A two-hop relay network is considered, in which an eavesdropper can overhear the relaying signal. To prevent the eavesdropper from decoding this signal, a destination transmits a jamming noise while a source transmits the data signal to the relay. At the same time, the relay can harvest energy from both the source signal and the jamming noise, and use this harvested energy to forward the received signal to the destination. In such a wireless-powered relay system with cooperative jamming, we propose two adaptive relaying schemes based on power splitting and time switching techniques. In the proposed power splitting-based relaying (PSR) and time switching-based relaying (TSR) schemes, the relay controls the power splitting ratio (ρ) and time switching ratio (α), respectively, in order to achieve a balance between signal processing and energy harvesting. We find analytically the optimal values of ρ and α in each scheme to maximize the secrecy capacity under the assumption of high signal-to-noise ratio. Interestingly, although the eavesdropper’s channel state information (CSI) is used in the derivation of the optimal control parameters (ρ and α), they are shown not to be affected by the eavesdropper’s CSI in a high SNR regime. This implies that the proposed schemes can be effective even for practical environments where there is no eavesdropper’s CSI. Furthermore, simulation results show that they well coincide with the exact solutions in practical environments even though the closed-form solutions are obtained with a high SNR assumption. Moreover, the comparisons of PSR and TSR in various scenarios show that the two relaying schemes have complementary performances depending on the network conditions. Specifically, PSR achieves greater secrecy capacity than TSR when the channel condition is unfavourable to the eavesdropper for wiretapping.

This paper considers a two-way relay network, in which two sources exchange data through a relay and a cooperative jammer transmits an artificial noise (AN) while a number of nearby eavesdroppers overhear to recover data from both sources. The relay harvests energy from the two source signals and the AN, and then, uses this harvested energy to forward the received signals to the two sources. Each source eliminates its own signal from the relaying signal by self-cancellation and then decodes the data signal received from the other source. For this wireless-powered two-way relay system, we propose two secure relay protocols based on power splitting and time switching techniques. The two protocols are power splitting-based two-way relaying (PS-TWR) and time switching-based two-way relaying (TS-TWR), in which the relay respectively controls the power splitting ratio (ρ) and time switching ratio (α), in order to achieve a balance between the data receiving and the energy harvesting. The optimal values of ρ and α for each protocol are found analytically to maximize the minimum guaranteed secrecy capacity (CminS) considering multiple eavesdroppers in high signal-to-noise ratio environments. Numerical results show that both the PS-TWR and TS-TWR protocols using the optimized values of ρ and α achieve the near-optimal CminS no matter how many eavesdroppers exist anywhere. Comparisons of the two protocols in various scenarios also show that PS-TWR achieves better CminS than TS-TWR because PS-TWR inherently has a shorter vulnerable time for eavesdropping than TS-TWR.

This paper proposes a compressed sensing based random access protocol (CS-RACH), which is suitable for servicing a large number of MTC devices in Internet of Things (IoT) network. In CS-RACH, we utilize a larger number of unique preambles compared to conventional LTE-RACH, however, the compressed sensing technique makes it possible to simultaneously detect the users with high accuracy. Compared to the user detection in conventional LTE-RACH, the proposed user detection can get rid of preamble collisions and decrease the collision probability, thereby the overall access latency is significantly reduced. To prove the benefits of the proposed CS-RACH, we mathematically analyze and compare access latency performance of LTE-RACH and CS-RACH. In particular, based on the least absolute shrinkage and selection operator (LASSO) approach, we derive a normalized throughput, access success probability, and average access latency. Our simulation results also exhibit that the proposed CS-RACH considerably reduces the access latency under reasonable conditions in IoT environments.

In this paper, we consider user scheduling and power allocation techniques for downlinks where transmitted message must be kept confidential from all other unintended users and the number of users is larger than that of access point (AP) antennas. In order to maximize network secrecy rate with a limited number of antennas, the AP schedules a user who can achieve the largest secrecy rate and exploits multiple antennas for transmitting artificial noise to the unintended users. We derive the closed-form expressions of the optimal power allocation for the artificial noise and the average network secrecy rate through asymptotic analysis. The analysis results provide the insightful information on the behaviors of the user scheduling and the effect of system parameters on the network secrecy rate performance. It is shown that opportunistic scheduling with artificial noise transmission technique is more effective than the conventional precoding techniques for multiple confidential message transmissions in terms of network secrecy rate. Furthermore, we have also discussed a transmission strategy for the worst case scenarios where all unintended users are treated as colluding eavesdroppers. The simulations confirm the validity of the analysis results and provide additional information on the network performance in general cases.

Recent advances in deep learning (DL) have significantly enhanced automatic modulation classification (AMC), reducing the dependence on intricate feature engineering and substantially improving classification accuracy. Nonetheless, the conventional DL-based AMC methods have mostly adopted frequentist approaches, often exhibit limited adaptability and struggle to offer reliable uncertainty estimates for their predictions, especially when trained on limited data. To address these challenges, we propose a novel Bayesian DL framework tailored for the uncertainty-aware incremental AMC. Our framework involves a two-stage process, beginning with the optimization of model parameters using a cumulative loss function in frequentist approach, followed by the application of Bayesian neural network (BNN) techniques, such as Laplace approximation and variational inference, for statistical inference. This framework ensures robust calibration and scalability, enabling the classifier to incrementally refine its predictions with the reception of additional signal samples and to terminate the refinement process once the confidence score meets the predetermined classification accuracy target. In addition, these enhanced capabilities of our classifier can extend to identifying a new, unseen modulation scheme by discerning distinct confidence score patterns associated with varying sequence lengths. Simulation results validate the efficacy of the proposed uncertainty-aware incremental AMC by showing enhanced classification accuracy and superior calibration relative to conventional DL-based AMC methods in practical scenarios with limited training data.