Cooperative base station beamforming in WiMAX systems

Chen, Z.; Peng, Mugen; Wang, W.; Chen, H.-H.

doi:10.1049/iet-com.2009.0155

Cited by 12 publications

(17 citation statements)

References 15 publications

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“…The ( i , j )th element of the channel matrix

H_{k}^{(m)}

is given by

H_{k}^{(m)} (i, j) = α_{i_{k}, j_{m}} C_{k}^{(m)} (i, j)

where

α_{i_{k}, j_{m}}

encompasses both medium‐scale and large‐scale fadings from the m th BS to the k th MS. In this paper, we study two possible situations: each BS allocates the same power level to the same MS as in and , we investigate a more general approach where the allocated power levels from the set of BSs to the same MS are not necessarily identical.…”

Section: System Modelmentioning

confidence: 99%

“…The key problem of joint transmit processing among cooperative BSs is to define a joint transmit beamforming matrix to cancel the ICI term of . Note that in the related work of and the authors assumed that each BS allocates the same power level to the same MS, which leads to a joint beamforming matrix

{\overset{true˜}{W}}_{k} = {[{(W_{k}^{(1)})}^{normalT}, \dots, {(W_{k}^{(M)})}^{normalT}]}^{normalT} \in ℂ^{M N_{t} \times N_{r}}

for the k th MS, that satisfies

{\overset{MathClass-op̃}{MathClass-opH}}_{k} {\overset{MathClass-op̃}{bold-italicW}}_{j} MathClass-rel= 0 MathClass-punc, MathClass-rel\forall k MathClass-rel\neq j

where

{\overset{true˜}{H}}_{k} = [H_{k}^{(1)}, \dots, H_{k}^{(M)}] \in ℂ^{N_{r} \times M N_{t}}

.…”

Section: System Modelmentioning

confidence: 99%

“…In practical situations, usually the perfect knowledge of the CSI could not be available at the transmitter side. As such, the channel matrix

H_{k}^{(m)}

can be expressed as

H_{k}^{(m)} = {({\overset{true^}{H}}^{e})}_{k}^{(m)} + Δ_{k}^{(m)}

where

{({\overset{true^}{H}}^{e})}_{k}^{(m)}

and

Δ_{k}^{(m)}

are the estimated channel and estimation error matrices, from m th BS to k th MS, respectively, and the superscript ‘ e ’ refers to the non‐perfect CSI case as the used entities are the estimates instead of their true values. The estimation error matrix

Δ_{k}^{(m)}

is due to either the thermal noise that befoul the channel estimation process when pilot symbols are transmitted or the resulting prediction errors.…”

Section: System Modelmentioning

confidence: 99%

See 2 more Smart Citations

Channel estimation errors impact on the sum rate maximisation in a JP‐CoMP transmission systems

Abdelkefi

Feki

Siala

et al. 2013

Trans. Emerging Tel. Tech.

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Cooperation between multicell base stations (BSs) is a promising technique that has the potential to mitigate inter‐cell interference (ICI) and increase data rates without sacrificing additional frequency spectrum. Classical beamforming techniques have been used to eliminate the existing ICI and improve the sum rate of downlink cooperative multiple input multiple output transmission systems. However, a perfect knowledge of the channel state information at the transmitter is needed to perform adequate beamforming such as block diagonalisation or zero‐forcing preprocessing. In practice, this is not the case due to the presence of channel estimation errors.In this paper, we consider one particular scenario of the transmission coordination among multiple cell sites, namely the joint processing coordinated multipoint transmission scheme. A joint processing coordinated multipoint is composed of several BSs that cooperate to serve a set of mobile stations (MSs) and where both BSs and MSs are equipped with multiple antennas. In the context of this transmission scenario, we model the multicell cooperative system under the presence of channel estimation errors that are often neglected but that can significantly influence the obtained results. Furthermore, we consider a general framework where the distributed power from the BSs to the MSs is not constrained to be identical. We then propose a modified version of the classical block diagonalisation and zero‐forcing beamforming to efficiently mitigate the existing ICI. Finally, we evaluate the sum rate expression of the considered cooperative system, and we propose efficient alternatives to improve it without coupling the beamforming and the power control processes. Simulation results are given to support our claims. Copyright © 2013 John Wiley & Sons, Ltd.

