Uploaded by Eimis Vilk

2011 Sensor Array and Multichannel Signal Processing

[in the SPOTLIGHT]
SPCOM is evolving to incorporate new
networking concepts. The smart grid adds
communication networking capability to
an infrastructure network like the power
grid or water grid. For example, the power
grid can deliver power much more efficiently when real-time information about
the grid state is available through networked meters and power infrastructure.
Challenges in the smart power grid
include developing machine-to-machine
network protocols for reporting measurements, better techniques for large-scale
network state estimation, and robustness
to cyber attacks. The smart grid is just
one example of a more general trend of
networks-of-networks where different networking concepts are used to make infrastructure more intelligent. More examples
include intelligent transportation systems, which use vehicle-to-vehicle networks to improve transportation network
safety and efficiency. Mathematical tools
from SPCOM are also being used to
understand noncommunication networks
like social networks.
Compressive sensing (CS) refers to efficient compression and reconstruction
of analog signals that are sparse in
some domain, e.g., space, time, or frequency. CS is a component of many different technical committees. In
SPCOM, CS has been applied to the
detection of impulse radio ultrawideband (exploits time-domain sparsity),
radar (exploits sparsity in angle,
Doppler, and/or range domain), and
spectrum sensing (exploits sparsity in
the spectrum). There are many applications of CS remaining in SPCOM,
including localization and tracking
through radar, or better navigation
through global network satellite systems. Challenges remain, especially in
evaluating the viability of CS versus
non-CS techniques.
Determining location is receiving
renewed interest in SPCOM coupled
with applications such as sensor networks for telemetry and satellite navigation. For low-energy sensor applications,
range-based localization is receiving
attention where distance measurements
between sensors and beacons and/or
among the sensors themselves are
exploited to compute the location of
the sensors. Assisted satellite navigation is also likely to become more
important, where signals of opportunity are exploited. New mathematical
tools that are being exploited in localization include CS and multidimensional scaling.
Shuguang (Robert) Cui (cui@ece.tamu.
edu) is an associate professor at Texas
A&M University.
Robert W. Heath Jr. (rheath@ece.
utexas.edu) is an associate professor at
The University of Texas
at Austin.
Geert Leus (G.J.T.Leus
@tudelft.nl) is an associate professor at the Delft
University of Technology.
A.M. Zoubir, V. Krishnamurthy,
and A.H. Sayed
Signal Processing Theory and Methods
he scope of the IEEE Signal
Processing Theory and
Methods (SPTM) Technical
Committee has a broad span,
ranging from digital filtering
and adaptive signal processing to statistical signal analysis, estimation, and detection. There have also been significant
advances in the estimation of sparse systems. These areas continue to play a key
role in classical and timely applications.
Digital Object Identifier 10.1109/MSP.2011.941987
Date of publication: 22 August 2011
Under the unifying theme “how simple local behavior generates rational
global behavior,” an SPTM expert session was organized by the authors during ICASSP 2011 in Prague. This article
summarizes the session and raises challenging questions for future research. It
is by no means representative of all
emerging topics in the areas of SPTM,
but it includes trends and challenges
that, in our opinion, will become
important activities in SPTM in the
coming years. The bibliography is not
exhaustive due to space limitations; it
only gives some representative references the readers may want to consult.
Cognitive or adaptive networks are composed of spatially distributed agents that
share information over a graph. The topology of the graph may evolve dynamically
over time due to movement of the agents
or because agents wish to collaborate with
other agents and form coalitions (see [1]
and the references therein). Each agent
possesses adaptation and learning abilities
[in the SPOTLIGHT]
[2], [3]; for example, each agent can have
the capability of running an adaptive or a
Bayesian signal processing algorithm
based on local data and also information
from other nodes. There are at least two
questions one can pose:
1) If each agent possesses limited capabilities, can the global behavior of the
network be sophisticated? This can be
viewed as an analysis problem—how
do simple algorithms interact resulting
in sophisticated behavior.
2) A related question is: what algorithm should each agent run for the
global behavior of the network to
achieve a particular objective? This can
be viewed as a synthesis problem, since
one is interested in designing distributed algorithms.
The combination of in-network processing
and adaptive cooperation leads to the
emergence of learning and self-organization features across the network. Nature
provides an abundance of examples of selforganization over biological networks
consisting of mobile agents. While individual agents tend to exhibit limited cognitive abilities, it is the coordinated behavior
among the agents that leads to the manifestation of decentralized intelligence and
enables the agents to perform sophisticated maneuvers to evade predators. In many
biological networks, no single agent is in
command and yet complex patterns of formation are evident. Examples include fish
joining together in schools [1], birds flying
in formation [4], bees seeking a new hive,
and bacteria foraging for food.
