Discrete Power Levels Research Articles

NOMA-Based Coded Slotted ALOHA for Machine-Type Communications

In this letter, non-orthogonal multiple access based coded slotted ALOHA (NOMA-CSA) for machine-type communications (MTC) is proposed, in which encoded segments are transmitted with discrete power levels. Most collided segments can be successfully decoded by using the successive interference cancellation (SIC) and maximum-a-posteriori (MAP) erasure decoding. To further improve performance, the code degree distribution and the power distribution for the different number of power levels are jointly optimized, which leads to considerable performance improvement. Numerical results verify our analysis and show that the NOMA-CSA greatly outperforms conventional schemes in terms of throughput and packet loss rate (PLR).

The quantum‐mode regulated power point tracking in a photovoltaic array for application under the quantised converter duty ratio

The objective of this work is to develop a realistic control technique for stably regulating the power output of a photovoltaic array. The associated process is referred to as the regulated power point tracking (RPPT). Ideally, the converter duty ratio is expected to be a continuous variable so that the photovoltaic power output can be freely adjusted. However, such a continuous-mode RPPT is possible only with the help of an analogue pulse-width-modulation (PWM) control. In contrast, the modern digital PWM control causes a duty ratio quantisation. As a result, the photovoltaic power output can, practically, be adjusted only at some discrete power levels. Therefore, existing RPPT control techniques, which optimistically assume a continuous power production range, may lead to persistent low-frequency power oscillations because of the digital PWM. To overcome the particular problem, a quantum-mode RPPT control technique is proposed in this paper. The quantum-mode RPPT basically limits its search only to the practically attainable discrete power levels by suitably modifying the given power reference command. Thus, the power tracking can always reach a converged state. For the faster RPPT, the perturbation step size is dynamically adapted. The proposed RPPT control technique is verified via both simulations and hardware experiments.

Joint Resource Allocation, User Association, and Power Control for 5G LTE-Based Heterogeneous Networks

The aim of 5G wireless networks to provide Mbps and Gbps data rates to end users is expected to be fulfilled by the advanced technologies such as multi-input multi-output (MIMO), carrier aggregation (CA), inter/intra-cell communication, and adaptive modulation and coding techniques, which would be all realized in the Long Term Evolution-Advanced (LTE-A) heterogeneous network constituted by macrocells (MCs) and small cells (SCs) adopting these 5G advanced techniques. Given the potential of significantly increasing the network performance, the resource allocation (RA) problem involved becomes harder than ever especially when MIMO and CA are included in the RA problem involving multiple types of resources to be concurrently determined for the global optimization. Facing this challenge, we develop a framework to jointly optimize energy efficiency (EE), spectrum efficiency (SE), and queue length for downlink transmissions with an overall and comprehensive consideration of dynamically allocating resource blocks (RBs), component carriers (CCs), modulation and coding schemes (MCSs), and deciding user association (UA) with a power control (PC) mechanism on discrete power levels (PLs) in the heterogeneous LTE-based MIMO wireless networks. Specially, for the complex joint RA, UA, and PC problem, we conduct a mixed integer programming model to accommodate the stochastic optimization problem involved with the drift-plus-penalty (DPP) approach for Lyapunov opportunistic optimization. In particular, although it involves a nondeterministic polynomial time (NP) problem, we can still show a reduced problem to be solved easily through linear relaxation when its coefficient matrix is totally unimodular (TUM), and to be solved efficiently as well even when the TUM property is not guaranteed. Based on the reduction, we further develop a distributed or semi-distributed algorithm operated on two levels to approach the optimal results with lower complexity if the UA requirement can be relaxed. Finally, apart from exhibiting its performance on the weighting parameters, the numerical experiments also show our approach to make a good tradeoff among SE, EE, and queue length, and outperform the greedy-based state-of-the-art algorithms.

