MMSE vector precoding with joint transmitter and receiver design for MIMO systems

Liu, Feng; Jiang, Lingge; He, Chen

doi:10.1016/j.sigpro.2007.05.025

Cited by 8 publications

(3 citation statements)

References 11 publications

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“…The latter procedures for all CTs are the same, i.e. the greedy user scheduling [9] and MMSE precoding [21] are operated based on the tracked channel under total power constraint.…”

Section: Transmission Scheme Designmentioning

confidence: 99%

Location‐aided channel tracking and downlink transmission for HST massive MIMO systems

Zhang

Huang

et al. 2017

IET Communications

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“…The latter procedures for all CTs are the same, i.e. the greedy user scheduling [9] and MMSE precoding [21] are operated based on the tracked channel under total power constraint.…”

Section: Transmission Scheme Designmentioning

confidence: 99%

Location‐aided channel tracking and downlink transmission for HST massive MIMO systems

Zhang

Huang

et al. 2017

IET Communications

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“…The ML-like sphere decoding technique [2] involves an exhaustive search in the hypersphere, the dimensions of which remain high in the case of large-scale MIMO, resulting in detection that is impossible to solve from a complexity point of view. Linear solutions such as Minimum Mean Square Error (MMSE) [3] and Zero Forcing (ZF) have a low computational complexity at the expense of a high performance loss. Successive interference cancellation (SIC) schemes [4], [5] have been proposed such as MMSE-SIC in [6] to improve linear detector performance at the expense of higher complexity.…”

Section: Introductionmentioning

confidence: 99%

Iterative Receivers for Large-Scale MIMO Systems With Finite-Alphabet Simplicity-Based Detection

2020

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In this paper, we consider large-scale MIMO systems and we define iterative receivers which use the simplicity-based detection algorithm referred to as Finite Alphabet Simplicity (FAS) algorithm. First, we focus on uncoded systems and we propose a novel successive interference cancellation algorithm with an iterative processing based on the shadow area principle and we optimize its parameters by exploiting the theoretical analysis of the detector output. Secondly, we assume FEC-encoded systems and we propose an iterative receiver based on a maximum likelihood-like detection with restricted candidate subset defined by the FAS algorithm output. We also introduce another receiver based on FAS detection whose criterion is penalized with the mean absolute error function. Simulations results show the efficiency of all proposed iterative receivers compared to the state-of-the-art methods.

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“…It is worth noticing that our method can be simply transfer into MMSE VP proposed in [14]. The complexity of MLRA algorithm is roughly 2K times of the basic LRA VP.…”

Section: Introductionmentioning

confidence: 99%

Multi-lattice-reduction aided vector precoding for multi-user MIMO downlink system

Shi

Jiang

2009

2009 Asia Pacific Conference on Postgraduate Research in Microelectronics &Amp; Electronics (PrimeAsia)

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We consider a multiuser MIMO downlink system with NT transmit antennas and N R users each with one antenna. We assume that the channel is quasi-static flat fading and the perfect channel state information is available at the transmit side. So the system model can be written as where x., E ceNT X1 is the transmitted signal, y c E ceNR x 1 is the received signal. He E ceNRXNT is the channel matrix with the entry hc,ml representing the complex transfer function from transmit antenna l to the mth user. Furthermore, hc,ml is circularly symmetric complex Gaussian distributed with unit variance. n., E ceNR x 1 denotes i.i.d. additive white Gaussian noise with zero mean and variance a~. Without loss of generality, we assume that NT = N R = K.It is noted that the real model has advantage over the complex model [11]. So we transform the complex system model to the equivalent real-valued system model by stacking real which is much simpler than the SD algorithm. Although the LRA method has the same diversity with SD, there has much performance gap between them. Our work attempts to fill the performance gap between the two methods above, thus sacrifices some of complexity advantage over LRA method. The original LRA method reduces the lattice only once to obtain searching space for perturbation vector, which may cause searching errors. In order to reduce searching errors, we alternately fix each of the elements of the original perturbation vector to the possible values and reduce the formulated lattice to get subspaces of searching space. For each subspace, there is a perturbation vector to be solved. Therefore, a set of candidates of perturbation vectors is created and the optimum one is chosen from this set. The searching procedure shows that our algorithm requires to reduce lattice several times which decided by the numbers of antennas, so that we name our method as multi-Iatticereduction aided (MLRA) algorithm. Although the proposed method has slightly more complexity than LRA algorithm, simulation shows that it leads to immense improvement of performance closing to SD only with polynomial complexity.Abstract-A multi-lattice-reduction aided(MLRA) vector precoding algorithm is proposed in this paper. Compared with traditionallattice-reduction-aided(LRA) method, the new method tries to find a set of candidates of perturbation vectors through reducing the formulated lattice to get subspaces of searching space. Within each subspace, a candidate vector is solved. Then the optimum vector is found in this candidate set. Although the searching procedure has slightly more complexity than LRA method, simulation results show that it outperforms the LRA method and fills the performance gap to the sphere decode algorithm with only polynomial complexity.

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MMSE vector precoding with joint transmitter and receiver design for MIMO systems

Cited by 8 publications

References 11 publications

Location‐aided channel tracking and downlink transmission for HST massive MIMO systems

Location‐aided channel tracking and downlink transmission for HST massive MIMO systems

Iterative Receivers for Large-Scale MIMO Systems With Finite-Alphabet Simplicity-Based Detection

Multi-lattice-reduction aided vector precoding for multi-user MIMO downlink system

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