Linear MIMO equalization for CDMA downlink signals with code reuse

Mailaender, L.

doi:10.1109/twc.2005.853887

Cited by 40 publications

(22 citation statements)

References 22 publications

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“…The signal b k is then processed in an MMSE based [10,11] or an interference aware [12,13] equalizer F to obtain an estimate of the transmitted chip stream…”

Section: Related Workmentioning

confidence: 99%

Joint throughput optimized CQI and precoding weight calculation for MIMO HSDPA

Mehlführer

Caban

Wrulich

et al. 2008

2008 42nd Asilomar Conference on Signals, Systems and Computers

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In MIMO High Speed Downlink Packet Access, the channel adaptation is performed by means of the Channel Quality Indicator (CQI) and the Precoding Control Indicator (PCI). The CQI value is utilized to determine the coding rate and modulation alphabet, as well as the number of spatially multiplexed data streams. The PCI value is associated to a specific precoding vector that is applied to the transmit signal at the basestation. In this work, we derive analytic expressions for the post equalization SINR. These SINR values are then evaluated at the receiver to jointly calculate the CQI and the PCI in order to maximize the data throughput. The SINR expressions are verified not only by simulations but also by outdoor MIMO HSDPA measurements.

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“…The signal b k is then processed in an MMSE based [10,11] or an interference aware [12,13] equalizer F to obtain an estimate of the transmitted chip stream…”

Section: Related Workmentioning

confidence: 99%

Joint throughput optimized CQI and precoding weight calculation for MIMO HSDPA

Mehlführer

Caban

Wrulich

et al. 2008

2008 42nd Asilomar Conference on Signals, Systems and Computers

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“…Considering the same contributions as T (α) for all users (thus not only mean but variance of the bias contribution), we can write in a similar fashion as in (9) 2 . In this case the contribution of nth symbols from all other codes can be considered to be eliminated when detecting the corresponding symbol of kth user.…”

Section: Joint ML Detection For K Usersmentioning

confidence: 99%

“…T cancels interchip as well as interstream interference [2]. Each component of the equalizer is given by the structure shown in Fig.…”

Section: Lmmse Extension For Mimomentioning

confidence: 99%

“…This receiver is one of the commonly accepted SISO/SIMO/MIMO receiver structures (see e.g., [2] and references therein). A Successive Decoding/Interference Canceling (SIC) receiver based on this LMMSE feedforward filter was shown to be mutual-information maximizing in [3] when operating at the chip-level (feeding back chip-sequence decisions).…”

Section: Introductionmentioning

confidence: 99%

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On LMMSE bias in CDMA SIMO/MIMO receivers

Ghauri¹,

Shenoy²,

Slock

2008

2008 IEEE International Conference on Acoustics, Speech and Signal Processing

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We revisit CDMA downlink receivers (RX) based on linear Minimum Mean-Square Error (LMMSE) chip-equalizer front-end followed by a Walsh code correlator for Single-Input-Single (Multi)-Output (SISO or SIMO) channels with the purpose of highlighting the non-trivial question of bias in the output of the equalizer. In a linear time-invariant channel, this bias is constant at chip-equalizer output, but evolves over time at code correlator output impacting Signal-to-Interference-plus-Noise Ratio (SINR) and thus achievable rates in such receivers. In principle, this bias must be taken into account in further RX/decoding stages even if its impact is small. It is shown that a new class of Maximum-Likelihood (ML) RX leading to potential performance gains is obtained when properly accounting for symbol-level bias across a set of user codes. These results are extended to the Multi-Input-Multi-Output (MIMO) case of UMTS High-Speed Downlink Packet Access (HSDPA).

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“…The other group of HSDPA receivers [3] (and references therein) we address recognize that interference arises from loss of orthogonality due to the multipath channel and circumvent this problem by attempting to bring back the orthogonality through a SINRmaximizing LMMSE equalizer (or a MMSE-ZF solution). When spatial multiplexing is considered, it is clear that MIMO equalizers have to suppress another spatio-temporal interferer rendering linear equalization less efficient.…”

Section: Introductionmentioning

confidence: 99%

Receiver Designs For MIMO HSDPA

Shenoy

Ghauri²,

Slock

2008

2008 IEEE International Conference on Communications

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Optimal linear receivers for MIMO HSDPA (as for SISO/SIMO) are symbol-level (deterministic) multiuser receivers, known unfortunately to be time-varying in nature and thus prohibitively complex. Traditional less complex alternative is dimensionality-reducing linear chip-equalization followed by further non-linear (interference canceling) or joint detection stages to improve symbol estimates. Well-known versions of former include inter-stream Successive Interference Canceling (SIC) involving all codes while the latter leads to per-code joint spatial maximum-likelihood (ML) receiver. We investigate the class of MIMO HSDPA receivers based upon LMMSE chiplevel MIMO (equalizer) front end, and introduce two (one static and the other time-varying) models of the resulting spatial channel, a consequence of treating the scrambler as random or deterministic. It is shown that in the random case, statistical properties can be exploited to design MIMO receivers while the deterministic point-of-view leads to another set of reduceddimensionality linear receivers or interference cancelers.

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Linear MIMO equalization for CDMA downlink signals with code reuse

Cited by 40 publications

References 22 publications

Joint throughput optimized CQI and precoding weight calculation for MIMO HSDPA

Joint throughput optimized CQI and precoding weight calculation for MIMO HSDPA

On LMMSE bias in CDMA SIMO/MIMO receivers

Receiver Designs For MIMO HSDPA

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