Multi-Stream Beam-Training For MmWave MIMO Networks

Authors:
Yasaman Ghasempour Rice University
Muhammad Kumail Haider Rice University
Carlos Cordeiro Intel Corporation
Dimitrios Koutsonikolas University at Buffalo, SUNY
Edward Knightly Rice University

Introduction:

In this paper, the authors present MUlti-stream beam-Training for mm-wavE networks (MUTE) a novel system that leverages channel sparsity, GHz-scale sampling rate, and the knowledge of mm-Wave RF codebook beam patterns to construct a set of candidate beams for multi-stream beam steering.

Abstract:

Multi-stream 60 GHz communication has the potential to achieve data rates up to 100 Gbps via multiplexing multiple data streams. Unfortunately, establishing multi-stream directional links can be a high overhead procedure as the search space increases with the number of spatial streams and the product of AP-client beam resolution. In this paper, we present MUlti-stream beam-Training for mm-wavE networks (MUTE) a novel system that leverages channel sparsity, GHz-scale sampling rate, and the knowledge of mm-Wave RF codebook beam patterns to construct a set of candidate beams for multi-stream beam steering. In 60 GHz WLANs, the AP establishes and maintains a directional link with every client through periodic beam training. MUTE repurposes these beam acquisition sweeps to estimate the Power Delay Profile (PDP) of each beam with zero additional overhead. Coupling PDP estimates with beam pattern knowledge, MUTE selects a set of candidate beams that capture diverse or ideally orthogonal paths to obtain maximum stream separability. Our experiments demonstrate that MUTE achieves 90% of the maximum achievable aggregate rate while incurring only 0.04% of exhaustive search's training overhead.

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