Leveraging inherent advantages of large bandwidth, high carrier frequency, and fine resolution, millimeter-wave (mmWave) technology is poised to play a pivotal role in integrated sensing and communication (ISAC) applications envisioned for sixth-generation (6G) networks. This paper proposes a stochastic geometry-based analytical framework to evaluate the performance of hybrid ISAC networks integrating sub-6 GHz and mmWave base stations (BSs). The framework explicitly incorporates band-specific propagation characteristics. Each mmWave BS is equipped with a large-scale antenna array to compensate for high-frequency propagation loss. Based on received signal power, we propose the maximum received echo signal power (Max-RESP) and maximum received average signal power (Max-RASP) association schemes to ensure that the target and user are associated with the BS providing better link conditions, respectively. Using stochastic geometry and probability theory, we derive analytical expressions for sensing distance accuracy and the communication achievable rate. The analytical results are validated via extensive Monte Carlo simulations. Numerical results show that hybrid sub-6 GHz and mmWave ISAC networks significantly outperform conventional pure sub-6 GHz networks and can approach the performance of pure mmWave networks by appropriately tuning the deployment density ratio. The appropriate density ratio provides practical guidance for balancing cost efficiency with performance enhancement. Moreover, the results reveal a performance bottleneck at higher density ratios, primarily due to the saturation of the signal-to-interference-plus-noise ratio (SINR). These findings highlight the crucial role of selecting an appropriate density ratio in hybrid sub-6GHz and mmWave ISAC networks.

In this work, we aim to effectively characterize the performance of cooperative integrated sensing and communication (ISAC) networks and to reveal how performance metrics relate to network parameters. To this end, we introduce a generalized stochastic geometry framework to model the cooperative ISAC networks, which approximates the spatial randomness of the network deployment. Based on this framework, we derive analytical expressions for key performance metrics in both communication and sensing domains, with a particular focus on communication coverage probability, radar information rate and coverage probability for sensing. The analytical expressions derived explicitly highlight how performance metrics depend on network parameters, thereby offering valuable insights into the deployment and design of cooperative ISAC networks. In the end, we validate the theoretical performance analysis through Monte Carlo simulation results. Our results demonstrate that increasing the number of cooperative base stations (BSs) significantly improves both metrics, while increasing the BS deployment density has a limited impact on communication coverage probability but substantially enhances the radar information rate. Additionally, increasing the number of transmit antennas is effective when the total number of transmit antennas is relatively small. The incremental performance gain reduces with the increase of the number of transmit antennas, suggesting that indiscriminately increasing antennas is not an efficient strategy to improve the performance of the system in cooperative ISAC networks.