scholarly journals Suboptimal Greedy Power Allocation Schemes for Discrete Bit Loading

2013 ◽  
Vol 2013 ◽  
pp. 1-11
Author(s):  
Waleed Al-Hanafy ◽  
Stephan Weiss
Keyword(s):  
Computational Complexity ◽  
Power Allocation ◽  
Low Cost ◽  
Extra Cost ◽  
Power Budget ◽  
Bit Loading ◽  
Data Throughput ◽  
Qam Modulation ◽  
Discrete Bit Loading ◽  
Residual Power

We consider low cost discrete bit loading based on greedy power allocation (GPA) under the constraints of total transmit power budget, target BER, and maximum permissible QAM modulation order. Compared to the standard GPA, which is optimal in terms of maximising the data throughput, three suboptimal schemes are proposed, which perform GPA on subsets of subchannels only. These subsets are created by considering the minimum SNR boundaries of QAM levels for a given target BER. We demonstrate how these schemes can significantly reduce the computational complexity required for power allocation, particularly in the case of a large number of subchannels. Two of the proposed algorithms can achieve near optimal performance including a transfer of residual power between subsets at the expense of a very small extra cost. By simulations, we show that the two near optimal schemes, while greatly reducing complexity, perform best in two separate and distinct SNR regions.

scholarly journals Computational Complexity Reduction of Neural Networks of Brain Tumor Image Segmentation by Introducing Fermi–Dirac Correction Functions

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 223
Author(s):  
Yen-Ling Tai ◽  
Shin-Jhe Huang ◽  
Chien-Chang Chen ◽  
Henry Horng-Shing Lu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Computational Complexity ◽  
High Performance ◽  
Low Cost ◽  
Structural Complexity ◽  
Correction Function ◽  
Computational Time ◽  
Learning Methods ◽  
Band Theory

Nowadays, deep learning methods with high structural complexity and flexibility inevitably lean on the computational capability of the hardware. A platform with high-performance GPUs and large amounts of memory could support neural networks having large numbers of layers and kernels. However, naively pursuing high-cost hardware would probably drag the technical development of deep learning methods. In the article, we thus establish a new preprocessing method to reduce the computational complexity of the neural networks. Inspired by the band theory of solids in physics, we map the image space into a noninteraction physical system isomorphically and then treat image voxels as particle-like clusters. Then, we reconstruct the Fermi–Dirac distribution to be a correction function for the normalization of the voxel intensity and as a filter of insignificant cluster components. The filtered clusters at the circumstance can delineate the morphological heterogeneity of the image voxels. We used the BraTS 2019 datasets and the dimensional fusion U-net for the algorithmic validation, and the proposed Fermi–Dirac correction function exhibited comparable performance to other employed preprocessing methods. By comparing to the conventional z-score normalization function and the Gamma correction function, the proposed algorithm can save at least 38% of computational time cost under a low-cost hardware architecture. Even though the correction function of global histogram equalization has the lowest computational time among the employed correction functions, the proposed Fermi–Dirac correction function exhibits better capabilities of image augmentation and segmentation.

Efficient Two-Stage Discrete Bit-Loading Algorithms for OFDM Systems

2010 ◽  
Vol 59 (7) ◽  
pp. 3407-3416 ◽  
Author(s):  
Deqiang Wang ◽  
Yewen Cao ◽  
Laibo Zheng
Keyword(s):  
Ofdm Systems ◽  
Two Stage ◽  
Bit Loading ◽  
Discrete Bit Loading

Development of Appropriate Power Distribution Design for Can-Sized satellite (canSAT)

2021 ◽  
Vol 02 (02) ◽  
Author(s):  
Devina Cristine Marubin ◽  
◽  
Sim Sy Yi ◽  
Keyword(s):  
Power System ◽  
Data Acquisition ◽  
Power Supply ◽  
Communication System ◽  
Power Distribution ◽  
Low Cost ◽  
Small Satellite ◽  
Power Budget ◽  
Space Agency ◽  
Rules Set

Can-Sized satellite (canSAT) is a small satellite that is used for educational purpose. CanSAT offer student to build their satellites with their creativity which make the learning process more effective. In Malaysia, SiswaSAT is held by the Malaysia Space Agency for students in different categories to participate and build their satellites according to rules set and it should be a low-cost project. CanSAT can be divided into few parts which are communication system, onboard data acquisition, ground control station and power system. The power system is one of the important and heaviest subsystems, it needed to supply power, but weight and size are one of the main concerned as the canSAT should not exceed the required weight and selecting power supply that is matched with the overall power budget that has small size and lightweight is challenging. Therefore, the power supply selection should consider this detail. The power distribution design should be able to supply an appropriate amount of current and voltage to the components according to their specification. This study aims to develop and test the proposed prototype which is named ScoreSAT able to provide data and have enough power supply for the whole operation. Therefore, an initiative to develop the appropriate power distribution design for canSAT is taken to overcome the problem of the power system. Moreover, each subsystem needs to be tested by obtaining the results from the onboard data acquisition and transmit the data using the communication system before integrating into the power system. ScoreSAT prototype needs to carry the system that is mounted inside, thus the space inside the prototype needs to be fully utilized for the whole system to fit in. ScoreSAT completes the mission by obtaining data acquisition during the operation.

scholarly journals Proportional Fair Power Allocation for Secondary Transmitters in the TV White Space

