Fixed-complexity vector perturbation with block diagonalization for MU-MIMO systems

Mohaisen, Manar; Hui, Bing; Chang, KyungHi; Ji, Seunghwan; Joung, Jinsoup

doi:10.1109/micc.2009.5431503

Cited by 22 publications

(21 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As for lattice basis reduction precoding, the number of iterations required to orthogonalize the lattice basis can be as high as theoretically infinite, depending on the conditioning of the channel matrix (i.e., lattice basis). We are proposing several low and fixed computational complexity algorithms that aim to find the best t vector such that the total transmit power is reduced [14][15][16][17]. That is:…”

Section: Statement Of Problemmentioning

confidence: 99%

“…To reduce the complexity of the users' receivers, we took into consideration that the channel effect is equalized for at the transmitter side by means of precoding. Moreover, we employed the already explained VP techniques [16,17].…”

Section: Block Diagonalizationmentioning

confidence: 99%

“…Note that the aforementioned precoding approaches conventionally assume that a single stream transmission is employed. Block diagonalization (BD), which supports multi-stream transmission, can be used to transform the MU-MIMO channel into several parallel SU-MIMO systems that can be independently precoded [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Review of Fixed-Complexity Vector Perturbation for MU-MIMO

Mohaisen

2015

J Inf Process Syst

View full text Add to dashboard Cite

Recently, there has been an increasing demand of high data rates services, where several multiuser multipleinput multiple-output (MU-MIMO) techniques were introduced to meet these demands. Among these techniques, vector perturbation combined with linear precoding techniques, such as zero-forcing and minimum mean-square error, have been proven to be efficient in reducing the transmit power and hence, perform close to the optimum algorithm. In this paper, we review several fixed-complexity vector perturbation techniques and investigate their performance under both perfect and imperfect channel knowledge at the transmitter. Also, we investigate the combination of block diagonalization with vector perturbation outline its merits.

show abstract

Section: Statement Of Problemmentioning

confidence: 99%

Section: Block Diagonalizationmentioning

confidence: 99%

See 1 more Smart Citation

A Review of Fixed-Complexity Vector Perturbation for MU-MIMO

Mohaisen

2015

J Inf Process Syst

View full text Add to dashboard Cite

show abstract

“…Fig. 4 shows the performance comparison between the LTE codebook with unitary precoding and the proposed codebook with Tomlinson-Harashima precoding (THP) [3] . The results are obtained in  ×  system, and show that the implementation of LTE codebook has much worse performance.…”

Section: ) Normalization Of the Csimentioning

confidence: 99%

Codebook based Direct Vector Quantization of MIMO Channel Matrix with Channel Normalization

Hui¹,

Chang²

2014

The Journal of Korean Institute of Communications and Informati

Self Cite

View full text Add to dashboard Cite

show abstract

“…Several practical precoding techniques have been proposed in the literature, including linear precoding techniques [4], Tomlison-Harashimaprecoding [5], [6], and vector perturbation techniquescombined with linear precoders [7]- [10]. Linear zero-forcing precoding(ZFP) can be seen as a beamforming algorithm, where the beamforming weights are the rows of the pseudo-inverse of the channel matrix, while in the minimum-mean square error precoder (MMSEP) the channel matrix is regularized so that a tradeoff between noise amplification and IUI is achieved.…”

Section: Iintroductionmentioning

confidence: 99%

Transmit Antenna Selection for Multi-user MIMO Precoding Systems with Limited Feedback

Mohaisen¹

2011

Journal of information and communication convergence engineerin

Self Cite

View full text Add to dashboard Cite

Abstract-Transmit antenna selection techniques are prominent since they exploit the spatial selectivity at the transmitter side. In the literature, antenna selection techniques assume full knowledge of the channel state information (CSI). In this paper, we consider that the CSI is not perfectly known at the transmitter; however, a quantized version of the channel coefficients is fed back by the users. We employ the non-uniform Lloyd-Max quantization algorithm which takes into consideration the distribution of the channel coefficients. Simulation results show that the degradation in the BER of the system with imperfect CSI at the transmitter is tolerable, especially when the transmit diversity order is high.Index Terms-Multi-user MIMO system, transmit antenna selection, imperfect channel state information, nonuniform quantization, Lloyd-Max quantizer. I.INTRODUCTIONIN recent broadband communication standards, multiuser multiple-input multiple-output (MU-MIMO) systems have been considered as a means for increasing the system throughput [1]- [2].In MU-MIMO systems, multiple users are assigned the same time and frequency resources, while the spatial resources can be either shared or orthogonally assigned. In the downlink MU-MIMO system, the base station (BS) has a pre-knowledge of the users' data and the fed back channel state information (CSI) from the users. As such, data can be precoded so that inter-user interference (IUI) can be cancelled, or highly reduced. Optimally, each user receives his data without experiencing the existence of other users.Dirty paper coding (DPC) is the optimal precoding scheme for downlink MU-MIMO systems [3]. The main idea behind DPC is that it considers the MIMO channel to be a white paper that includes some dirt, i.e., interference. Since the location of the dirt is well-known, the BS only writes on the clean parts of the paper. Therefore, the reader, i.e., the mobile station (MS) receiver, can clearly distinguish between the dirt and the useful writing.Several . Linear zero-forcing precoding(ZFP) can be seen as a beamforming algorithm, where the beamforming weights are the rows of the pseudo-inverse of the channel matrix, while in the minimum-mean square error precoder (MMSEP) the channel matrix is regularized so that a tradeoff between noise amplification and IUI is achieved. On the other hand, THP and VP techniques linearly perturb the data vector such that the required transmit power is reduced. Combining these techniques with linear precoders is interesting in future communication systems such as long-term evolution (LTE) and LTE-advanced (LTE-A). This is because BS is supposed to have a large number of transmit antennas, e.g., up to 8 antennas in LTE-A system, and to communicate simultaneously with several users [11]. Due to its simplicity, we will restrict our discussions in the sequel on the ZFP, unless otherwise mentioned.Transmit antenna selection improves the system performance by exploiting the spatial diversity at the transmitter side. Therefore, when the number of anten...

show abstract

Fixed-complexity vector perturbation with block diagonalization for MU-MIMO systems

Cited by 22 publications

References 18 publications

A Review of Fixed-Complexity Vector Perturbation for MU-MIMO

A Review of Fixed-Complexity Vector Perturbation for MU-MIMO

Codebook based Direct Vector Quantization of MIMO Channel Matrix with Channel Normalization

Transmit Antenna Selection for Multi-user MIMO Precoding Systems with Limited Feedback

Contact Info

Product

Resources

About