Power Allocation in Massive MIMO-HWSN Based on the Water-Filling Algorithm

Pilot power allocation for Internet of Things (IoT) devices in massive multi-input multioutput heterogeneous wireless sensor networks (MIMO-HWSN) is studied in this paper. The interference caused by fractional pilot reusing in adjacent cells had a negative effect on the MIMO-HWSN system performance. Reasonable power allocation for users can effectively weaken the interference. Motivated by the water-filling algorithm, we proposed a suboptimal pilot transmission power method to improve the system capacity. Simulation results show that the proposed method can significantly improve the uplink capacity of the system and explain the influence of different pilot transmission power on the performance of the system, but the complexity of the system almost does not increase.

A novel energy management strategy to reduce gird dependency using electric vehicles storage in coordination with solar power

The effective coordination of solar photovoltaic (solar PV) with Electrical Vehicles (EV) can substantially improve the micro grid(MG) stability and economic benefits. This paper presents a novel Energy Management System (EMS) that synchronizes EV storage with Solar PV and load variability. Reducing grid dependency and energy cost of the MGs are the key objectives of the proposed EMS. A smart EV prioritization based control strategy is developed using fuzzy controller. Probabilistic approach is designed to estimate the EV usage expectancy in the near time zone that helps smart decision on choosing EVs. Minimizing battery degradation and maximizing EV storage exploitation are the key objectives of EV prioritization. On the other hand, Water Filling Algorithm (WFA) is used for Optimal Storage Distribution (OSD) in each zone of energy need for load flattening. The proposed EMS is implemented in a real time on-grid MG scenario and different case studies have been investigated to realize the impact of proposed EMS. A comprehensive cost analysis has been conducted and the efficacy of the proposed EMS is analysed.

Power allocation in OFDM-based cognitive radio systems

In this thesis, we develop an subcarrier transmission suboptimal power allocation algorithm and an underlay subcarrier transmission optimal power allocation algorithm for the orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) systems with different statistical interference constraints imposed by different primary users (PUs). Given the fact that the interference constraints are met in a statistical manner, the CR transmitter does not require the instantaneous channel quality feed-back from the PU receivers. First an alternative subcarrier transmission suboptimal algorithm with reduced complexity has been proposed and the performance has been investigated. Presented numerical results show that with our proposed suboptimal power allocation algorithm CR user can achieve 10 percent higher transmission capacity for given statistical interference constraints and a given power budget compared to the traditional suboptimal power allocation algorithms, uniform and water-filling power allocation algorithms. The proposed suboptimal algorithm outperforms traditional suboptimal algorithm, water-filling algorithm and uniform power loading algorithm. Second,We introduce an underlay subcarrier transmission optimal power allocation algorithms which allows the secondary users use the bandwidth used by Pus. And at the same time we consider the individual peak power constraint as the forth constraint added to the objective function which is the transmission capacity rate of the secondary users.Third, we propose suboptimal algorithm using GWF which has less complexity level than traditional water-filling algorithm instead of conventional water-filling algorithm in calculating the assigned power while considering the satisfaction of the total power constraint. The proposed suboptimal algorithm gives an option of using a low complexity power allocation algorithm where complexity is an issue.

Power allocation in OFDM-based cognitive radio systems

In this thesis, we develop an subcarrier transmission suboptimal power allocation algorithm and an underlay subcarrier transmission optimal power allocation algorithm for the orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) systems with different statistical interference constraints imposed by different primary users (PUs). Given the fact that the interference constraints are met in a statistical manner, the CR transmitter does not require the instantaneous channel quality feed-back from the PU receivers. First an alternative subcarrier transmission suboptimal algorithm with reduced complexity has been proposed and the performance has been investigated. Presented numerical results show that with our proposed suboptimal power allocation algorithm CR user can achieve 10 percent higher transmission capacity for given statistical interference constraints and a given power budget compared to the traditional suboptimal power allocation algorithms, uniform and water-filling power allocation algorithms. The proposed suboptimal algorithm outperforms traditional suboptimal algorithm, water-filling algorithm and uniform power loading algorithm. Second,We introduce an underlay subcarrier transmission optimal power allocation algorithms which allows the secondary users use the bandwidth used by Pus. And at the same time we consider the individual peak power constraint as the forth constraint added to the objective function which is the transmission capacity rate of the secondary users.Third, we propose suboptimal algorithm using GWF which has less complexity level than traditional water-filling algorithm instead of conventional water-filling algorithm in calculating the assigned power while considering the satisfaction of the total power constraint. The proposed suboptimal algorithm gives an option of using a low complexity power allocation algorithm where complexity is an issue.

