6,192 research outputs found
Intelligent Wireless Communications Enabled by Cognitive Radio and Machine Learning
The ability to intelligently utilize resources to meet the need of growing
diversity in services and user behavior marks the future of wireless
communication systems. Intelligent wireless communications aims at enabling the
system to perceive and assess the available resources, to autonomously learn to
adapt to the perceived wireless environment, and to reconfigure its operating
mode to maximize the utility of the available resources. The perception
capability and reconfigurability are the essential features of cognitive radio
while modern machine learning techniques project great potential in system
adaptation. In this paper, we discuss the development of the cognitive radio
technology and machine learning techniques and emphasize their roles in
improving spectrum and energy utility of wireless communication systems. We
describe the state-of-the-art of relevant techniques, covering spectrum sensing
and access approaches and powerful machine learning algorithms that enable
spectrum- and energy-efficient communications in dynamic wireless environments.
We also present practical applications of these techniques and identify further
research challenges in cognitive radio and machine learning as applied to the
existing and future wireless communication systems
Data and Spectrum Trading Policies in a Trusted Cognitive Dynamic Network
Future wireless networks will progressively displace service provisioning
towards the edge to accommodate increasing growth in traffic. This paradigm
shift calls for smart policies to efficiently share network resources and
ensure service delivery. In this paper, we consider a cognitive dynamic network
architecture (CDNA) where primary users (PUs) are rewarded for sharing their
connectivities and acting as access points for secondary users (SUs). CDNA
creates opportunities for capacity increase by network-wide harvesting of
unused data plans and spectrum from different operators. Different policies for
data and spectrum trading are presented based on centralized, hybrid and
distributed schemes involving primary operator (PO), secondary operator (SO)
and their respective end users. In these schemes, PO and SO progressively
delegate trading to their end users and adopt more flexible cooperation
agreements to reduce computational time and track available resources
dynamically. A novel matching-with-pricing algorithm is presented to enable
self-organized SU-PU associations, channel allocation and pricing for data and
spectrum with low computational complexity. Since connectivity is provided by
the actual users, the success of the underlying collaborative market relies on
the trustworthiness of the connections. A behavioral-based access control
mechanism is developed to incentivize/penalize honest/dishonest behavior and
create a trusted collaborative network. Numerical results show that the
computational time of the hybrid scheme is one order of magnitude faster than
the benchmark centralized scheme and that the matching algorithm reconfigures
the network up to three orders of magnitude faster than in the centralized
scheme.Comment: 15 pages, 12 figures. A version of this paper has been published in
IEEE/ACM Transactions on Networking, 201
Dynamic Decentralized Algorithms for Cognitive Radio Relay Networks
We propose a distributed spectrum access algorithm for cognitive radio relay
networks with multiple primary users (PU) and multiple secondary users (SU).
The key idea behind the proposed algorithm is that the PUs negotiate with the
SUs on both the amount of monetary compensation, and the amount of time the SUs
are either (i) allowed spectrum access, or (ii) cooperatively relaying the PU's
data, such that both the PUs' and the SUs' minimum rate requirement are
satisfied. The proposed algorithm is shown to be flexible in prioritizing
either the primary or the secondary users. We prove that the proposed algorithm
will result in the best possible stable matching and is weak Pareto optimal.
Numerical analysis also reveal that the distributed algorithm can achieve a
performance comparable to an optimal centralized solution, but with
significantly less overhead and complexity
Generic Multiuser Coordinated Beamforming for Underlay Spectrum Sharing
The beamforming techniques have been recently studied as possible enablers
for underlay spectrum sharing. The existing beamforming techniques have several
common limitations: they are usually system model specific, cannot operate with
arbitrary number of transmit/receive antennas, and cannot serve arbitrary
number of users. Moreover, the beamforming techniques for underlay spectrum
sharing do not consider the interference originating from the incumbent primary
system. This work extends the common underlay sharing model by incorporating
the interference originating from the incumbent system into generic combined
beamforming design that can be applied on interference, broadcast or multiple
access channels. The paper proposes two novel multiuser beamforming algorithms
for user fairness and sum rate maximization, utilizing newly derived convex
optimization problems for transmit and receive beamformers calculation in a
recursive optimization. Both beamforming algorithms provide efficient operation
for the interference, broadcast and multiple access channels, as well as for
arbitrary number of antennas and secondary users in the system. Furthermore,
the paper proposes a successive transmit/receive optimization approach that
reduces the computational complexity of the proposed recursive algorithms. The
results show that the proposed complexity reduction significantly improves the
convergence rates and can facilitate their operation in scenarios which require
agile beamformers computation.Comment: 30 pages, 5 figure
On Green Energy Powered Cognitive Radio Networks
Green energy powered cognitive radio (CR) network is capable of liberating
the wireless access networks from spectral and energy constraints. The
limitation of the spectrum is alleviated by exploiting cognitive networking in
which wireless nodes sense and utilize the spare spectrum for data
communications, while dependence on the traditional unsustainable energy is
assuaged by adopting energy harvesting (EH) through which green energy can be
harnessed to power wireless networks. Green energy powered CR increases the
network availability and thus extends emerging network applications. Designing
green CR networks is challenging. It requires not only the optimization of
dynamic spectrum access but also the optimal utilization of green energy. This
paper surveys the energy efficient cognitive radio techniques and the
optimization of green energy powered wireless networks. Existing works on
energy aware spectrum sensing, management, and sharing are investigated in
detail. The state of the art of the energy efficient CR based wireless access
network is discussed in various aspects such as relay and cooperative radio and
small cells. Envisioning green energy as an important energy resource in the
future, network performance highly depends on the dynamics of the available
spectrum and green energy. As compared with the traditional energy source, the
arrival rate of green energy, which highly depends on the environment of the
energy harvesters, is rather random and intermittent. To optimize and adapt the
usage of green energy according to the opportunistic spectrum availability, we
discuss research challenges in designing cognitive radio networks which are
powered by energy harvesters
On Oligopoly Spectrum Allocation Game in Cognitive Radio Networks with Capacity Constraints
Dynamic spectrum sharing is a promising technology to improve spectrum
utilization in the future wireless networks. The flexible spectrum management
provides new opportunities for licensed primary user and unlicensed secondary
users to reallocate the spectrum resource efficiently. In this paper, we
present an oligopoly pricing framework for dynamic spectrum allocation in which
the primary users sell excessive spectrum to the secondary users for monetary
return. We present two approaches, the strict constraints (type-I) and the QoS
penalty (type-II), to model the realistic situation that the primary users have
limited capacities. In the oligopoly model with strict constraints, we propose
a low-complexity searching method to obtain the Nash Equilibrium and prove its
uniqueness. When reduced to a duopoly game, we analytically show the
interesting gaps in the leader-follower pricing strategy. In the QoS penalty
based oligopoly model, a novel variable transformation method is developed to
derive the unique Nash Equilibrium. When the market information is limited, we
provide three myopically optimal algorithms "StrictBEST", "StrictBR" and
"QoSBEST" that enable price adjustment for duopoly primary users based on the
Best Response Function (BRF) and the bounded rationality (BR) principles.
