The development of computation-intensive and delay-sensitive smart applications has put forward great challenge to the current cellular networks. To cope with this challenge, unmanned aerial vehicles (UAVs) providing additional on-demand communication and computing services has become a promising technology. This letter proposes a novel task offloading framework in UAV-aided mobile edge computing (MEC) networks. Specifically, the MEC queuing delay is considered in the formulated problem, where the average task delay is minimized via jointly optimizing user association and UAV deployment. The optimal transport theory is introduced to analyze the user association sub-problem, and the UAV deployment is optimized by the classical particle swarm optimization algorithm. Simulation results show that the delay performance is significantly improved by the proposed algorithm.

This paper addresses the spectrum sensing problem in an orthogonal frequency-division multiplexing (OFDM) system based on machine learning. To adapt to signal-to-noise ratio (SNR) variations, we first formulate the sensing problem into a novel SNR-related multi-class classification problem. Then, we train a naive Bayes classifier (NBC), and propose a class-reduction assisted prediction method to reduce spectrum sensing time. We derive the performance bounds by translating the Bayes error rate into spectrum sensing error rate. Compared with the conventional methods, the proposed method is shown by simulation to achieve higher spectrum sensing accuracy, in particular at critical areas of low SNRs. It offers a potential solution to the hidden node problem.

In recent years, deep neural network-based transfer learning (TL) has shown outstanding performance in EEG-based motor imagery (MI) brain-computer interface (BCI). However, due to the long preparation for pre-trained models and the arbitrariness of source domain selection, using deep transfer learning on different datasets and models is still challenging. In this paper, we proposed a multi-direction transfer learning (MDTL) strategy for cross-subject MI EEG-based BCI. This strategy utilizes data from multi-source domains to the target domain as well as from one multi-source domain to another multi-source domain. This strategy is model-independent so that it can be quickly deployed on existing models. Three generic deep learning models for MI classification (DeepConvNet, ShallowConvNet, and EEGNet) and two public motor imagery datasets (BCIC IV dataset 2a and Lee2019) are used in this study to verify the proposed strategy. For the four-classes dataset BCIC IV dataset 2a, the proposed MDTL achieves 80.86%, 81.95%, and 75.00% mean prediction accuracy using the three models, which outperforms those without MDTL by 5.79%, 6.64%, and 11.42%. For the binary-classes dataset Lee2019, MDTL achieves 88.2% mean accuracy using the model DeepConvNet. It outperforms the accuracy without MDTL by 23.48%. The achieved 81.95% and 88.2% are also better than the existing deep transfer learning strategy. Besides, the training time of MDTL is reduced by 93.94%. MDTL is an easy-to-deploy, scalable and reliable transfer learning strategy for existing deep learning models, which significantly improves model performance and reduces preparation time without changing model architecture.

With the rapid increase of the demand for the Internet of Things (IoT), spectrum resources have incremental challenges. Nonorthogonal multiple access (NOMA) and spectrum sensing (SS) are considered key candidate technologies for next-generation wireless communications to improve spectrum utilization. Nevertheless, using both technologies at the same time makes the system more complex and brings new challenges to user differentiation. In order to make better use of these advantages, we creatively propose a feature detection-based SS method for NOMA systems. To better distinguish the relationship between the presence or absence of signals from different NOMA users, we employ feature detection to obtain the feature values of each user. We propose workflows and transceiver architectures combining the two technologies. Based on the relationship among users’ priorities, power, and transmission in common scenarios, we design a downlink mode and two uplink modes and deduce the threshold settings of the corresponding modes. Meanwhile, we also customarily propose enhanced algorithms, to have a marked increase in the performance for the proposed method in various modes. Experimental results illustrate that the proposed technique is feasible and has prominent detection performance and satisfying throughput performance.

Brain-computer interfaces (BCIs) aim to directly bridge the human brain and the outside world through acquiring and processing the brain signals in real time. In recent two decades, tremendous progress has been made in terms of BCI paradigms and decoding algorithms. The information transfer rate of the BCI systems has been significantly enhanced, and the communication community are considering including the BCI technologies into the 6G networks and beyond. However, some critical problems and challenges remain. To some extent, the BCI systems resemble the wireless communication systems, as brain signals are generated and transmitted by the neurons and received by the sensors, propagating through multiple brain tissues which cause inevitable attenuation and mixing effects. Therefore, by following this idea, a systematic review of the BCI technologies is provided through comparing it with wireless communications. The main challenges of BCI lie in brain channel modelling, modulation, signal processing and detection. The latest and some classic researches are covered. In addition, typical applications of BCIs such as the Quality-of-Experience (QoE) metric and Internet-of-Brains (IoB) are predicted. Then, we present a prospect of BCIs being integrated with future wireless communication systems. Also, the technical challenges regarding communication and computation to fulfill the prospect are discussed, as well as privacy, security and ethical concerns. Therefore, this review aims to comprehensively overview BCI-inspired communication on two levels, the BCI itself being a communication system and future integration of BCI and communication as a whole system.

As a vital enabler for ubiquitous connectivity in space–air–ground integrated networks, the satellite-terrestrial communication system has been envisioned to be a crucial complement to terrestrial networks because of its superior capability of providing wide coverage. However, there are many practical limitations that degrade system performance, including on-board power constraints, severe path loss, high delay, and Doppler frequency shift. Reconfigurable intelligent surface (RIS), an attractive candidate technology for future networks, has been expected to be a promising solution to tackle these challenges. In this article, we propose an RIS-assisted multisatellite cooperative downlink transmission scheme, where an RIS is deployed near the terrestrial receiver to enhance the communication via joint design of beamforming vectors at satellites and phase shift optimization at RIS. Specifically, we first optimize the channel gain of the proposed transmission scheme under the continuous-time propagation model. Then, we formulate the problem as a maximization optimization of the received signal power during the duration of multisatellite communication service, and we apply an alternating optimization (AO) algorithm to solve the nonconvex problem. To reduce computational complexity, we further propose a low-complexity optimization algorithm under Line-of-Sight (LoS) transmission conditions and a scalable distributed optimization algorithm without the need to share the multisatellite individual channel state information. Simulation results validate the superiority of our proposed transmission scheme and demonstrate that the low-complexity algorithm and the distributed algorithm can achieve system performance comparable to that of the AO algorithm under LoS channel conditions.