Abstract
The use of beamforming technology in standalone (SA) millimeter wave communications results in directional transmission and reception modes at the mobile station (MS) and base station (BS). This results in initial beam access challenges, since the MS and BS are now compelled to perform spatial search to determine the best beam directions that return highest signal levels. The high number of signal measurements here prolongs access times and latencies, as well as increasing power and energy consumption. Hence this paper proposes a first study on leveraging deep learning schemes to simplify the beam access procedure in standalone mmWave networks. The proposed scheme combines bidirectional recurrent neural network (BRNN) and long short-term memory (LSTM) to achieve fast initial access times. Namely, the scheme predicts the best beam index for use in the next time step once a MS accesses the network, e.g., transition from sleep to active (or idle) modes. The scheme eliminates the need for beam scanning, thereby achieving ultra-low access times and energy efficiencies as compared to existing methods.
Highlights
Millimeter Wave frequencies constitute a major component of SA 5G networks for high data rates support in enhanced mobile broadband
In light of the above, this paper proposes a first use of a deep learning network model for initial beam access in mmWave communications, with the goal to develop one of the fastest beam access schemes
The proposed bidirectional recurrent neural network (BRNN)-long short-term memory (LSTM) prediction scheme is simulated in Figure 4 over various time steps, which shows high approximation between the ground truth and the prediction pattern
Summary
Millimeter Wave (mmWave) frequencies constitute a major component of SA 5G networks for high data rates support in enhanced mobile broadband (eMBB). Deep-learning-based beam selection is proposed in [11] to reduce the time overhead by exploiting sub-6 GHz channel information. The work in [17] presents a beam alignment technique with partial beams using neural networks for multi-user mmWave massive MIMO system in efforts to improve the spectral efficiency at reduced training overhead, as compared to hierarchical search and compressed sensing methods. Other limitations include indoor implementation and marginal enhancement to existing conventional methods (e.g., beam sweeping and exhaustive searches) These models overall lack time delay and power consumption models in the control plane and work is needed to investigate the delay in standalone beamforming-based mmWave networks. Consideration for power leakage Multi-user beam alignment, online prediction Multi-user beam alignment
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