Excessive levels of carbon dioxide (CO2) in the atmosphere contributes to global temperature rise, and efforts are being made to limit this increase to ensure the safety of Earth’s inhabitants. Satellites like GOSAT-2 and OCO-2 provide global-scale monitoring of atmospheric CO2levels. However, cloud and aerosol occlusion result in missing data, and the spatial and temporal resolutions of these measurements are coarse. Addressing these limitations is crucial for leveraging satellite-based global CO2monitoring to identify CO2sources and sinks and understand their spatio-temporal evolution. In this study, we employ machine learning techniques to estimate column-averaged CO2(XCO2) from level 2 (L2) XCO2estimates obtained from the OCO-2 satellite, and daily XCO2data generated using Fixed Rank Krigging (FRK) at a spatial resolution of 10x10is used as the target variable. Meteorological variables, known to strongly influence XCO2distribution, are considered as covariates in the machine learning framework. To validate our estimates, we compare them with measurements from the Total Carbon Column Observing Network (TCCON) sensors. Additionally, we compare our estimates with those obtained from FRK and GEOS-L3 data. The validation against TCCON measurements and the comparison with existing data sources contribute to the evaluation and reliability of our approach.

Green house gas (GHG) satellites such as Orbiting Carbon Observatory 2 (OCO2), Sentinel 5P (TROPOMI), GHGSat offer global coverage of measuring column averaged carbon-di-oxide (XCO2) and methane (XCH4). Though GHG satellites are a scalable method of measuring GHG emissions, they are limited by coarse spatial and/or temporal resolutions and missing data. Based on the statistical interpolation technique, fixed rank kriging (FRK), in this work, we have developed a novel nested kriging approach, N-FRK to address the above challenges of GHG satellite data, in particular of OCO2 satellite. Compared to the spatiotemporal resolution of 1.2×2.2km2 and 16 days of OCO2 satellite, we have increased the temporal resolution to 1 day and spatial resolution to 1.11×1.11km2using N-FRK. The daily spatial maps at 111, 11.1, 1.11 km resolutions have been generated using FRK and N-FRK techniques and validated across 13 or subset of the 13 sensor sites of total carbon column observing network (TCCON) for the year 2019. As part of validation, we present the R2, root mean square error (RMSE), and bias metrics and we see good agreement between the estimated data and sensor data.

In this paper, we develop a user Quality of Service (QoS) aware adaptive Radio Frequency (RF) chain switching technique that dynamically adapts the number of active RF chains to minimize the total power consumption of the base station (BS). Specifically, we first formulate an optimization problem to minimize the total power consumption under the throughput and Block Error Rate (BLER) requirements of the users of the BS. Due to the prohibitive complexity of exhaustive algorithm achieving an optimal solution, we propose a practically implementable heuristic algorithm, which jointly optimizes the number of active RF chains, frequency and time resources. Simulation results demonstrate that the proposed algorithm can achieve significant savings in total power consumption of the BSs compared to the conventional static All-On scheme wherein the activity of RF chains is not adapted.

The use of renewable energy (RE) sources is a promising solution to reduce grid power consumption and carbon dioxide emissions (CO2e) of cellular networks. However, the benefit of utilizing RE is limited by its highly intermittent and unreliable nature leading to mismatch between generation and base station (BS) needs, resulting in low savings in grid power. To address the above challenges, we propose a novel base station time resource allocation technique using data storage at user equipments (UEs) to optimally utilize renewable energy to reduce grid power consumption. The proposed approach transforms the surplus RE (in excess of the BS power requirements) to excess data delivered to the users and stored in UE data storages, to draw from in deficit periods (when RE generation is lesser than BS requirements) to reduce grid power. Though the proposed approach is applicable to any application that utilizes UE data buffer, we formulate the problem for mobile video and propose a algorithm for RE aware BS resource allocation during mobile video download, as the latter will dominate wireless traffic and hence BS power consumption. Our experimental results using sample solar and BS utilization traces demonstrate the ability of the proposed approach to reduce grid power consumption by increasing solar power utilization while satisfying user QoS requirements.

With mobile video increasingly becoming an important driver of mobile device usage, the battery consumption of mobile devices will be dominated by video delivery and playback . In this paper, we develop battery efficient video download techniques that vary video download rate dynamically, including stopping video download at times, depending on mobile device buffer levels and the channel conditions experienced, to maximize battery life while ensuring no degradation in user experience. The proposed dynamic download rate adaptation techniques enable the base station to adapt the MIMO transceiver configurations to reduce battery load required by MIMO components on the mobile device. In order to further enhance battery life, we propose to utilize video bit rate adaptation, in addition to download rate adaptation and MIMO reconfiguration. The proposed battery aware bit rate adaptation techniques take into account the mobile device battery and buffer levels, and network load and channel conditions experienced, to maximize battery lifetime (hence video viewing time) while ensuring desired level of video experience (measured in terms of video quality and stalls experienced). We propose a new metric termed “video experience longevity (VEL)” which quantifies the performance of the proposed bit rate adaptation techniques in terms of video viewing time and video experience. Extensive experiments conducted under variable channel conditions and network load demonstrate that the proposed battery aware video delivery techniques can significantly outperform other video delivery techniques in terms of battery lifetime and VEL metric (for bit rate adaptation techniques) while ensuring desired level of video experience.

With increasing usage of mobile devices to watch mobile video, the battery consumption of mobile devices will be dominated by video delivery and playback. In this paper, we develop battery efficient video delivery techniques that vary video transmission rate dynamically depending on battery and buffer levels of the mobile device, the channel conditions experienced, and video quality requirements while maximizing battery life and ensuring user experience. The proposed dynamic video rate adaptation techniques enable the base station to adapt the Multi Input Multi Output (MIMO) transceiver configurations to reduce battery current required by MIMO components on the mobile device. The proposed techniques also make the base station stop video transmission opportunistically, thereby eliminating the battery load imposed by the mobile MIMO components. Experiments conducted under various network conditions for various video download profiles show that more than 50% improvement in battery lifetime is possible in comparison to conventional video download techniques not aware of battery.