| Summary: | In millimeter wave (mmWave) communications, fast and reliable beam alignment is crucial to the achievable data rate. The problem is even more challenging when either the transmitter or the receiver, or both move that need timely update of the best beam pair. Some existing work tracks the channel changes by Kalman filter (KF). Alternatively, compressed sensing (CS) approaches are useful to reconstruct the channel based on the sparsity of mmWave channels. However, the aforementioned methods either need a full scan over all possible beam pairs or require frequent beam training, leading to high overhead. In this paper, a novel beam alignment scheme based on an integrated KF and CS framework is proposed for the single-user mmWave channel. Since the CS performance is heavily dependent on the sparsity level, the proposed method increases the signal sparsity level by applying adaptive CS on the observation residual computed from the previous estimate of the support to predict the angles of the dominant paths, while the corresponding path gains are tracked by the reduced-order KF. To further exploit the spatial correlation of sparse signals, our approach considers the adaptation of the sensing matrix based on the previous estimate to enhance the estimation accuracy, and the weighted CS is adopted for shrinking the possible range of solutions. To balance the estimation accuracy and alignment overhead, a switching mechanism is proposed that determines the timing for switching the estimation policy between beam training and beam tracking. Simulations are performed to evaluate the performance of the proposed method subject to numerous important factors, such as signal-to-noise ratio (SNR), number of available beams, overhead for beam alignment, beam variation rate, and the dominance of line-of-sight path.
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