127 research outputs found
Handshaking Protocols and Jamming Mechanisms for Blind Rendezvous in a Dynamic Spectrum Access Environment
Blind frequency rendezvous is an important process for bootstrapping communications between radios without the use of pre-existing infrastructure or common control channel in a Dynamic Spectrum Access (DSA) environment. In this process, radios attempt to arrive in the same frequency channel and recognize each other’s presence in changing, under-utilized spectrum. This paper refines existing blind rendezvous techniques by introducing a handshaking algorithm for setting up communications once two radios have arrived in the same frequency channel. It then investigates the effect of different jamming techniques on blind rendezvous algorithms that utilize this handshake. The handshake performance is measured by determining the probability of a handshake, the time to handshake, and the increase in time to rendezvous (TTR) with a handshake compared to that without. The handshake caused varying increases in TTR depending on the time spent in each channel. Four different jamming techniques are applied to the blind rendezvous process: noise, deceptive, sense, and Primary User Emulation (PUE). Each jammer type is analyzed to determine how they increase the TTR, how often they successfully jam over a period of time, and how long it takes to jam. The sense jammer was most effective, followed by PUE, deceptive, and noise, respectively
A Reinforcement Learning Approach for the Multichannel Rendezvous Problem
In this paper, we consider the multichannel rendezvous problem in cognitive
radio networks (CRNs) where the probability that two users hopping on the same
channel have a successful rendezvous is a function of channel states. The
channel states are modelled by two-state Markov chains that have a good state
and a bad state. These channel states are not observable by the users. For such
a multichannel rendezvous problem, we are interested in finding the optimal
policy to minimize the expected time-to-rendezvous (ETTR) among the class of
{\em dynamic blind rendezvous policies}, i.e., at the time slot each
user selects channel independently with probability , . By formulating such a multichannel rendezvous problem as an
adversarial bandit problem, we propose using a reinforcement learning approach
to learn the channel selection probabilities , . Our
experimental results show that the reinforcement learning approach is very
effective and yields comparable ETTRs when comparing to various approximation
policies in the literature.Comment: 5 pages, 9 figures. arXiv admin note: text overlap with
arXiv:1906.1042
Communication Primitives in Cognitive Radio Networks
Cognitive radio networks are a new type of multi-channel wireless network in
which different nodes can have access to different sets of channels. By
providing multiple channels, they improve the efficiency and reliability of
wireless communication. However, the heterogeneous nature of cognitive radio
networks also brings new challenges to the design and analysis of distributed
algorithms.
In this paper, we focus on two fundamental problems in cognitive radio
networks: neighbor discovery, and global broadcast. We consider a network
containing nodes, each of which has access to channels. We assume the
network has diameter , and each pair of neighbors have at least ,
and at most , shared channels. We also assume each node has at
most neighbors. For the neighbor discovery problem, we design a
randomized algorithm CSeek which has time complexity
. CSeek is flexible and robust,
which allows us to use it as a generic "filter" to find "well-connected"
neighbors with an even shorter running time. We then move on to the global
broadcast problem, and propose CGCast, a randomized algorithm which takes
time. CGCast uses
CSeek to achieve communication among neighbors, and uses edge coloring to
establish an efficient schedule for fast message dissemination.
Towards the end of the paper, we give lower bounds for solving the two
problems. These lower bounds demonstrate that in many situations, CSeek and
CGCast are near optimal
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