32,003 research outputs found
Location-Quality-aware Policy Optimisation for Relay Selection in Mobile Networks
Relaying can improve the coverage and performance of wireless access
networks. In presence of a localisation system at the mobile nodes, the use of
such location estimates for relay node selection can be advantageous as such
information can be collected by access points in linear effort with respect to
number of mobile nodes (while the number of links grows quadratically).
However, the localisation error and the chosen update rate of location
information in conjunction with the mobility model affect the performance of
such location-based relay schemes; these parameters also need to be taken into
account in the design of optimal policies. This paper develops a Markov model
that can capture the joint impact of localisation errors and inaccuracies of
location information due to forwarding delays and mobility; the Markov model is
used to develop algorithms to determine optimal location-based relay policies
that take the aforementioned factors into account. The model is subsequently
used to analyse the impact of deployment parameter choices on the performance
of location-based relaying in WLAN scenarios with free-space propagation
conditions and in an measurement-based indoor office scenario.Comment: Accepted for publication in ACM/Springer Wireless Network
Bayesian Symbol Detection in Wireless Relay Networks via Likelihood-Free Inference
This paper presents a general stochastic model developed for a class of
cooperative wireless relay networks, in which imperfect knowledge of the
channel state information at the destination node is assumed. The framework
incorporates multiple relay nodes operating under general known non-linear
processing functions. When a non-linear relay function is considered, the
likelihood function is generally intractable resulting in the maximum
likelihood and the maximum a posteriori detectors not admitting closed form
solutions. We illustrate our methodology to overcome this intractability under
the example of a popular optimal non-linear relay function choice and
demonstrate how our algorithms are capable of solving the previously
intractable detection problem. Overcoming this intractability involves
development of specialised Bayesian models. We develop three novel algorithms
to perform detection for this Bayesian model, these include a Markov chain
Monte Carlo Approximate Bayesian Computation (MCMC-ABC) approach; an Auxiliary
Variable MCMC (MCMC-AV) approach; and a Suboptimal Exhaustive Search Zero
Forcing (SES-ZF) approach. Finally, numerical examples comparing the symbol
error rate (SER) performance versus signal to noise ratio (SNR) of the three
detection algorithms are studied in simulated examples
Effective Capacity of Cognitive Radio Links: Accessing Primary Feedback Erroneously
We study the performance of a cognitive system modeled by one secondary and
one primary link and operating under statistical quality of service (QoS) delay
constraints. We analyze the effective capacity (EC) to quantify the secondary
user (SU) performance under delay constraints. The SU intends to maximize the
benefit of the feedback messages on the primary link to reduce SU interference
for primary user (PU) and makes opportunistic use of the channel to transmit
his packets. We assume that SU has erroneous access to feedback information of
PU. We propose a three power level scheme and study the tradeoff between
degradation in EC of SU and reliability of PU defined as the success rate of
the transmitted packets. Our analysis shows that increase in error in feedback
access causes more interference to PU and packet success rate decreases
correspondingly.Comment: Accepted for publication in International Symposium on Wireless
Communication Systems (ISWCS) 201
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
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