117 research outputs found
Globally Optimal Resource Allocation Design for Discrete Phase Shift IRS-Assisted Multiuser Networks with Perfect and Imperfect CSI
Intelligent reflecting surfaces (IRSs) are a promising low-cost solution for
achieving high spectral and energy efficiency in future communication systems
by enabling the customization of wireless propagation environments. Despite the
plethora of research on resource allocation design for IRS-assisted multiuser
communication systems, the optimal design and the corresponding performance
upper bound are still not fully understood. To bridge this gap in knowledge, in
this paper, we investigate the optimal resource allocation design for
IRS-assisted multiuser systems employing practical discrete IRS phase shifters.
In particular, we jointly optimize the beamforming vector at the base station
(BS) and the discrete IRS phase shifts to minimize the total transmit power for
the cases of perfect and imperfect channel state information (CSI) knowledge.
To this end, two novel algorithms based on the generalized Benders
decomposition (GBD) method are developed to obtain the globally optimal
solution for perfect and imperfect CSI, respectively. Moreover, to facilitate
practical implementation, we propose two corresponding low-complexity
suboptimal algorithms with guaranteed convergence by capitalizing on successive
convex approximation (SCA). In particular, for imperfect CSI, we adopt a
bounded error model to characterize the CSI uncertainty and propose a new
transformation to convexify the robust quality-of-service (QoS) constraints.
Our numerical results confirm the optimality of the proposed GBD-based
algorithms for the considered system for both perfect and imperfect CSI.
Furthermore, we unveil that both proposed SCA-based algorithms can achieve a
close-to-optimal performance within a few iterations. Moreover, compared with
the state-of-the-art solution based on the alternating optimization (AO)
method, the proposed SCA-based scheme achieves a significant performance gain
with low complexity
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