Integrated sensing and communication (ISAC), near-field communication, and extremely large-scale antenna arrays are transformative and indispensable technologies that revolutionize 6G communications. This work proposes a monostatic ISAC architecture for near-field systems employing extremely large-scale uniform planar arrays (XL-UPAs). A theoretical analysis of multi-user interference and sensing-over-communication interference is provided for the considered near-field ISAC system, where the sensing and communication channels are modeled using a non-uniform spherical wave (NUSW) model. In addition to communication, the monostatic ISAC system must ensure simultaneous target detection with a constant probability of false alarm, independent of noise statistics. A grid-based two-dimensional constant false alarm rate (2D-CFAR) detection scheme is proposed to address the issue. Furthermore, an analytical expression is derived to quantify the achievable range resolution in near-field ISAC systems.

This paper proposes novel Bayesian learning approaches for sparse channel estimation in a multi-user millimeter-wave massive multiple-input multiple-output underlay cognitive radio system. The intelligent reflecting surfaces (IRS)-aided secondary network adopts a two-phase transmission protocol comprising of silent and estimation phases. During the silent phase, the secondary base station(SBS) captures primary network pilot transmissions to estimate the cascaded channel between primary users and the SBS. Next, the estimation phase considers two pilot design policies with an inherent estimation accuracy and spectral efficiency trade-off, for cascaded channel estimation with respect to the secondary users, IRS, and SBS. Further, the associated hybrid and marginalized Cramér-Rao bounds are developed to benchmark the efficacy of proposed estimation schemes. Simulation results demonstrate the superior performance of the proposed approaches in comparison to existing compressed sensing methods such as orthogonal matching pursuit and subspace multi-user joint channel estimation.

Multiple-input-multiple-output (MIMO) ambient backscatter communication (ABC) is emerging as one of the promising techniques to combat the deeper fading effects offered by the backscatter link. Based on earlier studies, multiple-antenna backscatter setup has been exploited to ensure lower bit error rate (BER) performance and higher spectral efficiency (SE) with the utilization of spatial modulation (SM) in ABC systems, but its investigations in the literature have assumed that the perfect channel state information (CSI) is available at the Reader, which is practically unreasonable. In an attempt to retain the key feature of SM and eliminate the need for a channel estimation block, this paper takes the first step in establishing a preliminary study of the Differential Spatial Modulation (DSM) scheme in MIMO ABC systems, which interestingly enough exploits a newly set of linearized MIMO ABC channel for its implementation. Simulation results verify that the computational complexity of the proposed DSM backscatter scheme achieves 53% lower in comparison to the SM backscatter scheme over the linearized channel model for a 2 x$R$MIMO ABC system, where$R$indicates the number of receive antennas at the Reader.