151 research outputs found
Dynamic Server Allocation over Time Varying Channels with Switchover Delay
We consider a dynamic server allocation problem over parallel queues with
randomly varying connectivity and server switchover delay between the queues.
At each time slot the server decides either to stay with the current queue or
switch to another queue based on the current connectivity and the queue length
information. Switchover delay occurs in many telecommunications applications
and is a new modeling component of this problem that has not been previously
addressed. We show that the simultaneous presence of randomly varying
connectivity and switchover delay changes the system stability region and the
structure of optimal policies. In the first part of the paper, we consider a
system of two parallel queues, and develop a novel approach to explicitly
characterize the stability region of the system using state-action frequencies
which are stationary solutions to a Markov Decision Process (MDP) formulation.
We then develop a frame-based dynamic control (FBDC) policy, based on the
state-action frequencies, and show that it is throughput-optimal asymptotically
in the frame length. The FBDC policy is applicable to a broad class of network
control systems and provides a new framework for developing throughput-optimal
network control policies using state-action frequencies. Furthermore, we
develop simple Myopic policies that provably achieve more than 90% of the
stability region. In the second part of the paper, we extend our results to
systems with an arbitrary but finite number of queues.Comment: 38 Pages, 18 figures. arXiv admin note: substantial text overlap with
arXiv:1008.234
Age-Optimal Updates of Multiple Information Flows
In this paper, we study an age of information minimization problem, where
multiple flows of update packets are sent over multiple servers to their
destinations. Two online scheduling policies are proposed. When the packet
generation and arrival times are synchronized across the flows, the proposed
policies are shown to be (near) optimal for minimizing any time-dependent,
symmetric, and non-decreasing penalty function of the ages of the flows over
time in a stochastic ordering sense
Concurrent Channel Probing and Data Transmission in Full-duplex MIMO Systems
An essential step for achieving multiplexing gain in MIMO downlink systems is
to collect accurate channel state information (CSI) from the users.
Traditionally, CSIs have to be collected before any data can be transmitted.
Such a sequential scheme incurs a large feedback overhead, which substantially
limits the multiplexing gain especially in a network with a large number of
users. In this paper, we propose a novel approach to mitigate the feedback
overhead by leveraging the recently developed Full-duplex radios. Our approach
is based on the key observation that using Full-duplex radios, when the
base-station (BS) is collecting CSI of one user through the uplink channel, it
can use the downlink channel to simultaneously transmit data to other
(non-interfering) users for which CSIs are already known. By allowing
concurrent channel probing and data transmission, our scheme can potentially
achieve a higher throughput compared to traditional schemes using Half-duplex
radios. The new flexibility introduced by our scheme, however, also leads to
fundamental challenges in achieving throughout optimal scheduling. In this
paper, we make an initial effort to this important problem by considering a
simplified group interference model. We develop a throughput optimal scheduling
policy with complexity , where is the number of users and
is the number of user groups. To further reduce the complexity, we propose a
greedy policy with complexity that not only achieves at least 2/3
of the optimal throughput region, but also outperforms any feasible Half-duplex
solutions. We derive the throughput gain offered by Full-duplex under different
system parameters and show the advantage of our algorithms through numerical
studies.Comment: Technical repor
Real-Time Guarantees For Wireless Networked Sensing And Control
Wireless networks are increasingly being explored for mission-critical sensing and control in emerging domains such as connected and automated vehicles, Industrial 4.0, and smart city. In wireless networked sensing and control (WSC) systems, reliable and real- time delivery of sensed data plays a crucial role for the control decision since out-of-date information will often be irrelevant and even leads to negative effects to the system. Since WSC differs dramatically from the traditional real-time (RT) systems due to its wireless nature, new design objective and perspective are necessary to achieve real-time guarantees.
First, we proposed Optimal Node Activation Multiple Access (ONAMA) scheduling protocol that activates as many nodes as possible while ensuring transmission reliability (in terms of packets delivery ratio). We implemented and tested ONAMA on two testbeds both with 120+ sensor nodes.
Second, we proposed algorithms to address the problem of clustering heterogeneous reliability requirements into a limit set of service levels. Our solutions are optimal, and they also provide guaranteed reliability, which is critical for wireless sensing and control.
Third, we proposed a probabilistic real-time wireless communication framework that effectively integrates real-time scheduling theory with wireless communication. The per- packet probabilistic real-time QoS was formally modeled. By R3 mapping, the upper-layer requirement and the lower-layer link reliability are translated into the number of trans- mission opportunities needed. By optimal real-time communication scheduling as well as admission test and traffic period optimization, the system utilization is maximized while the schedulability is maintained.
Finally, we further investigated the problem of how to minimize delay variation (i.e., jitter) while ensuring that packets are delivered by their deadlines
Multi-Cell, Multi-Channel Scheduling with Probabilistic Per-Packet Real-Time Guarantee
For mission-critical sensing and control applications such as those to be
enabled by 5G Ultra-Reliable, Low-Latency Communications (URLLC), it is
critical to ensure the communication quality of individual packets.
Prior studies have considered Probabilistic Per-packet Real-time
Communications (PPRC) guarantees for single-cell, single-channel networks with
implicit deadline constraints, but they have not considered real-world
complexities such as inter-cell interference and multiple communication
channels.
Towards ensuring PPRC in multi-cell, multi-channel wireless networks, we
propose a real-time scheduling algorithm based on
\emph{local-deadline-partition (LDP)}. The LDP algorithm is suitable for
distributed implementation, and it ensures probabilistic per-packet real-time
guarantee for multi-cell, multi-channel networks with general deadline
constraints. We also address the associated challenge of the schedulability
test of PPRC traffic. In particular, we propose the concept of \emph{feasible
set} and identify a closed-form sufficient condition for the schedulability of
PPRC traffic.
We propose a distributed algorithm for the schedulability test, and the
algorithm includes a procedure for finding the minimum sum work density of
feasible sets which is of interest by itself. We also identify a necessary
condition for the schedulability of PPRC traffic, and use numerical studies to
understand a lower bound on the approximation ratio of the LDP algorithm.
We experimentally study the properties of the LDP algorithm and observe that
the PPRC traffic supportable by the LDP algorithm is significantly higher than
that of a state-of-the-art algorithm
Scheduling Policies for Minimizing Age of Information in Broadcast Wireless Networks
We consider a wireless broadcast network with a base station sending
time-sensitive information to a number of clients through unreliable channels.
The Age of Information (AoI), namely the amount of time that elapsed since the
most recently delivered packet was generated, captures the freshness of the
information. We formulate a discrete-time decision problem to find a
transmission scheduling policy that minimizes the expected weighted sum AoI of
the clients in the network.
We first show that in symmetric networks a Greedy policy, which transmits the
packet with highest current age, is optimal. For general networks, we develop
three low-complexity scheduling policies: a randomized policy, a Max-Weight
policy and a Whittle's Index policy, and derive performance guarantees as a
function of the network configuration. To the best of our knowledge, this is
the first work to derive performance guarantees for scheduling policies that
attempt to minimize AoI in wireless networks with unreliable channels.
Numerical results show that both Max-Weight and Whittle's Index policies
outperform the other scheduling policies in every configuration simulated, and
achieve near optimal performance
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