7 research outputs found
On the Combined Effect of Directional Antennas and Imperfect Spectrum Sensing upon Ergodic Capacity of Cognitive Radio Systems
We consider a cognitive radio system, consisting of a primary transmitter
(PUtx), a primary receiver (PUrx), a secondary transmitter (SUtx), and a
secondary receiver (SUrx). The secondary users (SUs) are equipped with
steerable directional antennas. We assume the SUs and primary users (PUs)
coexist and the SUtx knows the geometry of network. We find the ergodic
capacity of the channel between SUtx and SUrx , and study how spectrum sensing
errors affect the capacity. In our system, the SUtx first senses the spectrum
and then transmits data at two power levels, according to the result of
sensing. The optimal SUtx transmit power levels and the optimal directions of
SUtx transmit antenna and SUrx receive antenna are obtained by maximizing the
ergodic capacity, subject to average transmit power and average interference
power constraints. To study the effect of fading channel, we considered three
scenarios: 1) when SUtx knows fading channels between SUtx and PUrx, PUtx and
SUrx, SUtx and SUrx, 2) when SUtx knows only the channel between SUtx and SUrx,
and statistics of the other two channels, and, 3) when SUtx only knows the
statistics of these three fading channels. For each scenario, we explore the
optimal SUtx transmit power levels and the optimal directions of SUtx and SUrx
antennas, such that the ergodic capacity is maximized, while the power
constraints are satisfied
Beam Selection and Discrete Power Allocation in Opportunistic Cognitive Radio Systems with Limited Feedback Using ESPAR Antennas
We consider an opportunistic cognitive radio (CR) system consisting of a
primary user (PU), secondary transmitter (SUtx), and secondary receiver (SUrx),
where SUtx is equipped with an electrically steerable parasitic array radiator
(ESPAR) antenna with the capability of choosing one beam among M beams for
sensing and communication, and there is a limited feedback channel from SUrx to
SUtx. Taking a holistic approach, we develop a framework for integrated
sector-based spectrum sensing and sector-based data communication. Upon sensing
the channel busy, SUtx determines the beam corresponding to PU's orientation.
Upon sensing the channel idle, SUtx transmits data to SUrx, using the selected
beam corresponding to the strongest channel between SUtx and SUrx. We formulate
a constrained optimization problem, where SUtx-SUrx link ergodic capacity is
maximized, subject to average transmit and interference power constraints, and
the optimization variables are sensing duration, thresholds of channel
quantizer at SUrx, and transmit power levels at SUtx. Since this problem is
non-convex we develop a suboptimal computationally efficient iterative
algorithm to find the solution. Our results demonstrate that our CR system
yields a significantly higher capacity, and lower outage and symbol error
probabilities, compared with a CR system that its SUtx has an omni-directional
antenna.Comment: This paper has been submitted to IEEE Transactions on Cognitive
Communications and Networkin
Compressive Sensing Based Direction-Of-Arrival Estimation Using Reweighted Greedy Block Coordinate Descent Algorithm For Espar Antennas
In this paper, we consider the problem of direction-of-arrival (DoA) estimation using electronically steerable parasitic array radiator (ESPAR) antenna based on compressive sensing. For an ESPAR antenna, the beampatterns and sparse model of DoA estimation problem in terms of overcomplete dictionary and sampling grid is presented. The DoA estimation problem is formulated as a mixed-norm β2,1 minimization problem and the reactance domain multiple signal classification (RD-MUSIC) spatial spectrum for ESPAR antenna is introduced. Then, we propose reweighted greedy block coordinate descent (RW-GBCD) and reweighted β2,1-SVD (RW-β2,1-SVD) algorithms for DOA estimation using ESPAR. The performance of RW-GBCD for DoA estimation is compared to that of GBCD, β2,1-SVD and RD-MUSIC algorithms. RW-GBCD benefits from less computational complexity compared to RW-β2,1-SVD. Simulation results demonstrate that the performance of RW-GBCD is better than that of GBCD and β2,1-SVD. When angle separation is less than 10Β°, RW-β2,1-SVD outperforms RW-GBCD. However, when angle separation is more than 10Β°, the performance of RW-GBCD in terms of root mean square error (RMSE) is approximately the same as that of RW-β2,1-SVD