Extended Arimoto-Blahut Algorithms for Bistatic Integrated Sensing and Communications Systems.

Area of Science:

• Wireless Communication
• Signal Processing
• Information Theory

Background:

• Integrated Sensing and Communication (ISAC) is crucial for next-generation wireless networks.
• Accurate performance evaluation, particularly the capacity-distortion function, is essential for ISAC systems.
• The non-convex nature of the capacity-distortion optimization problem presents significant challenges.

Purpose of the Study:

• To develop effective algorithms for computing the capacity-distortion function in bistatic ISAC systems.
• To address the non-convex optimization problem inherent in ISAC performance evaluation.
• To provide a fundamental measure of the communication-sensing performance trade-off.

Main Methods:

• Proposed extended Arimoto-Blahut (AB) algorithms to solve the non-convex optimization problem.
• Introduced auxiliary variables to transform non-convex distortion constraints into linear constraints.
• Proved the equivalence of optimal solutions between the original and reformulated problems.
• Developed extended AB algorithms for squared error distortion and logarithmic loss distortion.

Main Results:

• The reformulated optimization problem with linear constraints retains the same optimal solution.
• The proposed extended AB algorithms are effective for bistatic ISAC systems.
• Numerical results validate the performance of the developed algorithms.

Conclusions:

• Extended Arimoto-Blahut algorithms provide an effective solution for the capacity-distortion trade-off in ISAC.
• The method successfully transforms non-convex problems into solvable linear constraints.
• This work advances the performance evaluation of ISAC systems.