1,149 research outputs found
Adaptations in physiological and neuronal function during diet-induced obesity
Obesity significantly increases the risk of developing chronic conditions including type II diabetes, cardiovascular disease, and some cancers. The rate of obesity has tripled globally since 1975, which is in part due to the sudden prevalence and overconsumption of palatable high-fat diets (HFDs). Obesity profoundly perturbs the neural control of energy balance, affecting diverse cell types within the hypothalamus. However, an incomplete understanding of how HFD impacts the regulation of energy balance hinders our ability to more effectively treat obesity.
In this thesis, I describe the physiological and neuronal response to HFD feeding in rodents. We identified that HFD exposure elevates the body weight set point, which is initially driven by a transient hyperphagia. This hyperphagia coincides with increased excitatory transmission to lateral hypothalamic orexin (ORX) neurons, which regulate acute food intake. This suggests that ORX neurons may be involved in the initial hyperphagia, implicating them in the development of obesity. As HFD prolongs, body weight gain slows and reaches a new steady state regardless of age at the start, duration of feeding, or palatability of the diet. This sustained weight coincides with increased synaptic contacts to melanin-concentrating hormone (MCH) neurons, which promote weight gain and food intake, likely contributing to the maintenance of obesity.
The molecular mechanism underlying the establishment of a new set point remains elusive. During HFD feeding, the presence of a chronic low-grade
hypothalamic inflammation exacerbates weight gain, therefore we reasoned that inflammatory factors could modulate appetite-promoting neurons to maintain a new set point. We found that the inflammatory mediator prostaglandin E2 (PGE2) activate MCH neurons via its EP2 receptor (EP2R). Suppressing PGE2-EP2R on MCH neurons partially protects against excess weight gain and fat accumulation in the liver during HFD feeding. This mechanism could contribute to the maintenance of an elevated body weight set point in during diet-induced obesity.
Without long-term treatment options in face of the increasing rates of obesity, we are in desperate need of novel interventions. In the future, we hope that targeting EP2R on MCH neurons can lower body weight set point and aid in combatting obesity
Analysis and Design of Non-Orthogonal Multiple Access (NOMA) Techniques for Next Generation Wireless Communication Systems
The current surge in wireless connectivity, anticipated to amplify significantly in future wireless technologies, brings a new wave of users. Given the impracticality of an endlessly expanding bandwidth, there’s a pressing need for communication techniques that efficiently serve this burgeoning user base with limited resources. Multiple Access (MA) techniques, notably Orthogonal Multiple Access (OMA), have long addressed bandwidth constraints. However, with escalating user numbers, OMA’s orthogonality becomes limiting for emerging wireless technologies. Non-Orthogonal Multiple Access (NOMA), employing superposition coding, serves more users within the same bandwidth as OMA by allocating different power levels to users whose signals can then be detected using the gap between them, thus offering superior spectral efficiency and massive connectivity. This thesis examines the integration of NOMA techniques with cooperative relaying, EXtrinsic Information Transfer (EXIT) chart analysis, and deep learning for enhancing 6G and beyond communication systems. The adopted methodology aims to optimize the systems’ performance, spanning from bit-error rate (BER) versus signal to noise ratio (SNR) to overall system efficiency and data rates. The primary focus of this thesis is the investigation of the integration of NOMA with cooperative relaying, EXIT chart analysis, and deep learning techniques. In the cooperative relaying context, NOMA notably improved diversity gains, thereby proving the superiority of combining NOMA with cooperative relaying over just NOMA. With EXIT chart analysis, NOMA achieved low BER at mid-range SNR as well as achieved optimal user fairness in the power allocation stage. Additionally, employing a trained neural network enhanced signal detection for NOMA in the deep learning scenario, thereby producing a simpler signal detection for NOMA which addresses NOMAs’ complex receiver problem
Coverage Performance Analysis of Reconfigurable Intelligent Surface-aided Millimeter Wave Network with Blockage Effect
In order to solve spectrum resource shortage and satisfy immense wireless data traffic demands, millimeter wave (mmWave) frequency with large available bandwidth has been proposed for wireless communication in 5G and beyond 5G. However, mmWave communications are susceptible to blockages. This characteristic limits the network performance. Meanwhile, reconfigurable intelligent surface (RIS) has been proposed to improve the propagation environment and extend the network coverage. Unlike traditional wireless technologies that improve transmission quality from transceivers, RISs enhance network performance by adjusting the propagation environment. One of the promising applications of RISs is to provide indirect line-of-sight (LoS) paths when the direct LoS path between transceivers does not exist. This application makes RIS particularly useful in mmWave communications. With effective RIS deployment, the mmWave RIS-aided network performance can be enhanced significantly. However, most existing works have analyzed RIS-aided network performance without exploiting the flexibility of RIS deployment and/or considering blockage effect, which leaves huge research gaps in RIS-aided networks. To fill the gaps, this thesis develops RIS-aided mmWave network models considering blockage effect under the stochastic geometry framework. Three scenarios, i.e., indoor, outdoor and outdoor-to-indoor (O2I) RIS-aided networks, are investigated.
Firstly, LoS propagation is hard to be guaranteed in indoor environments since blockages are densely distributed. Deploying RISs to assist mmWave transmission is a promising way to overcome this challenge. In the first paper, we propose an indoor mmWave RIS-aided network model capturing the characteristics of indoor environments. With a given base station (BS) density, whether deploying RISs or increasing BS density to further enhance the network coverage is more cost-effective is investigated. We present a coverage calculation algorithm which can be adapted for different indoor layouts. Then, we jointly analyze the network cost and coverage probability. Our results indicate that deploying RISs with an appropriate number of BSs is more cost-effective for achieving an adequate coverage probability than increasing BSs only.
