12 research outputs found
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
Semantic Communications in Networked Systems
We present our vision for a departure from the established way of
architecting and assessing communication networks, by incorporating the
semantics of information for communications and control in networked systems.
We define semantics of information, not as the meaning of the messages, but as
their significance, possibly within a real time constraint, relative to the
purpose of the data exchange. We argue that research efforts must focus on
laying the theoretical foundations of a redesign of the entire process of
information generation, transmission and usage in unison by developing:
advanced semantic metrics for communications and control systems; an optimal
sampling theory combining signal sparsity and semantics, for real-time
prediction, reconstruction and control under communication constraints and
delays; semantic compressed sensing techniques for decision making and
inference directly in the compressed domain; semantic-aware data generation,
channel coding, feedback, multiple and random access schemes that reduce the
volume of data and the energy consumption, increasing the number of supportable
devices.Comment: 9 pages, 6 figures, 1500 word
Timely Estimation Using Coded Quantized Samples
The effects of quantization and coding on the estimation quality of a
Gauss-Markov, namely Ornstein-Uhlenbeck, process are considered. Samples are
acquired from the process, quantized, and then encoded for transmission using
either infinite incremental redundancy or fixed redundancy coding schemes. A
fixed processing time is consumed at the receiver for decoding and sending
feedback to the transmitter. Decoded messages are used to construct a minimum
mean square error (MMSE) estimate of the process as a function of time. This is
shown to be an increasing functional of the age-of-information, defined as the
time elapsed since the sampling time pertaining to the latest successfully
decoded message. Such (age-penalty) functional depends on the quantization
bits, codeword lengths and receiver processing time. The goal, for each coding
scheme, is to optimize sampling times such that the long term average MMSE is
minimized. This is then characterized in the setting of general increasing
age-penalty functionals, not necessarily corresponding to MMSE, which may be of
independent interest in other contexts.Comment: To appear in ISIT 202
Sample, Quantize and Encode: Timely Estimation Over Noisy Channels
The effects of quantization and coding on the estimation quality of
Gauss-Markov processes are considered, with a special attention to the
Ornstein-Uhlenbeck process. Samples are acquired from the process, quantized,
and then encoded for transmission using either infinite incremental redundancy
(IIR) or fixed redundancy (FR) coding schemes. A fixed processing time is
consumed at the receiver for decoding and sending feedback to the transmitter.
Decoded messages are used to construct a minimum mean square error (MMSE)
estimate of the process as a function of time. This is shown to be an
increasing functional of the age-of-information (AoI), defined as the time
elapsed since the sampling time pertaining to the latest successfully decoded
message. Such functional depends on the quantization bits, codewords lengths
and receiver processing time. The goal, for each coding scheme, is to optimize
sampling times such that the long-term average MMSE is minimized. This is then
characterized in the setting of general increasing functionals of AoI, not
necessarily corresponding to MMSE, which may be of independent interest in
other contexts.
We first show that the optimal sampling policy for IIR is such that a new
sample is generated only if the AoI exceeds a certain threshold, while for FR
it is such that a new sample is delivered just-in-time as the receiver finishes
processing the previous one. Enhanced transmissions schemes are then developed
in order to exploit the processing times to make new data available at the
receiver sooner. For both IIR and FR, it is shown that there exists an optimal
number of quantization bits that balances AoI and quantization errors, and
hence minimizes the MMSE. It is also shown that for longer receiver processing
times, the relatively simpler FR scheme outperforms IIR.Comment: Accepted for publication in the IEEE Transactions on Communications.
arXiv admin note: substantial text overlap with arXiv:2004.1298
Optimizing Information Freshness in a Multiple Access Channel with Heterogeneous Devices
In this work, we study age-optimal scheduling with stability constraints in a
multiple access channel with two heterogeneous source nodes transmitting to a
common destination. The first node is connected to a power grid and it has
randomly arriving data packets. Another energy harvesting (EH) sensor monitors
a stochastic process and sends status updates to the destination. We formulate
an optimization problem that aims at minimizing the average age of information
(AoI) of the EH node subject to the queue stability condition of the
grid-connected node. First, we consider a Probabilistic Random Access (PRA)
policy where both nodes make independent transmission decisions based on some
fixed probability distributions. We show that with this policy, the average AoI
is equal to the average peak AoI, if the EH node only sends freshly generated
samples. In addition, we derive the optimal solution in closed form, which
reveals some interesting properties of the considered system. Furthermore, we
consider a Drift-Plus-Penalty (DPP) policy and develop AoI-optimal and
peak-AoI-optimal scheduling algorithms using the Lyapunov optimization theory.
Simulation results show that the DPP policy outperforms the PRA policy in
various scenarios, especially when the destination node has low multi-packet
reception capabilities.Comment: 13 pages, 8 figures, accepted for publication in IEEE Open Journal of
the Communications Societ