Abstract Driven by the vision of ubiquitous connectivity and wireless intelligence, the evolution of ultra-dense constellation-based satellite-integrated Internet is underway, now taking preliminary shape. Nevertheless, the entrenched institutional silos and limited, nonrenewable heterogeneous network resources leave current satellite systems struggling to accommodate the escalating demands of next-generation intelligent applications. In this context, the distributed satellite information networks (DSIN), exemplified by the cohesive clustered satellites (CCS) system, have emerged as an innovative architecture, bridging information gaps across diverse satellite systems, such as communication, navigation, and remote sensing, and establishing a unified, open information network paradigm to support resilient space information services. This survey first provides a profound discussion about innovative network architectures of DSIN, encompassing distributed regenerative satellite network architecture, distributed satellite computing network architecture, and reconfigurable satellite formation flying, to enable flexible and scalable communication, computing and control, fundamentally enhancing network resilience. The DSIN faces challenges from network heterogeneity, unpredictable channel dynamics, sparse resources, and decentralized collaboration frameworks. To address these issues, a series of enabling technologies is identified, including channel modeling and estimation, cloud-native distributed MIMO cooperation, new waveform design, grant-free massive access, nonorthogonal multicast, distributed phased array antennas, high-speed inter-satellite communication, network routing, and the proper combination of all these diversity techniques. Furthermore, to heighten the overall resource efficiency, the cross-layer optimization techniques are further developed to meet upper-layer deterministic, adaptive and secure information services requirements. In addition, emerging research directions and new opportunities are highlighted on the way to achieving the DSIN vision.

The integration of intelligent reflecting surfaces (IRS) onto structures takes advantage of its electrically thin and flexible properties, effectively transforming original obstructions into potential serving nodes. However, existing works model IRS as a planar array, which is not well coupled with the infrastructural shape, posing challenges to future large-scale deployment. Therefore, this paper introduces conformal IRS and proposes a reflection constraint model owing to the curvature imparted by the conformal deployment of IRS, based on which the conformal IRS-assisted network is modeled and system-level performance analysis is derived via stochastic geometry. Specifically, considering infrastructure facades non-planar feature, to balance the accuracy and analytical tractability, randomly located infrastructures are modeled as cylinders based on the Boolean scheme, and conformal IRSs are deployed around a fraction ρ of them. In addition, the surface curvature of conformal IRS modulates the distribution of Line-of-Sight paths within the system and its effective illumination. We obtain the successfully reflecting probability and the effective reflecting elements respectively under two different states of IRS perfect reflecting and random scattering, and further derive the coverage probability. Numerical results show that conformal IRS significantly improves network connectivity, though it slightly reduces coverage compared to 2D planar IRS.

Recently, a two-dimension (2D) modulation waveform of orthogonal time-frequency-space (OTFS) has been a popular 6G candidate to replace existing orthogonal frequency division multiplexing (OFDM). The extensive OTFS researches help to make both the advantages and limitations of OTFS more and more clear. The limitations are not easy to overcome as they come from OTFS on-grid 2D convolution channel model. Instead of solving OTFS inborn challenges, this paper proposes a novel 2D modulation waveform named orthogonal time-frequency division multiplexing (OTFDM). OTFDM uses a 2D dot-product channel model to cope with doubly-selectivity. Compared with OTFS, OTFDM supports grid-free channel delay and Doppler and gains a simple and efficient 2D equalization. The concise dot-division equalization can be easily combined with MIMO. The simulation result shows that OTFDM is able to bear high mobility and greatly outperforms OFDM in doubly-selective channel.

Joint communication and sensing (JCAS) is a very promising 6G technology, which attracts more and more research attention. Compared with communication, radar has many unique features in terms of waveform design criteria, self-interference cancelation (SIC), aperture-dependent resolution, and virtual aperture. This article proposes a novel waveform design named max-aperture radar slicing (MaRS) to gain a large time-frequency aperture, which is generated by orthogonal frequency division multiplexing (OFDM) and occupies only a tiny fraction of OFDM resources. The proposed MaRS keeps the radar advantages of constant modulus, zero auto-correlation sequence, and simple SIC. As MaRS consumes much less resources, conventional processing methods fail, and novel angle-Doppler map-based methods are proposed to obtain the range-velocity-angle information from MaRS echos and strong clutters. To avoid complex full-duplex communication, this article proposes a hybrid-duplex JCAS scheme composed of half-duplex communication and full-duplex radar. The half-duplex communication antenna array is reused, and a small sensing-dedicated antenna array is added. Using these two arrays, a large space-domain sensing aperture is virtually formed to greatly improve the angle resolution. The numerical results show that the proposed MaRS and hybrid duplex can achieve a high sensing resolution with only 0.4% OFDM resources, which reduces the overheads of conventional methods to less than one tenth.

Low density parity check (LDPC) code in the fifth generation (5G or New Radio, NR) was mainly designed for the enhanced Mobile Broadband (eMBB) scenario, and specific optimization has not been considered for both scenarios of Ultra-Reliable and Low Latency Communications (URLLC) and massive Machine Type Communications (mMTC), especially for small transport block sizes (TBSs). Therefore, we introduce a specific optimized design method of LDPC code for small TBSs of the latter two scenarios. According to the proposed method, a new LDPC code is designed and its performance is better than the LDPC code in 5G. Furthermore, an improved algorithm combining belief propagation (BP) and ordered statistic decoding (OSD) is considered in this paper. Compared with traditional BP+OSD algorithm, although there is slight performance loss, the number of OSD decoding implemented in the improved algorithm can be reduced to 1 or 2. Simulation results indicate that the performance of the improved BP+OSD decoding algorithm is obviously better than that of the popular BP decoding and also better than that of the polar code in 5G.

In this paper, we address the popular content distribution (PCD) problem in a highway scenario, in which popular files are distributed to a group of on-board units (OBUs) driving through a single roadside unit (RSU). Due to the high speeds, the OBUs may not finish downloading a large file within the limited time for vehicle-to-roadside (V2R) communication and a peer-to-peer (P2P) network consisting of OBUs out of the RSU coverage can be constructed for completing the file delivery process. However, due to fast and unpredictable topological changes of the vehicular ad hoc network (VANET), the static methods in traditional P2P networks can be inefficient. We model this problem as a coalition formation game with transferable utilities, and propose a coalition formation algorithm that converges into a Nash-stable partition adapting to environmental changes. Based on this algorithm, we further propose a distributed scheme for the overall PCD problem. Simulation results show that our scheme presents a considerable performance improvement relative to the non-cooperative case using the carrier sense multiple access with collision avoidance (CSMA/CA).

Existing minimum-symbol-error-rate equalizers were derived based on the symbol-error-rate objective function. Due to the complexity of the objective function the derivation is not straightforward. In this paper we present a new approach to derive the minimum-symbol-error-rate adaptive equalizers. The problem is formulated as minimizing the norm between two subsequent parameter vectors under the constraint of symbol-error-rate minimization. The constrained optimization problem then is solved with the Lagrange multiplier method, which results in an adaptive algorithm with normalization. Simulation results show that the proposed algorithm outperforms the existing adaptive minimum-symbol-error-rate equalizer in convergence speed and steady-state performance.