Noncoherent Trellis Coded Quantization: A Practical Limited Feedback Technique for Massive MIMO Systems
arXiv:1305.4976 · doi:10.1109/TCOMM.2013.111413.130379
Abstract
Accurate channel state information (CSI) is essential for attaining beamforming gains in single-user (SU) multiple-input multiple-output (MIMO) and multiplexing gains in multi-user (MU) MIMO wireless communication systems. State-of-the-art limited feedback schemes, which rely on pre-defined codebooks for channel quantization, are only appropriate for a small number of transmit antennas and low feedback overhead. In order to scale informed transmitter schemes to emerging massive MIMO systems with a large number of transmit antennas at the base station, one common approach is to employ time division duplexing (TDD) and to exploit the implicit feedback obtained from channel reciprocity. However, most existing cellular deployments are based on frequency division duplexing (FDD), hence it is of great interest to explore backwards compatible massive MIMO upgrades of such systems. For a fixed feedback rate per antenna, the number of codewords for quantizing the channel grows exponentially with the number of antennas, hence generating feedback based on look-up from a standard vector quantized codebook does not scale. In this paper, we propose noncoherent trellis-coded quantization (NTCQ), whose encoding complexity scales linearly with the number of antennas. The approach exploits the duality between source encoding in a Grassmannian manifold and noncoherent sequence detection. Furthermore, since noncoherent detection can be realized near-optimally using a bank of coherent detectors, we obtain a low-complexity implementation of NTCQ encoding using an off-the-shelf Viterbi algorithm applied to standard trellis coded quantization. We also develop advanced NTCQ schemes which utilize various channel properties such as temporal/spatial correlations. Simulation results show the proposed NTCQ and its extensions can achieve near-optimal performance with moderate complexity and feedback overhead.
30 pages, 13 figures, IEEE Transactions on Communications, accepted for publication (typos corrected)
References in corpus (2)
Cited by in corpus (31)
- Downlink Training Techniques for FDD Massive MIMO Systems: Open-Loop and Closed-Loop Training with Memory
- Pilot Beam Pattern Design for Channel Estimation in Massive MIMO Systems
- Antenna Grouping based Feedback Compression for FDD-based Massive MIMO Systems
- Sum-Rate and Power Scaling of Massive MIMO Systems with Channel Aging
- High-Dimensional Stochastic Gradient Quantization for Communication-Efficient Edge Learning
- Channel Feedback Based on AoD-Adaptive Subspace Codebook in FDD Massive MIMO Systems
- An Overview of Transmission Theory and Techniques of Large-scale Antenna Systems for 5G Wireless Communications
- RF Lens-Embedded Massive MIMO Systems: Fabrication Issues and Codebook Design
- Limited Feedback Channel Estimation in Massive MIMO with Non-uniform Directional Dictionaries
- Advanced Quantizer Designs for FDD-Based FD-MIMO Systems Using Uniform Planar Arrays
- On the Required Number of Antennas in a Point-to-Point Large-but-Finite MIMO System: Outage-Limited Scenario
- Downlink Channel Reconstruction for Spatial Multiplexing in Massive MIMO Systems
- Recursive CSI Quantization of Time-Correlated MIMO Channels by Deep Learning Classification
- Downlink Channel Covariance Matrix Reconstruction for FDD Massive MIMO Systems with Limited Feedback
- A Codebook-Based Limited Feedback System for Large-Scale MIMO
- Uplink Downlink Rate Balancing and throughput scaling in FDD Massive MIMO Systems
- Limited Feedback Massive MISO Systems with Trellis Coded Quantization for Correlated Channels
- Downlink MIMO Channel Estimation from Bits: Recoverability and Algorithm
- On the Convergence and Performance of MF Precoding in Distributed Massive MU-MIMO Systems
- An Effective Limited Feedback Scheme for FD-MIMO Based on Noncoherent Detection and Kronecker Product Codebook
- Trellis-Extended Codebooks and Successive Phase Adjustment: A Path from LTE-Advanced to FDD Massive MIMO Systems
- Efficient Feedback Mechanisms for FDD Massive MIMO under User-level Cooperation
- Soft Pilot Reuse and Multi-Cell Block Diagonalization Precoding for Massive MIMO Systems
- Spatial Modulation Assisted Multi-Antenna Non-Orthogonal Multiple Access
- Exploiting the Preferred Domain of FDD Massive MIMO Systems with Uniform Planar Arrays
- Downlink Extrapolation for FDD Multiple Antenna Systems Through Neural Network Using Extracted Uplink Path Gains
- Interleaving Channel Estimation and Limited Feedback for Point-to-Point Systems with a Large Number of Transmit Antennas
- Quantization and Feedback of Spatial Covariance Matrix for Massive MIMO Systems with Cascaded Precoding
- Structured Compressive Sensing Based Superimposed Pilot Design in Downlink Large-Scale MIMO Systems
- Training Sequence Design for Feedback Assisted Hybrid Beamforming in Massive MIMO Systems
- Joint Channel Direction Information Quantization For Spatially Correlated 3D MIMO Channels