Coarse NLOS detection algorithm based on discrete power levels

Indoor localisation has become increasingly important for many services. When the environment is not crowded, i.e. most links between the target and anchors do not suffer from non-line-of sight (NLOS) offsets, the centralised time-difference-of-arrival localisation can efficiently estimate the target position. However, such a method does not work in crowded environments unless a large number of anchors are installed, which is unrealistic for many applications. In this case, the objects that block the LOS links can serve as dynamic anchors (e.g. with round trip time-of-arrival localisation) to improve positioning accuracy. However, such a method tends to include too many unreliable dynamic anchors in estimation, which will significantly increase the computational complexity (e.g. to detect and discard such unreliable anchors in the NLOS mitigation stage). This study proposes a coarse NLOS detection algorithm based on discrete power levels to efficiently achieve the coarse NLOS mitigation, which automatically discards most unreliable dynamic anchors. The simulation results show that the proposed method can achieve very similar estimation performance as the optimal estimation method in the crowded environment, while analysis shows that it can significantly decrease the computational complexity.

NOMA-Based Irregular Repetition Slotted ALOHA for Satellite Networks

In this letter, a non-orthogonal multiple access (NOMA) scheme is employed for irregular repetition slotted ALOHA (IRSA). Specifically, packet replicas are transmitted with discrete power levels, which are pre-determined by the NOMA scheme. In this case, most packet collisions can be resolved in the power domain, contributing to a much lower packet loss rate. Density evolution analysis is formulated and the degree distributions are optimized for different numbers of power levels. The simulation results validate our analysis and show that the proposed scheme can outperform existing IRSA schemes.

Application of metaheuristic algorithms for solving inverse radiative boundary design problems with discrete power levels

In this study, an analysis for solving inverse radiative boundary design problem with discrete design variables is developed and presented when radiation is the dominant mode of heat transfer. An absorbing, emitting, and participating medium is considered in radiative equilibrium. The discrete ordinate method (DOM) is implemented to solve the radiative transfer equation (RTE). The goal of the design problem is to find the optimum power of heaters from a discrete set of powers in such a way that they produce the desired temperature and heat flux profile over the design surface. Therefore, several metaheuristic optimization methods including the genetic algorithm (GA), particle swarm optimization (PSO), and its modified forms such as PSO with Constriction Factor (PSO-CF), PSO with repulsion factor (PSO-RF) and PSO with adaptive inertia weight (PSO-W) are explored to solve the inverse problem. The accuracy of the direct solution of radiative heat transfer is examined by comparison of the present results with findings from the literature. Then the efficiency and accuracy of the optimization methods are assessed by their ability of finding and reconstructing of the results from the direct solution. Finally, the effects of some thermos-physical properties including the absorption coefficient, emissivity of the heater surface and design surface, on the optimum solutions are examined. The comparison of the results revealed that the genetic optimization algorithm converged faster and with fewer calculations of the objective function compared to other optimization techniques.

Localized topology control and on-demand power-efficient routing for wireless ad hoc and sensor networks

Power use is a crucial design concern in wireless ad hoc and sensor networks since it corresponds directly to the network operational time. In this paper, we study the issue of power-efficient use in the following two aspects: Selecting power-efficient routes and performing efficient localized topology control to assign reduced transmit powers at nodes while preserving the global optimal connectivity. We proposed a localized topology control algorithm using two-hop neighborhood knowledge, which works to build local shortest path tree at each node independently in order to generate a reduced topology while preserving the global optimal connectivity. We derive the energy stretch ratio and maximum degree performance of our proposed algorithm as well as several existing algorithms in this aspect. We then devise three power-efficient on-demand routing protocols on top of various localized topology control algorithms, which are to acquire minimum-power paths while minimizing the associated protocol overhead for route discovery by utilizing local network state information and also packet receipt status at neighbor nodes. We further derive the asymptotical performance of the routing strategy in our protocols in terms of energy stretch ratio and route acquisition latency when network nodes operate at limited discrete power levels. Simulation results are provided to demonstrate the high performance of our topology control algorithm and also the devised routing protocols.

