The space-terrestrial integrated network (STIN) is crucial for achieving ubiquitous connectivity in the 6G era. However, leveraging full potential of STIN is challenging due to the distinct characteristics and objectives of constituent networks. Inspired by symbiotic communication (SC), this paper proposes a service exchange-based symbiotic STIN system that optimizes objectives of different networks by exploiting their complementary features. Specifically, the ground network provides task offloading services to the space network, while the space network reciprocates with communication services. To minimize computation delay in the space network and maximize the energy efficiency (EE) of the ground network, we formulate a multi-objective optimization problem (MOOP) that jointly optimizes task offloading, resource allocation, and beamforming. We first transform the MOOP into a single-objective optimization problem (SOOP) via the ε-constraint method and then develop a successive convex approximation (SCA) algorithm to characterize its fundamental performance, which requires future state information. As obtaining such non-causal information is hard, we design a more practical multi-agent reinforcement learning (MARL) algorithm based on insights from the SCA. Besides, to address the challenges of storing multiple MARL policies for different EE-delay trade-offs, we develop a diffusion model-based behavior cloning (BC) algorithm to obtain a general policy suitable for varying trade-offs. Simulation results show that proposed algorithms outperform benchmarks and confirm that the proposed service exchange realizes a symbiotic STIN.

The space-terrestrial integrated network (STIN) is a pivotal architecture to support ubiquitous connectivity in the upcoming 6G era. Inter-operator resource and service sharing is a promising way to realize such a huge network, utilizing resources efficiently and reducing construction costs. Given the rationality of operators, the configuration of resources and services in STIN should focus on both the overall system performance and individual benefits of operators. Motivated by emerging symbiotic communication facilitating mutual benefits across different radio systems, we investigate the resource and service sharing in STIN from a symbiotic communication perspective in this paper. In particular, we consider a STIN consisting of a ground network operator (GNO) and a satellite network operator (SNO). Specifically, we aim to maximize the weighted sum rate (WSR) of the whole STIN by jointly optimizing the user association, resource allocation, and beamforming. Besides, we introduce a sharing coefficient to characterize the revenue of operators. Operators may suffer revenue loss when only focusing on maximizing the WSR. In pursuit of mutual benefits, we propose a mutual benefit constraint (MBC) to ensure that each operator obtains revenue gains. Then, we develop a centralized algorithm based on the successive convex approximation (SCA) method. Considering that the centralized algorithm is difficult to implement, we propose a distributed algorithm based on Lagrangian dual decomposition and the consensus alternating direction method of multipliers (ADMM). Finally, we provide extensive numerical simulations to demonstrate the effectiveness of the two proposed algorithms, and the distributed optimization algorithm can approach the performance of the centralized one. The results also reveal that the proposed MBCs can enable operators to achieve mutual benefits and realize a symbiotic resource and service sharing paradigm.

Spectrum and service sharing among multiple mobile network operators (MNOs) is regarded as a promising technology to construct future mobile networks, because it provides a higher utilization efficiency of the network resources, e.g., spectrum and network infrastructure. However, each individual MNO may not be able to benefit from this technology without joint optimizations. In this paper, we investigate a symbiotic spectrum and service sharing paradigm, which can improve the overall performance of the multi-MNO network while guaranteeing each MNO benefits as well. Particularly, we formulate two user association problems with novel mutual benefit constraints, namely, sum rate maximization and load balancing. Besides, we introduce a sharing coefficient for inter-MNO service sharing, which can be regarded as an inter-MNO service level agreement (SLA) accounting for the regulation of user association behaviors. Then, we propose a successive convex approximation (SCA) based algorithm and a fractional programming (FP) based algorithm to solve the sum rate maximization problems for different inter-MNO sharing strategies. In addition, we also develop a Lagrangian dual decomposition based algorithm for the load balancing problems. Finally, extensive numerical simulations are provided to demonstrate the effectiveness of the proposed algorithms. The results show that mutual benefit constraints can help multiple MNOs realize a symbiotic spectrum and service sharing paradigm. By comparing different inter-MNO sharing strategies, it can also be observed that the highest performance gain can be achieved when the multiple MNOs share their spectrum and service simultaneously.

To utilize network resources efficiently and reduce network construction costs, inter-operator spectrum and service sharing has been regarded as a promising technique for constructing future multi-operator networks. As the mobile operators are inherently competitors, one necessary prerequisite for inter-operator spectrum and service sharing is to guarantee each operator's revenue, namely, achieving mutual benefits among operators. Noting that the recently proposed symbiotic communication can achieve mutual benefits among different radio systems, in this paper, we investigate inter-operator spectrum and service sharing from a symbiotic communication perspective. Particularly, we propose a mutual benefit constraint to guarantee the revenue of each operator, and we aim to maximize the weighted sum rate (WSR) of the multi-operator network through joint optimization of the user association and beamforming design. Besides, considering the cost incurred by inter-operator spectrum and service sharing, we introduce a sharing coefficient to characterize the revenue of operators. Then, we develop an algorithm based on alternating optimization and successive convex approximation (SCA) methods for the WSR maximization problem. Simulation results demonstrate that the proposed mutual benefit constraints and optimization algorithms can enable operators to achieve mutual benefits.

Inter-operator resource and service sharing, which can utilize the network resources more efficiently and improve users' quality of services, is regarded as a promising method for the operators to construct future multi-operator networks. Particularly, an appropriate user association scheme is quite essential to enhance the network capacity. In this paper, our objective is to maximize the capacity of a multi-operator network by optimizing the user association scheme. Specifically, we investigate three inter-operator sharing scenarios, i.e., service-sharing scenario, spectrum-sharing scenario, and full-sharing scenario. Then, we propose a fractional programming (FP) based algorithm for the user association optimization problem. As inter-operator service sharing enables users to be served by other operators, a sharing coefficient is introduced for each operator to indicate its service level agreement (SLA) for regulating the users' association behaviors. As shown in the simulation results, the user association scheme obtained by the proposed algorithm can achieve a higher capacity than other alternative schemes. In comparison with the scenario without inter-operator sharing, both inter-operator spectrum sharing and service sharing can improve network capacity significantly, and the largest capacity is realized under the full-sharing scenario.