Alternating optimization; integrated sensing and communication; orthogonal frequency division multiplexing; green communication; econfigurable intelligent surfaces; resource allocation
Abstract :
[en] Integrated sensing and communications (ISAC) has
emerged as a key enabler for 6G and beyond. However, ISAC
systems face significant challenges, including the sensing func-
tion that introduces interference and degrades communication
performance, as well as high sensing power consumption that
reduces overall communication efficiency, particularly in complex
urban environments. To address these issues, we propose a
reconfigurable intelligent surface (RIS)-assisted orthogonal fre-
quency division multiplexing (OFDM) multiple-input multiple-
output (MIMO) ISAC system, where a RIS enhances connec-
tivity for users in localized coverage gaps. We formulate and
study two optimization problems: i) maximizing system sum
spectral efficiency and ii) maximizing global energy efficiency,
by jointly optimizing transmit precoding, subcarrier allocation,
and RIS phase shifts under power, quality of service, and sensing
accuracy constraints. These problems are classified as mixed-
integer nonlinear programs, which are generally difficult to solve
optimally. To tackle this, we develop efficient iterative algo-
rithms leveraging successive convex approximation, alternating
optimization, Riemannian manifolds, and Dinkelbach’s method
to obtain at least locally optimal solutions. Simulation results
validate the effectiveness of the proposed designs, demonstrating
their superiority over benchmark schemes, achieving up to 40%
higher spectral efficiency and up to 60% improvement in energy
efficiency compared to conventional overlap and random-phase
approaches.
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