A close synergy is emerging between
studies on self-organization in the biological [5] and social sciences and studies on
cognitive networks in signal processing
and communications. There are ample
opportunities for cross-disciplinary
research that seeks to understand and
reverse-engineer the decentralized intelligence encountered in socio-economic-biological networks, by exploring connections
with adaptation over networks and by
using enhanced signal processing techniques. Adaptive diffusion methods [1] and
game theoretic methods [6], [7] are ideal
tools for the synthesis and analysis of cognitive networks with varied capabilities. By
spreading intelligence throughout the sys-
tem, such methods eliminate the need to
transport information to and from a central point, while still allowing local information exchange to any desired degree.
An important feature of cognitive networks is that the individual nodes are not
expected to rely mainly on information
fed from their neighbors. Such cooperation among the nodes is only one factor
in the learning process. The individual
nodes should also possess local adaptive
processing abilities that enable them to
assess and react to the quality of the
information received from their neighbors against their own personal beliefs
[1], [4]. For this reason, cognitive networks do not expect all nodes to reach
global agreement over the state of the
environment, as is common in some useful consensus seeking strategies [8], [9];
nodes in a cognitive network do not need
to converge to the same global value [2].
Actually, such variations in the individual
levels of performance across a network
are commonly observed in nature.
Animals in a group do not act in absolute
synchrony. There are variations in their
patterns of motion and in their individual
reactions to obstacles in the environment
[1]. The same phenomenon is observed
even in agent-based models of macroeconomies: the nodes (such as sellers and
buyers) do not need to converge to the
same equilibrium state. Instead, their
state (and behavior) can fluctuate
depending on their individual beliefs and
Solving estimation and tracking problems over cognitive networks generally
require optimizing certain global cost
functions in a distributed manner.
Consensus-based techniques are useful in
enabling networks to evaluate average val-
ues across the network. Adaptive diffusion
techniques, on the other hand, allow networks to more generally optimize global
cost functions and to perform real-time
adaptation and learning [2]. This level of
generality is useful in modeling mobile
adaptive networks, which serve as good
models for various patterns of motion
observed in biological networks. The analysis of such learning algorithms poses several challenges. What assumptions on
graph connectivity, information patterns,
rate of information sharing, adaptation
rules, and learning dynamics are needed
to ensure convergence to within acceptable levels of performance?
Game-theoretic methods [6], [7] can also
derive rich dynamics through the interaction of simple components and can be
used either as descriptive tools, to predict
the outcome of complex interactions, or as
prescriptive tools, to design systems
around given interaction rules. Game theory is a complexity-based theory, along
with percolation theory, cellular automata, and ecology modeling. Game-theoretic
learning algorithms [6], [10] can allow
individual agents to perform simple algorithms and, under suitable assumptions,
ensure that the global performance converges to desired equilibrium sets.
The game theoretic approach has also
several appealing features for system
design and analysis. Simple devices with
limited awareness can be equipped with
preconfigured or configurable utility functions and routines for maximizing their
utility in an interactive, even unknown
environment. Such devices can then be
deployed to organize themselves effectively in a dynamic and unknown environment. As long as the utility functions are
properly specified, these networked devices can be made to exhibit many desired
behaviors [7]. The game theoretic design
approach echoes natural systems in a form
of biomimicry; biological agents such as
insects have long since evolved the proper
procedures and utility preferences to
accomplish collective tasks. The same can
be said of humans, who orient their utilities according to economics; the proper
specification of utilities in this case are
[in the SPOTLIGHT]
dictated by the structure of the economic
system and realized through such mechanisms as pricing. The self-organizing feature of game-theoretic (decentralized)
systems results in a specific set of benefits
and challenges.
Cognitive networks can be designed to
perform a variety of tasks. Examples of
applications include environmental monitoring, event detection, resource monitoring, target tracking, communications over
cognitive radio channels, processing and
control over smart power grids, analysis of
swarm and animal flocking behavior [1],
[4], design of multiagent systems, and
analysis of collective decision making.
While it is generally possible to find centralized or hierarchical processing mechanisms that are faster or more accurate in
performing a given task, cognitive
networks are generally more scalable,
adaptable, and resilient.
Cognitive networks can also be used
to model herding behavior in macroeconomic systems. In agent-based economic models, the individual agents such as
buyers, sellers, traders, brokers, and
dealers, are capable of behavioral adaptation. The nodes are embodied with goals
and beliefs related to patterns they see in
pricing and profitability, and they react
according to certain behavioral rules.
