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10
Cooperative MIMO channel modeling and multi-link spatial correlation properties
- IEEE J. Sel. Areas Commun
, 2012
"... Abstract—In this paper, a novel unified channel model framework is proposed for cooperative multiple-input multiple-output (MIMO) wireless channels. The proposed model framework is generic and adaptable to multiple cooperative MIMO scenarios by simply adjusting key model parameters. Based on the pro ..."
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Cited by 15 (8 self)
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Abstract—In this paper, a novel unified channel model framework is proposed for cooperative multiple-input multiple-output (MIMO) wireless channels. The proposed model framework is generic and adaptable to multiple cooperative MIMO scenarios by simply adjusting key model parameters. Based on the proposed model framework and using a typical cooperative MIMO communication environment as an example, we derive a novel geometry-based stochastic model (GBSM) applicable to multiple wireless propagation scenarios. The proposed GBSM is the first cooperative MIMO channel model that has the ability to investigate the impact of the local scattering density (LSD) on channel characteristics. From the derived GBSM, the corresponding multi-link spatial correlation functions are derived and numerically analyzed in detail. Index Terms—Cooperative MIMO channels, geometry-based stochastic model, spatial correlation, non-isotropic scattering.
On the Trade-off Between Feedback and Capacity in Measured MU-MIMO Channels
, 2009
"... In this work we study the capacity of multi-user multiple-input multiple-output (MU-MIMO) downlink channels with codebook-based limited feedback using real measurement data. Several aspects of MU-MIMO channels are evaluated. Firstly, we compare the sum rate of different MU-MIMO precoding schemes in ..."
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Cited by 11 (3 self)
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In this work we study the capacity of multi-user multiple-input multiple-output (MU-MIMO) downlink channels with codebook-based limited feedback using real measurement data. Several aspects of MU-MIMO channels are evaluated. Firstly, we compare the sum rate of different MU-MIMO precoding schemes in various channel conditions. Secondly, we study the effect of different codebooks on the performance of limited feedback MU-MIMO. Thirdly, we relate the required feedback rate with the achievable rate on the downlink channel. Real multi-user channel measurement data acquired with the Eurecom MIMO OpenAir Sounder (EMOS) is used. To the best of our knowledge, these are the first measurement results giving evidence of how MU-MIMO precoding schemes depend on the precoding scheme, channel characteristics, user separation, and codebook. For example, we show that having a large user separation as well as codebooks adapted to the second order statistics of the channel gives a sum rate close to the theoretical limit. A small user separation due to bad scheduling or a poorly adapted codebook on the other hand can impair the gain brought by MU-MIMO. The tools and the analysis presented in this paper allow the system designer to trade-off downlink rate with feedback rate by carefully choosing the codebook.
Differential feedback in mimo communications: Performance with delay and real channel measurements
- in Workshop on Smart Antennas (WSA 2009
, 2009
"... This work studies the performance of our recently proposed differential feedback scheme for multi-input-multi-output (MIMO) communication systems using real channel measurement data. The algorithm is applied to the channel correlation matrix exploiting geodesic curves and the intrinsic geometry of p ..."
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Cited by 4 (1 self)
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This work studies the performance of our recently proposed differential feedback scheme for multi-input-multi-output (MIMO) communication systems using real channel measurement data. The algorithm is applied to the channel correlation matrix exploiting geodesic curves and the intrinsic geometry of positive definite Hermitian matrices. The performance of this and a conventional non-differential feedback scheme are evaluated using real data and channel measurements obtained with the Eurecom MIMO OpenAir Sounder (EMOS). Additionally, the impact of having a delay in the feedback link is also studied in terms of a loss of performance in the communication through several simulations. The results show that the differential feedback strategy performs much better than the non-differential strategies in low mobility channels, while in high mobility channels the performance is similar. A delay in the feedback channel affects specially high mobility channels while having a negligible impact in the slow-varying cases. Topics: Precoding and limited feedback, Multi-antenna channel measurements, MIMO systems.
LOW-RATE FEEDBACK FOR REAL MEASURED TEMPORALLY CORRELATED MIMO CHANNELS
"... This work presents a summary of a proposal for a feedback scheme in a multi-input-multi-output (MIMO) communication system based on a differential quantization strategy applied to the channel response. The performance of this scheme is evaluated using real data and channel measurements obtained with ..."
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Cited by 1 (0 self)
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This work presents a summary of a proposal for a feedback scheme in a multi-input-multi-output (MIMO) communication system based on a differential quantization strategy applied to the channel response. The performance of this scheme is evaluated using real data and channel measurements obtained with the Eurecom’s MIMO OpenAir Sounder (EMOS). More concretely, the impact of having a delay in the feedback link is also studied in terms of a loss of performance in the communication through several simulation results. Topics: Precoding and limited feedback, Multi-antenna channel measurements, MIMO systems.
