IET Communications最新文献

This paper discusses a new symbol modulation scheme known as polarized amplitude and phase shift keying, ($��P�o�l�$-APSK) modulation with four rings in its basic form. The new scheme maps power-polarized symbol pairs on its constellation in order to increase the number of data symbols. $��P�o�l�$-APSK exploits a symbol mapper based on a conjugate power splitting algorithm. Product modulation is applied, where a voltage signal of a specified amplitude and phase is multiplied by another current signal of a specified amplitude and phase, thus forming polymorphic signals in the product constellation. At the receiver, an isolated detection is performed, where the voltage signal is detected independently of the received current signal. A selection combining scheme is then used. The results depict unity peak-to-average power ratio and low average symbol energy, which is desirable for Green communications in 5G networks. It presents lower bit error rates when compared with the state-of-the art $�M$-ary quadrature amplitude modulation, with a signal-to-noise ratio gap of at least $��10��\left[�\text{dB}�\right]�$. The proposed analytical framework closely matches the simulations for bit error rates under $��M�=�16�,�32�,�64��P�o�l�\text{-APSK}�$.

Traffic volume is increasing dramatically due to the quick development of technologies like online gaming, on-demand video streaming, and the Internet of Things (IoT). The telecommunications industry's large-scale expansion is increasing its energy usage and carbon footprint. Given the desire to minimize energy consumption and carbon emissions, one of the most essential concerns of future communication networks is ensuring rigorous performance restrictions of IoT services while improving energy efficiency. In this regard, a convolutional neural network-based hierarchical reinforcement learning approach is provided to lower total energy consumption and carbon emissions in the dynamic service function chaining situations. This method can more effectively lower energy consumption and carbon emissions when compared to other hierarchical algorithms based on conventional deep neural networks and non-hierarchical algorithms. The suggested method is tested in three typical complicated networks with different network parameters to show its suitability in different network scenarios.

Turbo codes play a crucial role in wireless communication systems, and their compiled code structures are key factors affecting the performance of the entire communication system. As a result, the study of turbo compiled code structures has been a focal point for researchers. The iterative decoding of turbo code structures has multiple limitations and large storage resource consumption, leading to poor system anti-interference ability and a rapid increase in BER. To address these issues, this paper proposes the mimic turbo compiled code structure (MTCCS) for wireless communication systems. MTCCS is based on the DHR idea, incorporating dynamic, heterogeneous, and redundancy characteristics. Dynamicity is achieved through a dynamic scheduling algorithm based on abnormal feedback information. Heterogeneity is achieved through a codec component collection design method based on intrinsic and extrinsic heterogeneity. Redundancy is achieved through a majority voting algorithm. At the beginning of information transmission, MTCCS randomly selects heterogeneous codecs from the heterogeneous codec collection to enter the runtime pool. After the information transmission is complete, the majority voting algorithm is used to adjudicate the multi-mode output of the codecs, resulting in a relatively accurate decoding outcome. Meanwhile, the dynamic scheduling module calculates the abnormal feedback information of each codec and accordingly dynamically schedules the mimic turbo codecs to replace the abnormal ones. Through the above process, MTCCS realizes the adaptive compilation code and improves the anti-interference ability of turbo code. Simulation experiments are conducted on MTCCS in both non-interference and interference scenarios. Simulation experiments show that MTCCS introducing the DHR idea achieves a balance between anti-interference and decoding performance. It effectively addresses the issue of poor anti-interference ability in turbo codes, and the decoding performance of MTCCS is superior to that of the previous single conventional turbo codes.

In the era of industry 4.0 and the widespread use of digital devices, the number of cyber attacks poses an escalating and diverse threat, jeopardizing users' online activities. Intrusion detection systems (IDS) emerge as pivotal solutions, playing a crucial role in detecting anomalous signals within network systems. To counter novel attack patterns, IDS systems require periodic rule updates for effective identification of unusual signals. Typically, these policies are updated based on rule-based or deep learning algorithms to enhance detection performance. However, the insufficient number of labeled samples remains a challenge for real-world deployment. In this article, an automated labeling method is presented that has shown high effectiveness, requiring minimal hardware resources, and applicable to IDS systems. Additionally, the approach utilizes transfer learning combined with attention mechanisms to boost the efficiency of abnormal signal detection. The results from the approach are compared with those of a reference model, illustrating an overall improvement of nearly 10% in our model's performance compared to the reference model. This underscores the effectiveness of automating rule adjustments for IDS, contributing significantly to reducing associated financial costs. The research addresses the challenges in deploying IDS in real-world scenarios and provides a valuable contribution to enhancing cyber threat detection capabilities.

A preprint has previously been published [11].

This study investigates the integration of dimmable constant weight polar-coded non-orthogonal multiple access (NOMA) with orthogonal space-time block coding (OSTBC) in visible light communication (VLC) systems over Nakagami-$�m$ fading environments. The proposed scheme aims to enhance the reliability of VLC systems by reducing the outage probability while accommodating dimming requirements. By allowing multiple users to utilize the same time-frequency resources and adjust power levels based on individual channel conditions, the system optimizes resource utilization. Additionally, the OSTBC method provides effective communication among multiple users by leveraging transmit diversity to improve system performance. The efficacy of the proposed approach is demonstrated through analytical analysis and Monte Carlo simulations, showcasing superior outage probability performance compared to conventional Alamouti-STBC schemes.

