Convex Optimization Theory Applied to Joint Transmitter‐Receiver Design in MIMO Channels

Palomar, Daniel P.; Pascual‐Iserte, Antonio; Cioffi, J.M.; Lagunas, M.A.

doi:10.1002/0470010045.ch8

Cited by 9 publications

(19 citation statements)

References 84 publications

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“…The results are derived without using the existing optimal solutions. For a given symbol error rate constraint and target bit rate , the power-minimizing problem A with real bit allocation can be formulated as [11]- [17] A minimize subject to (7) where is the symbol error rate of the th subchannel and is the set of nonnegative real numbers. Given a symbol error rate constraint and power constraint , the rate-maximizing problem A with real bit allocation is [19]- [22] A maximize subject to (8) In either problem, we need to design the transmit matrix and bit allocation jointly to maximize bit rate or minimize power.…”

Section: Power-minimizing and Rate-maximizing Problems With Non-mentioning

confidence: 99%

“…3) When the error rate is constrained to be equal to for all subchannels, it has been shown that equality in the power and bit rate constraints will hold [17], [22], [25] using majorization theorem [41] and optimization theorem [42]. Lemma 3 is more general in that we have considered inequality constraint on the error rate in addition to power and bit rate constraints.…”

Section: ) Lemma 3 Shows That All the Inequalities In Constraints Ofmentioning

confidence: 99%

“…There have been many papers on the topics of power minimization [11]- [17] and bit rate maximization [19]- [25]. For the power minimization problem with integer bit allocation, the solution has been found in [22].…”

Section: Optimal Solution For Transceiver Design With Bit Allocationmentioning

confidence: 99%

“…In [16], the transmit power is minimized with different quality of service requirements using the MMSE criterion. Optimal transceiver that minimizes transmit power with a global quality of service constraint is proposed in [17]. A two step transmitter design for minimizing the total transmit power for a target bit rate and BER constraint is proposed in [18].…”

Section: Introductionmentioning

confidence: 99%

“…These two problems have been treated as different problems in the literature and they have been addressed independently, power minimization problem in [11]- [17] and rate maximization in [19]- [22]. We will show that these two are actually dual problems.…”

Section: Introductionmentioning

confidence: 99%

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On the Duality of MIMO Transceiver Designs With Bit Allocation

Lin

2011

IEEE Trans. Signal Process.

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Abstract-Bit rate and power are two commonly used optimality criteria for MIMO transceiver design. In the literature, bit rate maximization and power minimization problems are viewed as different problems and solved independently. In this paper, we derive the duality between these two problems for both the cases with and without integer constraint on bit allocation. We will show that if a transceiver is optimal for the power-minimizing problem, it is also optimal for the rate maximizing problem, and the converse is true. Such a duality has not been stated and proved in the literature to the best of our knowledge. The derivation does not involve any existing optimal solution and we can establish duality result even for the rate maximization problem with integer bit constraint, which the optimal solution is not known. The duality also allows us to develop an algorithm for finding the rate-maximizing transceiver with integer bit allocation using the solution of power-minimizing system. We will also consider some possible generalizations of the problem, for example, when there is a constraint on the maximal constellation size and when the subchannel bit error rates (BERs) are constrained. For each of these cases, we will see that the duality between the two problems continued to hold. In the simulations, we will compute the optimal solutions for these two problems and demonstrate the duality between these two.

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Section: Power-minimizing and Rate-maximizing Problems With Non-mentioning

confidence: 99%

Section: ) Lemma 3 Shows That All the Inequalities In Constraints Ofmentioning

confidence: 99%

Section: Optimal Solution For Transceiver Design With Bit Allocationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On the Duality of MIMO Transceiver Designs With Bit Allocation

Lin

2011

IEEE Trans. Signal Process.

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Training signal designs for spatially correlated multi-user multi-input multi-output with orthogonal frequency-division multiplexing systems

et al. 2012

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Optimal training design and channel estimation for spatially correlated multi-user multi-input multi-output with orthogonal frequency-division multiplexing (MIMO-OFDM) systems is still an open research topic of great interest. This study first applies tractable semi-definite programming (SDP) to obtain the optimal training signal for the general case of spatial channel correlations for multi-user MIMO-OFDM. In order to reduce the computational complexity of the SDP-based solution, an approximate solution in closed-form is then presented. For a special case of transmit correlations, an optimal solution in closed-form expression is also derived. Analytical and simulation results demonstrate the excellent performance of the proposed designs and their performance advantage over the existing equi-powered training designs.

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Robust Linear Precoding for Uncertain Miso Broadcast Channels

Shenouda

Davidson

2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings

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Convex Optimization Theory Applied to Joint Transmitter‐Receiver Design in MIMO Channels

Cited by 9 publications

References 84 publications

On the Duality of MIMO Transceiver Designs With Bit Allocation

On the Duality of MIMO Transceiver Designs With Bit Allocation

Training signal designs for spatially correlated multi-user multi-input multi-output with orthogonal frequency-division multiplexing systems

Robust Linear Precoding for Uncertain Miso Broadcast Channels

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