Applications of Space Division Multiplexing and Those Performance in a MIMO Channel

Ohgane, Takeo; Nishimura, Toshihiko; Ogawa, Yasutaka

doi:10.1093/ietcom/e88-b.5.1843

Cited by 67 publications

(34 citation statements)

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“…The receiver demultiplexed the received signal using an MMSE spatial filter at each subcarrier and then passed the data to a soft-decision Viterbi decoder. To sufficiently reflect space-frequency diversity effect after demultiplexing, we multiplied a log-likelihood ratio by the corresponding SINR at the MMSE spatial filter output [10]. It was assumed that a fading condition was static over a frame, and that the receiver had perfect channel state information.…”

Section: Simulation Conditionsmentioning

confidence: 99%

Measurement-Based Performance Evaluation of Coded MIMO-OFDM Spatial Multiplexing with MMSE Spatial Filtering in an Indoor Line-of-Sight Environment

Nishimoto

Nishimura

Ohgane

et al. 2008

IEICE Transactions on Communications

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Hiroshi NISHIMOTO †a) , Student Member, Toshihiko NISHIMURA †b) , Takeo OHGANE †c) , and Yasutaka OGAWA †d) , Members SUMMARYThe MIMO system can meet the growing demand for higher capacity in wireless communication fields. So far, the authors have reported that, based on channel measurements, uncoded performance of narrowband MIMO spatial multiplexing in indoor line-of-sight (LOS) environments generally outperforms that in non-LOS (NLOS) ones under the same transmit power condition. In space-frequency coded MIMO-OFDM spatial multiplexing, however, we cannot expect high space-frequency diversity gain in LOS environments because of high fading correlations and low frequency selectivity of channels so that the performance may degrade unlike uncoded cases. In this letter, we present the practical performance of coded MIMO-OFDM spatial multiplexing based on indoor channel measurements. The results show that an LOS environment tends to provide lower space-frequency diversity effect whereas the MIMO-OFDM spatial multiplexing performance is still better in the environment compared with an NLOS environment. key words: MIMO, OFDM, spatial multiplexing, MMSE spatial filtering, indoor channel measurement, line-of-sight environments, space-frequency diversity

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Section: Simulation Conditionsmentioning

confidence: 99%

Measurement-Based Performance Evaluation of Coded MIMO-OFDM Spatial Multiplexing with MMSE Spatial Filtering in an Indoor Line-of-Sight Environment

Nishimoto

Nishimura

Ohgane

et al. 2008

IEICE Transactions on Communications

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“…For the sake of simplicity, in each case of corresponding bit 1 or 0, we used the squared Euclidean distance between the output symbol and the closest replica symbol candidate as its log-likelihood [32]. In order to reflect channel quality depending on the substream and subcarrier, we multiplied the obtained log-likelihood by the corresponding SINR of the MMSE spatial filter output, i.e., [33]. Eight coded bits were assigned per OFDM symbol per subcarrier.…”

Section: Simulation Conditionsmentioning

confidence: 99%

Pseudo Eigenbeam-Space Division Multiplexing (PE-SDM) in Frequency-Selective MIMO Channels

Nishimoto¹,

Nishimura²,

Ohgane³

et al. 2007

IEICE Transactions on Communications

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Hiroshi NISHIMOTO †a) , Student Member, Toshihiko NISHIMURA †b) , Takeo OHGANE †c) , and Yasutaka OGAWA †d) , Members SUMMARYIn a frequency-selective multiple-input multiple-output (MIMO) channel, the optimum transmission is achieved by beamforming with eigenvectors obtained at each discrete frequency point, i.e., an extension of eigenbeam-space division multiplexing (E-SDM). However, the calculation load of eigenvalue decomposition at the transmitter increases in proportion to the number of frequency points. In addition, frequencyindependent eigenvectors increase the delay spread of the effective channel observed at the receiver. In this paper, we propose a pseudo eigenvector scheme for the purpose of mitigating the calculation load and maintaining frequency continuity (or decreasing the delay spread). First, we demonstrate that pseudo eigenvectors reduce the delay spread of the effective channels with low computational complexity. Next, the practical performance of the pseudo E-SDM (PE-SDM) transmission is evaluated. The simulation results show that PE-SDM provides almost the same or better performance compared with E-SDM when the receiver employs a time-windowing-based channel estimation available in the low delay spread cases.

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“…In MIMO systems, high spectral efficiency is achieved by spatially multiplexing multiple data streams at the same time and frequency [1]. The MIMO system with space division multiplexing (SDM) technique is categorized into two cases, i.e., no channel state information (CSI) is needed at the transmitter or CSI is exploited in both transmitter and receiver.…”

Section: Introductionmentioning

confidence: 99%

“…The MIMO system with space division multiplexing (SDM) technique is categorized into two cases, i.e., no channel state information (CSI) is needed at the transmitter or CSI is exploited in both transmitter and receiver. As a method used in the former systems, spatial filtering and maximum likelihood detection (MLD) are known [1], where the received SDM signal is de-multiplexed with signal processing at the receiver. One of the latter MIMO systems is called the Eigenbeam SDM (E-SDM) [1] [2], where data streams are transmitted through multiple orthogonal eigenpath channels between the transmitter and the receiver.…”

Section: Introductionmentioning

confidence: 99%

“…As a method used in the former systems, spatial filtering and maximum likelihood detection (MLD) are known [1], where the received SDM signal is de-multiplexed with signal processing at the receiver. One of the latter MIMO systems is called the Eigenbeam SDM (E-SDM) [1] [2], where data streams are transmitted through multiple orthogonal eigenpath channels between the transmitter and the receiver. Thus, the E-SDM system with power control based on the water-filling theorem [3] improves the MIMO channel capacity, provided that accurate CSI is known to the transmitter and the receiver.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Iterative Optimization Algorithms to Determine Transmit and Receive Weights for MIMO Systems

Muta¹,

Tominaga²,

Fujii³

et al. 2011

MIMO Systems, Theory and Applications

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Applications of Space Division Multiplexing and Those Performance in a MIMO Channel

Cited by 67 publications

References 0 publications

Measurement-Based Performance Evaluation of Coded MIMO-OFDM Spatial Multiplexing with MMSE Spatial Filtering in an Indoor Line-of-Sight Environment

Measurement-Based Performance Evaluation of Coded MIMO-OFDM Spatial Multiplexing with MMSE Spatial Filtering in an Indoor Line-of-Sight Environment

Pseudo Eigenbeam-Space Division Multiplexing (PE-SDM) in Frequency-Selective MIMO Channels

Iterative Optimization Algorithms to Determine Transmit and Receive Weights for MIMO Systems

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