Power allocation in OFDM-based cognitive radio systems using iterative algorithms

As spectrum scarcity is becoming a serious problem, the worth of finding a general solution for such issue has become even serious due to the rapid development of wireless communications. The main objective of this thesis is to investigate the optimal power allocation procedure that maximizes the capacity in OFDM based Cognitive Radio Systems. The main purpose of the search is to modify the conventional water-filling algorithm applied in general OFDM based Cognitive Radio systems due to the per subchannel power constraints and individual peak power constraints. For Radio Resource Allocation (RRA), one of the most typical problems is to solve power allocation using the Conventional Water- filling. As communication system develops, the structures of the system models and the corresponding RRA problems evolve to more advanced and more complicated ones. In this thesis Iterative Partitioned Weighted Geometric Water-filling with Individual Peak Power Constraints (IGPP), a simple and elegant approach is proposed to solve the weighted radio resource allocation problem with peak power constraint and total subchannel power constraint with channel partitions. The proposed IGPP algorithm requires less computation than the Conventional Water-filling algorithm (CWF). Dynamic Channel Sensing Iterative (DCSI) approach is another algorithm proposed to optimally allocate power for OFDM based Cognitive Radio Systems. DCSI is a innovative concept which will allow us to solve the same problem intelligently with less complexity. It provides straight forward power allocation analysis, solutions and insights with reduced computation over other approaches under the same memory requirement and sorted parameters.

Power allocation in OFDM-based cognitive radio systems using iterative algorithms

As spectrum scarcity is becoming a serious problem, the worth of finding a general solution for such issue has become even serious due to the rapid development of wireless communications. The main objective of this thesis is to investigate the optimal power allocation procedure that maximizes the capacity in OFDM based Cognitive Radio Systems. The main purpose of the search is to modify the conventional water-filling algorithm applied in general OFDM based Cognitive Radio systems due to the per subchannel power constraints and individual peak power constraints. For Radio Resource Allocation (RRA), one of the most typical problems is to solve power allocation using the Conventional Water- filling. As communication system develops, the structures of the system models and the corresponding RRA problems evolve to more advanced and more complicated ones. In this thesis Iterative Partitioned Weighted Geometric Water-filling with Individual Peak Power Constraints (IGPP), a simple and elegant approach is proposed to solve the weighted radio resource allocation problem with peak power constraint and total subchannel power constraint with channel partitions. The proposed IGPP algorithm requires less computation than the Conventional Water-filling algorithm (CWF). Dynamic Channel Sensing Iterative (DCSI) approach is another algorithm proposed to optimally allocate power for OFDM based Cognitive Radio Systems. DCSI is a innovative concept which will allow us to solve the same problem intelligently with less complexity. It provides straight forward power allocation analysis, solutions and insights with reduced computation over other approaches under the same memory requirement and sorted parameters.

Local Water-Filling Algorithm for Shadow Detection and Removal of Document Images

Shadow detection and removal is an important task for digitized document applications. It is hard for many methods to distinguish shadow from printed text due to the high darkness similarity. In this paper, we propose a local water-filling method to remove shadows by mapping a document image into a structure of topographic surface. Firstly, we design a local water-filling approach including a flooding and effusing process to estimate the shading map, which can be used to detect umbra and penumbra. Then, the umbra is enhanced using Retinex Theory. For penumbra, we propose a binarized water-filling strategy to correct illumination distortions. Moreover, we build up a dataset called optical shadow removal (OSR dataset), which includes hundreds of shadow images. Experiments performed on OSR dataset show that our method achieves an average ErrorRatio of 0.685 with a computation time of 0.265 s to process an image size of 960×544 pixels on a desktop. The proposed method can remove the shading artifacts and outperform some state-of-the-art methods, especially for the removal of shadow boundaries.