted Tchebycheff and the Dinkelbach methods are also utilized to develop the optimal bandwidth allocation and power control algorithm. The performance of the proposed algorithms is verified by numerical results, which show that it is better to share the bandwidth orthogonally rather than non-orthogonally if the interference between each user pair is stronger than a given threshold.

Fair Non-Orthogonal Multiple Access for Visible Light Communication Downlinks

This letter investigates a downlink power control strategy for visible light communication (VLC) systems with non-orthogonal multiple access. Our goal is to maximize sum throughput of the multiuser VLC systems subject to both user fairness and unique intensity constraints for optical modulations. Though the original problem is nonconvex, we first transform it into an equivalent convex one by introducing auxiliary variables and applying variable transformation. Then, we present an optimal power control algorithm with low complexity. Numerical results show large rate gains compared to the conventional OFDM scheme.

Optimal Power Control for Underlay Cognitive Radio Systems With Arbitrary Input Distributions

This paper studies optimal power control policies that maximize the achievable rates of underlay cognitive radio systems with arbitrary input distributions under both peak/average transmit power and peak/average interference power constraints for general fading distributions. In particular, optimal power adaptation schemes are formulated and low-complexity optimal power control algorithms are proposed. Additionally, simpler approximations of optimal power control policies in the low-power regime are determined. By considering gamma distributed channel power gains of the interference link between the secondary transmitter and the primary receiver and of the transmission link between the secondary transmitter and the secondary receiver, closed-form expressions for the maximum achievable rate attained with optimal power control in the low-power regime are provided. Through numerical results, the impact of the fading severity of both interference and transmission links and transmit power and interference power constraints on the maximum achievable rate of the cognitive user for different practical constellations and Gaussian signals are investigated.

System Utility Maximization With Interference Processing for Cognitive Radio Networks

In spectrum underlay cognitive radio networks, secondary users (SUs) are allowed to reuse the spectrum allocated to a primary system. The interference between SUs actually carries information and can potentially be exploited to improve the network performance through information-theoretic interference processing. In this paper, we design an optimal joint power and rate control algorithm that maximizes the secondary system utility subject to the interference temperature constraints of primary users based on the capacity-approaching interference processing scheme called as the Han-Kobayashi scheme. The optimal solution is difficult to achieve because the optimization problem is in general non-convex. To make the optimization problem tractable, this paper first transforms the problem into a monotonic optimization problem through exploiting its hidden monotonicity. We then devise an effective algorithm to obtain the global optimal solution to the joint power and rate control problem in the Han-Kobayashi scheme. The key idea behind the proposed algorithm is to construct a sequence of shrinking polyblocks that approximate the upper boundary of the feasible region with increasing precision. Numerical results further show that the achieved utility of our scheme significantly outperforms the utility of conventional schemes which treat the interference between SUs as the noise.

Optimal Price-Based Power Control Algorithm in Cognitive Radio Networks

This paper investigates the price-based power control problem in the spectrum sharing cognitive radio networks. The primary user (PU) can admit secondary users (SUs) to access by pricing their interference power under the interference power constraint. We model the interaction between the PU and the SUs as a Stackelberg game. The revenue function of the PU is expressed as a nonconcave function of SU's transmit power by backward induction. Variable substitution is used to transform the nonconcave maximization problem into a concave maximization problem. Based on the equivalent concave maximization problem, a novel algorithm is proposed to find the optimal price for the PU to maximize its revenue. The optimal price of each SU is given as a closed-form expression with one parameter, which can be determined with the complexity of $O(K)$ , where $K$ is the number of the SUs. Furthermore, asymptotic analysis is exploited to derive the number of the admitted SUs at low and high interference-to-noise ratio. Simulation results show the effectiveness of the proposed algorithm in comparison with the nonuniform pricing algorithm.

Stochastic Modeling and Power Control of Time-Varying Wireless Communication Networks

Wireless networks are characterized by nodes mobility, which makes the propagation environment time-varying and subject to fading. As a consequence, the statistical characteristics of the received signal vary continuously, giving rise to a Doppler power spectral density (DPSD) that varies from one observation instant to the next. This paper is concerned with dynamical modeling of time-varying wireless fading channels, their estimation and parameter identification, and optimal power control from received signal measurement data. The wireless channel is characterized using a stochastic state-space form and derived by approximating the time-varying DPSD of the channel. The expected maximization and Kalman filter are employed to recursively identify and estimate the channel parameters and states, respectively, from online received signal strength measured data. Moreover, we investigate a centralized optimal power control algorithm based on predictable strategies and employing the estimated channel parameters and states. The proposed models together with the estimation and power control algorithms are tested using experimental measurement data and the results are presented.