The accelerated proliferation of visual content and the rapid development of machine vision technologies bring significant challenges in delivering visual data on a gigantic scale, which shall be effectively represented to satisfy both human and machine requirements. In this work, we investigate how hierarchical representations derived from the advanced generative prior facilitate constructing an efficient scalable coding paradigm for human-machine collaborative vision. Our key insight is that by exploiting the StyleGAN prior, we can learn three-layered representations encoding hierarchical semantics, which are elaborately designed into the basic, middle, and enhanced layers, supporting machine intelligence and human visual perception in a progressive fashion. With the aim of achieving efficient compression, we propose the layer-wise scalable entropy transformer to reduce the redundancy between layers. Based on the multi-task scalable rate-distortion objective, the proposed scheme is jointly optimized to achieve optimal machine analysis performance, human perception experience, and compression ratio. We validate the proposed paradigm’s feasibility in face image compression. Extensive qualitative and quantitative experimental results demonstrate the superiority of the proposed paradigm over the latest compression standard Versatile Video Coding (VVC) in terms of both machine analysis as well as human perception at extremely low bitrates (< 0.01 bpp), offering new insights for human-machine collaborative compression.

In this paper, we propose a nested tensor-based algorithm for integrated sensing and communication (ISAC) in reconfigurable intelligent surface (RIS)-assisted downlink terahertz (THz) multiple-input multiple-output (MIMO) systems. By exploiting the multi-group Khatri-Rao space-time (KRST) coding scheme at the base station (BS) and the structure of RIS phase shifts, we formulate the received signal at the vehicle terminal (VT) as a nested tensor that is composed of multiple outer parallel factor (PARAFAC) tensors and an inner PARATUCK tensor. With the outer PARAFAC tensors, a low-complexity least squares Khatri-Rao factorization (LS-KRF) algorithm is developed for joint channel estimation and symbol detection. Then, with the inner PARATUCK tensor, an optimized five-linear alternating least squares (OF-ALS) algorithm is designed to precisely estimate the sensing parameters such as angles of departure (AoDs), angles of arrival (AoAs), and time delays for VT and scatterer positioning. The proposed algorithm can be effectively applied to the scenario where the number of paths is unknown. Simulation results show that the proposed algorithm has more relaxed parameter conditions and provides superior ISAC performance compared with the existing state-of-the-art algorithms.

In this paper, we propose a robust tensor-based scheme for joint direction-of-departure (DOD) and direction-of-arrival (DOA) estimation in bistatic multiple-input multiple-output (MIMO) radar with unknown spatially colored noise. To eliminate the colored noise, the proposed algorithm first denoises the received signal from the temporal cross-correlation approach and constructs a third-order complex tensor model. Then, a real-valued compressed tensor model is developed by utilizing the characteristic of radar antenna arrays. Finally, Bayesian tensor factorization is derived to fit the constructed real-valued model and approximate factor matrices, from which DOD and DOA can be extracted. We also derive Cramér-Rao bound (CRB) results for angle estimation. The proposed algorithm is suitable for both uniform linear array (ULA) and uniform planar array (UPA) manifolds. Compared with existing angle estimation methods, the proposed one has better estimation accuracy and more stable performance. In addition, the proposed algorithm works well even if the number of targets is unknown. Simulation results demonstrate the effectiveness and improvement of the proposed angle estimation algorithm.

In this paper, we propose a nested tensor-based framework for the time-varying phase noise (PHN) estimation, channel estimation, and data detection in downlink reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) systems. Due to the structure of time-varying PHN and RIS phase shifts, we construct the received signal at the mobile station (MS) as a fourth-order tensor. By designing the multi-blocks time-domain transmission scheme, this fourth-order tensor can be converted into a third-order nested tensor model to facilitate the parallel factor (PARAFAC) decomposition. Based on the reconstructed nested PARAFAC model, we develop two algorithms to jointly estimate the time-varying PHN and channels. The first one achieves the minimum mean square error (MMSE) with iterative algorithm. The second one adopts certain approximation and could yield closed-form solutions by sacrificing a little bit performance. With the estimated time-varying PHN and channels, we further detect the data via the proposed vectorized Kronecker-based zero-forcing (VKBZF) approach. Moreover, to quantitatively evaluate the performance of the proposed algorithms, we derive the Cramér-Rao bound (CRB) as the benchmark. Simulation results demonstrate that the proposed algorithms provide superior estimation and detection performance compared with the existing state-of-the-art algorithm, and require less pilot overhead.

In this paper, we propose a nested tensor-based framework for integrated near-field sensing and far-field communication assisted by reconfigurable intelligent surface (RIS). With the multi-dimensional resources of the considered integrated sensing and communication (ISAC) scenario and the Khatri-Rao space-time (KRST) coding, we formulate the received ISAC signal as a fourth-order nested tensor. By utilizing the algebraic structure of the nested tensor, a nested tensor-based joint sensing and communication scheme is designed to realize symbol detection and target localization without sending the specialized pilots. Moreover, the detection and localization accuracy is further improved by combining the dimensions of sensing and communication signals. Simulation results show that the proposed scheme provides superior ISAC performance with low complexity, which confirms the potential advantage of nested tensor-based framework in RIS-assisted ISAC systems.

In order to support colourful multimedia services with strict quality-of-service (QoS) requirements of user equipments (UEs), the space-air-ground integrated networks (SAGIN) can be taken as a promising approach to enhance network capacity. Among them, millimeter wave (mmWave) and edge caching promise to significantly improve the SAGIN performance due to the advantage in rich bandwidth resource and low latency, respectively. In this paper, we investigate the joint caching and resource allocation for multimedia services in SAGIN, where multimedia content requests can be simultaneously served by multiple access points (APs). Considering the delay-constraint of multimedia services, we then formulate a mixed-integer non-linear programming (MINLP) problem aiming at minimizing the service delay, which involves jointly optimizing coded caching (CC), power allocation (PA) and UEs-to-APs association (UA). We propose to find the optimal solution by employing an alternating iteration optimization framework. The optimal CC and PA problems are firstly addressed by utilizing convex optimization technology. Then, two many-to-many swap matching algorithms are developed to slove the UA subproblem effectively. Numerical results demonstrate that our proposed algorithms can substantially reduce the service delay over other benchmarks.