Optimization of transceivers with bit allocation to maximize bit rate for MIMO transmission

Li, Chien-Chang; Lin, Yuan-Pei; Tsai, Shang-Ho; Vaidyanathan, P.P.

doi:10.1109/tcomm.2009.12.080194

Cited by 12 publications

(23 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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. The following lemmas will be useful for subsequent discussion.…”

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

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: 96%

“…In the context of transceiver design, there have been many studies on power minimization [11]- [18] and rate maximization [19]- [25]. ZF solutions with the aim of minimizing the total transmit power for a given BER are developed in [11].…”

Section: Introductionmentioning

confidence: 99%

“…A two step transmitter design for minimizing the total transmit power for a target bit rate and BER constraint is proposed in [18]. ZF transceiver design with bit allocation for maximizing bit rate is proposed in [19] and [20]. An unified framework for designing MMSE MIMO systems with a power constraint is proposed in [21].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

On the Duality of MIMO Transceiver Designs With Bit Allocation

Lin

2011

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

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.

show abstract

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

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On the Duality of MIMO Transceiver Designs With Bit Allocation

Lin

2011

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

show abstract

“…For the case that such instantaneous CSI is also available at the transmitter, the design of linear minimum mean-square error (MMSE) transmitters and receivers is addressed in [1]. Other contributions in this context include [2] and [3].…”

Section: Introductionmentioning

confidence: 99%

Bit loading for MIMO with statistical channel information at the transmitter and MMSE receivers

Armada

Lozano

2012

2012 IEEE Wireless Communications and Networking Conference (WCNC)

View full text Add to dashboard Cite

Abstract-Bit and power loading schemes for a single-user multiple-input multiple-output channel using a minimum meansquare error receiver are proposed. Such schemes are derived without the benefit of instantaneous channel state information (CSI) at the transmitter, on the basis of only statistical CSI. They exploit the relationships between the average bit error probability at the receiver and the power allocation at the transmitter, in the context of spatially correlated Rayleigh fading. The performance of the proposed schemes is then benchmarked with respect to both non-adaptive transmission and to an adaptive counterpart with a zero-forcing receiver.Index Terms-bit loading, minimum mean-square error (MMSE), bit error rate (BER), multiple-input multiple-output (MIMO), correlated channel.

show abstract

Optimal Bit Allocation-Based Hybrid Precoder-Combiner Design Techniques for mmWave MIMO-OFDM Systems

et al. 2021

View full text Add to dashboard Cite

This work conceives techniques for the design of hybrid precoders/combiners for optimal bit allocation in frequency selective millimeter wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems, toward transmission rate maximization. Initially, the optimal fully digital ideal precoder/ combiner design is derived together with a closed-form expression for the optimal bit allocation in the above system. This is followed by the development of a framework for optimal transceiver design and bit allocation in a practical mmWave MIMO-OFDM implementation with a hybrid architecture. It is demonstrated that the pertinent problem can be formulated as a multiple measurement vector (MMV)-based sparse signal recovery problem for joint design of the RF and baseband components across all the subcarriers, and an explicit algorithm is derived to solve this using the simultaneous orthogonal matching pursuit (SOMP). To overcome the shortcomings of the SOMP-based greedy approach, an MMV sparse Bayesian learning (MSBL)-based state-of-the-art algorithm is subsequently developed, which is seen to lead to improved performance due to the superior sparse recovery properties of the Bayesian learning framework. Simulation results verify the efficacy of the proposed designs and also demonstrate that the performance of the hybrid transceiver is close to that of its fully-digital counterpart.

show abstract

Optimization of transceivers with bit allocation to maximize bit rate for MIMO transmission

Cited by 12 publications

References 15 publications

On the Duality of MIMO Transceiver Designs With Bit Allocation

On the Duality of MIMO Transceiver Designs With Bit Allocation

Bit loading for MIMO with statistical channel information at the transmitter and MMSE receivers

Optimal Bit Allocation-Based Hybrid Precoder-Combiner Design Techniques for mmWave MIMO-OFDM Systems

Contact Info

Product

Resources

About