Carrier Aggregation Enabled MIMO-OFDM Integrated Sensing and Communication

TL;DR

This paper proposes a carrier aggregation-enabled MIMO-OFDM ISAC system that fuses high and low-frequency sensing data for enhanced communication and high-precision sensing, addressing phase misalignment and data fusion challenges.

Contribution

It introduces a novel CA-enabled MIMO-OFDM ISAC system with symbol-level data fusion and phase compensation, providing theoretical performance bounds and validation.

Findings

Achieves high-accuracy sensing through phase compensation and data fusion.

Derives mutual information and CRLB bounds for system performance.

Simulation confirms the system's feasibility and superiority.

Abstract

In the evolution towards the forthcoming era of sixth-generation (6G) mobile communication systems characterized by ubiquitous intelligence, integrated sensing and communication (ISAC) is in a phase of burgeoning development. However, the capabilities of communication and sensing within single frequency band fall short of meeting the escalating demands. To this end, this paper introduces a carrier aggregation (CA)-enabled multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) ISAC system fusing the sensing data on high and low-frequency bands by symbol-level fusion for ultimate communication experience and high-accuracy sensing. The challenges in sensing signal processing introduced by CA include the initial phase misalignment of the echo signals on high and low-frequency bands due to attenuation and radar cross section, and the fusion of the sensing data on high and low-frequency bands with different physical-layer parameters. To this end, the sensing signal processing is decomposed into two stages. In the first stage, the problem of initial phase misalignment of the echo signals on high and low-frequency bands is solved by the angle compensation, spatial filtering and cyclic cross-correlation operations. In the second stage, this paper realizes symbol-level fusion of the sensing data on high and low-frequency bands through sensing vector rearrangement and cyclic prefix adjustment operations, thereby obtaining high-precision sensing performance. Then, the closed-form communication mutual information (MI) and sensing Cram\'er-Rao lower bound (CRLB) for the proposed ISAC system are derived to explore the theoretical performance bound with CA. Simulation results validate the feasibility and superiority of the proposed ISAC system.