With the expansive deployment of ground base stations, low Earth orbit (LEO) satellites, and aerial platforms such as unmanned aerial vehicles (UAVs) and high altitude platforms (HAPs), the concept of space-air-ground integrated network (SAGIN) has emerged as a promising architecture for future 6G wireless systems. In general, SAGIN aims to amalgamate terrestrial nodes, aerial platforms, and satellites to enhance global coverage and ensure seamless connectivity. Moreover, beyond mere communication functionality, computing capability is increasingly recognized as a critical attribute of sixth generation (6G) networks. To address this, integrated communication and computing have recently been advocated as a viable approach. Additionally, to overcome the technical challenges of complicated systems such as high mobility, unbalanced traffics, limited resources, and various demands in communication and computing among different network segments, various solutions have been introduced recently. Consequently, this paper offers a comprehensive survey of the technological advances in communication and computing within SAGIN for 6G, including system architecture, network characteristics, general communication, and computing technologies. Subsequently, we summarize the pivotal technologies of SAGIN-enabled 6G, including the physical layer, medium access control (MAC) layer, and network layer. Finally, we explore the technical challenges and future trends in this field.

This paper investigates the issue of resource allocation for unmanned aerial vehicle and reconfigurable intelligent surface (UAV-RIS) assisted vehicular communication systems. To adapt the high dynamics of vehicular networks, we conceive a digital twin-based system over RIS-embedded environment towards environmental-aware communications. Specifically, a digital twin system can leverage data-driven models to predict the large-scale fading of future stages, while RIS is capable of controlling the propagation environments in real time, which can be utilized to mitigate prediction errors imposed by the small-scale fading. Using the capabilities of “prediction" and “reconfiguration", we expect to comprehensively foresee the dynamic changes in vehicular networks. In particular, the above-mentioned issue is formulated as a multi-slot total power consumption minimization problem under the quality of service (QoS) and energy constraints. Considering the finite battery energy of the UAV and the circuit power of the RIS, the transmit power of the UAV and the number of active reflecting elements (REs) are jointly scheduled for a finite time horizon. To tackle this mixed integer non-linear programming (MINLP) problem, we transform the original model into a discrete-time dynamic system. According to whether the dynamics of radio environments are predictable or not, the optimal offline and online policies are derived by using the deterministic and stochastic dynamic programming algorithms, respectively. To reduce the computation complexity, we further propose a novel online policy based on the idea of double-strategy selection. Finally, numerical results demonstrate that the proposed online policy exhibits near-optimal performances and outperforms other benchmarks in terms of transmission failure probability and effective power consumption.

In smart ocean Internet of Things (IoT) systems, autonomous underwater vehicles (AUVs) are responsible for underwater information collection. Due to the nature of the medium, the acoustic communications for AUVs are of low bandwidth and adverse environmental conditions causing severe transmission problems. In order to realize cross-media transmission from AUVs to the offshore platform, unmanned surface vehicles (USVs) have been suggested to forward the collected information in a coordinated manner. Against this backdrop, this contribution develops a cooperative USV-to-USV (U2U) cross-media cooperative communications scheme. Then, we formulate a rate maximization problem with the objective of optimizing the reputation-aware USV selection strategy. Furthermore, to characterize the impact of the mobility of AUVs/USVs, a long-term dynamic process is constructed. Meanwhile, we also develop an efficient algorithm which transforms the reputation-aided dynamic USVs selection problem into the infinite-time horizon average one restricted by time average rate constraints in the collection of penalty processes with the help of the Lyapunov optimization framework and drift-plus-penalty method. Finally, numerical results are presented to validate the convergence behavior and the performance for the designed dynamic USVs selection algorithm.

In this contribution, we consider the construction of the communication link between two terminals working on different media as underwater acoustics and traditional microwave, with the aid of a cross-media bidirectional relay. Our goal is to develop an optimal power allocation algorithm in order to minimize the outage probability. Through theoretical analysis and simulation results, we demonstrate the effectiveness of the above-mentioned cross-media structure and power optimization scheme, so as to adapt the scenario of hybrid underwater acoustic communication (UAC) and radio frequency (RF) transmission.

