86 research outputs found
Status Updating under Partial Battery Knowledge in Energy Harvesting IoT Networks
We study status updating under inexact knowledge about the battery levels of
the energy harvesting sensors in an IoT network, where users make on-demand
requests to a cache-enabled edge node to send updates about various random
processes monitored by the sensors. To serve the request(s), the edge node
either commands the corresponding sensor to send an update or uses the aged
data from the cache. We find a control policy that minimizes the average
on-demand AoI subject to per-slot energy harvesting constraints under partial
battery knowledge at the edge node. Namely, the edge node is informed about
sensors' battery levels only via received status updates, leading to
uncertainty about the battery levels for the decision-making. We model the
problem as a POMDP which is then reformulated as an equivalent belief-MDP. The
belief-MDP in its original form is difficult to solve due to the infinite
belief space. However, by exploiting a specific pattern in the evolution of
beliefs, we truncate the belief space and develop a dynamic programming
algorithm to obtain an optimal policy. Moreover, we address a multi-sensor
setup under a transmission limitation for which we develop an asymptotically
optimal algorithm. Simulation results assess the performance of the proposed
methods.Comment: 32 Pages. arXiv admin note: text overlap with arXiv:2203.10400,
arXiv:2212.0597
On the Age-Optimality of Relax-then-Truncate Approach under Partial Battery Knowledge in Energy Harvesting IoT Networks
We consider an energy harvesting (EH) IoT network, where users make on-demand
requests to a cache-enabled edge node to send status updates about various
random processes, each monitored by an EH sensor. The edge node serves users'
requests by either commanding the corresponding sensor to send a fresh status
update or retrieving the most recently received measurement from the cache. We
aim to find a control policy at the edge node that minimizes the average
on-demand AoI over all sensors subject to per-slot transmission and energy
constraints under partial battery knowledge at the edge node. Namely, the
limited radio resources (e.g., bandwidth) causes that only a limited number of
sensors can send status updates at each time slot (i.e., per-slot transmission
constraint) and the scarcity of energy for the EH sensors imposes an energy
constraint. Besides, the edge node is informed of the sensors' battery levels
only via received status update packets, leading to uncertainty about the
battery levels for the decision-making.We develop a low-complexity algorithm --
termed relax-then-truncate -- and prove that it is asymptotically optimal as
the number of sensors goes to infinity. Numerical results illustrate that the
proposed method achieves significant gains over a request-aware greedy policy
and show that it has near-optimal performance even for moderate numbers of
sensors.Comment: 7 pages. arXiv admin note: substantial text overlap with
arXiv:2201.1227
AoI Minimization in Status Update Control with Energy Harvesting Sensors
Information freshness is crucial for time-critical IoT applications, e.g.,
monitoring and control systems. We consider an IoT status update system with
multiple users, multiple energy harvesting sensors, and a wireless edge node.
The users receive time-sensitive information about physical quantities, each
measured by a sensor. Users send requests to the edge node where a cache
contains the most recently received measurements from each sensor. To serve a
request, the edge node either commands the sensor to send a status update or
retrieves the aged measurement from the cache. We aim at finding the best
actions of the edge node to minimize the age of information of the served
measurements. We model this problem as a Markov decision process and develop
reinforcement learning (RL) algorithms: model-based value iteration and
model-free Q-learning methods. We also propose a Q-learning method for the
realistic case where the edge node is informed about the sensors' battery
levels only via the status updates. The case under transmission limitations is
also addressed. Furthermore, properties of an optimal policy are analytically
characterized. Simulation results show that an optimal policy is a
threshold-based policy and that the proposed RL methods significantly reduce
the average cost compared to several baselines.Comment: 31 pages, 6 figures, submitted journa
Minimizing the AoI in Resource-Constrained Multi-Source Relaying Systems: Dynamic and Learning-based Scheduling
We consider a multi-source relaying system where the independent sources
randomly generate status update packets which are sent to the destination with
the aid of a relay through unreliable links. We develop transmission scheduling
policies to minimize the sum average age of information (AoI) subject to
transmission capacity and long-run average resource constraints. We formulate a
stochastic control optimization problem. To solve the problem, a constrained
Markov decision process (CMDP) approach and a drift-plus-penalty method are
proposed. The CMDP problem is solved by transforming it into an MDP problem
using the Lagrangian relaxation method. We theoretically analyze the structure
of optimal policies for the MDP problem and subsequently propose a
structure-aware algorithm that returns a practical near-optimal policy. By the
drift-plus-penalty method, we devise a dynamic near-optimal low-complexity
policy. We also develop a model-free deep reinforcement learning policy, which
does not require the full knowledge of system statistics. To do so, we employ
the Lyapunov optimization theory and a dueling double deep Q-network.