Power Beacon-Assisted Millimeter Wave Ad Hoc Networks

Deployment of low cost power beacons (PBs) is a promising solution for dedicated wireless power transfer (WPT) in future wireless networks. In this paper, we present a tractable model for PB-assisted millimeter wave (mmWave) wireless ad hoc networks, where each transmitter (TX) harvests energy from all PBs and then uses the harvested energy to transmit information to its desired receiver. Our model accounts for realistic aspects of WPT and mmWave transmissions, such as power circuit activation threshold, allowed maximum harvested power, maximum transmit power, beamforming and blockage. Using stochastic geometry, we obtain the Laplace transform of the aggregate received power at the TX to calculate the power coverage probability. We approximate and discretize the transmit power of each TX into a finite number of discrete power levels in log scale to compute the channel and total coverage probability. We compare our analytical predictions to simulations and observe good accuracy. The proposed model allows insights into effect of system parameters, such as transmit power of PBs, PB density, main lobe beam-width and power circuit activation threshold on the overall coverage probability. The results confirm that it is feasible and safe to power TXs in a mmWave ad hoc network using PBs.

Beacon Deployment for Unambiguous Positioning

Instant and precise localization of a mobile user is fundamental for supporting various sophisticated indoor location-aware services. This paper focuses on achieving unambiguous user positioning using practical Bluetooth low energy (BLE) beacons with multiple discrete power levels. By receiving the beacon coverage status from a user’s device, the cloud server can unambiguously pinpoint the user’s location and react correspondingly. We first define the problem of beacon deployment for positioning (BDP) and provide several theoretic bounds on the number of required beacons to gain sufficient understanding on its performance behavior. The BDP problem is further formulated into an integer linear program (ILP) and solved in extensive case studies. We claim that this is the first systematic and in-depth research on beacon deployment for unambiguous user positioning. Our analysis and experiments show that the proposed solution takes ${O(\sqrt {N})}$ to ${O({N}/{2})}$ beacons for ${N}$ test positions, which is 2–8 times less beacons compared to that by the naive approach, while the analytical bounds are tight with the ILP results with 20% of gap.

A power-aware 2-covered path routing for wireless body area networks with variable transmission ranges

Wireless body area networks (WBAN) are an emerging form of technology which provides a base for various implantable and wearable sensors. This paper presents a 2-covered path routing that provides each WBAN along a path sheltered from an intersectional coverage of at least two WBAN hubs to provide high availability and reliable multi-hop communication in WBANs. In radio frequency (RF) systems, the power consumption required to transmit data grows at least quadratic times as its transmission range. Therefore, this paper studies the 2-covered path problem in which antennas have discrete transmission power levels. Given a set of n antennas with m available radii, a source and a sink on the plane, we propose three power-aware methods to construct 2-covered paths between source and sink. The proposed methods are graph transformation planning (GTP), 2-covered area stretching planning (TASP) and radii shrinking planning (RSP) which apply different strategies to reduce overall power consumption of the network. Experiments show that GTP obtains the maximum power saving while RSP and TASP are polynomial-time algorithms and save power up to 96% of those achieved by GTP.

Optimal thresholds for discrete power levels using adaptive modulation in presence of imperfect channel state information

Optimal thresholds for discrete power levels using adaptive modulation in presence of imperfect channel state information

Joint Dimming and Communication Design for Visible Light Communication

Multi-pulse position modulation (M-PPM) is advantageous in a visible light communication system due to its overall satisfactory performance in spectrum efficiency and dimming control. In this letter, we consider the joint dimming and communication design with M-PPM. We provide a model for the dimming control and address the balance between the dimming loss minimization and the transmission rate maximization. The dimming loss is modeled by the quantization loss within the dimming range for the discrete transmission power levels. The transmission rate is evaluated by the channel capacity. We propose two optimization formulations on the dimming requirement service and the transmission rate maximization. Numerical results show that the objective loss function may increase as either the lower limit of the dimming range approaches zero or the upper limit approaches the peak transmission power.

