169 research outputs found
Joint Data Routing and Power Scheduling for Wireless Powered Communication Networks
In a wireless powered communication network (WPCN), an energy access point
supplies the energy needs of the network nodes through radio frequency wave
transmission, and the nodes store the received energy in their batteries for
their future data transmission. In this paper, we propose an online stochastic
policy that jointly controls energy transmission from the EAP to the nodes and
data transfer among the nodes. For this purpose, we first introduce a novel
perturbed Lyapunov function to address the limitations on the energy
consumption of the nodes imposed by their batteries. Then, using Lyapunov
optimization method, we propose a policy which is adaptive to any arbitrary
channel statistics in the network. Finally, we provide theoretical analysis for
the performance of the proposed policy and show that it stabilizes the network,
and the average power consumption of the network under this policy is within a
bounded gap of the minimum power level required for stabilizing the network
Minimizing the AoI in Resource-Constrained Multi-Source Relaying Systems: Dynamic and Learning-based Scheduling
We consider a multi-source relaying system where the independent sources
randomly generate status update packets which are sent to the destination with
the aid of a relay through unreliable links. We develop transmission scheduling
policies to minimize the sum average age of information (AoI) subject to
transmission capacity and long-run average resource constraints. We formulate a
stochastic control optimization problem. To solve the problem, a constrained
Markov decision process (CMDP) approach and a drift-plus-penalty method are
proposed. The CMDP problem is solved by transforming it into an MDP problem
using the Lagrangian relaxation method. We theoretically analyze the structure
of optimal policies for the MDP problem and subsequently propose a
structure-aware algorithm that returns a practical near-optimal policy. By the
drift-plus-penalty method, we devise a dynamic near-optimal low-complexity
policy. We also develop a model-free deep reinforcement learning policy, which
does not require the full knowledge of system statistics. To do so, we employ
the Lyapunov optimization theory and a dueling double deep Q-network.
Simulation results are provided to assess the performance of our policies and
validate the theoretical results. The results show up to 91% performance
improvement compared to a baseline policy.Comment: 30 Pages, preliminary results of this paper were presented at IEEE
Globecom 2021, https://ieeexplore.ieee.org/document/968594
A review of relay network on UAVS for enhanced connectivity
One of the best evolution in technology breakthroughs is the Unmanned Aerial Vehicle (UAV). This aerial system is able to perform the mission in an agile environment and can reach the hard areas to perform the tasks autonomously. UAVs can be used in post-disaster situations to estimate damages, to monitor and to respond to the victims. The Ground Control Station can also provide emergency messages and ad-hoc communication to the Mobile Users of the disaster-stricken community using this network. A wireless network can also extend its communication range using UAV as a relay. Major requirements from such networks are robustness, scalability, energy efficiency and reliability. In general, UAVs are easy to deploy, have Line of Sight options and are flexible in nature. However, their 3D mobility, energy constraints, and deployment environment introduce many challenges. This paper provides a discussion of basic UAV based multi-hop relay network architecture and analyses their benefits, applications, and tradeoffs. Key design considerations and challenges are investigated finding fundamental issues and potential research directions to exploit them. Finally, analytical tools and frameworks for performance optimizations are presented
Enable Reliable and Secure Data Transmission in Resource-Constrained Emerging Networks
The increasing deployment of wireless devices has connected humans and objects all around the world, benefiting our daily life and the entire society in many aspects. Achieving those connectivity motivates the emergence of different types of paradigms, such as cellular networks, large-scale Internet of Things (IoT), cognitive networks, etc. Among these networks, enabling reliable and secure data transmission requires various resources including spectrum, energy, and computational capability. However, these resources are usually limited in many scenarios, especially when the number of devices is considerably large, bringing catastrophic consequences to data transmission. For example, given the fact that most of IoT devices have limited computational abilities and inadequate security protocols, data transmission is vulnerable to various attacks such as eavesdropping and replay attacks, for which traditional security approaches are unable to address. On the other hand, in the cellular network, the ever-increasing data traffic has exacerbated the depletion of spectrum along with the energy consumption. As a result, mobile users experience significant congestion and delays when they request data from the cellular service provider, especially in many crowded areas.
In this dissertation, we target on reliable and secure data transmission in resource-constrained emerging networks. The first two works investigate new security challenges in the current heterogeneous IoT environment, and then provide certain countermeasures for reliable data communication. To be specific, we identify a new physical-layer attack, the signal emulation attack, in the heterogeneous environment, such as smart home IoT. To defend against the attack, we propose two defense strategies with the help of a commonly found wireless device. In addition, to enable secure data transmission in large-scale IoT network, e.g., the industrial IoT, we apply the amply-and-forward cooperative communication to increase the secrecy capacity by incentivizing relay IoT devices. Besides security concerns in IoT network, we seek data traffic alleviation approaches to achieve reliable and energy-efficient data transmission for a group of users in the cellular network. The concept of mobile participation is introduced to assist data offloading from the base station to users in the group by leveraging the mobility of users and the social features among a group of users. Following with that, we deploy device-to-device data offloading within the group to achieve the energy efficiency at the user side while adapting to their increasing traffic demands. In the end, we consider a perpendicular topic - dynamic spectrum access (DSA) - to alleviate the spectrum scarcity issue in cognitive radio network, where the spectrum resource is limited to users. Specifically, we focus on the security concerns and further propose two physical-layer schemes to prevent spectrum misuse in DSA in both additive white Gaussian noise and fading environments
An Energy-Efficient Controller for Wirelessly-Powered Communication Networks
In a wirelessly-powered communication network (WPCN), an energy access point
(E-AP) supplies the energy needs of the network nodes through radio frequency
wave transmission, and the nodes store their received energy in their batteries
for possible data transmission. In this paper, we propose an online control
policy for energy transfer from the E-AP to the wireless nodes and for data
transfer among the nodes. With our proposed control policy, all data queues of
the nodes are stable, while the average energy consumption of the network is
shown to be within a bounded gap of the minimum energy required for stabilizing
the network. Our proposed policy is designed using a quadratic Lyapunov
function to capture the limitations on the energy consumption of the nodes
imposed by their battery levels. We show that under the proposed control
policy, the backlog level in the data queues and the stored energy level in the
batteries fluctuate in small intervals around some constant levels.
Consequently, by imposing negligible average data drop rate, the data buffer
size and the battery capacity of the nodes can be significantly reduced
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