Search

Massive Machine-Type Communication (mMTC) is proposed by ITU to provide a large scale of connectivity services for billions of machine-type communications devices (MTCDs) via cellular networks. However, MTCDs are generally energy-constrained, security capability limited and short-packet transmission dominated. It has great challenges for massive MTCDs to perform short-packet transmission simultaneously while guaranteeing the confidentiality and energy efficiency of the information transmission. In this paper, we investigate the energy-efficient secure short-packet transmission of Non-Orthogonal Multiple Access (NOMA) assisted mMTC networks, in which MTCDs aim to transmit secrecy messages to the destination base station via trusted relays with a passive eavesdropper present. To realize secure, reliable and energy-efficient transmission, we maximize the secrecy energy efficiency (SEE) by implementing joint relay and power level selection. Furthermore, we formulate a decentralized stateless Q-learning-based multi-agent reinforcement learning (MARL) algorithm for the dynamic and complex mMTC systems. The simulation results show that adopting the proposed decentralized MARL algorithm and relaying with NOMA can improve the performance of SEE while reducing information exchange overhead. [ABSTRACT FROM AUTHOR]

The theme of the environmental upgrades and Sustainable Development Goals (SDGs) of the United Nations is "Better Environment, Better tomorrow." This study uses regulatory quality (RgQ) and energy consumption per capita (EC) as explanatory variables and foreign direct investment (FDI) as an integrating variable with RgQ and EC and examines their impacts on greenhouse gas (GHG) emissions from 2001 to 2018 in panel Asian economies. The findings indicate that EC negatively impacts GHG emissions, while RgQ positively promotes the reduction of GHG emissions in Asia. In addition, FDI has an integrating role, as sufficient FDI inflows significantly enhance clean energy use and EC efficiency. Such inflows also support and improve Asia's regulation quality by upgrading standardization investments and technology deployment to enhance institutional quality and achieve SDGs. FDI inflows have thus been vital in environment upgrading in Asia and regional sustainable environmental development. [ABSTRACT FROM AUTHOR]

Millimeter-wave (mmWave) and ultra-dense networks are two key technologies for the fifth-generation (5G) and beyond communication system. However, the ultra-dense deployment of small base stations (SBSs) might introduce severe interference to users that connect to SBSs. This paper analyzes the performance of 5G communication networks where the SBSs with coordinated beamforming, operating at mmWave frequency band and macro base stations (MBSs) operating at sub-6 GHz coexist. First, by utilizing a stochastic geometry approach, we obtain the cell association probability expressions in terms of different cell association biases, base station density ratios and probabilities of line of sight (LoS) link. Furthermore, we propose a clustering method to choose some SBSs to eliminate intra-cell interference. Then, we put forward an average distance from the Kth SBS to a user to obtain signal-to-interference-ratio (SINR) and rate coverage probability expressions. The simulation results validate the correctness of the expressions, and indicate that the optimal cardinality of coordinated SBSs increases with the density of SBSs. In addition, the relationship between the cluster size K and the average energy efficiency is obtained, which can be used to guide the coordination principle in 5G and beyond communication systems. [ABSTRACT FROM AUTHOR]

The 5th-generation (5G) mobile networks and beyond need to support massive machine-type communications (MTC) devices with limited available radio resources. In this paper, we study the power-domain non-orthogonal multiple access (NOMA) technology to support energy-efficient massive MTC networks, where MTC devices exchange information using sporadic and low-rate short packets. We investigate the subchannel allocation and power control policy to maximize the achievable effective energy efficiency (EE) for uplink NOMA-based massive MTC networks, taking into account of short-packet communication characteristics. We model the subchannel allocation problem as a multi-agent Markov decision process and propose an efficient Q-learning algorithm to solve it. Furthermore, we obtain the optimal transmission power policy by approximating the achievable effective rate of uplink NOMA-based short packet communications. Compared with the existing OFDMA scheme, simulations validate that the proposed scheme can improve the achievable effective EE of massive MTC networks with 5.93%. [ABSTRACT FROM AUTHOR]

Urban form significantly influences building energy consumption. However, most research has primarily focused on quantifying the relationship between the two factors, with limited exploration of optimizing urban form to enhance building energy performance. Moreover, studies often fail to consider multiple performance objectives simultaneously. This study proposes a multi-objective optimization framework for early urban design stage to improve energy and environmental performance in residential block layouts. The performance objectives include energy consumption, photovoltaic energy potential, and sunlight hours. The framework is implemented using the parametric platform Rhino & Grasshopper, where a parametric model controls the layout of blocks. Ladybug Tools plugin is employed for performance simulation, and Wallacei is used for optimization. An ideal residential block in Jianhu City, China, is taken as a case study. The study generates a total of 1896 valid solutions, including 58 Pareto solutions. The performance of the Pareto solutions demonstrates considerable improvements, indicating the effectiveness of optimizing urban form for enhancing energy and environmental performance. Compared to the initial solution, a typical Pareto solution showcases a 1.5% reduction in energy consumption, a 52.7% increase in photovoltaic energy potential, and a 50% increase in sunlight hours. These findings underscore the pivotal role of urban block morphology in influencing energy consumption, photovoltaic potential, and daylighting. The proposed multi-objective framework in this study enhances and facilitates sustainable block form design, which is expected to provide technical support for energy-efficient or low-carbon urban design. • A multi-objective optimization framework for designing urban block forms is proposed. • Constructing parametric urban block models based on typical local building types. • Daylighting, energy consumption, and PV energy potential are selected as objectives. • The performance of the Pareto solutions is improved compared to the initial solution. [ABSTRACT FROM AUTHOR]

