Crowd counting has long been a crucial topic in the domains of computer vision and video surveillance. In particular, with the widespread use of thermal or depth cameras, RGB-X crowd counting has emerged as a prominent research focus. Although depth or infrared images provide complementary information, the core challenge remains in effectively unifying heterogeneous cross-modality and cross-scale information to form a comprehensive representation of crowd distributions in complex scenes. To address this problem, we propose a novel Mamba-based framework, termed UMS-VMamba-CC for multi-modal (RGB-X) crowd counting. Specifically, we design the cross-modality disentanglement fusion visual Mamba (CMDF-VMamba) that uses self-supervised learning to decompose modality-invariant and modality-specific features in spatial-frequency domains, followed by multi-modal feature aggregation via the gating mechanism. For cross-scale fusion, we design the cross-scale pyramid fusion visual Mamba (CSPF-VMamba), which adopts the bi-directional pyramid structure to incorporate low-level features into high-level representations during downsampling, while upsampling high-level features and aggregating them with low-level features through state space contextual modeling. Comprehensive experiments on multiple mainstream datasets demonstrate that the UMS-VMamba-CC framework achieves competitive performance for RGB-X crowd counting.

We present the first investigation into the transmission of multi-stream information from a base station equipped with reconfigurable holographic surfaces (RHS) to multiple users with the aid of multi-antenna arrays. Building upon this, we propose the joint design of RHS and baseband beamformers that enables multi-stream delivery at fair rates across all users. Specifically, we first introduce a max-min rate optimization approach, which aims for maximizing the minimum rate for all users through iterative solutions of quadratic problems. To reduce complexity, we then propose a surrogate-based optimization approach that offers a low-complexity design alternative relying on closed-form updates. Our simulations show that the surrogate-based approach achieves nearly the same minimum rate as max-min optimization, while delivering sum-rates comparable to those of sum-rate maximization, overcoming the rate-fairness deficiency typical of the latter.

This paper investigates an integrated sensing and communication (ISAC) network operating over millimeter-wave and sub-Terahertz bands, where a base station serves downlink communication users (CUs), while simultaneously sensing targets. First, we propose a novel hybrid beamforming structure that reduces power consumption in high-frequency bands by using a low number of phase shifters for analog beamforming and enhances spatial diversity in baseband beamforming through improper Gaussian signaling (IGS), addressing the limitations of having only a few radio frequency chains by boosting the number of supported data streams. Together, these techniques establish a new phased-MIMO radar structure and an energy-efficient signaling strategy designed for joint sensing and communication. Second, we formulate a new beampattern-optimization objective that enables computationally efficient algorithms, which iteratively update the hybrid beamformers through closed-form expressions. This design ensures tight mainlobe concentration for sensing while simultaneously serving multiple CUs. A new soft-min function, paired with a closed-form algorithm, secures both strong worst-rate and sum-rate performance. By unifying sensing and communication objectives, the proposed framework offers a well-balanced trade-off between high CU rates and high-quality sensing beampatterns, while maintaining computational complexity scalable. Simulation results validate the practicality of the proposed approach.

This is the first treatise on multi-user (MU) beamforming designed for achieving long-term rate-fairness in full-dimensional MU massive multi-input multi-output (m-MIMO) systems. Explicitly, based on the channel covariances, which can be assumed to be known beforehand, we address this problem by optimizing the following objective functions: the users’ signal-to-leakage-noise ratios (SLNRs) using SLNR max-min optimization, geometric mean of SLNRs (GM-SLNR) based optimization, and SLNR soft max-min optimization. We develop a convex-solver based algorithm, which invokes a convex subproblem of cubic time-complexity at each iteration for solving the SLNR max-min problem. We then develop closed-form expression based algorithms of scalable complexity for the solution of the GM-SLNR and of the SLNR soft max-min problem. The simulations provided confirm the users’ improved-fairness ergodic rate distributions.

A wireless network serving multiple users in the millimeter-wave or the sub-terahertz band by a base station is considered. High-throughput multi-user hybrid-transmit beamforming is conceived by maximizing the minimum rate of the users. For the sake of energy-efficient signal transmission, the array-of-subarrays structure is used for analog beamforming relying on low-resolution phase shifters. We develop a convex-solver based algorithm, which iteratively invokes a convex problem of the same beamformer size for its solution. We then introduce the soft max-min rate objective function and develop a scalable algorithm for its optimization. Our simulation results demonstrate the striking fact that soft max-min rate optimization not only approaches the minimum user rate obtained by max-min rate optimization but it also achieves a sum rate similar to that of sum-rate maximization. Thus, the soft max-min rate optimization based beamforming design conceived offers a new technique of simultaneously achieving a high individual quality-of-service for all users and a high total network throughput.

A multi-user fog radio access network (F-RAN) is designed for supporting content-centric services. The requested contents are partitioned into sub-contents, which are then ‘beamformed’ by the remote radio heads (RRHs) for transmission to the users. Since a large number of beamformers must be designed, this poses a computational challenge. We tackle this challenge by proposing a new class of regularized zero forcing beamforming (RZFB) for directly mitigating the inter-content interferences, while the ‘intra-content interference’ is mitigated by successive interference cancellation at the user end. Thus each beamformer is decided by a single real variable (for proper Gaussian signaling) or by a pair of complex variables (for improper Gaussian signaling). Hence the total number of decision variables is substantially reduced to facilitate tractable computation. To address the problem of energy efficiency optimization subject to multiple constraints, such as individual user-rate requirement and the fronthauling constraint of the links between the RRHs and the centralized baseband signal processing unit, as well as the total transmit power budget, we develop low-complexity path-following algorithms. Finally, we confirm their performance by simulations.