Telecommunication networks are growing exponentially due to their significant role in civilization and industry. As a result of this very significant role, diverse applications have been appeared, which require secured links for data transmission. However, Internet-of-Things (IoT) devices are a substantial field that utilizes the wireless communication infrastructure. However, the IoT, besides the diversity of communications, are more vulnerable to attacks due to the physical distribution in real world. Attackers may prevent the services from running or even forward all of the critical data across the network. That is, an Intrusion Detection System (IDS) has to be integrated into the communication networks. In the literature, there are numerous methodologies to implement the IDSs. In this paper, two distinct models are proposed. In the first model, a custom Convolutional Neural Network (CNN) was constructed and combined with Long Short Term Memory (LSTM) deep network layers. The second model was built about the all fully connected layers (dense layers) to construct an Artificial Neural Network (ANN). Thus, the second model, which is a custom of an ANN layers with various dimensions, is proposed. Results were outstanding a compared to the Logistic Regression algorithm (LR), where an accuracy of 97.01% was obtained in the second model and 96.08% in the first model, compared to the LR algorithm, which showed an accuracy of 92.8%.

Partial transmit sequence (PTS) technique is considered as one of the efficient methods to reduce the high peak-to-average power ratio (PAPR) problem in 4G waveform design such as the orthogonal frequency division multiplexing (OFDM) systems. Filtered-OFDM (F-OFDM) is a new candidate in the 5G waveform development with maintaining a high level of commonality with legacy OFDM systems, but the high PAPR value is still a considerable problem because F-OFDM supports the orthogonal transmission. Although the PTS technique improves the PAPR reduction performance significantly, the high computational complexity level for optimizing the phases of the transmitting signal restricts this technique in practical applications. Therefore, the low computational complexity level of the PTS technique leads to the adoption of this technique for PAPR reduction in the multicarrier systems such as OFDM and F-OFDM. In this paper, a new low complexity algorithm (Gray-PF-PTS) by combining the Gray code and the left feedback shift register operation based on a specific mapping rule has been proposed. The numerical results indicate that the Gray-PF-PTS algorithm extensively reduces the computational complexity level without degradation the PAPR reduction performance compared with the conventional PTS method. Furthermore, the OFDM and F-OFDM systems based on the Gray-PF-PTS algorithm have been compared regarding the PAPR, bit error rate (BER), and power spectrum density (PSD) performances.

Orthogonal frequency division multiplexing (OFDM) has been the overwhelmingly prevalent choice for high‐data‐rate systems due to its superior advantages compared with other modulation techniques. In contrast, a high peak‐to‐average‐power ratio (PAPR) is considered the fundamental obstacle in OFDM systems since it drives the system to suffer from in‐band distortion and out‐of‐band radiation. The partial transmit sequence (PTS) technique is viewed as one of several strategies that have been suggested to diminish the high PAPR trend. The PTS relies upon dividing an input data sequence into a number of subblocks. Hence, three common types of the subblock segmentation methods have been adopted—interleaving (IL‐PTS), adjacent (Ad‐PTS), and pseudorandom (PR‐PTS). In this study, a new type of subblock division scheme is proposed to improve the PAPR reduction capacity with a low computational complexity. The results indicate that the proposed scheme can enhance the PAPR reduction performance better than the IL‐PTS and Ad‐PTS schemes. Additionally, the computational complexity of the proposed scheme is lower than that of the PR‐PTS and Ad‐PTS schemes.

Sliding the SLM-technique to Reduce the Non-Linear Distortion in OFDM Systems