Distributed power control for 5G millimeter wave dense small cell

The millimeter wave (mm-wave) is one of the key enabling elements in the fifth generation (5G) technology that uses highly directional beamforming to mitigate path loss by using antenna arrays. The mmwave for massive multiple-input-multiple-output (MIMO) is able to reduce the cross-tier interference between multiple antennas to assist the number of active users (UEs). The dense small cell is very important to increase the capacity and high coverage in cell edge. This paper focuses on achievable high data rate in a dense small cell based on the use of mm-wave. In order to perform the achievable high data rate, a novel distributed power allocation is proposed in this work that reduces the high path loss and suppresses cross-tier interference under constraint transmission power in mm-wave. The condition of the Nash Equilibrium is also applied to reduce the cross-interference by guiding every femtocell user equipment's to achieve the target signal-to-interference noise ratio (SINR). From the numerical results, reduction in the high path loss on the desired signal in the heterogeneous downlink networks can be achieved by spatially reducing the larger antenna arrays and occurred when the mm-wave for distributed transmit power is larger than the threshold power

Low computational complexity for optimizing energy efficiency in mm-wave hybrid precoding system for 5G

Millimeter-wave (mm-wave) communication is the spectral frontier to meet the anticipated significant volume of high data traffic processing in next-generation systems. The primary challenges in mm-wave can be overcome by reducing complexity and power consumption by large antenna arrays for massive multiple-input multiple-output (mMIMO) systems. However, the circuit power consumption is expected to increase rapidly. The precoding in mm-wave mMIMO systems cannot be successfully achieved at baseband using digital precoders, owing to the high cost and power consumption of signal mixers and analog-to-digital converters. Nevertheless, hybrid analog–digital precoders are considered a cost-effective solution. In this work, we introduce a novel method for optimizing energy efficiency (EE) in the upper-bound multiuser (MU) - mMIMO system and the cost efficiency of quantized hybrid precoding (HP) design. We propose effective alternating minimization algorithms based on the zero gradient method to establish fully-connected structures (FCSs) and partially-connected structures (PCSs). In the alternating minimization algorithms, low complexity is proposed by enforcing an orthogonal constraint on the digital precoders to realize the joint optimization of computational complexity and communication power. Therefore, the alternating minimization algorithm enhances HP by improving the performance of the FCS through advanced phase extraction, which involves high complexity. Meanwhile, the alternating minimization algorithm develops a PCS to achieve low complexity using HP. The simulation results demonstrate that the proposed algorithm for MU - mMIMO systems improves EE. The power-saving ratio is also enhanced for PCS and FCS by 48.3% and 17.12%, respectively

Identification of Homogeneous Areas for Drought Frequency Analysis

Owing to high spatial and temporal rainfall variability, rationale water management decision-making is complex. Hence, it is essential to identify homogeneous areas to assist water management. This paper focusses on separating the study area into homogeneous groups to predict the risk of occurrence of a drought event. The severity-duration-frequency (SDF) curves were developed to determine the relationship between the probability of a drought occurring with a certain severity and frequency at the selected stations in Victoria, Australia. Two techniques namely cluster analysis and modified Andrews curve were used in grouping study area that have similar climate characteristics with respect to risk of occurrence of drought (i.e. rainfall variability). The study area was divided into six clusters and they adequately covered the study area. A mean drought frequency curve was developed for each homogeneous group to determine the probability of vulnerability to a drought event with a certain severity. The advantage of separating stations into homogenous groups based on similar drought characteristics is that it eliminates the necessity to carry out a detailed drought characteristic analysis for any location of interest. The measurable characteristics of this station will determine its best match with the existing cluster groups

A new technique for improving energy efficiency in 5g mm-wave hybrid precoding systems

