Root Mean Square Decomposition for EST-Based Spatial Multiplexing Systems

Hwang, Taewon; Kwon, Younggap

doi:10.1109/tsp.2011.2179649

Cited by 9 publications

(11 citation statements)

References 25 publications

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“…Consider more general cases of K > 2 users in one multicasting system with an extension version of c ∈ R K , e ∈ R K , and G ∈ R K×3 , respectively. It is clear from (6) that no proper solution x ∈ R 3 will exist with probability 1. Therefore, we only focus on precoding for multicasting systems with two users.…”

Section: Jgmd For Complex-valued Matrix Casementioning

confidence: 99%

“…For instance, singular value decomposition (SVD) probably serves as a prevailing way to construct the capacity-achieving precoder for a single-user scenario by diagonalizing the MIMO channel matrix. To avoid introducing rather complicated bit loading strategies at the transmit side, the precoding scheme in [2] using geometric mean decomposition (GMD) [3,4] *Correspondence: xdxu@ustc.edu.cn Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, Anhui 230027, People's Republic of China and later the unitary channel decomposition (UCD) scheme [5] and the root-mean-square decomposition (RMSD) scheme [6] are successively proposed to achieve optimum bit error rate (BER) and channel throughput simultaneously.…”

Section: Introductionmentioning

confidence: 99%

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An improved block diagonal precoding scheme for MIMO multicast channel with two users

Zhang

2014

EURASIP J. Adv. Signal Process.

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Matrix theory plays an important role in precoding methodology for multiple input multiple output (MIMO) systems. In this paper, an improved block diagonal (BD) precoding scheme is proposed for a MIMO multicast channel with two users, where the unitary precoding matrix is constructed in a block-wise form by joint triangularization decomposition. In order to reduce large signal-to-noise ratios (SNRs) spread across different transmitted data streams and users, the combination of joint equi-diagonal triangularization (JET) and joint geometric mean decomposition (JGMD) is applied to submatrix construction in the inner process of this precoding scheme. An elaborate implementation is presented, and the existence condition of JGMD is also investigated for two complex-valued matrices with two columns, where the analytical result reveals the connection with the particular channel realization and essentially determines when to consider JGMD for submatrix construction. In addition, the properties of the diagonal elements generated by joint triangularization decomposition are discussed as well as the computational complexity of the proposed scheme. Simulation results indicate that in general, JGMD is employed with high probability in the hybrid model, and the proposed scheme readily outperforms the JET scheme in terms of bit error rate (BER) performance in the moderate to high SNR regimes.

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Section: Jgmd For Complex-valued Matrix Casementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An improved block diagonal precoding scheme for MIMO multicast channel with two users

Zhang

2014

EURASIP J. Adv. Signal Process.

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“…2), the number of VAR samples (also the number of QoC constraints) and their positions depend on the shape of the SINR transfer curve and have to be chosen appropriately case by case. However, as will be shown in Section IV-C, the number of QoC constraints determines the computational cost of solving the problem (15). Therefore, there should be a trade-off between the precision of the approximation and the total computational load of solving the optimization problem.…”

Section: A Quality-of-convergence (Qoc)mentioning

confidence: 99%

“…It was shown in [10], [11] that iterative FDE approaches the matched filter bound (MFB), a theoretical lower bound for equalization systems in frequency selective channels, while it keeps the complexity comparable to that of the linear minimum-mean-square-error (MMSE) equalizer and the decision feedback equalizer (DFE) [12]. Moreover, the basic concept of iterative FDE has already been extended to single-user multiplexing (SU-MIMO) systems [13]- [15] and to multiple access (MAC) systems [16], [17]. Further applications and extensions of iterative FDE can be found in [3], [18]- [20] and the references therein.…”

Section: Introductionmentioning

confidence: 99%

“…In this framework, the statistical behavior of the equalizer and the soft decider is carefully studied, such that the iterative exchange of information between these two devices can be precisely characterized in a semi-analytical way. This framework is directly applicable to existing studies on iterative FDE [8]- [10], and can be readily extended to SU-MIMO [13]- [15] and MAC [16], [17] schemes as well. The evolutional analysis also leads to special formulations of both QoS and QoC constraints, which further facilitate recasting the original power allocation problem into a structured convex optimization problem.…”

Section: Introductionmentioning

confidence: 99%

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Joint Transceiver Design for Iterative FDE

Han

Yin

Bai

et al. 2013

IEEE Trans. Signal Process.

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This paper considers the wideband single-carrier transmission through frequency-selective wireless channels with a quality-of-service (QoS) requirement. Power allocation at the transmitter and iterative frequency domain equalization (FDE) at the receiver are jointly designed to achieve the target QoS with minimum transmission power consumption. The joint transceiver design is based on a novel evolutional analysis framework for iterative FDE without the aid of decoding during the iterative processing. It introduces a set of QoS and quality-of-convergence (QoC) constraints, which recast the design problem into a convex and structured joint QoS/QoC constrained power allocation problem. Thanks to the underlying sparsity in the mathematical structure of the problem, a specific interior-point method (IPM) solver is developed, which significantly reduces the computational complexity, as compared to the generic solvers. A duality gap analysis is then applied to show that the performance gap between the numerical algorithms and the theoretical optimum can be controlled by adjusting its key parameters, which, in turn, determines the convergence behavior of the iterative solver. Extensive simulation results are given to support the mathematical development of the problem.Index Terms-Joint transmit-receive equalization, frequency domain equalization, iterative processing, power allocation, interior-point method.

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