ON THE CHARACTERIZATION OF MU-MIMO CHANNELS
"... In this work we study the divergence of different links in wide-band multi-user multiple-input multiple-output (MU-MIMO) channels. The divergence is measured on several levels: (i) spatial separation of the user’s correlation matrices, (ii) co-linearity of the MIMO channel matrices, and (iii) correl ..."
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In this work we study the divergence of different links in wide-band multi-user multiple-input multiple-output (MU-MIMO) channels. The divergence is measured on several levels: (i) spatial separation of the user’s correlation matrices, (ii) co-linearity of the MIMO channel matrices, and (iii) correlation of large scale fading. The measurement data has been acquired using Eurecom’s MIMO Openair Sounder (EMOS). The EMOS can perform real-time MIMO channel measurements synchronously over multiple users. For this work we have used an outdoor measurement with two transmit antennas and two users with two antennas each. Several measurements with different distances between users were acquired. We find that the structure of the MIMO channel matrices changes significantly with the inter-user distance. This is best captured by the co-linearity measure. The transmit and the full correlation matrix also show some dependence on the inter-user distance whereas the receive correlation matrices are independent of the inter-user distance. The shadowing correlation was found to be very low in all cases. These findings are important for MU-MIMO precoding and scheduling algorithms. 1.
1 Experimental Characterization and Modeling of Outdoor-to-Indoor and Indoor-to-Indoor Distributed Channels
"... Abstract — We propose and parameterize an empirical model of the outdoor-to-indoor and indoor-to-indoor distributed (cooperative) radio channel, using experimental data in the 2.4 GHz band. In addition to the well-known physical effects of path loss, shadowing, and fading, we include several new asp ..."
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Abstract — We propose and parameterize an empirical model of the outdoor-to-indoor and indoor-to-indoor distributed (cooperative) radio channel, using experimental data in the 2.4 GHz band. In addition to the well-known physical effects of path loss, shadowing, and fading, we include several new aspects in our model that are specific to multi-user distributed channels: (i) correlated shadowing between different point to point links which has a strong impact on cooperative system performance, (ii) different types of indoor node mobility with respect to the transmitter and/or receiver nodes, implying a distinction between static and dynamic shadowing motivated by the measurement data, and (iii) a small-scale fading distribution that captures more severe fading than given by the Rayleigh distribution. I.
Characterization of MU-MIMO Channels Using the Spectral Divergence Measure
, 2008
"... In this work we apply the spectral divergence to characterize the (dis-)similarity of different links in wide-band multi-user multiple-input multiple-output (MU-MIMO) channels. The spectral divergence measures the distance between strictly positive, non-normalized spectral densities, such as the pow ..."
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In this work we apply the spectral divergence to characterize the (dis-)similarity of different links in wide-band multi-user multiple-input multiple-output (MU-MIMO) channels. The spectral divergence measures the distance between strictly positive, non-normalized spectral densities, such as the power delay profile. The measurement data has been acquired using Eurecom’s MIMO Openair Sounder (EMOS). The EMOS can perform real-time MIMO channel measurements synchronously over multiple users. For this work we have used a line-of-sight measurement with two transmit antennas and two users with two antennas each. We compare the spectral divergence of different links with respect to the distance of the users. We find that the spectral divergence can be quite low when the users are close together. These findings are important for MU-MIMO precoding and scheduling algorithms. 1 I.
1Distributed Compressive CSIT Estimation and Feedback for FDD Multi-user Massive MIMO Systems
"... Abstract—To fully utilize the spatial multiplexing gains or array gains of massive MIMO, the channel state information must be obtained at the transmitter side (CSIT). However, conventional CSIT estimation approaches are not suitable for FDD massive MIMO systems because of the overwhelming training ..."
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Abstract—To fully utilize the spatial multiplexing gains or array gains of massive MIMO, the channel state information must be obtained at the transmitter side (CSIT). However, conventional CSIT estimation approaches are not suitable for FDD massive MIMO systems because of the overwhelming training and feedback overhead. In this paper, we consider multi-user massive MIMO systems and deploy the compressive sensing (CS) technique to reduce the training as well as the feedback overhead in the CSIT estimation. The multi-user massive MIMO systems exhibits a hidden joint sparsity structure in the user channel matrices due to the shared local scatterers in the physical propagation environment. As such, instead of naively applying the conventional CS to the CSIT estimation, we propose a distributed compressive CSIT estimation scheme so that the compressed measurements are observed at the users locally, while the CSIT recovery is performed at the base station jointly. A joint orthogonal matching pursuit recovery algorithm is proposed to perform the CSIT recovery, with the capability of exploiting the hidden joint sparsity in the user channel matrices. We analyze the obtained CSIT quality in terms of the normalized mean absolute error, and through the closed-form expressions, we obtain simple insights into how the joint channel sparsity can be exploited to improve the CSIT recovery performance. Index Terms—Massive MIMO, CSIT estimation and feedback, compressive sensing, joint orthogonal matching pursuit (J-OMP). I.