CMOS-integrated GaN LED array for discrete power level stepping in visible light communications

We report a CMOS integrated micro-LED array capable of generating discrete optical output power levels. A 16 × 16 array of individually addressable pixels are on-off controlled through parallel logic signals. With carefully selected groups of LEDs driven together, signals suitable for discrete transmission schemes are produced. The linearity of the device is assessed, and data transmission using pulse amplitude modulation (PAM) and orthogonal frequency division multiplexing (OFDM) is performed. Error-free transmission at a symbol rate of 100 MSamples/s is demonstrated with 4-PAM, yielding a data rate of 200 Mb/s. For 8-PAM, encoding is required to overcome the baseline wander from the receiver, reducing the data rate to 150 Mb/s. We also present an experimental proof-of-concept demonstration of discrete-level OFDM, achieving a spectral efficiency of 3.96 bits/s/Hz.

Optimization of Discrete Power and Resource Block Allocation for Achieving Maximum Energy Efficiency in OFDMA Networks

Most of the resource allocation literature on the energy-efficient orthogonal frequency division multiple access (OFDMA)-based wireless communication systems assume continuous power allocation/control, while, in practice, the power levels are discrete (such as in 3GPP LTE). This convenient continuous power assumption has mainly been due to either the limitations of the used optimization tools and/or the high computational complexity involved in addressing the more realistic discrete power allocation/control. In this paper, we introduce a new optimization framework to maximize the energy efficiency of the downlink transmission of cellular OFDMA networks subject to power budget and quality-of-service constraints, while considering discrete power and resource blocks (RBs) allocations. The proposed framework consists of two parts: 1) we model the predefined discrete power levels and RBs allocations by a single binary variable and 2) we propose a close-to-optimal semidefinite relaxation algorithm with Gaussian randomization to efficiently solve this non-convex combinatorial optimization problem with polynomial time complexity. We notice that a small number of power levels suffice to approach the energy efficiency performance of the continuous power allocation. Based on this observation, we propose an iterative suboptimal heuristic to further reduce the computational complexity. Simulation results show the effectiveness of the proposed schemes in maximizing the energy efficiency, while considering the practical discrete power levels.

Discrete Transmit Power Devices in Dense Wireless Networks: Methodology and Case Study

Machine-type communications (MTC) in 5G will be mostly realized using low-cost transceivers with a small, discrete set of possible configurations. This is in contrast to more capable devices with software defined radio capabilities that support configurations over a practically continuous domain. We find that existing theoretical work assuming continuous domains cannot be immediately applied to such constrained MTC devices. Therefore, we propose a methodology that guides researchers in the process of developing effective MTC wireless systems with devices that have restricted capabilities. By following the proposed methodology, existing theories can be experimentally evaluated and replicated. We show how this methodology can be applied for developing and evaluating efficient interference mitigation systems on a case study using devices that support only discrete transmit power levels. In this case study, we formulate an interference mitigation problem and a corresponding game-theoretic formalism that can support discrete output power levels. We validated some of the existing results obtained analytically and in simulation and we also found that: 1) in practice, devices have to be penalized stronger than in theory to determine them to select all the available transmit power levels; 2) in practice, for the discrete case of the power allocation algorithm, the convergence speed does not increase exponentially with the number of devices; and 3) the proposed power selection algorithm converges in two to four iterations. Similarly, as in the presented case study, the methodology can be used to adapt and test other resource management solutions in an operating environment with real-world restrictions.

Experimental investigation on the heat transfer performance of a PCM based pin fin heat sink with discrete heating

This paper reports the results of an experimental investigation of the heat transfer performance of a phase change material (PCM) based composite 72 pin fin heat sink subjected to individual heat loading of 4 discrete heaters of equal area. The investigation is then followed by multi objective optimization of the individual heat inputs, wherein the total power of the heaters is fixed. The PCM used is n-eicosane and the heat sink is made of aluminium. First, in house experiments are conducted for different combinations of power. The hotspots imposed by spatially non uniform heat flux are seen to have a considerable effect on the melting and solidification cycle and hence on the thermal performance of the heat sink. The diversity in the performance of heat sink motivates us to perform a multi objective optimization using Non-dominated sorting genetic algorithm (NSGA-II) to determine the optimum combination of the discrete power levels, that stretches the charging period and minimizes the discharging period of the heat sink simultaneously. The solutions obtained thus obtained are validated by conducting in house experiments. This study establishes the fact that discrete heating scenarios have to be considered in the future optimization of the heat sinks.