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“…The ( i , j )th element of the channel matrix

H_{k}^{(m)}

is given by

H_{k}^{(m)} (i, j) = α_{i_{k}, j_{m}} C_{k}^{(m)} (i, j)

where

α_{i_{k}, j_{m}}

Section: System Modelmentioning

confidence: 99%

{\overset{true˜}{W}}_{k} = {[{(W_{k}^{(1)})}^{normalT}, \dots, {(W_{k}^{(M)})}^{normalT}]}^{normalT} \in ℂ^{M N_{t} \times N_{r}}

for the k th MS, that satisfies

{\overset{MathClass-op̃}{MathClass-opH}}_{k} {\overset{MathClass-op̃}{bold-italicW}}_{j} MathClass-rel= 0 MathClass-punc, MathClass-rel\forall k MathClass-rel\neq j

where

{\overset{true˜}{H}}_{k} = [H_{k}^{(1)}, \dots, H_{k}^{(M)}] \in ℂ^{N_{r} \times M N_{t}}

.…”

Section: System Modelmentioning

confidence: 99%

“…In practical situations, usually the perfect knowledge of the CSI could not be available at the transmitter side. As such, the channel matrix

H_{k}^{(m)}

can be expressed as

H_{k}^{(m)} = {({\overset{true^}{H}}^{e})}_{k}^{(m)} + Δ_{k}^{(m)}

where

{({\overset{true^}{H}}^{e})}_{k}^{(m)}

and

Δ_{k}^{(m)}

Δ_{k}^{(m)}

is due to either the thermal noise that befoul the channel estimation process when pilot symbols are transmitted or the resulting prediction errors.…”

Section: System Modelmentioning

confidence: 99%

See 1 more Smart Citation

Channel estimation errors impact on the sum rate maximisation in a JP‐CoMP transmission systems

Abdelkefi

Feki

Siala

et al. 2013

Trans. Emerging Tel. Tech.

View full text Add to dashboard Cite

show abstract

“…It is shown that the universal frequency reuse scheme achieves a very poor performance in a realistic femto-cell environment, whereas the ZF cooperative BS transmission provides a good option for femto-cell systems. Besides, the cooperative BS ZF beamforming for the WiMAX system has been analysed in [15].…”

Section: Introductionmentioning

confidence: 99%

High diversity downlink two‐cell coordination with low backhaul load

Zhang

et al. 2014

IET Communications

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In this study, the authors present a novel low backhaul load cooperative transmission framework for the two-cell multiple-input-single-output systems. In this framework, the neighbouring two base stations (BSs) take turns to transmit data in two consecutive slots. In each slot, only one BS is active, transmitting the preprocessed data symbols of both its own serving user and the cooperative user in neighbouring cell, and sharing its preprocessed data symbols to the other cooperative BS for next transmission. Linear constellation spreading is utilised for preprocessing which helps the system to exploit the macro-diversity without reducing the multiplexing gain. Besides, zero-forcing beamforming is applied in each transmission slot so as to cancel the multiuser interference. In this way, the inter-cell links become beneficial rather than detrimental. Pairwise error probability analysis demonstrates that the multi-cell spatial diversity gain can be achieved for each data stream. Both theoretical analysis and simulation results confirm that the proposed scheme outperforms the existing relevant strategies with less channel estimation overhead. It is shown that because of the higher diversity order it achieved, the proposed scheme can significantly improve the error performance in a distributed manner while maintaining the same multiplexing gain.

show abstract

“…Nevertheless, to exploit the full potential of cooperative communication accurate estimates for the different links are required. Although some work has evaluated the impact of the imperfect channel estimation in cooperative schemes (Chen et al, 2009), (Fouillot et al, 2010), (Gedik & Uysal, 2009), (Hadizadeh & Muhaidat, 2010), (Han et al, 2009), (Ikki et al 2010), (Muhaidat et al, 2009), new techniques have been derived to address the specificities of such systems. Channel estimation for cooperative communication depends on the employed relaying protocol, e.g., decode and forward (DF) (Laneman et al, 2004) when the relay has the capability to regenerate and re-encode the whole frame; amplify and forward (AF) (Laneman et al, 2004) where only amplification takes place; and what we term equalize and forward (EF) (Moco et al, 2010), (Teodoro et al, 2009), where more sophisticated filtering operations are used.…”

Section: Introductionmentioning

confidence: 99%