Simulation results are provided to assess the performance of our policies and
validate the theoretical results. The results show up to 91% performance
improvement compared to a baseline policy.Comment: 30 Pages, preliminary results of this paper were presented at IEEE
Globecom 2021, https://ieeexplore.ieee.org/document/968594
Multi-Source AoI-Constrained Resource Minimization under HARQ: Heterogeneous Sampling Processes
We consider a multi-source hybrid automatic repeat request (HARQ) based
system, where a transmitter sends status update packets of random arrival
(i.e., uncontrollable sampling) and generate-atwill (i.e., controllable
sampling) sources to a destination through an error-prone channel. We develop
transmission scheduling policies to minimize the average number of
transmissions subject to an average age of information (AoI) constraint. First,
we consider known environment (i.e., known system statistics) and develop a
near-optimal deterministic transmission policy and a low-complexity dynamic
transmission (LC-DT) policy. The former policy is derived by casting the main
problem into a constrained Markov decision process (CMDP) problem, which is
then solved using the Lagrangian relaxation, relative value iteration
algorithm, and bisection. The LC-DT policy is developed via the
drift-plus-penalty (DPP) method by transforming the main problem into a
sequence of per-slot problems. Finally, we consider unknown environment and
devise a learning-based transmission policy by relaxing the CMDP problem into
an MDP problem using the DPP method and then adopting the deep Q-learning
algorithm. Numerical results show that the proposed policies achieve
near-optimal performance and illustrate the benefits of HARQ in status
updating
On Greedy Methods for EXIT Chart Based Transmission Power Allocation
This paper addresses the problem of power allocation for single carrier point-to-point multiple input multiple output (MIMO) systems with iterative frequency-domain (FD) soft cancellation (SC) minimum mean squared error (MMSE) equalization. Two novel heuristic power allocation methods are proposed. The proposed methods explicitly take into account the convergence properties of the iterative equalizer while transmission power is minimized. The proposed heuristic schemes are based on, convergence constraint power allocation (CCPA), technique that decouples the spatial interference between streams using singular value decomposition (SVD), and minimize the transmission power while achieving the target mutual information for each stream after iterations at the receiver side. The proposed heuristic transmission schemes are inspired by well-known greedy algorithm resulting in a simple and efficient solutions to the power allocation problem. Numerical results show that the proposed heuristic schemes can achieve close to optimal performance in the terms of equalizer convergence as well as the total transmission power
EXIT Chart-Based Power Allocation for Iterative Frequency Domain MIMO Detector
Transmission power allocation in single-carrier multiple-input multiple-output (MIMO) systems with iterative frequency-domain (FD) soft cancellation (SC) minimum mean-squared error (MMSE) equalization is considered. A novel framework for transmission power minimization subject to equalizer convergence constraints, referred as convergence constrained power allocation (CCPA) method, is proposed based on extrinsic information transfer (EXIT) chart analysis. The proposed method decouples the spatial interference between the streams using singular value decomposition (SVD), and minimizes the transmission power while achieving the target mutual information for each stream after iterations at the receiver. We show that the transmission power optimization can be formulated as a convex optimization problem. Three CCPA methods, one approximately optimal, and other two heuristic methods inspired by the Lagrange duality are derived. The numerical results demonstrate that the proposed scheme outperforms the existing linear precoding schemes. Moreover, the proposed heuristic schemes can achieve performance close with that of the approximately optimal method in terms of the equalizer convergence properties as well as transmission power
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
Multidimensional adaptive radio links for broadband communications
Abstract
Advanced multiple-input multiple-output (MIMO) transceiver structures which utilize the knowledge of channel state information (CSI) at the transmitter side to optimize certain link parameters (e.g., throughput, fairness, spectral efficiency, etc.) under different constraints (e.g., maximum transmitted power, minimum quality of services (QoS), etc.) are considered in this thesis.
Adaptive transmission schemes for point-to-point MIMO systems are considered first. A robust link adaptation method for time-division duplex systems employing MIMO-OFDM channel eigenmode based transmission is developed. A low complexity bit and power loading algorithm which requires low signaling overhead is proposed.
Two algorithms for computing the sum-capacity of MIMO downlink channels with full CSI knowledge are derived. The first one is based on the iterative waterfilling method. The convergence of the algorithm is proved analytically and the computer simulations show that the algorithm converges faster than the earlier variants of sum power constrained iterative waterfilling algorithms. The second algorithm is based on the dual decomposition method. By tracking the instantaneous error in the inner loop, a faster version is developed.
The problem of linear transceiver design in MIMO downlink channels is considered for a case when the full CSI of scheduled users only is available at the transmitter. General methods for joint power control and linear transmit and receive beamformers design are provided. The proposed algorithms can handle multiple antennas at the base station and at the mobile terminals with an arbitrary number of data streams per scheduled user. The optimization criteria are fairly general and include sum power minimization under the minimum signal-to-interference-plus-noise ratio (SINR) constraint per data stream, the balancing of SINR values among data streams, minimum SINR maximization, weighted sum-rate maximization, and weighted sum mean square error minimization. Besides the traditional sum power constraint on the transmit beamformers, multiple sum power constraints can be imposed on arbitrary subsets of the transmit antennas.This extends the applicability of the results to novel system architectures, such as cooperative base station transmission using distributed MIMO antennas. By imposing per antenna power constraints, issues related to the linearity of the power amplifiers can be handled as well.
The original linear transceiver design problems are decomposed as a series of remarkably simpler optimization problems which can be efficiently solved by using standard convex optimization techniques. The advantage of this approach is that it can be easily extended to accommodate various supplementary constraints such as upper and/or lower bounds for the SINR values and guaranteed QoS for different subsets of users. The ability to handle transceiver optimization problems where a network-centric objective (e.g., aggregate throughput or transmitted power) is optimized subject to user-centric constraints (e.g., minimum QoS requirements) is an important feature which must be supported by future broadband communication systems
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