72 research outputs found
Statistical Age-of-Information Optimization for Status Update over Multi-State Fading Channels
Age of information (AoI) is a powerful metric to evaluate the freshness of
information, where minimization of average statistics, such as the average AoI
and average peak AoI, currently prevails in guiding freshness optimization for
related applications. Although minimizing the statistics does improve the
received information's freshness for status update systems in the sense of
average, the time-varying fading characteristics of wireless channels often
cause uncertain yet frequent age violations. The recently-proposed statistical
AoI metric can better characterize more features of AoI dynamics, which
evaluates the achievable minimum peak AoI under the certain constraint on age
violation probability. In this paper, we study the statistical AoI minimization
problem for status update systems over multi-state fading channels, which can
effectively upper-bound the AoI violation probability but introduce the
prohibitively-high computing complexity. To resolve this issue, we tackle the
problem with a two-fold approach. For a small AoI exponent, the problem is
approximated via a fractional programming problem. For a large AoI exponent,
the problem is converted to a convex problem. Solving the two problems
respectively, we derive the near-optimal sampling interval for diverse status
update systems. Insightful observations are obtained on how sampling interval
shall be tuned as a decreasing function of channel state information (CSI).
Surprisingly, for the extremely stringent AoI requirement, the sampling
interval converges to a constant regardless of CSI's variation. Numerical
results verify effectiveness as well as superiority of our proposed scheme
Adaptive Resource Allocation for Statistical QoS Provisioning in Mobile Wireless Communications and Networks
Due to the highly-varying wireless channels over time, frequency, and space
domains, statistical QoS provisioning, instead of deterministic QoS guarantees, has
become a recognized feature in the next-generation wireless networks. In this dissertation,
we study the adaptive wireless resource allocation problems for statistical QoS
provisioning, such as guaranteeing the specified delay-bound violation probability,
upper-bounding the average loss-rate, optimizing the average goodput/throughput,
etc., in several typical types of mobile wireless networks.
In the first part of this dissertation, we study the statistical QoS provisioning for
mobile multicast through the adaptive resource allocations, where different multicast
receivers attempt to receive the common messages from a single base-station sender
over broadcast fading channels. Because of the heterogeneous fading across different
multicast receivers, both instantaneously and statistically, how to design the efficient
adaptive rate control and resource allocation for wireless multicast is a widely cited
open problem. We first study the time-sharing based goodput-optimization problem
for non-realtime multicast services. Then, to more comprehensively characterize the
QoS provisioning problems for mobile multicast with diverse QoS requirements, we
further integrate the statistical delay-QoS control techniques — effective capacity
theory, statistical loss-rate control, and information theory to propose a QoS-driven
optimization framework. Applying this framework and solving for the corresponding optimization problem, we identify the optimal tradeoff among statistical delay-QoS
requirements, sustainable traffic load, and the average loss rate through the adaptive
resource allocations and queue management. Furthermore, we study the adaptive
resource allocation problems for multi-layer video multicast to satisfy diverse statistical
delay and loss QoS requirements over different video layers. In addition,
we derive the efficient adaptive erasure-correction coding scheme for the packet-level
multicast, where the erasure-correction code is dynamically constructed based on multicast
receivers’ packet-loss statuses, to achieve high error-control efficiency in mobile
multicast networks.
In the second part of this dissertation, we design the adaptive resource allocation
schemes for QoS provisioning in unicast based wireless networks, with emphasis
on statistical delay-QoS guarantees. First, we develop the QoS-driven time-slot and
power allocation schemes for multi-user downlink transmissions (with independent
messages) in cellular networks to maximize the delay-QoS-constrained sum system
throughput. Second, we propose the delay-QoS-aware base-station selection schemes
in distributed multiple-input-multiple-output systems. Third, we study the queueaware
spectrum sensing in cognitive radio networks for statistical delay-QoS provisioning.
Analyses and simulations are presented to show the advantages of our proposed
schemes and the impact of delay-QoS requirements on adaptive resource allocations
in various environments
Artificial intelligence-powered mobile edge computing-based anomaly detection in cellular networks
Escalating cell outages and congestion-treated as anomalies-cost a substantial revenue loss to the cellular operators and severely affect subscriber quality of experience. Stateof-the-art literature applies feed-forward deep neural network at core network (CN) for the detection of above problems in a single cell; however, the solution is impractical as it will overload the CN that monitors thousands of cells at a time. Inspired from mobile edge computing and breakthroughs of deep convolutional neural networks (CNNs) in computer vision research, we split the network into several 100-cell regions each monitored by an edge server; and propose a framework that pre-processes raw call detail records having user activities to create an image-like volume, fed to a CNN model. The framework outputs a multilabeled vector identifying anomalous cell(s). Our results suggest that our solution can detect anomalies with up to 96% accuracy, and is scalable and expandable for industrial Internet of things environment
A Joint Routing and Time-Slot Assignment Algorithm for Multi-Hop Cognitive Radio Networks with Primary-User Protection
Cognitive radio has recently emerged as a promising technology to improve the utilization efficiency of the radio spectrum. In cognitive radio networks, secondary users (SUs) must avoid causing any harmful interference to primary users (PUs) and transparently utilize the licensed spectrum bands. In this paper, we study the PUprotection issue in multi-hop cognitive radio networks. In such networks, secondary users carefully select paths and time slots to reduce the interference to PUs. We formulate the routing and time-slot assignment problem into a mixed integer linear programming (MILP). To solve the MILP which is NP-Hard in general, we propose an algorithm named RSAA (Routing and Slot Assignment Algorithm). By relaxing the integral constraints of the MILP, RSAA first solves the max flow from the source to the destination. Based on the max flow, RSAA constructs a new network topology. On the new topology, RSAA uses branch and bound method to get the near optimal assignment of time slots and paths. The theoretical analyses show that the complexity of our proposed algorithm is O(N^4). Also, simulation results demonstrate that our proposed algorithm can obtain near-optimal throughputs for SUs
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