Numerical results validate the effectiveness of our analysis and demonstrate
the fast convergence of "StrictBEST" as well as "QoSBEST" to the Nash
Equilibrium. For the "StrictBR" algorithm, we reveal the chaotic behaviors of
dynamic price adaptation in response to the learning rates.Comment: 40 pages, 22 figure
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
Hierarchic Power Allocation for Spectrum Sharing in OFDM-Based Cognitive Radio Networks
In this paper, a Stackelberg game is built to model the hierarchic power
allocation of primary user (PU) network and secondary user (SU) network in
OFDM-based cognitive radio (CR) networks. We formulate the PU and the SUs as
the leader and the followers, respectively. We consider two constraints: the
total power constraint and the interference-to-signal ratio (ISR) constraint,
in which the ratio between the accumulated interference and the received signal
power at each PU should not exceed certain threshold. Firstly, we focus on the
single-PU and multi-SU scenario. Based on the analysis of the Stackelberg
Equilibrium (SE) for the proposed Stackelberg game, an analytical hierarchic
power allocation method is proposed when the PU can acquire the additional
information to anticipate SUs' reaction. The analytical algorithm has two
steps: 1) The PU optimizes its power allocation with considering the reaction
of SUs to its action. In the power optimization of the PU, there is a sub-game
for power allocation of SUs given fixed transmit power of the PU. The existence
and uniqueness for the Nash Equilibrium (NE) of the sub-game are investigated.
We also propose an iterative algorithm to obtain the NE, and derive the
closed-form solutions of NE for the perfectly symmetric channel. 2) The SUs
allocate the power according to the NE of the sub-game given PU's optimal power
allocation. Furthermore, we design two distributed iterative algorithms for the
general channel even when private information of the SUs is unavailable at the
PU. The first iterative algorithm has a guaranteed convergence performance, and
the second iterative algorithm employs asynchronous power update to improve
time efficiency. Finally, we extend to the multi-PU and multi-SU scenario, and
a distributed iterative algorithm is presented
Performance Analysis of Wireless Network with Opportunistic Spectrum Sharing via Cognitive Radio Nodes
Cognitive radio (CR) is found to be an emerging key for efficient spectrum
utilization. In this paper, spectrum sharing among service providers with the
help of cognitive radio has been investigated. The technique of spectrum
sharing among service providers to share the licensed spectrum of licensed
service providers in a dynamic manner is considered. The performance of the
wireless network with opportunistic spectrum sharing techniques is analyzed.
Thus, the spectral utilization and efficiency of sensing is increased, the
interference is minimized, and the call blockage is reduced.Comment: 10 Pages, Journal of Electronic Science and Technology, Vol. 10, No.
4, December 2012. arXiv admin note: text overlap with arXiv:1210.3435; and
with arXiv:1201.1964 by other authors without attributio
Distributed Learning for Channel Allocation Over a Shared Spectrum
Channel allocation is the task of assigning channels to users such that some
objective (e.g., sum-rate) is maximized. In centralized networks such as
cellular networks, this task is carried by the base station which gathers the
channel state information (CSI) from the users and computes the optimal
solution. In distributed networks such as ad-hoc and device-to-device (D2D)
networks, no base station exists and conveying global CSI between users is
costly or simply impractical. When the CSI is time varying and unknown to the
users, the users face the challenge of both learning the channel statistics
online and converge to a good channel allocation. This introduces a multi-armed
bandit (MAB) scenario with multiple decision makers. If two users or more
choose the same channel, a collision occurs and they all receive zero reward.
We propose a distributed channel allocation algorithm that each user runs and
converges to the optimal allocation while achieving an order optimal regret of
O\left(\log T\right). The algorithm is based on a carrier sensing multiple
access (CSMA) implementation of the distributed auction algorithm. It does not
require any exchange of information between users. Users need only to observe a
single channel at a time and sense if there is a transmission on that channel,
without decoding the transmissions or identifying the transmitting users. We
demonstrate the performance of our algorithm using simulated LTE and 5G
channels
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