This paper introduces a protocol for quantum communication that employs entangled and separable pairs of particles, enabling classical information transmission without the need for classical communication between the parties. In this system, one party, Alice, manipulates the entanglement degree of photon pairs within two distinct channels, |ψ⟩ 1 and |ψ⟩ 2. The other party, Bob, subsequently measures the state of these photons. This study uses the entanglement status of each photon pair to encode classical bits: entangled pairs |00⟩ + |11⟩ represent the bit '1', while separable pairs |00⟩ represent '0'. The proposed protocol incorporates a secondary quantum channel that enhances entanglement determination accuracy through additional measurements and phase shifts. The proposed approach stands apart from conventional quantum teleportation, which requires inter-party classical communication, by only requiring an internal classical link at the receiver's end for entanglement status resolution. The proposed method offers benefits when classical communication implementation is impractical or costly. The research highlights the potential of a fully quantum-based communication system, bridging the gap between quantum entanglement and quantum teleportation in a novel, pragmatic manner. While there is a need for improvements to enhance efficiency, security, and reliability, the proposed protocol offers a promising path for future explorations in quantum channel communication.

The Quantum Key Distribution (QKD) method leverages the principles of quantum mechanics to securely create and distribute private keys using quantum systems and an authenticated public classical channel. Despite offering informationtheoretical security, its physical implementations often suffer from unintended information leakage, known as side channels, which eavesdroppers can exploit to obtain the private key. This study introduces a classical-side-channel attack on the privacyamplification step of a general QKD protocol based on matrix hashing, utilizing machine-learning techniques to analyze powerconsumption leakage. Through multiple simulated scenarios, the study found that the gradient-boosting machine consistently outperformed other models, recovering the entire private key for high measuring-instrument sampling rates, regardless of the hashing matrix size and noise level tested. Additionally, the study proposes a strategy based on analyzing the confusion matrices of the model to facilitate a brute-force search for the critical feasible in the case of a non-perfect model. Furthermore, the study discusses countermeasures such as noise insertion, masking, and randomization techniques, potentially restoring the QKD protocol's information-theoretical security. This work demonstrates that machine-learning techniques can effectively characterize the leakages in a QKD implementation and create potent attacks.

As technology advances, the world becomes more interconnected, and cellular networks play a crucial role. However, these networks are increasingly vulnerable to cyber-attacks, notably denial of service (DoS) attacks. Such attacks can be disastrous, causing significant communication disruptions and even financial losses. This paper focuses on illustrating the most common vulnerabilities in the user authentication system that the attackers exploit, thereby exposing the networks to such attacks. Additionally, the paper will conduct a detailed comparison of network security between the fifth generation and previous generations of cellular networks. This will enable us to understand the advancements in network security and the challenges that still need to be addressed to safeguard these networks from potential cyber threats.

The latest wireless technology, 5G, promises increased network capacity, reduced costs, and improved energy efficiency. To achieve these benefits, businesses and service providers use advanced technologies such as Virtualized Network Functions (VNFs) and Software Defined Networking (SDN) to minimize expenses. This study focuses on the challenge of optimizing virtual network functions and routing data through them to create a seamless service chain. To test this scenario, a network was designed with specific requirements and an optimization problem was formulated to minimize costs while meeting service and cloud provider requirements. Quality of service, network, and topology constraints were considered, including a congestion limit on links. As this is a complex problem, a small-scale network was simulated using MOSEK in MATLAB to obtain optimal results, and a heuristic algorithm was developed and compared to the optimal results.

This study states and analyzes a family of classical algorithms that solves Raz's problem, a problem in the communication complexity framework of distributed computing. The protocols' probabilistic convergence to the answer, their communication complexities, and how each protocol behaves in special cases of input variables are discussed. The family of protocols behaves in the following way: O(log d) communication complexity when stabilizer states and Clifford gates are guaranteed, O(d log d) when only the quantum gates are Clifford, O(d2 log d) when the input vector is a stabilizer state, and O(d3 log d) when there are no specifications on the input vector or gates. It performs as well as the quantum algorithm in the stabilizer sub-theory, but performs poorly in the general case.

It is crucial to share maximally entangled pairs in quantum information processing. However, unwanted interactions with the environment can reduce the entanglement fidelity of the produced pairs. Entanglement distillation is a method to overcome this problem by creating higher-fidelity entangled pairs from a more significant number of lower-fidelity pairs. This study uses Werner states and imperfect gates to analyse the performance of BBPSSW, DEJMPS, 3-to-1, and EPL protocols for entanglement distillation. Specifically, this study focuses on how the increase in fidelity depends on the input pair and gate fidelities. The results are obtained through numerical simulation using the NetSquid simulator. After characterising the numerical errors, the study demonstrates that all four protocols increase the pair fidelity for a gate higher than 0.96. The study also reveals that the higher the success fidelity, the lower the probability, as predicted theoretically. Additionally, based on a list of requirements and hardware performance, we developed a simple recipe for selecting the most suitable protocol. The simulation results highlight the practical limitations of entanglement distillation protocols, indicating the need for further investigation. This includes the development of a theoretical model for the EPL protocol implemented with Werner states, incorporating noisy measurements in the simulated protocols and increasing the number of simulations for higher accuracy.

Next generation communication networks promise seamless connectivity. This mandates high user coverage. Determining optimal access point locations is essential to maximize the total user coverage. However, users are mobile in real-time. Deployment of unmanned aerial vehicle access points (UAPs) has been proposed to improve the coverage. However, the onboard battery in UAPs pose energy limitations. This paper derives the UAP trajectory with optimal energy planning using a deep reinforcement learning approach.

Recent advances in deep learning have enhanced the ability of sound source localization in noise and reverberation. However, the single feature input and relatively simple network design hinder the further improvement of such ability. Therefore, a multi-feature fusion sound source localization method is proposed based on residuals and channel attention. The strategy of multi-feature fusion provides a deep neural network with more comprehensive discriminative features. The deep neural network fully extracts the sound source location information from the fused features by introducing residuals and channel attention. The simulations show that the localization accuracies of the proposed method in single and multiple sound source scenarios are respectively 8.24% and 15.54% higher than those of the single feature convolutional neural network (SF-CNN). In addition, the proposed method has excellent performance under different signal-to-noise ratios and reverberation times, which verifies its robustness to noise and reverberation. In the experiment, the proposed method is still effective in localizing single and multiple sound sources, and its localization accuracies are 5.48% and 7.57% higher than that of SF-CNN. With high accuracy and strong robustness, this method is significant for sound source localization in complex environments, such as noise, reverberation, and the presence of multiple sound sources.

This paper investigates the implementation of a physics informed neural network (PINN) using a hardware platform and hardware description language (HDL). The main objective is to investigate how real-time physics informed neural networks (PINNs) can be utilized on edge devices for digital twin applications. To make the best use of limited resources in these edge devices, they adopt a piece-wise non-linear approximation in the design of the PINN. To validate the effectiveness of their approach, they implement the PINN on a field programmable gate array (FPGA) to solve a non-linear ordinary differential equation (ODE) related to the Reynolds equation. According to the experimental findings, the hardware-based PINN achieves an impressive 95% accuracy compared to the actual solution.