Multi-access edge computing (MEC) is regarded as a promising solution to overcome the limit on computation capacity of mobile devices. This paper investigates an energy efficient unmanned aerial vehicle (UAV) enabled MEC framework incorporating non-orthogonal multiple access (NOMA), where multiple UAVs are deployed as edge servers to provide computation assistance to terrestrial users and NOMA is adopted to reduce the energy consumption of task offloading. A utility is formed to mathematically evaluate the weighted energy cost of the system. Due to the coupling of parameters, the minimization of utility is a highly non-convex problem and therefore the problem is decomposed into two more tractable subproblems, i.e., the optimal allocation of radio and computation resources given UAV trajectories, and the trajectory planning based on given resource allocation schemes. These two problems are converted to convex ones via successive convex approximation (SCA) and quadratic approximation respectively. Then an efficient iterative algorithm is proposed where these two subproblems are alternately solved to gradually approach the optimal resource management of the proposed system. Sufficient numerical results show that our proposed strategy has a remarkable advantage over existing systems in terms of energy efficiency.

Exploiting unmanned aerial vehicles (UAVs) as flying base stations (BSs) to assist the terrestrial cellular networks is promising in 5G and beyond. Despite the inherent potentials, one challenging problem is how to optimally deploy multiple UAVs to achieve on-demand coverage for ground user equipment (UE). In this paper, we model the deployment problem as minimizing the number of UAVs and maximizing the load balance among them, which is subject to two main constraints, i.e., UAVs should form a robust backbone network and they should keep connected with the fixed BSs. To solve this optimization problem with low complexity, we decompose the problem into two subproblems and propose a hybrid algorithm to solve them stepwise. First, a centralized greedy search algorithm is used to heuristically obtain the minimum number of UAVs and their suboptimal positions in a dis-continuous space. Then, a distributed motion algorithm is adopted which enables each UAV to autonomously control its motion towards the optimal position in a continuous space. The proposed algorithm is applicable to various scenarios where UAVs are deployed alone or with fixed BSs regardless of the UE distribution. Extensive simulations validate the proposed algorithm.

Unmanned aerial vehicles as base stations (UAV-BSs) are recognized as effective means for tackling eruptive communication service requirements especially when terrestrial infrastructures are unavailable. Quality of service (QoS) received by ground terminals (GTs) highly depends on the spatial movement of UAV-BSs. In this paper, we investigate the cooperative trajectory design problem of multiple UAV-BSs towards fair throughput maximization of GTs. Considering the restriction of coverage and sensing, we first propose a heterogeneous-graph-based formulation of relations between GTs and UAV-BSs. Subsequently, we design a framework named graph vision and communication (GVis&Comm) to 1) let each UAV-BS efficiently manage time-varying local observations; 2) facilitate cooperation between UAV-BSs through explicit information exchange. To further reduce the overhead of over-the-air cooperation, we realize discretization of the message passing process among UAV-BSs while still enabling end-to-end training. By leveraging multi-agent reinforcement learning (MARL), UAV-BSs as agents learn a distributed trajectory design policy. Extensive numerical simulation shows that our framework on the one hand achieves remarkable efficiency in processing local observations of each UAV-BS, and on the other improves the overall network performance via close cooperation among UAV-BSs.

Semantic communication is a promising technology used to overcome the challenges of large bandwidth and power requirements caused by the data explosion. Semantic representation is an important issue in semantic communication. The knowledge graph, powered by deep learning, can improve the accuracy of semantic representation while removing semantic ambiguity. Therefore, we propose a semantic communication system based on the knowledge graph. Specifically, in our system, the transmitted sentences are converted into triplets by using the knowledge graph. Triplets can be viewed as basic semantic symbols for semantic extraction and restoration and can be sorted based on semantic importance. Moreover, the proposed communication system adaptively adjusts the transmitted contents according to channel quality and allocates more transmission resources to important triplets to enhance communication reliability. Simulation results show that the proposed system significantly enhances the reliability of the communication in the low signal-to-noise regime compared to the traditional schemes.

Detecting ever increasing anomalous signals is critical to effective spectrum management. In this letter, we present a radio anomaly detection algorithm based on modified generative adversarial network (GAN). Firstly, short time fourier transform (STFT) is applied to obtain the spectrogram image from the received signal. Then, a novel encoder-GAN (E-GAN) structure is proposed by incorporating an encoder network into the original GAN to reconstruct the spectrogram. As a result, the existence of anomalies can be detected based on the reconstruction error and discriminator loss. In addition, the reconstruction error can also be exploited to locate the anomalies in time-frequency domain. Simulation results show that the proposed algorithm brings a performance improvement of up to 10 dB compared with the spectrum anomaly detector with interpretable features (SAIFE).

Devices in the Internet of Things (IoT) are usually limited in computing resources and energy capacity, which means that encryption schemes with higher complexity are not suitable for them to ensure secure communication. As a promising solution to this problem, physical layer key generation suggests that shared secret keys can be generated from noisy wireless channel measurements to enhance the security of wireless communications. In this article, we propose a key generation scheme with extremely low implementation complexity, which allows physical layer key generation to be implemented on IoT nodes. First, we preprocess the channel measurements with simple moving average filtering before quantization to improve channel reciprocity. Next, a bidirectional difference quantization scheme is proposed to realize reliable quantization of channel measurements, which is ingenious in that the quantization process does not depend on quantization thresholds, and thus, the mismatched key bits caused by measurements close to quantization thresholds can be effectively avoided. Then, we propose an improved Cascade protocol to achieve lightweight and efficient information reconciliation. The simulation results show that our scheme can well balance the reliability and efficiency of key generation, and has excellent performance in terms of implementation complexity and key randomness.