2013 ◽  
Vol 2013 ◽  
pp. 1-8
Author(s):  
Konstantinos Koufos ◽  
Riku Jäntti
Keyword(s):  
Computational Complexity ◽  
Power Allocation ◽  
Spectrum Allocation ◽  
Primary System ◽  
White Space ◽  
Tv White Space ◽  
Performance Loss ◽  
The Cost ◽  
Allocation Algorithms ◽  
Modified Algorithm

The key bottleneck for secondary spectrum usage is the aggregate interference to the primary system receivers due to simultaneous secondary transmissions. Existing power allocation algorithms for multiple secondary transmitters in the TV white space either fail to protect the TV service in all cases or they allocate extremely low power levels to some of the transmitters. In this paper, we propose a power allocation algorithm that favors equally the secondary transmitters and it is able to protect the TV service in all cases. When the number of secondary transmitters is high, the computational complexity of the proposed algorithm becomes high too. We show how the algorithm could be modified to reduce its computational complexity at the cost of negligible performance loss. The modified algorithm could permit a spectrum allocation database to allocate near optimal transmit power levels to tens of thousands of secondary transmitters in real time. In addition, we describe how the modified algorithm could be applied to allow decentralized power allocation for mobile secondary transmitters. In that case, the proposed algorithm outperforms the existing algorithms because it allows reducing the communication signalling overhead between mobile secondary transmitters and the spectrum allocation database.

An iterative scheme for optimal and energy effective power allocation using multi-scale resource GOA-EM in distributed antenna system

2021 ◽  
pp. 1-12
Author(s):  
K. Sakthidasan Sankaran ◽  
Xiao-Zhi Gao
Keyword(s):  
Computational Complexity ◽  
Power Allocation ◽  
Clustering Algorithms ◽  
Antenna System ◽  
Optimization Approach ◽  
Distributed Antenna System ◽  
Multi Scale ◽  
Clustering Model ◽  
Distributed Antenna ◽  
Grasshopper Optimization Algorithm

Nowadays, numerous algorithms on power allocation have been proposed for maximizing the EE (Energy efficiency) and SE (Spectral efficiency) in the Distributed Antenna System (DAS). Moreover, the conservative techniques employed for power allocation seem to be problematic, due to their high computational complexity. The main objective of this paper focuses on optimizing the power allocation in order to enhance the EE and SE along with the improved antenna capacity using an effective optimization approach with the clustering model. To obtain the optimized power allocation and antenna capacity, Multi-scale resource Grasshopper Optimization Algorithm (Multi-scale resource GOA) scheme is proposed and employed. Furthermore, clustering is developed based on the Discriminative cluster-based Expectation maximization (DC-EM) clustering algorithms, which also helps to reduce the interference rate and computational complexity. The performance analysis is made under various scenarios and circumstances. The proposed system (DAS with GOA-EM) is assessed and compared with the existing approaches in terms of both the EE and SE, which demonstrates that its superiority.

scholarly journals Energy and Spectrally Efficient Modulation Scheme for IoT Applications

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4382 ◽  
Author(s):  
Hany Hussein ◽  
Mohamed Elsayed ◽  
Mahmoud Fakhry ◽  
Usama Sayed Mohamed
Keyword(s):  
Low Power ◽  
Closed Form ◽  
Fading Channels ◽  
Low Cost ◽  
Superior Performance ◽  
Closed Form Expression ◽  
Modulation Scheme ◽  
Power Budget ◽  
Iot Applications ◽  
Phy Layer

Due to the Internet of Things (IoT) requirements for a high-density network with low-cost and low-power physical (PHY) layer design, the low-power budget transceiver systems have drawn momentous attention lately owing to their superior performance enhancement in both energy efficiency and hardware complexity reduction. As the power budget of the classical transceivers is envisioned by using inefficient linear power amplifiers (PAs) at the transmitter (TX) side and by applying high-resolution analog to digital converters (ADCs) at the receiver (RX) side, the transceiver architectures with low-cost PHY layer design (i.e., nonlinear PA at the TX and one-bit ADC at the RX) are mandated to cope with the vast IoT applications. Therefore, in this paper, we propose the orthogonal shaping pulses minimum shift keying (OSP-MSK) as a multiple-input multiple-output (MIMO) modulation/demodulation scheme in order to design the low-cost transceiver architectures associated with the IoT devices. The OSP-MSK fulfills a low-power budget by using constant envelope modulation (CEM) techniques at the TX side, and by applying a low-resolution one-bit ADC at the RX side. Furthermore, the OSP-MSK provides a higher spectral efficiency compared to the recently introduced MIMO-CEM with the one-bit ADC. In this context, the orthogonality between the in-phase and quadrature-phase components of the OSP are exploited to increase the number of transmitted bits per symbol (bps) without the need for extra bandwidth. The performance of the proposed scheme is investigated analytically and via Monte Carlo simulations. For the mathematical analysis, we derive closed-form expressions for assessing the average bit error rate (ABER) performance of the OSP-MSK modulation in conjunction with Rayleigh and Nakagami-m fading channels. Moreover, a closed-form expression for evaluating the power spectral density (PSD) of the proposed scheme is obtained as well. The simulation results corroborate the potency of the conducted analysis by revealing a high consistency with the obtained analytical formulas.

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