10,506 research outputs found
Joint Cooperative Spectrum Sensing and MAC Protocol Design for Multi-channel Cognitive Radio Networks
In this paper, we propose a semi-distributed cooperative spectrum sen sing
(SDCSS) and channel access framework for multi-channel cognitive radio networks
(CRNs). In particular, we c onsider a SDCSS scheme where secondary users (SUs)
perform sensing and exchange sensing outcomes with ea ch other to locate
spectrum holes. In addition, we devise the p -persistent CSMA-based cognitive
MAC protocol integrating the SDCSS to enable efficient spectrum sharing among
SUs. We then perform throughput analysis and develop an algorithm to determine
the spectrum sensing and access parameters to maximize the throughput for a
given allocation of channel sensing sets. Moreover, we consider the spectrum
sensing set optimization problem for SUs to maxim ize the overall system
throughput. We present both exhaustive search and low-complexity greedy
algorithms to determine the sensing sets for SUs and analyze their complexity.
We also show how our design and analysis can be extended to consider reporting
errors. Finally, extensive numerical results are presented to demonstrate the
sig nificant performance gain of our optimized design framework with respect to
non-optimized designs as well as the imp acts of different protocol parameters
on the throughput performance.Comment: accepted for publication EURASIP Journal on Wireless Communications
and Networking, 201
General analytical framework for cooperative sensing and access trade-off optimization
In this paper, we investigate the joint cooperative spectrum sensing and
access design problem for multi-channel cognitive radio networks. A general
heterogeneous setting is considered where the probabilities that different
channels are available, SNRs of the signals received at secondary users (SUs)
due to transmissions from primary users (PUs) for different users and channels
can be different. We assume a cooperative sensing strategy with a general
a-out-of-b aggregation rule and design a synchronized MAC protocol so that SUs
can exploit available channels. We analyze the sensing performance and the
throughput achieved by the joint sensing and access design. Based on this
analysis, we develop algorithms to find optimal parameters for the sensing and
access protocols and to determine channel assignment for SUs to maximize the
system throughput. Finally, numerical results are presented to verify the
effectiveness of our design and demonstrate the relative performance of our
proposed algorithms and the optimal ones.Comment: arXiv admin note: text overlap with arXiv:1404.167
An LSPI based reinforcement learning approach to enable network cooperation in cognitive wireless sensor networks
The number of wirelessly communicating devices increases every day, along with the number of communication standards and technologies that they use to exchange data. A relatively new form of research is trying to find a way to make all these co-located devices not only capable of detecting each other's presence, but to go one step further - to make them cooperate. One recently proposed way to tackle this problem is to engage into cooperation by activating 'network services' (such as internet sharing, interference avoidance, etc.) that offer benefits for other co-located networks. This approach reduces the problem to the following research topic: how to determine which network services would be beneficial for all the cooperating networks. In this paper we analyze and propose a conceptual solution for this problem using the reinforcement learning technique known as the Least Square Policy Iteration (LSPI). The proposes solution uses a self-learning entity that negotiates between different independent and co-located networks. First, the reasoning entity uses self-learning techniques to determine which service configuration should be used to optimize the network performance of each single network. Afterwards, this performance is used as a reference point and LSPI is used to deduce if cooperating with other co-located networks can lead to even further performance improvements
Byzantine Attack and Defense in Cognitive Radio Networks: A Survey
The Byzantine attack in cooperative spectrum sensing (CSS), also known as the
spectrum sensing data falsification (SSDF) attack in the literature, is one of
the key adversaries to the success of cognitive radio networks (CRNs). In the
past couple of years, the research on the Byzantine attack and defense
strategies has gained worldwide increasing attention. In this paper, we provide
a comprehensive survey and tutorial on the recent advances in the Byzantine
attack and defense for CSS in CRNs. Specifically, we first briefly present the
preliminaries of CSS for general readers, including signal detection
techniques, hypothesis testing, and data fusion. Second, we analyze the spear
and shield relation between Byzantine attack and defense from three aspects:
the vulnerability of CSS to attack, the obstacles in CSS to defense, and the
games between attack and defense. Then, we propose a taxonomy of the existing
Byzantine attack behaviors and elaborate on the corresponding attack
parameters, which determine where, who, how, and when to launch attacks. Next,
from the perspectives of homogeneous or heterogeneous scenarios, we classify
the existing defense algorithms, and provide an in-depth tutorial on the
state-of-the-art Byzantine defense schemes, commonly known as robust or secure
CSS in the literature. Furthermore, we highlight the unsolved research
challenges and depict the future research directions.Comment: Accepted by IEEE Communications Surveys and Tutoiral
- …