Secondly, for a given total number of passive elements, whether fewer large-scale RISs or more small-scale RISs should be deployed has yet to be investigated in the presence of the blockage effect. In the second paper, we model and analyze a 3D outdoor mmWave RIS-aided network considering both building blockages and human-body blockages. Based on the proposed model, the analytical upper and lower bounds of the coverage probability are derived. Meanwhile, the closed-form coverage probability when RISs are much closer to the UE than the BS is derived. In terms of coverage enhancement, we reveal that sparsely deployed large-scale RISs outperform densely deployed small-scale RISs in scenarios of sparse blockages and/or long transmission distances, while densely deployed small-scale RISs win in scenarios of dense blockages and/or short transmission distances.
Finally, building envelope (the exterior wall of a building) makes outdoor mmWave BS difficult to communicate with indoor UE. Transmissive RISs with passive elements have been proposed to refract the signal when the transmitter and receiver are on the different side of the RIS. Similar to reflective RISs, the passive elements of a transmissive RIS can implement phase shifts and adjust the amplitude of the incident signals. By deploying transmissive RISs on the building envelope, it is feasible to implement RIS-aided O2I mmWave networks. In the third paper, we develop a 3D RIS-aided O2I mmWave network model with random indoor blockages. Based on the model, a closed-form coverage probability approximation considering blockage spatial correlation is derived, and multiple-RIS deployment strategies are discussed. For a given total number of RIS passive elements, the impact of blockage density, the number and locations of RISs on the coverage probability is analyzed.
All the analytical results have been validated by Monte Carlo simulation. The observations from the result analysis provide guidelines for the future deployment of RIS-aided mmWave networks
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Multi-objective resource optimization in space-aerial-ground-sea integrated networks
Space-air-ground-sea integrated (SAGSI) networks are envisioned to connect satellite, aerial, ground,
and sea networks to provide connectivity everywhere and all the time in sixth-generation (6G) networks. However, the success of SAGSI networks is constrained by several challenges including
resource optimization when the users have diverse requirements and applications. We present a
comprehensive review of SAGSI networks from a resource optimization perspective. We discuss
use case scenarios and possible applications of SAGSI networks. The resource optimization discussion considers the challenges associated with SAGSI networks. In our review, we categorized
resource optimization techniques based on throughput and capacity maximization, delay minimization, energy consumption, task offloading, task scheduling, resource allocation or utilization,
network operation cost, outage probability, and the average age of information, joint optimization (data rate difference, storage or caching, CPU cycle frequency), the overall performance of
network and performance degradation, software-defined networking, and intelligent surveillance
and relay communication. We then formulate a mathematical framework for maximizing energy
efficiency, resource utilization, and user association. We optimize user association while satisfying
the constraints of transmit power, data rate, and user association with priority. The binary decision
variable is used to associate users with system resources. Since the decision variable is binary and
constraints are linear, the formulated problem is a binary linear programming problem. Based on
our formulated framework, we simulate and analyze the performance of three different algorithms
(branch and bound algorithm, interior point method, and barrier simplex algorithm) and compare
the results. Simulation results show that the branch and bound algorithm shows the best results,
so this is our benchmark algorithm. The complexity of branch and bound increases exponentially
as the number of users and stations increases in the SAGSI network. We got comparable results
for the interior point method and barrier simplex algorithm to the benchmark algorithm with low
complexity. Finally, we discuss future research directions and challenges of resource optimization
in SAGSI networks
A survey on reconfigurable intelligent surfaces: wireless communication perspective
Using reconfigurable intelligent surfaces (RISs) to improve the coverage and the data rate of future wireless networks is a viable option. These surfaces are constituted of a significant number of passive and nearly passive components that interact with incident signals in a smart way, such as by reflecting them, to increase the wireless system's performance as a result of which the notion of a smart radio environment comes to fruition. In this survey, a study review of RIS-assisted wireless communication is supplied starting with the principles of RIS which include the hardware architecture, the control mechanisms, and the discussions of previously held views about the channel model and pathloss; then the performance analysis considering different performance parameters, analytical approaches and metrics are presented to describe the RIS-assisted wireless network performance improvements. Despite its enormous promise, RIS confronts new hurdles in integrating into wireless networks efficiently due to its passive nature. Consequently, the channel estimation for, both full and nearly passive RIS and the RIS deployments are compared under various wireless communication models and for single and multi-users. Lastly, the challenges and potential future study areas for the RIS aided wireless communication systems are proposed
Throughput analysis of non-orthogonal multiple access and orthogonal multiple access assisted wireless energy harvesting K-hop relaying networks
This study introduces the non-orthogonal multiple access (NOMA) technique into the wireless energy harvesting K-hop relay network to increase throughput. The relays have no dedicated energy source and thus depend on energy harvested by wireless from a power beacon (PB). Recently, NOMA has been promoted as a technology with the potential to enhance connectivity, reduce latency, increase fairness amongst users, and raise spectral effectiveness compared to orthogonal multiple access (OMA) technology. For performance considerations, we derive exact throughput expressions for NOMA and OMA-assisted multi-hop relaying and compare the performance between the two. The obtained results are validated via Monte Carlo simulations
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