Agent-based models are also prevalent in
social networks, where they are used to
model social interactions and the spread
of disease or information. Extensive
studies in computer science and graph
theory have been devoted to understanding the structure of social networks in
terms of properties such as their centrality (a measure of the influence of a
node), closeness (how close individuals
are on a social network), and clustering,
and network degree (how many connections a node has).
There has already been
extensive work in the
literature on exploiting naturally occurring phenomena in the
development of biolog-
ically inspired techniques for application in various domains such as
robotics and optimization. For
example, the ant colony optimization
(ACO) procedure is based on how ants
find the shortest path to food, and the
particle swarm optimization (PSO) procedure is based on how birds flock to
find food. Other research efforts have
focused instead on rules that emulate
the emergence of organized behavior in
animal colonies. For example, in consensus-seeking models, the individual
members in a colony adjust their velocities according to the average velocity
of their neighbors. While consensus
methods can be effective in emulating
the coordinated motion of (animal)
agents, they are nevertheless limited in
their ability to model the remarkable
adaptation, learning, and tracking
capabilities that moving (animal) networks exhibit, especially when traversing an environment with unpredictable
obstacles and predators. Adaptive diffusion methods provide effective modeling tools in these situations [1], [4].
Research efforts are needed to address
broader questions such as understanding how and why complex patterns of
behavior arise in biological networks
under highly dynamic conditions. How
do mobility and the changing topologies influence learning and cognitive
abilities? How does information flow
through a cognitive network? Are there
similarities across different domains?
Interestingly, there is evidence to suggest that certain patterns of behavior
may be independent of the population.
For example, when faced with two
identical food sources, ants have been
observed to focus on one of these
sources for some time before switching
to the other source. The same behavior
has been observed in humans choosing
between two restaurants—this is modeled by social learning where agents
learn from the actions of other agents.
A related question is: how can a decision maker make global decisions
based on local decisions made by selfish individual agents? It can be shown
that even for elementary sequential
detection problems, the optimal decision policy no longer has a threshold
behavior [11].
A.M. Zoubir (zoubir@spg.tu-darmstadt.
de) is a professor with Technische
Universität Darmstadt, Germany.
V. Krishnamurthy (vikramk@ece.ubc.
ca) is a professor with The University of
British Columbia, Canada.
A.H. Sayed (sayed@ee.ucla.edu) is a
professor with the University of California,
Los Angeles, United States.
[1] S.-Y. Tu and A. H. Sayed, “Mobile adaptive networks,” IEEE J. Select. Topics Signal Processing, vol.
5, 2011.
[2] F. Cattivelli and A. H. Sayed, “Diffusion LMS strategies for distributed estimation,” IEEE Trans. Signal
Processing, vol. 58, no. 3, pp. 1035–1048, Mar. 2010.
[3] C. G. Lopes and A. H. Sayed, “Diffusion least-mean
squares over adaptive networks: Formulation and performance analysis,” IEEE Trans. Signal Processing,
vol. 56, no. 7, pp. 3122–3136, July 2008.
[4] F. Cattivelli and A. H. Sayed, “Modeling bird flight
formations using diffusion adaptation,” IEEE Trans.
Signal Processing, vol. 59, no. 5, pp. 2038–2051, 2011.
[5] S. Camazine, J. L. Deneubourg, N. R. Franks, J.
Sneyd, G. Theraulaz, and E. Bonabeau, SelfOrganization in Biological Systems. Princeton, NJ:
Princeton Univ. Press, 2003.
[6] S. Hart, “Adaptive heuristics,” Econometrica, vol.
73, no. 5, pp. 1401–1430, 2005.
[7] M. Maskery, V. Krishnamurthy, and Q. Zhang,
“Decentralized dynamic spectrum access for cognitive radios: Cooperative design of a non-cooperative
game,” IEEE Trans. Commun., vol. 57, pp. 459–469,
Feb. 2008.
[8] S. Kar and J. Moura, “Sensor networks with random links: Topology design for distributed consensus,”
IEEE Trans. Signal Processing, vol. 56, no. 7, pp.
3315–3326, July 2008.
[9] V. Krishnamurthy K Topley, and G. Yin, “Consensus formation in a two-time-scale Markovian system,”
SIAM J. Multiscale Model. Simul., vol. 7, no. 4, pp.
1898–1927, 2009.
[10] M. Benaim, J. Hofbauer, and S. Sorin, “Stochastic
approximations and differential inclusions,” SIAM J.
Control Optim., vol. 44, pp. 328–348, Jan. 2005.
[11] V. Krishnamurthy. (2011).
Bayesian sequential detection with phase-distributed
change time and nonlinear
penalty—A lattice programming approach. IEEE Trans.
Inform. Theory [Online].
Available: http://arxiv.org/