With the evolution of mobile terminals and the development of user demands, the existing mobile system is facing with serious challenges, such as the nearly saturated spectrum, limited processing capability of mobile terminals, and the high complexity of emerging technologies. To deal with these challenges, 6G is attracting extensive attentions. In this paper, we firstly investigate the potential technologies for 6G from the view of network design and resource management, then highlight the potential challenges for resource allocation, imposed for satisfying multi-demands in future hyper-heterogeneous networks with massive different devices and applications. In addition, the existing criteria for resource allocation are summarized for their common forms and limitations when dealing with multi-demands. Considering the limitations of the existing criteria, a novel quality-of-experience (QoE)-aware criterion on demand side is introduced toward efficient resource allocation in 6G, including its modeling, the potential application forms and typical scenarios, the future directions and challenges.

A novel reconfigurable intelligent surface (RIS)-aided hybrid reflection/transmitter design is proposed for achieving information exchange in cross-media communications. In pursuit of the balance between energy efficiency and low-cost implementations, the cloud-management transmission protocol is adopted in the integrated multi-media system. Specifically, the messages of devices using heterogeneous propagation media, are firstly transmitted to the medium-matched AP, with the aid of the RIS-based dual-hop transmission. After the operation of intermediate frequency conversion, the access point (AP) uploads the received signals to the cloud for further demodulating and decoding process. Based on time division multiple access (TDMA), the cloud is able to distinguish the downlink data transmitted to different devices and transforms them into the input of the RIS controller via the dedicated control channel. Thereby, the RIS can passively reflect the incident carrier back into the original receiver with the exchanged information during the preallocated slots, following the idea of an index modulation-based transmitter. Moreover, the iterative optimization algorithm is utilized for optimizing the RIS phase, transmit rate and time allocation jointly in the delay-constrained cross-media communication model. Our simulation results demonstrate that the proposed RIS-based scheme can improve the end-to-end throughput than that of the AP-based transmission, the equal time allocation, the random and the discrete phase adjustment benchmarks.

Water-fat separation is a non-linear non-convex parameter estimation problem in magnetic resonance imaging typically solved using spatial constraints. However, there is still limited knowledge on how to separate in vivo three chemical species in the presence of magnetic field inhomogeneities. The proposed method uses multiple graph-cuts in a hierarchical multi-resolution framework to perform robust chemical species separation in the breast for subjects with and without silicone implants. Experimental results show that the proposed method can decrease the computational time for water-fat separation and perform accurate water-fat-silicone separation with only a limited number of acquired echo images at 3 T. The silicone-separated images have an improved spatial resolution and image contrast compared to conventional scans used for regular monitoring of the silicone implant's integrity.

A novel social-tie motivated relay selection scheme is proposed for dynamic device-to-device (D2D) communications overlaying cellular networks. Using the non-edge cellular users to forward data, the proposed relay selection scheme can improve the transmission performance of the cell-edge users as an explicit benefit of D2D relays. Meanwhile, the effects of both the physical layer and social layer on the relay selection are jointly considered, where social ties are regarded as not only the motivation of relay services, but also the metric of security performance. Moreover, a generalized satisfaction index is introduced for designing a uniform quality of experience (QoE) criterion that can map different quality of service (QoS) metrics such as rate, throughput, delay, into a unified metric, and hence, is beneficial for the tradeoff between QoE and resource efficiency of relay selection. Furthermore, a dynamic optimization process is constructed for analyzing the effects of both the mobility of users and the randomness of channel on the relay selection, with the aid of the Lyapunov framework and drift-plus-penalty (DPP) algorithm. Finally, numerical results validate the effects of the proposed relay selection scheme.

In this paper, we propose a blockchain (BC)-enabled relay selection method in distributed cooperative communication networks, where non-cell-edge users (NCEUs) consume transmit power to relay cell edge users (CEUs) for uplink transmission in exchange for payments from CEUs. The proposed BC-enabled relay selection method aims at eliminating the failure of cooperative device to device (D2D) communication while maintaining privacy protection. By exploiting BC in the probe-reply phase, both CEU request and NCEU reply messages can be recorded in a verifiable manner. Once the feedback messages are received, the next step is decision making, which can be implemented by a two-sided matching game, in which the players include the CEUs party and the NCEUs one. In addition, the information recorded on the BC contains not only the probe-reply messages but also the optimal matching profile (e.g., transmission power sequence of NCEUs and the corresponding payment sequence of CEUs) in the second phase. The simulation results show that the proposed method is improved compared with the traditional matching scheme.

