619 research outputs found
Packet Relaying Control in Sensing-based Spectrum Sharing Systems
Cognitive relaying has been introduced for opportunistic spectrum access
systems by which a secondary node forwards primary packets whenever the primary
link faces an outage condition. For spectrum sharing systems, cognitive
relaying is parametrized by an interference power constraint level imposed on
the transmit power of the secondary user. For sensing-based spectrum sharing,
the probability of detection is also involved in packet relaying control. This
paper considers the choice of these two parameters so as to maximize the
secondary nodes' throughput under certain constraints. The analysis leads to a
Markov decision process using dynamic programming approach. The problem is
solved using value iteration. Finally, the structural properties of the
resulting optimal control are highlighted
Adaptive Modulation in Multi-user Cognitive Radio Networks over Fading Channels
In this paper, the performance of adaptive modulation in multi-user cognitive
radio networks over fading channels is analyzed. Multi-user diversity is
considered for opportunistic user selection among multiple secondary users. The
analysis is obtained for Nakagami- fading channels. Both adaptive continuous
rate and adaptive discrete rate schemes are analysed in opportunistic spectrum
access and spectrum sharing. Numerical results are obtained and depicted to
quantify the effects of multi-user fading environments on adaptive modulation
operating in cognitive radio networks
Finite-Blocklength Bounds for Wiretap Channels
This paper investigates the maximal secrecy rate over a wiretap channel
subject to reliability and secrecy constraints at a given blocklength. New
achievability and converse bounds are derived, which are shown to be tighter
than existing bounds. The bounds also lead to the tightest second-order coding
rate for discrete memoryless and Gaussian wiretap channels.Comment: extended version of a paper submitted to ISIT 201
-Learning: A Collaborative Distributed Strategy for Multi-Agent Reinforcement Learning Through Consensus + Innovations
The paper considers a class of multi-agent Markov decision processes (MDPs),
in which the network agents respond differently (as manifested by the
instantaneous one-stage random costs) to a global controlled state and the
control actions of a remote controller. The paper investigates a distributed
reinforcement learning setup with no prior information on the global state
transition and local agent cost statistics. Specifically, with the agents'
objective consisting of minimizing a network-averaged infinite horizon
discounted cost, the paper proposes a distributed version of -learning,
-learning, in which the network agents collaborate by means of
local processing and mutual information exchange over a sparse (possibly
stochastic) communication network to achieve the network goal. Under the
assumption that each agent is only aware of its local online cost data and the
inter-agent communication network is \emph{weakly} connected, the proposed
distributed scheme is almost surely (a.s.) shown to yield asymptotically the
desired value function and the optimal stationary control policy at each
network agent. The analytical techniques developed in the paper to address the
mixed time-scale stochastic dynamics of the \emph{consensus + innovations}
form, which arise as a result of the proposed interactive distributed scheme,
are of independent interest.Comment: Submitted to the IEEE Transactions on Signal Processing, 33 page
Distributed Linear Parameter Estimation: Asymptotically Efficient Adaptive Strategies
The paper considers the problem of distributed adaptive linear parameter
estimation in multi-agent inference networks. Local sensing model information
is only partially available at the agents and inter-agent communication is
assumed to be unpredictable. The paper develops a generic mixed time-scale
stochastic procedure consisting of simultaneous distributed learning and
estimation, in which the agents adaptively assess their relative observation
quality over time and fuse the innovations accordingly. Under rather weak
assumptions on the statistical model and the inter-agent communication, it is
shown that, by properly tuning the consensus potential with respect to the
innovation potential, the asymptotic information rate loss incurred in the
learning process may be made negligible. As such, it is shown that the agent
estimates are asymptotically efficient, in that their asymptotic covariance
coincides with that of a centralized estimator (the inverse of the centralized
Fisher information rate for Gaussian systems) with perfect global model
information and having access to all observations at all times. The proof
techniques are mainly based on convergence arguments for non-Markovian mixed
time scale stochastic approximation procedures. Several approximation results
developed in the process are of independent interest.Comment: Submitted to SIAM Journal on Control and Optimization journal.
Initial Submission: Sept. 2011. Revised: Aug. 201
The Trade-off between Processing Gains of an Impulse Radio UWB System in the Presence of Timing Jitter
In time hopping impulse radio, pulses of duration are transmitted
for each information symbol. This gives rise to two types of processing gain:
(i) pulse combining gain, which is a factor , and (ii) pulse spreading
gain, which is , where is the mean interval between two
subsequent pulses. This paper investigates the trade-off between these two
types of processing gain in the presence of timing jitter. First, an additive
white Gaussian noise (AWGN) channel is considered and approximate closed form
expressions for bit error probability are derived for impulse radio systems
with and without pulse-based polarity randomization. Both symbol-synchronous
and chip-synchronous scenarios are considered. The effects of multiple-access
interference and timing jitter on the selection of optimal system parameters
are explained through theoretical analysis. Finally, a multipath scenario is
considered and the trade-off between processing gains of a synchronous impulse
radio system with pulse-based polarity randomization is analyzed. The effects
of the timing jitter, multiple-access interference and inter-frame interference
are investigated. Simulation studies support the theoretical results.Comment: To appear in the IEEE Transactions on Communication
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