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Keywords
(14)
Cognitive Systems
Community Networks
Cooperative Communication
Iterative Algorithm
Iterative Solution
Multiple Antennas
Objective Function
Optimization Problem
Rate of Convergence
Satisfiability
Signal To Noise Ratio
Power Control
Primary User
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Jointly Optimal Source Power Control and Relay Matrix Design in MultipointtoMultipoint Cooperative Communication Networks
Jointly Optimal Source Power Control and Relay Matrix Design in MultipointtoMultipoint Cooperative Communication Networks,10.1109/TSP.2011.2158426,I
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Jointly Optimal Source Power Control and Relay Matrix Design in MultipointtoMultipoint Cooperative Communication Networks
(
Citations: 2
)
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Keyvan Zarifi
,
Ali Ghrayeb
,
Sofiène Affes
A
cooperative communication
network is considered wherein sources aim to transmit to their designated destina tions through the use of a multipleantenna relay. All sources transmit to the relay in a shared channel in the first transmission phase. Then, the relay linearly processes its received signal vector using relaying matrices and retransmits the resultant signals towards the destinations in dedicated channels in the second transmission phase. The goal is to jointly optimize the sources' transmit powers and the relaying matrices such that the worst normalized signaltointerferenceplusnoise ratio (SINR) among all destinations is maximized while the relays' transmit powers in the dedicated channels as well as the sources' individual and total transmit powers do not exceed predetermined thresholds. It is shown that the jointly optimal sources' transmit powers and the relaying matrices are the solutions to an
optimization problem
with a nonconvex
objective function
and multiple non convex constraints. To solve this problem, it is first proved that all normalized SINRs are equal at the optimal point of the objective function. Then, the
optimization problem
is transformed through multiple stages into an equivalent problem that is amenable to an iterative solution. Finally, an efficient
iterative algorithm
is developed that offers the jointly optimal sources' transmit powers and the relaying matrices. An extension to the above problem is then studied in the case when the
cooperative communication
network acts as a cognitive system that is expected to operate such that its interfering effect on the primary users is below some admissibility thresholds. In such a case, the sources' and relay's transmit powers should further satisfy some additional constraints that compel a new technique to tackle the problem of the joint optimization of the sources' transmit powers and the relaying matrices. An
iterative solution
to the latter problem is also proposed and the efficiency and the high
rate of convergence
of the proposed iterative algorithms in both the original and the cognitive cases are verified by simulation examples.
Journal:
IEEE Transactions on Signal Processing  TSP
, vol. 59, no. 9, pp. 43134330, 2011
DOI:
10.1109/TSP.2011.2158426
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Citation Context
(2)
...There is a growing research interest in the applications of multipoint to multipoint wireless networks wherein multiple sources communicate with their dedicated destinations [1][
8
]...
...Unfortunately, the technique introduced in [14] cannot be used to solve (12) as (6) and (8) pose M +1 additional constraints that jointly depend on w and p and, further, are nonconvex with respect to the total design parameters (w, p). Assuming that the transmissions from the relays to the destinations are carried out in dedicated orthogonal channels, we have developed in [7] (also [
8
]) a technique that jointly optimizes the sources’ ...
...It can be shown that Λl(w) are nonnegative primitive matrices for l =1 ,..., 2L +1 [
8
]...
...This requirement is met if and only if [
8
], [15]...
Keyvan Zarifi
,
et al.
Joint Source Power Control and Relay Beamforming in AmplifyandForwar...
...G ll , (5) can be equivalently represented as [
8
]...
...It can be proved that [
8
] Λl(W) is a nonnegative primitive matrix 3...
...The following theorem obtains Eo(W) and proves that λmax (Λm(W)) = λmax (Λn(W)) and pm(W )= pn(W) for m, n ∈E o(W). The proof of the theorem is given in [
8
]...
...Observation 3: Wo and po satisfy all constraints in (6) with equality [
8
], that is,...
...Observation 4: When Wo and po are jointly used, there is a constraint in (1) that holds with equality [
8
], that is, u T po = Pl (25)...
...The following theorem whose proof is given in [
8
] holds...
...L . Then, Wo and po are jointly optimal if and only if [
8
]...
...The convergence of W[n] and p[n] to Wo and po is guaranteed and is shown in [
8
]...
Keyvan Zarifi
,
et al.
Joint Power Control and Relay Matrix Design for Cooperative Communicat...
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(
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Journal:
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, vol. 6, no. 6, pp. 22822293, 2007
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Citations
(2)
Joint Source Power Control and Relay Beamforming in AmplifyandForward Cognitive Networks with Multiple SourceDestination Pairs
(
Citations: 1
)
Keyvan Zarifi
,
Sofiene Affes
,
Ali Ghrayeb
Published in 2011.
Joint Power Control and Relay Matrix Design for Cooperative Communication Networks with Multiple SourceDestination Pairs
(
Citations: 2
)
Keyvan Zarifi
,
Ali Ghrayeb
,
Sofiène Affes
Conference:
Global Telecommunications Conference, . GLOBECOM . IEEE  GLOBECOM
, pp. 16, 2010