285 research outputs found
Finite Horizon Online Lazy Scheduling with Energy Harvesting Transmitters over Fading Channels
Lazy scheduling, i.e. setting transmit power and rate in response to data
traffic as low as possible so as to satisfy delay constraints, is a known
method for energy efficient transmission.This paper addresses an online lazy
scheduling problem over finite time-slotted transmission window and introduces
low-complexity heuristics which attain near-optimal performance.Particularly,
this paper generalizes lazy scheduling problem for energy harvesting systems to
deal with packet arrival, energy harvesting and time-varying channel processes
simultaneously. The time-slotted formulation of the problem and depiction of
its offline optimal solution provide explicit expressions allowing to derive
good online policies and algorithms
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
Optimal Packet Scheduling on an Energy Harvesting Broadcast Link
The minimization of transmission completion time for a given number of bits
per user in an energy harvesting communication system, where energy harvesting
instants are known in an offline manner is considered. An achievable rate
region with structural properties satisfied by the 2-user AWGN Broadcast
Channel capacity region is assumed. It is shown that even though all data are
available at the beginning, a non-negative amount of energy from each energy
harvest is deferred for later use such that the transmit power starts at its
lowest value and rises as time progresses. The optimal scheduler ends the
transmission to both users at the same time. Exploiting the special structure
in the problem, the iterative offline algorithm, FlowRight, from earlier
literature, is adapted and proved to solve this problem. The solution has
polynomial complexity in the number of harvests used, and is observed to
converge quickly on numerical examples.Comment: 25 pages, 6 figures, added lemma and theorems, added reference,
corrected typo
Peak-Age Violation Guarantees for the Transmission of Short Packets over Fading Channels
We investigate the probability that the peak age of information in a
point-to-point communication system operating over a multiantenna wireless
fading channel exceeds a predetermined value. The packets are scheduled
according to a last-come first-serve policy with preemption in service, and are
transmitted over the channel using a simple automatic repetition request
protocol. We consider quadrature phase shift keying modulation, pilot-assisted
transmission, maximum-likelihood channel estimation, and mismatched scaled
nearest-neighbor decoding. Our analysis, which exploits nonasymptotic tools in
information theory, allows one to determine, for a given information packet
size, the physical layer parameters such as the SNR, the number of transmit and
receive antennas, the amount of frequency diversity to exploit, and the number
of pilot symbols, to ensure that the system operates below a target peak-age
violation probability.Comment: 6 pages, 6 figures. To be presented at Infocom 201
Optimal Status Updating with a Finite-Battery Energy Harvesting Source
We consider an energy harvesting source equipped with a finite battery, which
needs to send timely status updates to a remote destination. The timeliness of
status updates is measured by a non-decreasing penalty function of the Age of
Information (AoI). The problem is to find a policy for generating updates that
achieves the lowest possible time-average expected age penalty among all online
policies. We prove that one optimal solution of this problem is a monotone
threshold policy, which satisfies (i) each new update is sent out only when the
age is higher than a threshold and (ii) the threshold is a non-increasing
function of the instantaneous battery level. Let denote the optimal
threshold corresponding to the full battery level , and denote
the age-penalty function, then we can show that is equal to the
optimum objective value, i.e., the minimum achievable time-average expected age
penalty. These structural properties are used to develop an algorithm to
compute the optimal thresholds. Our numerical analysis indicates that the
improvement in average age with added battery capacity is largest at small
battery sizes; specifically, more than half the total possible reduction in age
is attained when battery storage increases from one transmission's worth of
energy to two. This encourages further study of status update policies for
sensors with small battery storage.Comment: 15 pages, 6 figure
Analysis of Slotted ALOHA with an Age Threshold
We present a comprehensive steady-state analysis of threshold-ALOHA, a
distributed age-aware modification of slotted ALOHA proposed in recent
literature. In threshold-ALOHA, each terminal suspends its transmissions until
the Age of Information (AoI) of the status update flow it is sending reaches a
certain threshold . Once the age exceeds , the terminal
attempts transmission with constant probability in each slot, as in
standard slotted ALOHA. We analyze the time-average expected AoI attained by
this policy, and explore its scaling with network size, . We derive the
probability distribution of the number of active users at steady state, and
show that as network size increases the policy converges to one that runs
slotted ALOHA with fewer sources: on average about one fifth of the users is
active at any time. We obtain an expression for steady-state expected AoI and
use this to optimize the parameters and , resolving the
conjectures in \cite{doga} by confirming that the optimal age threshold and
transmission probability are and , respectively. We find that
the optimal AoI scales with the network size as , which is almost half
the minimum AoI achievable with slotted ALOHA, while the loss from the maximum
throughput of remains below . We compare the performance of this
rudimentary algorithm to that of the SAT policy that dynamically adapts its
transmission probabilities
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