1,790 research outputs found
Interference Alignment for Cognitive Radio Communications and Networks: A Survey
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Interference alignment (IA) is an innovative wireless transmission strategy that has shown to be a promising technique for achieving optimal capacity scaling of a multiuser interference channel at asymptotically high-signal-to-noise ratio (SNR). Transmitters exploit the availability of multiple signaling dimensions in order to align their mutual interference at the receivers. Most of the research has focused on developing algorithms for determining alignment solutions as well as proving interference alignment’s theoretical ability to achieve the maximum degrees of freedom in a wireless network. Cognitive radio, on the other hand, is a technique used to improve the utilization of the radio spectrum by opportunistically sensing and accessing unused licensed frequency spectrum, without causing harmful interference to the licensed users. With the increased deployment of wireless services, the possibility of detecting unused frequency spectrum becomes diminished. Thus, the concept of introducing interference alignment in cognitive radio has become a very attractive proposition. This paper provides a survey of the implementation of IA in cognitive radio under the main research paradigms, along with a summary and analysis of results under each system model.Peer reviewe
Optimal Real-time Spectrum Sharing between Cooperative Relay and Ad-hoc Networks
Optimization based spectrum sharing strategies have been widely studied.
However, these strategies usually require a great amount of real-time
computation and significant signaling delay, and thus are hard to be fulfilled
in practical scenarios. This paper investigates optimal real-time spectrum
sharing between a cooperative relay network (CRN) and a nearby ad-hoc network.
Specifically, we optimize the spectrum access and resource allocation
strategies of the CRN so that the average traffic collision time between the
two networks can be minimized while maintaining a required throughput for the
CRN. The development is first for a frame-level setting, and then is extended
to an ergodic setting. For the latter setting, we propose an appealing optimal
real-time spectrum sharing strategy via Lagrangian dual optimization. The
proposed method only involves a small amount of real-time computation and
negligible control delay, and thus is suitable for practical implementations.
Simulation results are presented to demonstrate the efficiency of the proposed
strategies.Comment: One typo in the caption of Figure 5 is correcte
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)
Interference-Aware Resource Control in Multi-Antenna Cognitive Ad Hoc Networks with Heterogeneous Delay Constraints
In this work, we consider a multi-antenna cognitive ad hoc network (CAHNet)
with heterogeneous delay requirements. To fulfill the interference and delay
constraints simultaneously, we propose to perform adaptive zero-forcing
beamforming (ZFBF) at cognitive transmitters according to interference channel
state information (CSI). To assist the CAHNet to obtain the interference CSI,
we use a win-win inter-network cooperation strategy, namely quantized
interference CSI feedback from the primary network to CAHNet through a feedback
link, under the condition that the CAHNet pays a proper price for it.
Considering the scarcity of feedback and power resources, we focus on the
minimization of the overall resource cost subject to both interference and
delay constraints. To solve the problem, we derive a joint feedback and power
control algorithm amongst multiple links of CAHNet. Finally, simulation results
validate the effectiveness of the proposed algorithm.Comment: 4 pages, 2 figure
Optimal Random Access and Random Spectrum Sensing for an Energy Harvesting Cognitive Radio
We consider a secondary user with energy harvesting capability. We design
access schemes for the secondary user which incorporate random spectrum sensing
and random access, and which make use of the primary automatic repeat request
(ARQ) feedback. The sensing and access probabilities are obtained such that the
secondary throughput is maximized under the constraints that both the primary
and secondary queues are stable and that the primary queueing delay is kept
lower than a specified value needed to guarantee a certain quality of service
(QoS) for the primary user. We consider spectrum sensing errors and assume
multipacket reception (MPR) capabilities. Numerical results are presented to
show the enhanced performance of our proposed system over a random access
system, and to demonstrate the benefit of leveraging the primary feedback.Comment: in WiMob 201
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