Orthogonal Time Frequency Space (OTFS) enables reliable communication in high-speed mobility scenarios, making it ideal for Low Earth Orbit (LEO) satellite communication. Furthermore sparse Code Multiple Access (SCMA) supports massive connection in uplink mobile communications. This paper proposes an OTFS-SCMA scheme for LEO satellite communications and the corresponding cross domain detection algorithm. At the transmitter, users are grouped, and a practical codebook is employed. At the receiver, cross-domain detection is utilized to obtain initial estimates, which are then refined using the Message Passing Algorithm (MPA) for optimized detection. Comparative analysis with other baseline schemes demonstrates the performance gain.

Space-air-ground integrated networks (SAGIN) are a key technology in 6G, enabling seamless global connectivity through a unified communication framework, while integrated sensing and communication (ISAC) mitigates spectrum congestion by reusing spectrum and transceivers for dual communication and sensing functions. Despite its potential, existing ISAC research has largely focused on terrestrial networks, with limited exploration in SAGIN environments. This paper proposes a novel ISAC framework for SAGIN, incorporating orthogonal time frequency space (OTFS) modulation and non-orthogonal multiple access. OTFS enhances the system's robustness in doubly-dispersive channels and supports precise communication and sensing integration. These channels experience both time dispersion caused by multipath propagation and frequency dispersion caused by Doppler shifts. In this framework, high-altitude platform stations serve as relay nodes, amplifying communication signals and processing echo signals for accurate target parameter estimation. To further enhance system performance, we design a beamforming optimization strategy using successive convex approximation to improve communication energy efficiency. Simulation results demonstrate that the proposed scheme significantly enhances the overall performance of the SAGIN system.

In the visionary ideals of “Internet of Everything” and “Digital Twins”, the future 6G will deeply integrate diverse heterogeneous networks such as satellite and aerial networks to support seamless connectivity and efficient interoperability, also known as space-air-ground integrated networks (SAGIN), in which the grant-free uplink random access based on Slotted ALOHA (S-ALOHA) can reduce access latency and complexity for massive Internet of Things (IoT) devices. However, with the increasing number of IoT users, the collision probability of S-ALOHA escalates and further degrades the system performance. In this paper, we focus on the massive IoT device uplink access in SAGIN aided by high altitude platform stations (HAPS), investigating power allocation for IoT devices to maximize system access capability and spectral efficiency (SE). Specifically, we first optimize 3D deployment of HAPS. Then the resilient massive access (RMA) based on flexible fusion of S-ALOHA and non-orthogonal multiple access methods is proposed. To maximize system SE with device power constraints, we model the sequential decision problem as a Markov decision process and solve it with the Advantage Actor-Critic (A2C) algorithm. Simulation results demonstrate the proposed RMA can significantly improve the IoT terminal successful access probability and the resource scheduling based on A2C also significantly increases the system SE with low complexity.

Integrated sensing and communication (ISAC) and space-air-ground integrated networks (SAGIN) have been considered as key technologies of 6G. The challenge of achieving ISAC in uplink massive access scenarios within the SAGIN has become a major research topic. This paper introduces the Resilient Massive Access (RMA) protocol, which deeply integrates S-ALOHA and NOMA, effectively enhancing the system's access success probability. Additionally, a cascaded uplink detection algorithm based on LS and MUSIC (MUSIC-NOMA-TSA) is proposed, enabling signal decoding and target sensing even in the presence of collisions at the receiver. Simulation results demonstrate that, compared to traditional algorithms, the proposed algorithm not only achieves more accurate signal decoding and target sensing but also significantly improves the access success probability.

Sparse code multiple access (SCMA) is a new nonorthogonal multiple access scheme, which effectively exploits the constellation shaping gain of multi-dimensional codebook. In this paper, we propose a new SCMA architecture of uplink satellite communication system. At the transmitter, we group the users and design a practical spherical codebook. At the receiver, a low-complexity multi-user detection algorithm, namely logarithm domain message passing algorithm (Log-MPA) is implemented. The results show that the reliability of the proposed codebook outperforms the existing SCMA codebook schemes in both AWGN and Rayleigh channels.

Cell-free (CF) massive MIMO is considered one of the key technologies for 6G to achieve high spectral efficiency (SE) and ultralow latency. However, as the number of users increases, pilot contamination becomes more serious, and the optimal SE can not be achieved when the number of users exceeds the access points (APs). Therefore, we study the CF massive MIMO-NOMA system. Specifically, we design a user clustering algorithm based on the average Signal to Interference plus Noise Ratio (SINR), using orthogonal pilots between different clusters, and different users in the cluster using the same pilot, thereby reducing pilot contamination. Then we propose a flexible power allocation problem to maximize the system SE while taking into account user fairness. We model the problem as a Markov Decision Process (MDP) and then solve it using the asynchronous advantage actor-critic (A3C) algorithm in deep reinforcement learning. Simulation results show that the proposed A3C based power allocation scheme in CF massive MIMO-NOMA outperforms the baseline schemes in terms of fairness and SE.

Nonorthogonal multiple access (NOMA) and symbiotic ambient backscatter communications (AmBCs) are both considered promising technologies for beyond 5G mobile communication technology by enabling low-powered and spectrum-efficient access in large-scale Internet of Things. In this article, we investigate the uplink symbiotic communication in AmBC enabled NOMA system. In contrast with existing works, we assume the carrier transmitter (CT) transmits information while providing energy to the backscatter devices (BDs), which improves the energy efficiency. However, the signal power transmitted by the CT to integrated receiver (IR) through the direct link is much larger than the signal reflected by the BD, which can seriously affect the detection of the reflected signal by BD. Therefore, we jointly design the signal waveforms of CT and BDs, and propose a multiuser blind detection algorithm based on interference cancellation at IR. The simulation results demonstrate that the proposed multiuser detection algorithm achieves improved performance even the active BD number is unknown or without direct link.

Traditional cellular system deploys base station at the center of each cell, which leads to inter-user/cell interference and limited spectral efficiency. In contrast, the cell-free massive MIMO can effectively mitigate these interference by deploying multiple access points distributed in the coverage that jointly serve all the users. In this paper, we investigate a cell-free massive MIMO system assisted by the non-orthogonal multiple-access (NOMA) and propose a sparse signal reconstruction algorithm based on extended approximate message-passing (EAMP). The simulation results show that the proposed algorithm outperforms the traditional baselines in terms of recovery rate, calculation time and system capacity.