  In this study, energy-efficient deterministic adaptive beamforming algorithms
are proposed for distributed sensor/relay networks. Specifically, DBSA, D-QESA,
D-QESA-E, and a hybrid algorithm, hybrid-QESA, that combines the benefits of
both deterministic and random adaptive beamforming algorithms, are proposed.
Rigorous convergence analyses are provided for all our proposed algorithms and
convergence to the global optimal solution is shown for all our proposed
algorithms. Through extensive numerical simulations, we demonstrate that
superior performance is achieved by our proposed DBSA and D-QESA over random
adaptive beamforming algorithms for static channels. Surprisingly, D-QESA is
also more robust against random node removal than random adaptive beamforming
algorithms. For time-varying channels, hybrid-QESA indeed achieves the best
performance since it combines the benefits of both types of adaptive
beamforming algorithms. In summary, our proposed deterministic algorithms
demonstrate superior performance both in terms of convergence time and
robustness against channel and network uncertainties.
