1 research outputs found
Cooperative Beamforming with Predictive Relay Selection for Urban mmWave Communications
While millimeter wave (mmWave) communications promise high data rates, their
sensitivity to blockage and severe signal attenuation presents challenges in
their deployment in urban settings. To overcome these effects, we consider a
distributed cooperative beamforming system, which relies on static relays
deployed in clusters with similar channel characteristics, and where, at every
time instance, only one relay from each cluster is selected to participate in
beamforming to the destination. To meet the quality-of-service guarantees of
the network, a key prerequisite for beamforming is relay selection. However, as
the channels change with time, relay selection becomes a resource demanding
task. Indeed, estimation of channel state information for all candidate relays,
essential for relay selection, is a process that takes up bandwidth, wastes
power and introduces latency and interference in the network. We instead
propose a unique, predictive scheme for resource efficient relay selection,
which exploits the special propagation patterns of the mmWave medium, and can
be executed distributively across clusters, and in parallel to optimal
beamforming-based communication. The proposed predictive scheme efficiently
exploits spatiotemporal channel correlations with current and past networkwide
Received Signal Strength (RSS), the latter being invariant to relay cluster
size, measured sequentially during the operation of the system. Our numerical
results confirm that our proposed relay selection strategy outperforms any
randomized selection policy that does not exploit channel correlations,
whereas, at the same time, it performs very close to an ideal scheme that uses
complete, cluster size dependent RSS, and offers significant savings in terms
of channel estimation overhead, providing substantially better network
utilization, especially in dense topologies, typical in mmWave networks