In this article, we present a new approach to optimizing the energy efficiency of the cost-efficiency of quantized hybrid pre-encoding (HP) design. We present effective alternating minimization algorithms (AMA) based on the zero gradient method to produce completely connected structures (CCSs) and partially connected structures (PCSs). Alternative minimization algorithms offer lower complexity by introducing orthogonal constraints on digital pre-codes to concurrently maximize computing complexity and communication power. As a result, by improving CCS through advanced phase extraction, the alternating minimization technique enhances hybrid pre-encoding. For PCS, the energy-saving ratio grew by 45.3 %, while for CCS, it increased by 18.12 %

Long-baseline, sub-decimeter kinematic GPS positioning of moving object, with potential application to monitor ocean surface wave

Precise relative kinematic positioning of moving platforms using GPS carrier phase observables has numerous applications. One prominent application is utilization of highly stabilized GPS technology mounted on the buoy, which is specially designed for detecting tsunami wave at open sea. The essential point of this research is to investigate a potential use of a GPS tsunami buoy for the purpose of tsunami early warning system with long-baseline kinematic GPS processing method. The rule of thumb GPS positioning concept, GPS position results are affected by. baseline length mostly due to de-correlation of atmospheric errors. As baseline lengths increase, position results degrade due to the difficulty to correctly fix the cariier phase ambiguity to its integer value. carrier phase fixed ambiguity solutions are more accurate that float arnbiguify solutions. It is generally accepted that carrier phase can be successfUlly fixed for baselines of up to 10 km. After that, fixing ambiguities becomes more difficult and risky. It would be certainty more advantageous to have a reliable float solution rather than an unreliable fixed solution. In this study, we have developed a new quasi-real time long-baseline kinematic analysis method using dual-frequency carrier phase with floated ambiguities, implemented in the Bernese GPS Software Version 5.0. We demonstrate that early detection of a damaging tsunami can be achieved by tracking the anomalous changes in sea surface height. The movements of a GPS buoy relative to a base station with baseline length of 500 km have been monitored in quasi-real time mode, and the tsunami waves caused by the 5th September 2004 Off Kii Peninsula earthquake, Japan, have been successful detected as they went by, even though these were only 15 cm high. The filtered record of the solution closely resembles that of short baseline, with RMS of 3.4 cm over 2.5 hours. To test the robustness of our Iong-baseline kinematic GPS method under various meteorological, we conducted the GPS tsunami buoy data analysis continuously for 8 days to monitor the motion of the buoy. The average scatterings of GPS buoy heights by the low-pass filtered 1 -Hz positioning result after tidal correction are about 3.4 cm and 1.2 cm under both typhoon and calm weather conditions. This accuracy is precise enough to be applicable to a tsunami early warning system. Since our long-baseline kinematic GPS analysis is effective to a long baseline up to 500 km, we can place a GPS buoy far offshore, which ensures an adequate evacuation time even, for people living on the coast

Energy-Efficient Federated Learning With Resource Allocation for Green IoT Edge Intelligence in B5G

An edge intelligence-aided Internet-of-Things (IoT) network has been proposed to accelerate the response of IoT services by deploying edge intelligence near IoT devices. The transmission of data from IoT devices to the edge nodes leads to large network traffic in the wireless connections. Federated Learning (FL) is proposed to solve the high computational complexity by training the model locally on IoT devices and sharing the model parameters in the edge nodes. This paper focuses on developing an efficient integration of joint edge intelligence nodes depending on investigating an energy-efficient bandwidth allocation, computing Central Processing Unit (CPU) frequency, optimization transmission power, and the desired level of learning accuracy to minimize the energy consumption and satisfy the FL time requirement for all IoT devices. The proposal efficiently optimized the computation frequency allocation and reduced energy consumption in IoT devices by solving the bandwidth optimization problem in closed form. The remaining computational frequency allocation, transmission power allocation, and loss could be resolved with an Alternative Direction Algorithm (ADA) to reduce energy consumption and complexity at every iteration of FL time from IoT devices to edge intelligence nodes. The simulation results indicated that the proposed ADA can adapt the central processing unit frequency and power transmission control to reduce energy consumption at the cost of a small growth of FL time

A new technique for improving energy efficiency in 5G Mm-wave hybrid precoding systems

