Resilient Full-Duplex ISAC in the Face of Imperfect SI Cancellation: Globally Optimal Timeslot Allocation and Beam Selection

TL;DR

This work tackles downlink full-duplex ISAC resource management under imperfect self-interference cancellation by jointly optimizing discrete timeslot allocation and beam selection. The authors formulate a semi-infinite, nonconvex MINLP and devise a sequence of reformulations that yield an exact MILP, enabling globally optimal solutions for the resource allocation problem. They introduce auxiliary variables and linearization techniques to decouple beam choices and convert logical constraints into linear forms, resulting in a reformulated problem $\\mathcal{P}'(\boldsymbol{\Omega}')$ with solvable MILP structure. Through simulations with Rician/mmWave-like settings, the study demonstrates how user-target angular alignment, residual SI, sensing thresholds, and physical array separation govern throughput and sensing performance, highlighting practical design guidelines for robust full-duplex ISAC systems.

Abstract

This work addresses the radio resource management (RRM) design in downlink full-duplex integrated sensing and communications (ISAC) systems, jointly optimizing timeslot allocation and beam selection under imperfect self-interference cancellation. Timeslot allocation governs the distribution of discrete channel uses between sensing and communication tasks, while beam selection determines transmit and receive directions along with adaptive beamwidths. The joint design leads to a semi-infinite, nonconvex mixed-integer nonlinear program (MINLP), which is difficult to solve. To overcome this, we develop a tailored reformulation strategy that transforms the problem into a tractable mixed-integer linear program (MILP), enabling globally optimal solutions. Our approach provides insights into the coordinated optimization of timeslot allocation and beam selection, enhancing the efficiency of full-duplex ISAC systems while ensuring resilience against residual self-interference.

Figures (5)

• Figure 1: System model comprising a BS, a user, and a target.
• Figure 2: Impact of user and target alignment on beam adaptation.
• Figure 3: Impact of residual SI and sensing threshold.
• Figure 4: Impact of residual SI and sensing timeslots.
• Figure 5: Impact of array separation.

Theorems & Definitions (1)

• Remark 1