Beam Tracking for Dynamic mmWave Channels: A New Training Beam Sequence Design Approach

TL;DR

This paper presents a novel training beam sequence design for millimeter wave MISO systems, using a Markov process model and particle swarm optimization to improve tracking accuracy of the angle of departure.

Contribution

It introduces a new approach combining Markov modeling and particle swarm optimization for training beam sequence design in mmWave tracking systems.

Findings

Proposed method outperforms existing approaches in tracking accuracy.

Closed-form upper bound of tracking error probability used as optimization objective.

Numerical results validate the effectiveness of the new design.

Abstract

In this paper, we develop an efficient training beam sequence design approach for millimeter wave MISO tracking systems. We impose a discrete state Markov process assumption on the evolution of the angle of departure and introduce the maximum a posteriori criterion to track it in each beam training period. Since it is infeasible to derive an explicit expression for the resultant tracking error probability, we turn to its upper bound, which possesses a closed-form expression and is therefore leveraged as the objective function to optimize the training beam sequence. Considering the complicated objective function and the unit modulus constraints imposed by analog phase shifters, we resort to the particle swarm algorithm to solve the formulated optimization problem. Numerical results validate the superiority of the proposed training beam sequence design approach.