2010
DOI: 10.1109/lcomm.2010.05.100017
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A computationally efficient near-optimal algorithm for capacity-maximization based joint transmit and receive antenna selection

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Cited by 23 publications
(27 citation statements)
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“…Values for each scenario are shown in Table II. Taking into consideration the number of channel realizations executed in [14], and in order to ensure that we included a vast variety of them, we executed a Monte Carlo simulation consisted of 10.000 channel realizations, in total.…”
Section: Simulation Resultsmentioning
confidence: 99%
“…Values for each scenario are shown in Table II. Taking into consideration the number of channel realizations executed in [14], and in order to ensure that we included a vast variety of them, we executed a Monte Carlo simulation consisted of 10.000 channel realizations, in total.…”
Section: Simulation Resultsmentioning
confidence: 99%
“…Thus, in light of Theorem 1, and the sub-modularity of the objective function in the receive antenna selection problem (Theorem 4), a greedy algorithm (found in [6], [7]) that selects L out of the N r receive antennas is as follows.…”
Section: A Sub-modularity Of the Receive Antenna Selection Problemmentioning
confidence: 99%
“…By making use of results available in the approximation algorithms literature, we have been able to justify the use of greedy approaches for receive antenna selection and derive a lower bound on their performance. Prior work on finding the optimal antenna subset includes the greedy algorithm [1], [6], [7], however, provides only simulation results with no theoretical bounds. The simulated performance of a greedy algorithm in [1], [6], [7] is close to the optimal, and hence better than the theoretical result we have derived.…”
Section: A Sub-modularity Of the Receive Antenna Selection Problemmentioning
confidence: 99%
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