Internet of Things (IoT) as a prospective platform to develop mobile applications, is facing with significant challenges posed by the tension between resource-constrained mobile smart devices and low-latency demanding applications. Recently, mobile edge computing (MEC) is emerging as a cornerstone technology to address such challenges in IoT. In this paper, by leveraging social ties in human social networks, we investigate the optimal dynamic computation offloading mode selection to jointly minimize the total tasks' execution latency and the mobile smart devices' energy consumption in MEC-aided low-latency IoT. Different from the previous studies, which mostly focus on how to exploit social tie structure among mobile smart device users to construct the permutation of all the feasible modes, we consider dynamic computation offloading mode selection with social awareness-aided network resource assignment, involving both the computing resources and transmit power from heterogeneous mobile smart devices. On the one hand, we formulate the dynamic computation offloading mode selection into the infinite-horizon time-average renewal-reward problems subject to time average latency constraints on a collection of penalty processes. On the other hand, an efficient solution is also developed, which elaborates on a Lyapunov optimization-based approach, i.e., drift-plus-penalty (DPP) algorithm. Numerical simulations are provided to validate the theoretical analysis and assess the performance of the proposed dynamic social-aware computation offloading mode selection method considering different configurations of the IoT network parameters.

In this paper, we investigate the optimal dynamic social-aware peer selection with spectrum-power trading to maximize the average sum energy efficiency (EE) of cellular users (CUs) for uplink transmission for an orthogonal frequency division multiple access cellular network with device-to-device (D2D) communications. Different from the previous studies, which mostly focus on how to exploit social ties in human social networks to construct the permutation of all the feasible peers, we consider dynamic peer selection with social awareness-aided spectrum-power trading in D2D overlaying communications. Specifically, the amount of transmit power from the D2D transmitters to relay the CUs for uplink transmission is determined by their social trust levels. Likewise, the D2D transmitters can gain the corresponding amount of spectrum from the CUs for D2D pair link communications, which can be regarded as the compensation of the power consumption for relaying CUs. We formulate the dynamic peer selection problems with social awareness-aided spectrum-power trading in cooperative D2D communications into the infinite-horizon time-average renewal-reward problems subject to time average constraints on a collection of penalty processes. And the Lyapunov optimization concepts-based drift-plus-penalty algorithms are proposed to solve them. The simulation results demonstrate the effectiveness of the proposed dynamic peer selection algorithms. And further performance comparison indicates that the proposed dynamic peer selection algorithms not only maximize the average EE of CUs but also guarantee higher privacy protection.

We investigate the energy-efficient power allocation for downlink transmission in orthogonal frequency division multiple access-based long term evolution systems. Aiming at realizing on-demand power allocation in cellular networks, we explore the available coordination between the base station and multiple users, and propose a new performance merit, namely, the demand side coordination energy efficiency (DSC-EE), which captures the system normalized EE and the demand side information. The proposed DSC-EE is designed to exploit individual disparities from both the entire system and the individual own expected utility perspectives. Our goal is to maximize the DSC-EE of the system via power allocation with a constraint on the maximum transmit power. Specifically, the objective function of DSC-EE maximization problem in a fractional form can be transformed into a subtractive form that is more tractable based on the fractional programming theory. The convergence property of the proposed algorithms and the meaningfulness of the proposed performance merit related to the EE are demonstrated by simulations. The comparison of four EE metrics, the EE and the rate fairness, global-EE, Sum-EE, and DSC-EE, shows that the DSC-EE maximization tends to achieve high implementation level of on-demand power allocation while ensuring the EE of the system. In addition, when the minimum rate replaces the expected rate in the DSC-EE, further performance comparison indicates the necessity and impact of the expected rate in the tolerable quality of service bias function.

In frequency hopping (FH) wireless communications, finding an effective transmission strategy to properly mitigate jamming has been recently considered as a critical issue, due to the inherent broadcast nature of wireless communications. Recently, game theory has been proposed as a powerful tool for dealing with the jamming problem, which can be considered as a player (jammer) playing against a user (transmitter). Different from existing results, in this paper, a bimatrix game framework is developed for modeling the interaction process between the transmitter and the jammer, and the sufficient and necessary conditions for Nash equilibrium (NE) strategy of the game are obtained under the linear constraints. Furthermore, the relationship between the NE solution and the global optimal solution of the corresponding quadratic programming is presented. In addition, a special analysis case is developed based on the continuous game framework in which each player has a continuum of strategies. Finally, we show that the performance can be improved based on our game theoretic framework, which is verified by numerical investigations.