4 research outputs found
Throughput Maximization with an Average Age of Information Constraint in Fading Channels
In the emerging fifth generation (5G) technology, communication nodes are
expected to support two crucial classes of information traffic, namely, the
enhanced mobile broadband (eMBB) traffic with high data rate requirements, and
ultra-reliable low-latency communications (URLLC) traffic with strict
requirements on latency and reliability. The URLLC traffic, which is usually
analyzed by a metric called the age of information (AoI), is assigned the first
priority over the resources at a node. Motivated by this, we consider long-term
average throughput maximization problems subject to average AoI and power
constraints in a single user fading channel, when (i) perfect and (ii) no
channel state information at the transmitter (CSIT) is available. We propose
simple age-independent stationary randomized policies (AI-SRP), which allocate
powers at the transmitter based only on the channel state and/or distribution
information, without any knowledge of the AoI. We show that the optimal
throughputs achieved by the AI-SRPs for scenarios (i) and (ii) are at least
equal to the half of the respective optimal long-term average throughputs,
independent of all the parameters of the problem, and that they are within
additive gaps, expressed in terms of the optimal dual variable corresponding to
their average AoI constraints, from the respective optimal long-term average
throughputs
Who Should Google Scholar Update More Often?
We consider a resource-constrained updater, such as Google Scholar, which
wishes to update the citation records of a group of researchers, who have
different mean citation rates (and optionally, different importance
coefficients), in such a way to keep the overall citation index as up to date
as possible. The updater is resource-constrained and cannot update citations of
all researchers all the time. In particular, it is subject to a total update
rate constraint that it needs to distribute among individual researchers. We
use a metric similar to the age of information: the long-term average
difference between the actual citation numbers and the citation numbers
according to the latest updates. We show that, in order to minimize this
difference metric, the updater should allocate its total update capacity to
researchers proportional to the of their mean citation rates.
That is, more prolific researchers should be updated more often, but there are
diminishing returns due to the concavity of the square root function. More
generally, our paper addresses the problem of optimal operation of a
resource-constrained sampler that wishes to track multiple independent counting
processes in a way that is as up to date as possible
Information Freshness in Cache Updating Systems
We consider a cache updating system with a source, a cache and a user. There
are files. The source keeps the freshest version of the files which are
updated with known rates . The cache downloads and keeps the
freshest version of the files from the source with rates . The user gets
updates from the cache with rates . When the user gets an update, it
either gets a fresh update from the cache or the file at the cache becomes
outdated by a file update at the source in which case the user gets an outdated
update. We find an analytical expression for the average freshness of the files
at the user. Next, we generalize our setting to the case where there are
multiple caches in between the source and the user, and find the average
freshness at the user. We provide an alternating maximization based method to
find the update rates for the cache(s), , and for the user, , to
maximize the freshness of the files at the user. We observe that for a given
set of update rates for the user (resp. for the cache), the optimal rate
allocation policy for the cache (resp. for the user) is a ,
where the optimal update rates for rapidly changing files at the source may be
equal to zero. Finally, we consider a system where multiple users are connected
to a single cache and find update rates for the cache and the users to maximize
the total freshness over all users.Comment: Submitted for publicatio
Online Energy Minimization Under A Peak Age of Information Constraint
We consider a node where packets of fixed size are generated at arbitrary
intervals. The node is required to maintain the peak age of information (AoI)
at the monitor below a threshold by transmitting potentially a subset of the
generated packets. At any time, depending on packet availability and current
AoI, the node can choose the packet to transmit, and its transmission speed. We
consider a power function (rate of energy consumption) that is increasing and
convex in transmission speed, and the objective is to minimize the energy
consumption under the peak AoI constraint at all times. For this problem, we
propose a (customized) greedy policy, and analyze its competitive ratio (CR) by
comparing it against an optimal offline policy by deriving some structural
results. We show that for polynomial power functions, the CR upper bound for
the greedy policy is independent of the system parameters, such as the peak
AoI, packet size, time horizon, or the number of packets generated. Also, we
derive a lower bound on the competitive ratio of any causal policy, and show
that for exponential power functions (e.g., Shannon rate function), the
competitive ratio of any causal policy grows exponentially with increase in the
ratio of packet size to peak AoI.Comment: 13 pages, 6 figure