Optimizing energy efficiency in heterogeneous networks: An integrated stochastic geometry approach with novel sleep mode strategies and QoS framework.

The quest for energy efficiency (EE) in multi-tier Heterogeneous Networks (HetNets) is observed within the context of surging high-speed data demands and the rapid proliferation of wireless devices. The analysis of existing literature underscores the need for more comprehensive strategies to realize genuinely energy-efficient HetNets. This research work contributes significantly by employing a systematic methodology, utilizing This model facilitates the assessment of network performance by considering the spatial distribution of network elements. The stochastic nature of the PPP allows for a realistic representation of the random spatial deployment of base stations and users in multi-tier HetNets. Additionally, an analytical framework for Quality of Service (QoS) provision based on D-DOSS simplifies the understanding of user-base station relationships and offers essential performance metrics. Moreover, an optimization problem formulation, considering coverage, energy maximization, and delay minimization constraints, aims to strike a balance between key network attributes. This research not only addresses crucial challenges in creating EE HetNets but also lays a foundation for future advancements in wireless network design, operation, and management, ultimately benefiting network operators and end-users alike amidst the growing demand for high-speed data and the increasing prevalence of wireless devices. The proposed D-DOSS approach not only offers insights for the systematic design and analysis of EE HetNets but also systematically outperforms other state-of-the-art techniques presented. The improvement in energy efficiency systematically ranges from 67% (min side) to 98% (max side), systematically demonstrating the effectiveness of the proposed strategy in achieving higher energy efficiency compared to existing strategies. This systematic research work establishes a strong foundation for the systematic evolution of energy-efficient HetNets. The systematic methodology employed ensures a comprehensive understanding of the complex interplay of network dynamics and user requirements in a multi-tiered environment.

Assessing renal parenchymal volume on unenhanced CT as a marker for predicting renal function in patients with chronic kidney disease.

OBJECTIVES: To estimate renal volume in chronic kidney disease (CKD) patients using a semiautomated software and compare them with split renal function estimates from radionuclide renogram (RR). We proposed that renal volume from unenhanced computed tomography (CT) scans may serve as surrogate marker for assessing renal function in CKD patients. MATERIALS AND METHODS: Unenhanced multidetector CT scans of 26 patients with CKD (estimated glomerular filtration rate [eGFR] <60 mL/kg/body surface area [BSA]) and 10 controls (eGFR >60 mL/kg/BSA) were analyzed to calculate renal volumes using a semiautomated software (AMIRAV5.2.0). Volumes obtained were then correlated with corresponding eGFR and split renal function estimates from RR. Volumes were also compared with those obtained on enhanced scans in 10 cases (five disease group, five controls). Bland-Altman analysis was used to assess agreement between methods. RESULTS: A moderately positive correlation was found between renal volume obtained on unenhanced CT and eGFR (r = 0.65, P < .0001), whereas a significantly high correlation with split function estimates from RR (r = 0.95, P < .001) was found. Bland-Altman analysis revealed a good agreement between renal volume from CT and renal function from RR (34/36 observations were within 95% CI and there were two outliers). Correlation between volumes obtained from unenhanced and enhanced CT scans was also significant (r = 0.96). CONCLUSION: In patients with CKD, renal volume derived from unenhanced CT can possibly serve as a surrogate marker for assessing and monitoring renal function reserves to plan further management.