Due to its ability to significantly improve data rate, intelligent reflecting surface (IRS) is a potential crucial technique for the future generation wireless networks like six-generation (6G). In this paper, we focus on the analysis of degree of freedom (DoF) in IRS-aided multi-user multi-input multi-output (MIMO) network. Firstly, the DoF upper bound of IRS-aided single-user MIMO network, i.e., the achievable maximum DoF of such a system, is derived, and the corresponding results are extended to the case of IRS-aided multiuser MIMO by using the matrix rank inequalities. In particular, in serious rank-deficient, also called low-rank, like line-of-sight channel, the network DoF may double over no-IRS with the help of IRS. To verify the rate performance gain from augmented DoF, three closed-form beamforming methods, null-space projection plus maximize transmit power and maximize receive power (NSP-MTP-MRP), Schmidt orthogonalization plus (SO-MMSE) and two-layer leakage plus minimum mean square error (TLL-MMSE) are proposed to achieve the maximum DoF. Simulation results show that IRS does make a dramatic rate enhancement. For example, in a serious rank-deficient channel, also called low-rank, the sum-rate of the proposed TLL-MMSE aided by IRS is up to 2.54 times that of no IRS. This means that IRS may make a significant DoF improvement in such a channel.

With the advancement of integrated sensing and communication (ISAC), multi-target sensing and tracking in communication systems has emerged as a key application scenario. However, the limited angular resolution of single base station hinders high-precision sensing. To address this, cell-free technology offers a promising solution for distributed collaborative sensing. In this paper, we investigate the coop-erative hierarchy of cell-free ISAC networks and propose a novel architecture that supports functional partitioning be-tween distributed and central units. Specifically, an efficient multi-target tracking framework, named ISAC-SORT, is in-troduced, which utilizes a cascaded multi-target manager and specialized calibration-association algorithms to facilitate robust perception. Furthermore, an extended Kalman filter (EKF) is designed to fuse observation data from multiple nodes, enabling direct prediction and estimation of target positions based on range and Doppler measurements. Sim-ulation results demonstrate the framework’s robustness in dynamic scenarios involving temporary occlusions, sudden target emergence, and clutter interference, while maintaining decimeter-level precision even at low signal-to-noise ratios (SNRs). Finally, the fifth generation new radio (5G NR) standard-compliant outdoor experiments confirm that the system can achieve a sensing accuracy of 0.8 m.