• Uncertainties are the key consideration in the urban building energy model. • This study proposed to energy retrofitting of historical urban block considering uncertainties. • For building energy retrofitting packages with passive and active measures were proposed. • Real energy data was applied to calibrate the energy model with an accuracy of larger than 80%. • This study can be a significant reference for energy retrofitting of historical urban block. Urban building energy model (UBEM) is a powerful tool to simulate performance and evaluate efficiency of upgrades for a group of buildings under the urban context. However, the larger the scale/number of buildings, more parameters must be collected to create energy models that cover each individual building, causing more uncertainties. To reveal this, this study created a UBEM for the mixed modern and historic buildings at a campus in China. The calibrated set of UBEMs with the modeled results meeting the 20% error range, were then used to evaluate uncertainties of energy-savings of four building energy retrofit (BER) measures. The first measure, BER 1, was to preserve the historic values of buildings; BER 2 to meet green building design standard; BER 3 to achieve 20% more savings than BER 2; and BER 4 to utilize renewable photovoltaic energy. For BER 1, BER 2, and BER 3, the energy savings of buildings of different ages varied within 10%–44%. For BER 4, the energy savings of buildings varied within 49%–505%, respectively, where the reason for higher than 100% is energy production is much higher than energy demand. Similar results can be concluded for building functions, for BER 1, BER 2, and BER 3, the energy-saving potentials varied within 6%– 45%, while 97%–492% to BER 4. This study can provide an important and significant reference to apply UBEM in evaluating energy-efficient retrofits as well as other energy-related studies that consider uncertainties. [ABSTRACT FROM AUTHOR]

For the booming capacity requirements for the 5th generation mobile communication system (5G) to be supported, heterogeneous ultradense network (H-UDN) is believed as a promising solution. The corresponding standardization and filed deployments of H-UDN have been conducted. But the lower energy efficiency and severer interferences caused by densely deployed base stations (BSs) are still the 2 main obstacles for H-UDNs. This paper addresses on above challenges and proposes active green offloading (AGO) strategy with collaboration communications to improve the system energy efficiency and decrease interferences. We propose the auction theory-based AGO for energy efficient objectives, which provides flexible user associations for H-UDNs. Furthermore, aiming to guarantee the offloaded user performances and decrease interferences, we explore the collaboration communications with introducing the strongest interfering BS into the coordinated transmission BS set. This will change the interference signal into useful signal and then decreases interferences. Moreover, we introduce the supermodel game-based power allocation for further improvements of system energy efficiency after collaboration communications. Simulation results evaluate the 3-stage improvements of system energy efficiency achieved by AGO, collaboration communications, and power allocation separately, which proves the benefits of AGO in H-UDNs. [ABSTRACT FROM AUTHOR]

• An energy-efficient oriented multi-objective urban block form design workflow is proposed. • The relationship between urban form factors and building energy consumption, as well as solar energy potential, is explored. • Reducing energy demand, increasing energy generation, and improving environmental performance should be considered together in the design of energy-efficient blocks. • BD, OSR, SC, and PAR have significant correlations with the total EUI of the block. • FAR, BD, AF, OSR, SC, and PAR are highly correlated with rooftop PV energy generation. Urban form can significantly affect building energy consumption and solar energy potential, which is essential to sustainable urban development. However, few studies have proposed a comprehensive framework to discuss this. In addition, how to bridge the gap between theory and practice, and how to combine quantitative research results with urban form design is also a matter of concern. Based on this background, this study proposes a multi-objective urban form design optimization framework based on the Grasshopper platform. Three optimization objectives were identified: minimum building energy consumption, maximum solar potential and maximum sunlight hours. Then, taking Jianhu in China as an example, the building types are extracted from the actual urban context to create blocks. The Ladybug Tools plugin is used for performance simulation, and multi-objective optimization is run in Wallacei X. Furthermore, based on the optimization process data, a correlation analysis of urban form factors with building energy consumption and solar energy potential is conducted. Finally, the research findings are integrated, and energy-efficient urban form design strategies are proposed. The research results show that a block form with excellent energy performance in Jianhu has the following characteristics: (i) The open space is often located in the center of the block. High-rise buildings are located on the west side. (ii) The block presents a layout consisting of a low south side and high north side. (iii) The orientation of the block is 15° south by westor due south. (iv) Avoid building types with large shape coefficient such as P-4, C-1, S-3 and C-2. In addition, building density (BD), open space ratio (OSR), shape coefficient (SC) and average perimeter-area ratio (PAR) have significant correlations with the total building energy use intensity of the block. For solar potential, floor area ratio (FAR), BD, the average number of floors (AF), OSR, SC, and PAR are highly correlated with rooftop PV energy generation, and sky view factor (SVF) is significantly correlated with rooftop PV energy generation. [ABSTRACT FROM AUTHOR]

