The growing interests in Internet of Underwater Things (IoUT) put forward more demands for underwater acoustic (UWA) communication technologies. However, the conventional orthogonal frequency division multiplexing (OFDM) technology underperforms in terms of the resistance to carrier frequency offset (CFO), symbol time offset (STO), and applicability to multiple application scenarios. To remedy the deficiencies, this paper introduces the emerging universal filtered multi-carrier (UFMC) technology into UWA communications and devises a two-stage index modulated UFMC (TSIM-UFMC) system for performance improvement. In addition, a deep learning-aided signal detector termed as TSIMNet is proposed for the TSIM-UFMC system over multipath UWA channels. Specifically, at the transmitter, the TSIM-UFMC system adopts two-stage index modulation to characterize the different position information of data, pilot, and inactive subcarriers in each subblock for ameliorating the bit error rate (BER) performance under channel interferences. At the receiver, the TSIMNet detector exploits configurable deep neural networks to encapsulate the index demodulation, channel estimation, and constellation demodulation for optimizing the system structure. Furthermore, TSIMNet can be deployed to detect the received signal online for the TSIM-UFMC system after trained by offline data. Numerical results reveal that the proposed TSIM-UFMC system exhibits achievable advantages in combating CFO and STO, and the TSIMNet detector actualizes a near-optimal performance on BER with preferable computational complexity.

With the rapid growth of data traffic, low earth orbit (LEO) satellite communication networks have gradually ushered in a new trend of development due to its advantages of low latency, wide coverage, and high capacity. However, as a result of the limited on-board resources and rapidly changing traffic demand, it is increasingly urgent to design an efficient resource-allocation scheme to satisfy the traffic demand. In this paper, we propose two resource allocation algorithms in the multi-satellite system based on beam-hopping technology. In the offline case, it is assumed that the channel gains in all time-slots are known in advance, and we propose a heuristic algorithm to allocate time and frequency resources, and a successive convex approximation (SCA) algorithm to allocate power resources. In the online case, it is assumed that only the instant channel gains information is known; therefore, we apply the dynamic programming (DP) algorithm to maximize the system throughput. The simulation results prove that the proposed resource-allocation algorithms based on beam-hopping technology have better performance than the traditional average allocation method, and the online algorithm has acceptable performance loss compared with the offline algorithm.

In future sixth-generation (6G) communication systems, it is foreseen that complex communication scenarios and critical performance requirements will necessitate more flexible air interface configurations. Traditional air interface adaptation will no longer be applicable to 6G due to issues such as high computational complexity, sub-optimal trade-offs among multi-objective performance metrics, outdated configurations due to fast-varying channels, etc. In this paper, the relevant user behaviors, communication environment, and system are virtualized via the digital twinning technique. Then, a knowledge graph-based multi-objective recommendation framework is proposed to configure the digital twinning air interface to adapt to channel conditions, while balancing various service requirements. First, the knowledge graph is applied to reveal complex dependencies between the air interface and the service requirements, and more importantly, to reconcile possibly contradictory performance targets. Furthermore, the air interface configuration, empowered by the digital twin technique, is able to exploit predicted prior knowledge about user behavior and the channel characteristics, thus improving the utilization efficiency of wireless resources promptly. Moreover, the digital twin technique allows the candidate air interfaces to be virtually verified and compared with little effort. Finally, two case studies are presented to demonstrate the potential of the knowledge graph-based recommendation method for the digital twinning air interface.