The advent of the 6G era aims for ubiquitous connectivity, with the integration of non-terrestrial networks (NTN) offering extensive coverage and enhanced capacity. As manufacturing advances and user demands evolve, space-air-ground integrated networks (SAGIN) with computational capabilities emerge as a viable solution for services requiring low latency and high computational power. Resource management within joint communication and computing-embedded SAGIN (JCC-SAGIN) presents greater complexity than traditional terrestrial networks. This complexity arises from the spatiotemporal dynamics of network topology and service demand, the interdependency of large-scale resource variables, and intricate tradeoffs among various performance metrics. Thus, a thorough examination of resource management strategies in JCC-SAGIN is crucial, emphasizing the role of non-terrestrial platforms with processing capabilities in 6G. This paper begins by reviewing the architecture, enabling technologies, and applications in JCC-SAGIN. Then, we offer a detailed overview of resource management modeling and optimization methods, encompassing both traditional optimization approaches and learning-based intelligent decision-making frameworks. Finally, we outline the prospective research directions in JCC-SAGIN.

Sparse code multiple access (SCMA) is a novel type of non-orthogonal multiple access technology that combines the concepts of CDMA and OFDMA. The advantages of SCMA include high capacity, low time delay and high date rate. In this paper, a power allocation algorithm is proposed for SCMA downlink systems where each tone is taken by more than one user to maximize the system’s sum capacity. In SCMA systems, users are divided into different user groups. Thus, our proposed algorithm includes three-level power allocation. Since the power allocation problem is non-convex, the complexity of finding the optimal solutions is prohibitive. The Lagrange dual decomposition method is employed to efficiently solve the nonconvex optimization problem. Results show that the optimized algorithm can significantly improve the sum capacity.

With the large available bandwidth, millimeter wave (mmWave) communications have attracted considerable research interests because of their potential to achieve multi-gigabps rates. However, one of the main challenges for mmWave is high pathloss. To address this problem, full-duplex (FD) relaying can be used to increase the effective transmission distance and the spectral efficiency. Thus, studying the application of FD relaying in mmWave communications will be of value. However, one of the main challenges in FD mmWave relaying is the residual selfinterference (SI), which includes line-of-sight (LOS) and nonline- of-sight (NLOS) parts. To eliminate the SI and improve the spectral efficiency, we propose an orthogonal matching pursuit (OMP)-based SI-cancellation precoding algorithm. Then, we propose an energy consumption model and analyze the energy efficiency performance. We formulate the joint spectral efficiency and energy efficiency optimization problem, which can be transformed into a convex problem. The numerical results show that the FD precoding scheme can effectively eliminate the residual SI and achieve approximately twice the spectral efficiency of the conventional half-duplex (HD) system. We also show that in low-spectral-efficiency regions, the optimal energy efficiency can be achieved, but the achievable energy efficiency will decrease in high-spectral-efficiency regions.

In this paper, we propose S-Phaser, an indoor localization system that uses a single Wi-Fi access point (AP) to locate terminals. Compared to traditional indoor localization systems based on the fingerprint positioning technology, S-Phaser does not need to deploy a large number of APs and has better localization accuracy. S-Phaser utilizes channel state information (CSI) to compute the direct path length between a single AP and terminals, thereby efficiently improving the accuracy in the line-of-sight (LOS) scenario. S-Phaser is still able to get satisfied accuracy in the non-LOS scenario even not as good as in the LOS scenario. Using the feature of multiple channels in 802.11n, we design an interpolation-based algorithm, named as the interpolation elimination method to calibrate signal phases from CSI, and then we use two different algorithms, the Chinese residue theorem and broadband angle ranging, to compute the real distance of the direct path from the calibrated phases. Then, S-Phaser uses a geometric positioning algorithm to determine the user’s location. To verify the practicability of the proposed S-Phaser, we set up a localization system in an actual environment. S-Phaser can improve the median localization error to 1.5 m with a single Wi-Fi AP.

Traditional WiFi fingerprint positioning normally uses a time consuming and labor intensive process called site survey. This paper proposes a crowdsourcing and multi-source fusion based fingerprint sensing (CMFS) to replace the traditional site survey approach. In CMFS, motion sensors are used to construct the radio map by volunteers and Pedestrian Dead Reckoning (PDR) based on motion sensors equipped in smartphones is used for positioning. In positioning phase, an Extended Kalman Filter (EKF) based multi-source fusion algorithm is further developed to tackle the nonlinear fusion so that both positioning accuracy and robustness can be improved. Both experimental and simulation results verify that the performance of proposed schemes is comparable to the traditional fingerprint approaches. Further the EKF based fusion scheme is found to improve smoothness and stability of CMFS greatly.

The backscatter communications (BackCom) and physical layer security are respected to realize extremely low-power secure communications in the imminent sixth generation (6G). In a BackCom system, the backscatter device without radio frequency components sends messages to users by reflecting the external signals. However, the double-fading effect limits BackCom’s performance and the commonly used broadcast mode is vulnerable to eavesdropping. Two promising technologies, intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV), show excellent potential in handling these problems. In this paper, we propose a UAV-empowered IRS-BackCom network, where an IRS acts as the backscatter device and uses the received signals from a UAV for BackCom. We aim to guarantee secure transmission and maximize the broadcast secrecy rate by jointly optimizing the UAV’s beamformer and trajectory and the IRS’s reflection coefficient. To tackle the non-convex problem, we leverage the block coordinate descent method to decompose it into three subproblems. Specifically, the UAV’s beamformer and trajectory and the IRS’s reflection matrix are optimized alternatively. Further, we adopt reinforcement learning to facilitate the intractable UAV’s trajectory optimization. Simulation results verify the feasibility and effectiveness of the proposed system model and the optimization scheme.