In this paper, we develop a non-orthogonal multiple access (NOMA)-based two-way relay network with secrecy considerations, in which two users wish to exchange their NOMA signals via a trusted relay in the presence of single and multiple eavesdroppers. To ensure secure communications, the relay not only forwards confidential information to the legitimate users but also keeps emitting jamming signals all the time to degrade the performance of any potential eavesdropper. Moreover, we equip the relay and each user with the full-duplex technique in the multiple-access phase to combat the eavesdropping and improve the data transmission efficiency, respectively.We propose different decoding schemes based on the successive interference cancellation (SIC) for the legitimate users, relay, and eavesdroppers. Closed-form expressions for the achievable ergodic secrecy rates of all data symbols under both single- and multipleeavesdropper cases are derived, validated by the excellent fitting to the computer simulation results for our proposed network.

In this paper, we study the performance of a downlink non-orthogonal multiple access (NOMA) based cooperative relay system with a single relay over Nakagami-m fading channels, where both decode-and-forward (DF) and amplify-andforward (AF) protocols are considered. We assume that only statistical channel state information is available to the system and used to determine the decoding order of cell-edge users’ data. For DF relaying, both ergodic sum rate and outage probability are solved in closed form. For AF relaying, closed-form asymptotic ergodic sum rate and outage probability are provided. Numerical results verify the accuracy of the analysis and demonstrate that the DF protocol significantly outperforms the AF one in terms of ergodic sum rate even the channel’s near-far effect weakens. In addition, the DF protocol exhibits better outage performance than the AF one at low signal-to-noise ratio (SNR), though the superiority becomes negligible with the increasing SNR.

In this paper, we propose an energy-efficient spatial modulation based molecular communication (SM-MC) scheme, in which a transmitted symbol is composed of two parts, i.e., a space derived symbol and a concentration derived symbol. The space symbol is transmitted by embedding the information into the index of a single activated transmitter nanomachine. The concentration symbol is drawn according to the conventional concentration shift keying (CSK) constellation. Benefiting from a single active transmitter during each symbol period, SMMC can avoid the inter-link interference problem existing in the current multiple-input multiple-output (MIMO) based MC schemes, which hence enables low-complexity symbol detection and performance improvement. Correspondingly, we propose a low-complexity scheme, which first detects the space symbol by energy comparison, and then detects the concentration symbol by the maximum ratio combining assisted CSK demodulation. In this paper, we analyze the symbol error rate (SER) of the SM-MC and of its special case, namely the space shift keying based MC (SSK-MC), where only space symbol is transmitted and no CSK modulation is invoked. Finally, the analytical results are validated by computer simulations. Our studies demonstrate that both the SSK-MC and SM-MC are capable of achieving better SER performance than the conventional MIMO-MC and single-input single-output based MC, when given the same symbol rate.

In this letter, we propose a novel sparse Bayesian learning (SBL) algorithm using Generalized Double Pareto (GDP) prior to enhance the performance of direction of arrival (DOA) estimation for complex signals. Firstly, a novel hierarchical prior model is formulated for complex signals so that the marginal distribution of the complex signal is the GDP distribution, which promotes the sparsity more significantly than conventional priors used in SBL. Secondly, a novel fixed-point update rule of the hyperparameters is derived to speed up the convergence of the proposed SBL. Finally, a refined DOA searching method is also derived to tackle the grid-mismatch problem. Simulation results demonstrate the improved accuracy and efficiency of the proposed algorithm in low SNR and limited snapshots scenarios compared with other SBL-based DOA estimation methods.

Marine science and Internet of Underwater Things applications rely significantly on collecting data from underwater sensors. Data collection using long-distance underwater acoustic communications consumes a lot of energy in underwater sensor nodes, which are powered by batteries. To achieve low-energy consumption, we can use the autonomous underwater vehicle (AUV) to move close to sensor nodes and exploit the short-range and high-rate communications. Most of the existing AUV-based data collection schemes consider the scenarios having the knowledge of node positions, where the cruising trajectory can be computed before the AUV’s departure. These schemes cannot apply to some scenarios such as turtle tracking for a certain sea area having no position information. To this end, we first propose a planning-while-detecting approach to dynamically detect the sensors on turtles and adjust the AUV cruising direction to collect data. To further improve data efficiency under the energy limit of the AUV, we group the sensors that can share the same trajectory using their detected directions. A grouping-based dynamic trajectory planning (GDTP) is then proposed to determine the next cruising direction that can visit the group of sensors having the largest amount of data and demanding the least cruising energy at the risk of detection errors. Simulation results show that GDTP achieves significantly higher data collection efficiency than the existing trajectory planning algorithms in dynamic scenarios, and as the communication range increases, it can even outperform the existing algorithms with node locations.