DeepBeam uses deep neural networks for passive, efficient 5G beam alignment, reducing latency and overhead.
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Background:
The surge in mobile data traffic necessitates a new generation of wireless systems, leading to the development of 5G networks designed for high data rates and capacity. While 5G utilizes frequencies in the 24-52 GHz range to offer wide bandwidths, it faces propagation challenges and reduced range, requiring precise beamforming for effective communication. Traditional beamforming in mobile networks is a resource-intensive process involving coordinated signaling and pilots, which induces latency during initial link setups and beam adjustments due to movement. These limitations underscore the need for a faster, more efficient beam alignment process.
Description:
Northeastern researchers have created an innovative solution called DeepBeam, which revolutionizes beam alignment in 5G networks by eliminating the need for explicit coordination between devices. Utilizing deep neural networks, DeepBeam enables receivers to passively learn about data transmissions to other users within the network, assessing beam quality and determining the transmitter’s position relative to the receiver. Unlike traditional methods that rely on control signaling and pilots, DeepBeam reduces latency and overhead by deriving necessary information directly from low-level antenna array signals. This approach not only addresses the inefficiencies of current techniques but also enhances versatility, making it compatible with various millimeter-wave (mmWave) networking standards without involving the devices' protocol stacks.
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