Codeword position index based sparse code multiple access (CPI-SCMA) is an effective scheme that expands codeword positions in the time domain and carries extra information by their index. This paper proposes a dual-mode CPI-SCMA (DCPI-SCMA) scheme to improve the error propagation caused by the mismatch between SCMA codewords and information bits. Furthermore, a DCPI-SCMA with transmit diversity scheme is proposed where index bits are repeatedly transmitted to obtain a transmit diversity gain; hence, a higher reliability can be achieved with a slight loss of spectral efficiency. An optimal approach for the codebook index pattern is derived where the decision of index bits is involved in the demodulation of SCMA. It can be seen from simulation results and system analysis that the proposed schemes achieve better bit error rate performance with a decrease in complexity and obtain better robustness and flexibility compared with the existing CPI-based SCMA schemes under the same spectral efficiency.

In recent years, the Integrated Satellite Aerial Terrestrial (I-SAT) network has garnered significant attention as an innovative and integrated communication system. However, it still encounters interference in the face of the complex external environment. In this context, reconfigurable intelligent surface (RIS) provides a key way of solving this problem and effectively improves the performance and stability of the I-SAT network. This article considers the combination of unmanned aerial vehicle (UAV) and RIS and proposes a novel architecture for sub-connected active RIS (ARIS) under the energy consumption constraints of UAV and ARIS. The authors first provide a UAV-ARIS based position prediction strategy for the vehicle. Then, a joint RIS phase shift, amplification and UAV trail optimization algorithm is proposed to pursue a high achievable rate. The interference between each link and the total energy consumption are all taken into consideration. In addition, a deep deterministic policy gradient (DDPG) algorithm is utilized for the optimization problem, and achieves convergence in continuous action space. Finally, the simulation results affirm the precision of the proposed method in significantly enhancing performance compared to other schemes.

Massive multiple-input multiple-output and non-orthogonal multiple access (MIMO-NOMA) with low-resolution analog-to-digital converters (ADCs) have been widely considered for the next-generation wireless communication systems. However, the performance of the system including power-scaling law has not been well investigated for the practical low complexity receivers. Employing the additive quantization noise model, we derive asymptotic approximate expressions of the spectrum efficiency for the system with group successive interference cancellation (GSIC) receivers over Rician fading channels. Based on these approximations, we conduct a unified asymptotic analysis for the system with linear, SIC, and GSIC receivers. The analysis reveals the transmission power can be scaled by the number of antennas for the system with GSIC receivers and shows the effects of crucial parameters including the number of groups, resolution bits, and antennas on the performance. Given a quality of service, the minimum data transmission power is also calculated for each user and the corresponding approximate power allocation is derived. The asymptotic analysis and the accuracy of the power allocation approximation are then verified by simulation results. Numerical results also demonstrate that high spectrum efficiency and energy efficiency can be achieved by the system with medium-resolution ADCs and low complexity maximum ratio combining-GSIC receivers with a small number of groups.

Deploying the Internet of Things (IoT) in integrated edge-cloud environments exposes the IoT traffic data to performance issues such as delay, bandwidth limitation etc. Recently, Software-Defined Wide Area Network (SD-WAN) has emerged as an architecture that originates from the Software-Defined Network (SDN) paradigm and provides solutions for networking multiple data centers by allowing network administrators to manage and control network layers. In this article, an SDWAN-based policy for traffic management in IoT is introduced in which the Quality of Service (QoS) metrics such as end-to-end delay and bandwidth utilization are optimized. The proposed method implements the traffic management policy in the SDWAN controller. When the IoT traffic flows reach the SDWAN infrastructure network, graph search algorithms are performed to find the near-optimal paths that affect the end-to-end delay of traffic flows. Because of the ability of deep learning to process complex data, a deep RNN model is used to predict the network state information, such as link latency and available bandwidth, before the traffic flows reach the infrastructure network. The proposed method consists of four key modules to predict the routes for future time intervals: (a) an SD-WAN topology updater unit that checks the link changes and availability, (b) the network state information collector, which collects the network state information to create a dataset, (c) the learning unit, which trains a deep RNN model using the created dataset, and (d) the route predictor unit, which uses the trained model to predict the network state information using a heuristic algorithm to determine the routes. The simulation results showed that the deep RNN model can achieve high accuracy and low Mean Absolute Error (MAE), and the proposed method outperforms shortest-path algorithms in terms of latency. At the same time, the available bandwidth is almost fairly distributed among all network links.

This paper investigates the changes in the waveform of a sinusoidal carrier resulting from amplitude modulation (AM) process. Based on this analysis, a novel method for extracting amplitude information is proposed. The proposed method uses the behaviour of the amplitude limitation which does not significantly affect the slope of the sinusoidal signal near zero crossing points. A simple comparator is used to convert the changes in sinusoidal slope near zero crossing points into pulse width changes. A simple circuit is proposed which keeps the output pulse width of the comparator constant by a simple control loop. The accuracy of the method is evaluated through simulation and is experimentally tested. If the modulation index is high and the amplitude of the input signal to the detector is limited, the proposed method can yield up at least 9 dB improvement in relative error power. However, if the modulation index is small, the improvement in relative error power can be at least 35 dB compared to other conventional types of AM demodulators.