In this article, we present a new approach to optimizing the energy efficiency of the cost-efficiency of quantized hybrid pre-encoding (HP) design. We present effective alternating minimization algorithms (AMA) based on the zero gradient method to produce completely connected structures (CCSs) and partially connected structures (PCSs). Alternative minimization algorithms offer lower complexity by introducing orthogonal constraints on digital pre-codes to concurrently maximize computing complexity and communication power. As a result, by improving CCS through advanced phase extraction, the alternating minimization technique enhances hybrid pre-encoding. For PCS, the energy-saving ratio grew by 45.3 %, while for CCS, it increased by 18.12 %

Ownership structure, corporate governance and firm performance in Malaysia

Highly corporate concentrated ownership was among the significant factor that brought Malaysia into the 1997/98 financial crisis. Concentrated ownership, as agency theory states, has contributed to lower the effectiveness of corporate governance by considering the interests of majority shareholders at the expense of minorities, having the motivation and power to punish management and either appointing independent directors or sitting personally on the board to protect their interests. To overcome the problem, the MCCG, which largely followed recommendations of the United Kingdom (UK) code, was issued in 2001. However, it was argued that the same requirements of corporate governance practices in the UK code many not work effectively in a country which has a different legal system, business culture and corporate structure. Despite many studies have been conducted to examine the influence among the ownership structure corporate governance and firm performance, the results of the previous studies are still indeterminate. Unlike many previous studies, this study aimed to examine corporate governance in Malaysia by investigating ownership structure independently of corporate governance. Ownership structure was measured by government ownership, local nominees, and foreign nominees, while corporate governance was measured by CEO's duality, number of independent directors, board size, frequency of board meetings, number of women directors and audit committee. Firm performance was measured by return on assets and earnings per share. Data on ownership structure and corporate governance were collected from companies' annual reports, while data regarding firm performance were gathered from Bloomberg database sources and Annual Reports. Data were collected from secondary sources for the period 2003 to 2013 involving 341 Malaysian Public Listed Companies selected using a purposive sampling method involving the companies that have been existed throughout the period of 2003 to 2013. The data were analyzed using descriptive statistics, correlation and panel data regression model. Results of testing the influences among ownership structure, corporate governance and firm performance are found to be mixed. For example, local nominee, CEO duality and board meeting showed weak and negative influences on return on asset while foreign nominee and independent directors had weak and positive influences on earnings per share. The same mixed results were also found between concentrated ownership and corporate governance. This study has added to the body of knowledge from a different perspective of considering ownership structure as an independent variable separated from corporate governance. Finally, the findings of this study expect to assist the relevant authorities to evaluate the present listing requirements, corporate governance practices and the current ownership structure trends in enhancing future corporate performance

Energy-Efficient Federated Learning With Resource Allocation for Green IoT Edge Intelligence in B5G

An edge intelligence-aided Internet-of-Things (IoT) network has been proposed to accelerate the response of IoT services by deploying edge intelligence near IoT devices. The transmission of data from IoT devices to the edge nodes leads to large network traffic in the wireless connections. Federated Learning (FL) is proposed to solve the high computational complexity by training the model locally on IoT devices and sharing the model parameters in the edge nodes. This paper focuses on developing an efficient integration of joint edge intelligence nodes depending on investigating an energy-efficient bandwidth allocation, computing Central Processing Unit (CPU) frequency, optimization transmission power, and the desired level of learning accuracy to minimize the energy consumption and satisfy the FL time requirement for all IoT devices. The proposal efficiently optimized the computation frequency allocation and reduced energy consumption in IoT devices by solving the bandwidth optimization problem in closed form. The remaining computational frequency allocation, transmission power allocation, and loss could be resolved with an Alternative Direction Algorithm (ADA) to reduce energy consumption and complexity at every iteration of FL time from IoT devices to edge intelligence nodes. The simulation results indicated that the proposed ADA can adapt the central processing unit frequency and power transmission control to reduce energy consumption at the cost of a small growth of FL time