With the increasing number of wireless devices connecting to networks and sharing the same spectrum resources, interference has become a significant obstacle to improving network performance. Existing interference management (IM) methods treat interference as a negative factor and mainly focus on suppressing or eliminating its impact on desired transmissions. However, this often comes at the cost of consuming communication resources. Therefore, design of low-cost IM method that can exploit interference is of research importance. To achieve this goal, we leverage the cost-effectiveness and adaptable deployment capabilities of Intelligent Reflecting Surface (IRS) to propose an IRS Empowered Interference Utilization (IRS-IU) method to realize efficient desired data transmission. By appropriately designing the reflection coefficient of the IRS, a phase shift is introduced to the incident interference, allowing the reflecting interference to interact with its direct counterpart at the interfered receiver (Rx). As a result, the interfered Rx can retrieve its desired data from the mixed interference. In this way, IRS-IU can make full use of the interference to enhance the desired data transmission. Our theoretical analysis and simulation results show that the proposed method can significantly improve the spectral efficiency (SE) of the interfered communication-pair.

With the rapid development of wireless technologies, future communication networks should not only enhance data transmission but also provide accurate and reliable location services. In complex indoor environment, traditional positioning methods encounter challenges related to accuracy and cost due to unpredictable attenuation, multipath interference, and other factors. This paper designs a Machine Learning Enhanced Indoor Positioning method that utilizes RIS-aided Channel Configuration and Analysis, called RCCA-MLEIP, for large-scale warehouse applications. This method consists of two stages: the RIS-aided Channel Configuration and Analysis (RCCA) stage, and the Machine Learning Enhanced Indoor Positioning (MLEIP) stage. In the RCCA stage, multiple RISs are deployed to create reflection paths associated with product tags. The phase coefficients of the RISs are adjusted multiple times, and the mixed signals observed by the reader after each adjustment are recorded. Based on these mixed signals, a system of equations is established to resolve both the phase fading and amplitude fading of each reflection path. In the MLEIP stage, a feature dataset is created using simulation methods, consisting of the phase fading and amplitude fading of multiple reference tags analyzed in the RCCA stage. We use the coordinates of the reference tags as the label dataset to train a Back Propagation (BP) neural network. The trained model can then output the coordinates of a target product tag based on the channel fading features associated with that tag, which are solved/obtained in the RCCA stage. The proposed RCCA-MLEIP utilizes RIS to create a multipath environment for positioning, effectively reducing the hardware costs of the positioning system. Moreover, employing machine learning techniques to estimate the target position can enhance both accuracy and response speed.

Blockchain technology has been widely used in many fields. However, the proof of work (PoW) problem in the mining process of mobile devices requires a large amount of computing resources and energy consumption, which brings huge challenges to mobile devices. Mobile edge computing (MEC) can effectively solve the above problems, allowing mobile devices to offload tasks to edge servers to relieve the pressure of limited computing resources on mobile devices. Nonorthogonal multiple access (NOMA) is good at improving spectrum efficiency, so that the system can accommodate more users. In this paper, we propose a new NOMA-based MEC-enabled blockchain framework. Under the conditions of a given task execution deadline, the decision of offloading, local computing resource allocation, user clustering and admission control, and transmit power control is jointly optimized to minimize the total cost of the system. Since the problem is hard to solve, we decouple it into subproblems for low-complexity solutions. First, we propose two heuristic algorithms to obtain the binary offloading decision and user association, and then closed-form solutions of local resource allocation and transmit power control are obtained under the required delay constraints. Simulation results show that our proposed algorithms perform good in cost reduction compared with other baseline algorithms.

Abstract The traditional design of voice collector has poor anti attack ability, which makes the encryption effect of voice transmission poor. Therefore, taking the mobile network voice collector as the research object, 3des-ecc algorithm is applied to the information transmission encryption of the mobile network voice collector.An improved speech signal collector is designed, which combines 3DES and ECC algorithm to realize the encryption of speech transmission information. An improved voice signal collector is designed, which combines 3DES and ECC algorithm to realize the encryption of voice transmission information. In the process of encryption, 168-bit random key is generated first, and it is grouped according to 56 bits as 3DES key, and then the plaintext is encrypted by the key to generate ciphertext; the random key is encrypted by ECC public key of the receiver. The experimental results show that the encryption time of this method is less than 1 s, the data integrity is 93%, and the data loss rate is only 0.33%. It has better anti attack ability, fast encryption speed and good encryption effect.