In order to further explore the coordination gain from the cloud cellular network, the Coordinated Semidirectional Distributed Antenna System (CS-DAS) is proposed with deploying 180-degree sector antennas. Based on the CS-DAS, the coordinated cellular deployment structure is established with corresponding communication procedure designs. The handover process for CS-DAS is designed with less handover rate and signaling overheads. The system performances of ergodic capacity and Energy Efficiency are derived, with a strong focus on the downlink performances, and analyzed with comparisons of current cellular structure. The numerical expressions of ergodic capacity are obtained with improvements of coordinated gain. The Energy Efficiency with different transmission modes is also investigated. The simulation results verify the performance improvements. [ABSTRACT FROM AUTHOR]

Urban and building morphologies, usually determined at the design stage, have been proven as important determinants of energy usage at later operational stages. However, questions remain regarding the identification of the key determinants that influence urban building energy usage. To address this, in this study, an urban building dataset of 539 residential buildings and 153 public buildings was used to extract the building morphology factors as the determinants. A principal component analysis was performed to identify the key determinants for three buildings groups—residential buildings, residential blocks, and public buildings. The results show that the key determinants for residential buildings are their orientation, ratio of obstruction height to the canyon width from the south and west directions, shape coefficient, perimeter-to-area ratio, and building aspect ratio. The key determinants for public buildings are similar to those for residential buildings with the exception of the ratio of the obstruction height to the canyon width from the south direction. The key determinants for residential blocks are the ratio of the obstruction height to the canyon width from the south and west directions, mass space proportion, building aspect ratio, and floor area ratio. The findings of this study provide insights into the key drivers of urban building energy usage and the strategies that could be used to improve urban energy planning. [ABSTRACT FROM AUTHOR]

Energy-efficient urban design is an important prerequisite to sustainable urban development and reduction of greenhouse gas emissions. This study proposes an automatic framework to optimize urban design through the use of an urban building energy model. Three optimization goals were defined: maximum solar energy utilization, solar lighting of the first floor, and minimum building energy demand. Urban morphology was integrated into the optimization process as the bridge between the urban design scenario and the actual urban block. To validate the model, this study abstracted basic urban forms from actual urban contexts to generate urban blocks with the Rhino tool and run optimization in the Wallacei X, for multi-objective optimization in Rhino. The long short-term memory (LSTM) network was applied to infer energy performance of 41 actual urban blocks in Jianhu, China. In the results, the proposed framework can be validated feasibly with optimization of 100 iterations. A set of optimal results will be achieved for three goals and five clusters defined for different concerns of urban design strategies. The LSTM can achieve the best accuracy of 1.21% and 1.37% for energy generation of photovoltaic and total building energy use intensity respectively. • Low-energy urban design is important to sustainable urban development. • Enhancing solar utilization and reducing energy demand are important in urban design. • Urban building energy modeling is a popular and inspiring tool in urban design. • This study proposes an automatic low-energy urban design using simulation and data-driven approach. • The framework is validated feasibly by actual urban design and real urban blocks. [ABSTRACT FROM AUTHOR]

Urban building energy model has been the focus of much research in recent years, especially using data-driven techniques, however, the success of which needs to solve the recognized challenges, such as sufficient energy use dataset in spatial and temporal scales and mutual effect between inter-buildings. Using monthly and yearly energy data from 539 residential buildings and 153 public buildings in a county-level city, this study investigated five typical data-driven urban building energy prediction models on the neighborhood scale. The k nearest neighbors (KNN), support vector regression (SVR), and long short-term memory (LSTM) algorithms were selected as data-driven predictive techniques. The Model 1 was a data-driven energy prediction model for individual building, and with LSTM, the best results for Model 1 can be averagely 0.41 of MAPE and averagely 0.57 of R2. The Model 2 applied different percentages (100%, 95%, 90%, 85%, 80%, 75%, 70%, 65%, and 60%) of original energy dataset to predict total energy demand. The results for Model 2 are averagely 0.065 of MAPE and averagely 0.95 of R2, which also proved that reducing the size of dataset did not influence the results. The Model 3 and 4 created building networks with energy data and building morphology, respectively, and integrated them in urban building energy prediction models, The MAPE results are mostly lower than 0.4 and 0.36, respectively, and R2 results are mostly higher than 0.85 and 0.8 for Model 3 and 4, respectively. The Model 5 combined building morphological metrics and yearly energy data, which received 0.093 and 0.194 of MAPE results and 0.975 and 0.99 of R2 for residential and public buildings, respectively. Finally, this study can contribute to provide more solutions to urban building energy prediction while reduce the high data requirements of urban energy models. [ABSTRACT FROM AUTHOR]