Active Beam Tracking with Reconfigurable Intelligent Surface

Han, Han; Jiang, Tao; Yu, Wei

doi:10.1109/icassp49357.2023.10097134

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…For the implementation of active sensing framework, we follow the settings in IV-A. The fully connected neural networks in (27), (28) and (29) We compare with the following benchmarks: BCD with perfect CSI: Given perfect CSI, the beamforming gain |w H r R H diag(v H )T H w t | 2 can be optimized by the block coordinate descent (BCD) algorithm [46]. That is, beamformers {w t , w r } and reflection coefficients v are optimized alternately until the algorithm converges.…”

Section: E Simulation Resultsmentioning

confidence: 99%

“…We mention here that the beam alignment problem has also been investigated from the deep learning perspective in [22]- [26], but these works are all limited to the one-sided beam alignment problem. We remark that this paper considers a beam alignment problem within a coherence block where the Tx and Rx are not moving and the channel between the Tx and the Rx remains constant, while [27]- [29] address the beam tracking problem in the context of a time-varying channel model where the Rx moves following some Markov chain.…”

Section: A Related Workmentioning

confidence: 99%

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Active Sensing for Two-Sided Beam Alignment and Reflection Design Using Ping-Pong Pilots

Jiang

Sohrabi

2023

IEEE J. Sel. Areas Inf. Theory

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Beam alignment is an important task for millimeterwave (mmWave) communication, because constructing aligned narrow beams both at the transmitter (Tx) and the receiver (Rx) is crucial in terms of compensating the significant path loss in very high-frequency bands. However, beam alignment is also a highly nontrivial task because large antenna arrays typically have a limited number of radio-frequency chains, allowing only low-dimensional measurements of the high-dimensional channel. This paper considers a two-sided beam alignment problem based on an alternating ping-pong pilot scheme between Tx and Rx over multiple rounds without explicit feedback. We propose a deep active sensing framework in which two long short-term memory (LSTM) based neural networks are employed to learn the adaptive sensing strategies (i.e., measurement vectors) and to produce the final aligned beamformers at both sides. In the proposed ping-pong protocol, the Tx and the Rx alternately send pilots so that both sides can leverage local observations to sequentially design their respective sensing and data transmission beamformers. The proposed strategy can be extended to scenarios with a reconfigurable intelligent surface (RIS) for designing, in addition, the reflection coefficients at the RIS for both sensing and communications. Numerical experiments demonstrate significant and interpretable performance improvement. The proposed strategy works well even for the challenging multipath channel environments.

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