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205
Multi-Cell MIMO Cooperative Networks: A New Look at Interference
- J. Selec. Areas in Commun. (JSAC
, 2010
"... Abstract—This paper presents an overview of the theory and currently known techniques for multi-cell MIMO (multiple input multiple output) cooperation in wireless networks. In dense networks where interference emerges as the key capacitylimiting factor, multi-cell cooperation can dramatically improv ..."
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Cited by 257 (40 self)
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Abstract—This paper presents an overview of the theory and currently known techniques for multi-cell MIMO (multiple input multiple output) cooperation in wireless networks. In dense networks where interference emerges as the key capacitylimiting factor, multi-cell cooperation can dramatically improve the system performance. Remarkably, such techniques literally exploit inter-cell interference by allowing the user data to be jointly processed by several interfering base stations, thus mimicking the benefits of a large virtual MIMO array. Multicell MIMO cooperation concepts are examined from different perspectives, including an examination of the fundamental information-theoretic limits, a review of the coding and signal processing algorithmic developments, and, going beyond that, consideration of very practical issues related to scalability and system-level integration. A few promising and quite fundamental research avenues are also suggested. Index Terms—Cooperation, MIMO, cellular networks, relays, interference, beamforming, coordination, multi-cell, distributed.
Large system analysis of linear precoding in correlated MISO broadcast channels under limited feedback
- IEEE TRANS. INF. THEORY
, 2012
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Limited feedback beamforming over temporally-correlated channels
- IEEE Trans. Signal Process
, 2009
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Joint Base Station Clustering and Beamformer Design for Partial Coordinated Transmission in Heterogeneous Networks
, 2012
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Large System Analysis of Linear Precoding in MISO Broadcast Channels with Limited Feedback
, 2010
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Multi-mode transmission for the MIMO broadcast channel with imperfect channel state information
- IEEE Transactions on Communications
"... This paper proposes a multi-mode transmission strategy to improve the spectral efficiency achieved by the multiple-input multiple-output (MIMO) broadcast channel with delayed and quantized channel state information. It adaptively adjusts the number of active users, denoted as the transmission mode, ..."
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Cited by 25 (10 self)
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This paper proposes a multi-mode transmission strategy to improve the spectral efficiency achieved by the multiple-input multiple-output (MIMO) broadcast channel with delayed and quantized channel state information. It adaptively adjusts the number of active users, denoted as the transmission mode, to balance transmit array gain, spatial division multiplexing gain, and residual inter-user interference. Accurate closed-form approximations are derived for the achievable rates for different modes, which are used to select the active mode that maximizes the ergodic throughput. User scheduling algorithms based on multi-mode transmission are then proposed for the network with a large number of users, to reduce the overall amount of feedback. It is shown that the proposed algorithms provide throughput gains at moderate yet practically relevant signal-to-noise ratio. Index Terms MIMO systems, space division multiplexing, broadcast channels, scheduling, feedback, delay effects, adaptive systems. I.
Transmit Diversity vs. Spatial Multiplexing in Modern MIMO Systems
, 2010
"... A contemporary perspective on transmit antenna diversity and spatial multiplexing is provided. It is argued that, in the context of most modern wireless systems and for the operating points of interest, transmission techniques that utilize all available spatial degrees of freedom for multiplexing o ..."
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Cited by 24 (1 self)
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A contemporary perspective on transmit antenna diversity and spatial multiplexing is provided. It is argued that, in the context of most modern wireless systems and for the operating points of interest, transmission techniques that utilize all available spatial degrees of freedom for multiplexing outperform techniques that explicitly sacrifice spatial multiplexing for diversity. Reaching this conclusion, however, requires that the channel and some key system features be adequately modeled and that suitable performance metrics be adopted; failure to do so may bring about starkly different conclusions. As a specific example, this contrast is illustrated using the 3GPP Long-Term Evolution system design.
Optimization of Training and Feedback Overhead for Beamforming over Block Fading Channels
, 2009
"... We examine the capacity of beamforming over a single-user, multi-antenna link taking into account the overhead due to channel estimation and limited feedback of channel state information. Multi-input single-output (MISO) and multi-input multi-output (MIMO) channels are considered subject to block Ra ..."
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Cited by 17 (0 self)
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We examine the capacity of beamforming over a single-user, multi-antenna link taking into account the overhead due to channel estimation and limited feedback of channel state information. Multi-input single-output (MISO) and multi-input multi-output (MIMO) channels are considered subject to block Rayleigh fading. Each coherence block contains L symbols, and is spanned by T training symbols, B feedback bits, and the data symbols. The training symbols are used to obtain a Minimum Mean Squared Error estimate of the channel matrix. Given this estimate, the receiver selects a transmit beamforming vector from a codebook containing 2B i.i.d. random vectors, and sends the corresponding B bits back to the transmitter. We derive bounds on the beamforming capacity for MISO and MIMO channels and characterize the optimal (rate-maximizing) training and feedback overhead (T and B) as L and the number of transmit antennas Nt both become large. The optimal Nt is limited by the coherence time, and increases as L / logL. For the MISO channel the optimal T/L and B/L (fractional overhead due to training and feedback) are asymptotically the same, and tend to zero at the rate 1 / logNt. For the MIMO channel the optimal feedback overhead B/L tends to zero faster (as 1 / log² Nt).
Downlink training techniques for FDD massive MIMO systems: open-loop and closed-loop training with memory
- IEEE Journal of Selected Topics in Signal Processing
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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.