Maximum-Eigenvalue-Based Sensing and Power Recognition for Multiantenna Cognitive Radio System

Spectrum sensing, as a way for a secondary user (SU) to detect the on/off status of the primary user (PU), has been well studied in the area of cognitive radio (CR) for the past ten years. It is noticed that most exiting techniques only assumed that the PU has constant transmit power, although in practice, the PU could possibly operate under more than one discrete power levels, as can be seen from many practical standards, e.g., 802.11, Global System for Mobile Communications, and Long-Term Evolution. In this paper, we investigate this new multiple primary transmit power (MPTP) scenario and propose the corresponding detection/recognition framework based on multiple-antenna configuration at the SU. Although the primary target in MPTP is still to detect the on/off status of the PU, a secondary target appears in order to identify PU's transmit power level once the PU is “detected” in the first place. To achieve these two targets, an eigenvalue-based approach is applied due to its robustness and least requirement on the knowledge of channels. To obtain the closed-form threshold expressions and the performance analysis, we adopt nonasymptotic Gaussian and Gamma approximation for the distribution of the eigenvalues of the sample covariance matrix. Simulation results are provided to verify the proposed studies.

Effects of Mica2-based discrete energy levels on the lifetime of cooperation neighbor sensor networks

Abstract:We investigated the impact of transmission power control using Mica2 mote discrete power levels on neighbor sensor network lifetime. We built a linear programming framework to qualify the cooperation of sensor networks using a discrete energy model in comparison to noncooperating networks. Our results showed that a wireless sensor neighbor network that uses a discrete radio model can be more energy efficient than a network that uses a nondiscrete energy model.

Economical and Balanced Energy Usage in the Smart Home Infrastructure: A Tutorial and New Results

The smart home infrastructure features the automatic control of various household appliances in the advanced metering infrastructure, which enables the connection of individual smart home systems to a smart grid. In such an infrastructure, each smart meter receives electricity price from utilities and uses a smart controller to schedule the household appliances accordingly. This helps shift the heavy energy load from peak hours to nonpeak hours. Such an architecture significantly improves the reliability of the power grid through reducing the peak energy usage, while benefiting the customers through reducing electricity bills. This paper presents a tutorial on the development of the smart controller to schedule household appliances, which is also known as smart home scheduling. For each individual user, a dynamic programming-based algorithm that schedules household appliances with discrete power levels is introduced. Based on it, a game theoretic framework is designed for multi-user smart home scheduling to mitigate the accumulated energy usage during the peak hours. The simulation results demonstrate that it can reduce the electricity bill by 30.11% while still improving peak-to-average ratio (PAR) in the power grid. Furthermore, the deployment of smart home scheduling techniques in a big city is discussed. In such a context, the parallel computation is explored to tackle the large computational complexity, a machine assignment approximation algorithm is proposed to accelerate the smart home scheduling, and a new hieratical framework is proposed to reduce the communication overhead. The simulation results on large test cases demonstrate that the city level hierarchical smart home scheduling can achieve the bill reduction of 43.04% and the PAR reduction of 47.50% on average.

A Simple Random Access Scheme With Multilevel Power Allocation

This letter proposes a decentralized power allocation approach for random access with capabilities of multi-packet reception (MPR) and successive interference cancellation (SIC). Considering specific features of SIC, a bottom-up per-level algorithm is proposed to obtain discrete transmission power levels and the corresponding probabilities. Comparing with the conventional power allocation scheme with MPR and SIC, our approach significantly improves the system sum rate; this is confirmed by computer simulations.