5,046 research outputs found
Adaptive Channel Recommendation For Opportunistic Spectrum Access
We propose a dynamic spectrum access scheme where secondary users recommend
"good" channels to each other and access accordingly. We formulate the problem
as an average reward based Markov decision process. We show the existence of
the optimal stationary spectrum access policy, and explore its structure
properties in two asymptotic cases. Since the action space of the Markov
decision process is continuous, it is difficult to find the optimal policy by
simply discretizing the action space and use the policy iteration, value
iteration, or Q-learning methods. Instead, we propose a new algorithm based on
the Model Reference Adaptive Search method, and prove its convergence to the
optimal policy. Numerical results show that the proposed algorithms achieve up
to 18% and 100% performance improvement than the static channel recommendation
scheme in homogeneous and heterogeneous channel environments, respectively, and
is more robust to channel dynamics
Power-Optimal Feedback-Based Random Spectrum Access for an Energy Harvesting Cognitive User
In this paper, we study and analyze cognitive radio networks in which
secondary users (SUs) are equipped with Energy Harvesting (EH) capability. We
design a random spectrum sensing and access protocol for the SU that exploits
the primary link's feedback and requires less average sensing time. Unlike
previous works proposed earlier in literature, we do not assume perfect
feedback. Instead, we take into account the more practical possibilities of
overhearing unreliable feedback signals and accommodate spectrum sensing
errors. Moreover, we assume an interference-based channel model where the
receivers are equipped with multi-packet reception (MPR) capability.
Furthermore, we perform power allocation at the SU with the objective of
maximizing the secondary throughput under constraints that maintain certain
quality-of-service (QoS) measures for the primary user (PU)
Energy-aware cooperative wireless networks with multiple cognitive users
In this paper, we study and analyze cooperative cognitive radio networks with arbitrary number of secondary users (SUs). Each SU is considered a prospective relay for the primary user (PU) besides having its own data transmission demand. We consider a multi-packet transmission framework that allows multiple SUs to transmit simultaneously because of dirty-paper coding. We propose power allocation and scheduling policies that optimize the throughput for both PU and SU with minimum energy expenditure. The performance of the system is evaluated in terms of throughput and delay under different opportunistic relay selection policies. Toward this objective, we present a mathematical framework for deriving stability conditions for all queues in the system. Consequently, the throughput of both primary and secondary links is quantified. Furthermore, a moment generating function approach is employed to derive a closed-form expression for the average delay encountered by the PU packets. Results reveal that we achieve better performance in terms of throughput and delay at lower energy cost as compared with equal power allocation schemes proposed earlier in the literature. Extensive simulations are conducted to validate our theoretical findings
To Stay Or To Switch: Multiuser Dynamic Channel Access
In this paper we study opportunistic spectrum access (OSA) policies in a
multiuser multichannel random access cognitive radio network, where users
perform channel probing and switching in order to obtain better channel
condition or higher instantaneous transmission quality. However, unlikely many
prior works in this area, including those on channel probing and switching
policies for a single user to exploit spectral diversity, and on probing and
access policies for multiple users over a single channel to exploit temporal
and multiuser diversity, in this study we consider the collective switching of
multiple users over multiple channels. In addition, we consider finite
arrivals, i.e., users are not assumed to always have data to send and demand
for channel follow a certain arrival process. Under such a scenario, the users'
ability to opportunistically exploit temporal diversity (the temporal variation
in channel quality over a single channel) and spectral diversity (quality
variation across multiple channels at a given time) is greatly affected by the
level of congestion in the system. We investigate the optimal decision process
in this case, and evaluate the extent to which congestion affects potential
gains from opportunistic dynamic channel switching
Optimal Spectrum Access for a Rechargeable Cognitive Radio User Based on Energy Buffer State
This paper investigates the maximum throughput for a rechargeable secondary
user (SU) sharing the spectrum with a primary user (PU) plugged to a reliable
power supply. The SU maintains a finite energy queue and harvests energy from
natural resources, e.g., solar, wind and acoustic noise. We propose a
probabilistic access strategy by the SU based on the number of packets at its
energy queue. We investigate the effect of the energy arrival rate, the amount
of energy per energy packet, and the capacity of the energy queue on the SU
throughput under fading channels. Results reveal that the proposed access
strategy can enhance the performance of the SU.Comment: arXiv admin note: text overlap with arXiv:1407.726
Optimal Selection of Spectrum Sensing Duration for an Energy Harvesting Cognitive Radio
In this paper, we consider a time-slotted cognitive radio (CR) setting with
buffered and energy harvesting primary and CR users. At the beginning of each
time slot, the CR user probabilistically chooses the spectrum sensing duration
from a predefined set. If the primary user (PU) is sensed to be inactive, the
CR user accesses the channel immediately. The CR user optimizes the sensing
duration probabilities in order to maximize its mean data service rate with
constraints on the stability of the primary and cognitive queues. The
optimization problem is split into two subproblems. The first is a
linear-fractional program, and the other is a linear program. Both subproblems
can be solved efficiently.Comment: Accepted in GLOBECOM 201
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