10,081 research outputs found
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
Optimizing Average-Maximum TTR Trade-off for Cognitive Radio Rendezvous
In cognitive radio (CR) networks, "TTR", a.k.a. time-to-rendezvous, is one of
the most important metrics for evaluating the performance of a channel hopping
(CH) rendezvous protocol, and it characterizes the rendezvous delay when two
CRs perform channel hopping. There exists a trade-off of optimizing the average
or maximum TTR in the CH rendezvous protocol design. On one hand, the random CH
protocol leads to the best "average" TTR without ensuring a finite "maximum"
TTR (two CRs may never rendezvous in the worst case), or a high rendezvous
diversity (multiple rendezvous channels). On the other hand, many
sequence-based CH protocols ensure a finite maximum TTR (upper bound of TTR)
and a high rendezvous diversity, while they inevitably yield a larger average
TTR. In this paper, we strike a balance in the average-maximum TTR trade-off
for CR rendezvous by leveraging the advantages of both random and
sequence-based CH protocols. Inspired by the neighbor discovery problem, we
establish a design framework of creating a wake-up schedule whereby every CR
follows the sequence-based (or random) CH protocol in the awake (or asleep)
mode. Analytical and simulation results show that the hybrid CH protocols under
this framework are able to achieve a greatly improved average TTR as well as a
low upper-bound of TTR, without sacrificing the rendezvous diversity.Comment: Accepted by IEEE International Conference on Communications (ICC
2015, http://icc2015.ieee-icc.org/
Coalition Formation Games for Collaborative Spectrum Sensing
Collaborative Spectrum Sensing (CSS) between secondary users (SUs) in
cognitive networks exhibits an inherent tradeoff between minimizing the
probability of missing the detection of the primary user (PU) and maintaining a
reasonable false alarm probability (e.g., for maintaining a good spectrum
utilization). In this paper, we study the impact of this tradeoff on the
network structure and the cooperative incentives of the SUs that seek to
cooperate for improving their detection performance. We model the CSS problem
as a non-transferable coalitional game, and we propose distributed algorithms
for coalition formation. First, we construct a distributed coalition formation
(CF) algorithm that allows the SUs to self-organize into disjoint coalitions
while accounting for the CSS tradeoff. Then, the CF algorithm is complemented
with a coalitional voting game for enabling distributed coalition formation
with detection probability guarantees (CF-PD) when required by the PU. The
CF-PD algorithm allows the SUs to form minimal winning coalitions (MWCs), i.e.,
coalitions that achieve the target detection probability with minimal costs.
For both algorithms, we study and prove various properties pertaining to
network structure, adaptation to mobility and stability. Simulation results
show that CF reduces the average probability of miss per SU up to 88.45%
relative to the non-cooperative case, while maintaining a desired false alarm.
For CF-PD, the results show that up to 87.25% of the SUs achieve the required
detection probability through MWCComment: IEEE Transactions on Vehicular Technology, to appea
Distributed Clustering in Cognitive Radio Ad Hoc Networks Using Soft-Constraint Affinity Propagation
Absence of network infrastructure and heterogeneous spectrum availability in cognitive radio ad hoc networks (CRAHNs) necessitate the self-organization of cognitive radio users (CRs) for efficient spectrum coordination. The cluster-based structure is known to be effective in both guaranteeing system performance and reducing communication overhead in variable network environment. In this paper, we propose a distributed clustering algorithm based on soft-constraint affinity propagation message passing model (DCSCAP). Without dependence on predefined common control channel (CCC), DCSCAP relies on the distributed message passing among CRs through their available channels, making the algorithm applicable for large scale networks. Different from original soft-constraint affinity propagation algorithm, the maximal iterations of message passing is controlled to a relatively small number to accommodate to the dynamic environment of CRAHNs. Based on the accumulated evidence for clustering from the message passing process, clusters are formed with the objective of grouping the CRs with similar spectrum availability into smaller number of clusters while guaranteeing at least one CCC in each cluster. Extensive simulation results demonstrate the preference of DCSCAP compared with existing algorithms in both efficiency and robustness of the clusters
A Hybrid Model to Extend Vehicular Intercommunication V2V through D2D Architecture
In the recent years, many solutions for Vehicle to Vehicle (V2V)
communication were proposed to overcome failure problems (also known as dead
ends). This paper proposes a novel framework for V2V failure recovery using
Device-to-Device (D2D) communications. Based on the unified Intelligent
Transportation Systems (ITS) architecture, LTE-based D2D mechanisms can improve
V2V dead ends failure recovery delays. This new paradigm of hybrid V2V-D2D
communications overcomes the limitations of traditional V2V routing techniques.
According to NS2 simulation results, the proposed hybrid model decreases the
end to end delay (E2E) of messages delivery. A complete comparison of different
D2D use cases (best & worst scenarios) is presented to show the enhancements
brought by our solution compared to traditional V2V techniques.Comment: 6 page
IEEE Access special section editorial: Artificial intelligence enabled networking
With today’s computer networks becoming increasingly dynamic, heterogeneous, and complex, there is great interest in deploying artificial intelligence (AI) based techniques for optimization and management of computer networks. AI techniques—that subsume multidisciplinary techniques from machine learning, optimization theory, game theory, control theory, and meta-heuristics—have long been applied to optimize computer networks in many diverse settings. Such an approach is gaining increased traction with the emergence of novel networking paradigms that promise to simplify network management (e.g., cloud computing, network functions virtualization, and software-defined networking) and provide intelligent services (e.g., future 5G mobile networks). Looking ahead, greater integration of AI into networking architectures can help develop a future vision of cognitive networks that will show network-wide intelligent behavior to solve problems of network heterogeneity, performance, and quality of service (QoS)
Cross Layer Aware Adaptive MAC based on Knowledge Based Reasoning for Cognitive Radio Computer Networks
In this paper we are proposing a new concept in MAC layer protocol design for
Cognitive radio by combining information held by physical layer and MAC layer
with analytical engine based on knowledge based reasoning approach. In the
proposed system a cross layer information regarding signal to interference and
noise ratio (SINR) and received power are analyzed with help of knowledge based
reasoning system to determine minimum power to transmit and size of contention
window, to minimize backoff, collision, save power and drop packets. The
performance analysis of the proposed protocol indicates improvement in power
saving, lowering backoff and significant decrease in number of drop packets.
The simulation environment was implement using OMNET++ discrete simulation tool
with Mobilty framework and MiXiM simulation library.Comment: 8 page
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