Tree-Structured Random Vector Quantization for Limited-Feedback Wireless Channels

Abstract

We consider the quantization of a transmit beamforming vector in multiantenna channels and of a signature vector in code division multiple access (CDMA) systems. Assuming perfect channel knowledge, the receiver selects for a transmitter the vector that maximizes the performance from a random vector quantization (RVQ) codebook, which consists of independent isotropically distributed unit-norm vectors. The quantized vector is then relayed to the transmitter via a rate-limited feedback channel. The RVQ codebook requires an exhaustive search to locate the selected entry. To reduce the search complexity, we apply generalized Lloyd or $k$-dimensional (kd)-tree algorithms to organize RVQ entries into a tree. In examples shown, the search complexity of tree-structured (TS) RVQ can be a few orders of magnitude less than that of the unstructured RVQ for the same performance. We also derive the performance approximation for TS-RVQ in a large system limit, which predicts the performance of a moderate-size system very well.

References in corpus (1)

• GLRT-Optimal Noncoherent Lattice Decoding

Cited by in corpus (6)

• On Optimizing Feedback Interval for Temporally Correlated MIMO Channels With Transmit Beamforming And Finite-Rate Feedback
• Waveform Design for Wireless Power Transfer with Limited Feedback
• Variable-Length Channel Quantizers for Maximum Diversity and Array Gains
• Statistical Precoder Design for Space-Time-Frequency Block Codes in Multiuser MISO-MC-CDMA Systems
• Machine Learning-based Signal Detection for PMH Signals in Load-modulated MIMO System
• Fixed-rank Rayleigh Quotient Maximization by an $M$PSK Sequence