3,821 research outputs found
Nomographic Functions: Efficient Computation in Clustered Gaussian Sensor Networks
In this paper, a clustered wireless sensor network is considered that is
modeled as a set of coupled Gaussian multiple-access channels. The objective of
the network is not to reconstruct individual sensor readings at designated
fusion centers but rather to reliably compute some functions thereof. Our
particular attention is on real-valued functions that can be represented as a
post-processed sum of pre-processed sensor readings. Such functions are called
nomographic functions and their special structure permits the utilization of
the interference property of the Gaussian multiple-access channel to reliably
compute many linear and nonlinear functions at significantly higher rates than
those achievable with standard schemes that combat interference. Motivated by
this observation, a computation scheme is proposed that combines a suitable
data pre- and post-processing strategy with a nested lattice code designed to
protect the sum of pre-processed sensor readings against the channel noise.
After analyzing its computation rate performance, it is shown that at the cost
of a reduced rate, the scheme can be extended to compute every continuous
function of the sensor readings in a finite succession of steps, where in each
step a different nomographic function is computed. This demonstrates the
fundamental role of nomographic representations.Comment: to appear in IEEE Transactions on Wireless Communication
Short Packet Structure for Ultra-Reliable Machine-type Communication: Tradeoff between Detection and Decoding
Machine-type communication requires rethinking of the structure of short
packets due to the coding limitations and the significant role of the control
information. In ultra-reliable low-latency communication (URLLC), it is crucial
to optimally use the limited degrees of freedom (DoFs) to send data and control
information. We consider a URLLC model for short packet transmission with
acknowledgement (ACK). We compare the detection/decoding performance of two
short packet structures: (1) time-multiplexed detection sequence and data; and
(2) structure in which both packet detection and data decoding use all DoFs.
Specifically, as an instance of the second structure we use superimposed
sequences for detection and data. We derive the probabilities of false alarm
and misdetection for an AWGN channel and numerically minimize the packet error
probability (PER), showing that for delay-constrained data and ACK exchange,
there is a tradeoff between the resources spent for detection and decoding. We
show that the optimal PER for the superimposed structure is achieved for higher
detection overhead. For this reason, the PER is also higher than in the
preamble case. However, the superimposed structure is advantageous due to its
flexibility to achieve optimal operation without the need to use multiple
codebooks.Comment: Accepted at ICASSP 2018, special session on "Signal Processing for
Machine-Type Communications
Empowerment and State-dependent Noise : An Intrinsic Motivation for Avoiding Unpredictable Agents
Empowerment is a recently introduced intrinsic motivation algorithm based on the embodiment of an agent and the dynamics of the world the agent is situated in. Computed as the channel capacity from an agentâs actuators to an agentâs sensors, it offers a quantitative measure of how much an agent is in control of the world it can perceive. In this paper, we expand the approximation of empowerment as a Gaussian linear channel to compute empowerment based on the covariance matrix between actuators and sensors, incorporating state dependent noise. This allows for the first time the study of continuous systems with several agents. We found that if the behaviour of another agent cannot be predicted accurately, then interacting with that agent will decrease the empowerment of the original agent. This leads to behaviour realizing collision avoidance with other agents, purely from maximising an agentâs empowermentFinal Accepted Versio
Random Matrix Theories in Quantum Physics: Common Concepts
We review the development of random-matrix theory (RMT) during the last
decade. We emphasize both the theoretical aspects, and the application of the
theory to a number of fields. These comprise chaotic and disordered systems,
the localization problem, many-body quantum systems, the Calogero-Sutherland
model, chiral symmetry breaking in QCD, and quantum gravity in two dimensions.
The review is preceded by a brief historical survey of the developments of RMT
and of localization theory since their inception. We emphasize the concepts
common to the above-mentioned fields as well as the great diversity of RMT. In
view of the universality of RMT, we suggest that the current development
signals the emergence of a new "statistical mechanics": Stochasticity and
general symmetry requirements lead to universal laws not based on dynamical
principles.Comment: 178 pages, Revtex, 45 figures, submitted to Physics Report
Statistical Mechanics and Information-Theoretic Perspectives on Complexity in the Earth System
Peer reviewedPublisher PD
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