Semantic communications have shown its great potential to improve the transmission reliability, especially in the low signal-to-noise regime. However, resource allocation for semantic communications still remains unexplored, which is a critical issue in guaranteeing the semantic transmission reliability and the communication efficiency. To fill this gap, we investigate the spectral efficiency in the semantic domain and rethink the semantic-aware resource allocation issue. Specifically, taking text semantic communication as an example, the semantic spectral efficiency (S-SE) is defined for the first time, and is used to optimize resource allocation in terms of channel assignment and the number of transmitted semantic symbols. Additionally, for fair comparison of semantic and conventional communication systems, a transform method is developed to convert the conventional bit-based spectral efficiency to the S-SE. Simulation results demonstrate the validity and feasibility of the proposed resource allocation method, as well as the superiority of semantic communications in terms of the S-SE.

With the ever increasing spectrum demand of broadband multimedia services, cognitive satellite terrestrial networks have emerged as a promising paradigm for future space information networks. To provide services with diverse delay quality-of-service (QoS) requirements in an energy-limited system, in this paper, we investigate energy efficient power allocation for cognitive satellite terrestrial networks. Employing statistical delay-QoS metric, power allocation schemes are formulated as optimization problems to maximize effective energy efficiency of secondary satellite communications while satisfying interference constraints imposed by primary terrestrial communications. Specifically, allowing for the availability of instantaneous channel state information (CSI) of the secondary transmitter-primary receiver link, optimal transmit powers are derived for both cases of statistical and instantaneous interference constraints. Moreover, to provide a theoretical insight on the performance of the considered network, we derive closed-form expressions for the outage probability based on the obtained optimal transmit powers. Simulation results demonstrate the validity of the theoretical results and show the impacts of the delay exponent, interference constraint, and aggregate interference from terrestrial networks on the performance of satellite networks.

Allowing frequency reuse between satellite and terrestrial networks, the integrated satellite-terrestrial network can spatially optimize the usage of scarce spectrum resource and is becoming one of the most promising infrastructure for future multimedia services. Taking the requirements of both efficiency and reliability in satellite communications into account, we propose an adaptive transmission scheme for the integrated network in this paper, where the satellite can communicate with the destination user either in direct mode or in cooperative mode. Specifically, we first investigate the symbol error rate (SER) performance of two transmission modes with co-channel interference under composite multipath/shadowing fading. Taking the derived SERs as constraints, we formulate the adaptive transmission scheme as an optimization problem with the objective of maximizing energy efficiency (EE) and discuss the tradeoff among EE, spectral efficiency (SE), and SER. Furthermore, economic efficiency (ECE) is also analyzed as a complementary performance measure to SE and EE. Simulation results show that the proposed scheme can increase the attainable EE of satellite communications, which indicates that we should choose the transmission mode adaptively according to different interfering scenarios and shadowing degrees, rather than adopting cooperative transmission aggressively.

This paper considers a simultaneous wireless information and power transfer (SWIPT) based decode-and-forward relaying network, where the "harvest-then-forward" strategy is employed. We focus on designing optimal static and dynamic transmission schemes of joint time allocation and power splitting to conduct the relay in terms of outage performance and ergodic performance, respectively. In particular, the analytical expressions for the outage probability and ergodic capacity are derived to determine the optimal static power splitting (PS) and time allocation (TA) ratios. Moreover, we study the dynamic transmission scheme and formulate two joint optimization problems to minimize the outage probability and maximize the instantaneous channel capacity. Considering that the two optimization problems are non-convex, a split-step iterative method is proposed to obtain the optimal dynamic PS ratio and TA ratio for minimizing the outage probability, and an alternate convex optimization method is employed to solve the problem of maximizing the instantaneous channel capacity. Simulation results verify the advantages of the proposed schemes over three peer schemes.

Recent studies have demonstrated that using deep learning (DL) methods to classify drones based on radio frequency (RF) signal is effective. As known, the rich and diverse data is an important guarantee for good identification performance. In reality, due to the complexity and high dynamic of wireless environments, the costs of signal collection and labeling with sufficient diversity are often unacceptable. In this work, we propose a low-cost data augmentation (DA) method to improve the robustness of the signal identification neural network (NN). It generates extra training data by mixing pure drone signals with practical background signals and trains the NN to distinguish drone signals from interferences. Moreover, to further improve performance, we design a spectrogram segmentation (SS) method that directly splits the entire spectrogram into several sub-spectrograms to separate the interference signals working outside of the drone bandwidth. Finally, using five representative drone signals collected in multiple scenarios, we examine the identification accuracy and generalization capability of the proposed algorithm. Experiment results reveal that the proposed algorithm performs better than the NN trained without SS or DA.

By transmitting task-related information only, semantic communications yield significant performance gains over conventional communications. However, the lack of mature semantic theory about semantic information quantification and performance evaluation makes it challenging to perform resource allocation for semantic communications, especially when multiple tasks coexist in the network. To cope with this challenge, we propose a quality-of-experience (QoE) based semantic-aware resource allocation method for multi-task networks in this paper. First, semantic entropy is defined to quantify the semantic information for different tasks, and the relationship between semantic entropy and Shannon entropy is analyzed. Then, we develop a novel QoE model to formulate the semantic-aware resource allocation in terms of semantic compression, channel assignment, and transmit power. The compatibility of the formulated problem with conventional communications is further demonstrated. To solve this problem, we decouple it into two subproblems and solved them by a developed deep Q-network (DQN) based method and a proposed low-complexity matching algorithm, respectively. Finally, simulation results validate the effectiveness and superiority of the proposed method, as well as its compatibility with conventional communications.