Radio Networks Research Articles

Framework for Emerging Spectrum Sensing Techniques and Coexistence of Symbiotic Radio in Internet of Things Environment

The evolution of networking technologies is poised to revolutionize communication systems, challenging the conventional landscape. ITU estimated that the number of global mobile subscriptions could be 17.1 billion in 2030. The persistent growth in mobile data traffic underscores the urgent requirement for growing international research emphasis on cognitive radio networks. A cognitive radio uses spectrum sensing to accurately assess the target spectrum's state. This is an extremely difficult task for which numerous approaches have been studied throughout time. This paper undertakes the task of delivering a thorough examination of the current advancements in this field. The survey delves into the realm of conventional spectrum sensing techniques, providing insightful comparisons. Moreover, it unveils advanced spectrum sensing techniques integral to the fabric of 5G and the uncharted territories beyond, aiming to optimize spectrum utilization. In this survey we have analyzed and simulated the modern spectrum sensing Techniques such as IRS assisted spectrum sensing, Cooperative spectrum sensing, and Hybrid spectrum sensing in MATLAB environment and suggested potential applications in low SNR region. It is inferred through simulations that use of IRS will overcome the disadvantages of conventional spectrum sensing techniques of CRN. Also it is suggested that use of AI and Machine learning can enhance the performance of CR-IoT networks. There is a notable demand for further exploration into novel spectrum sensing methods tailored for 6G cognitive radio networks. Additionally, new Researchers can explore spectrum sensing techniques that utilize 6G signals as the main signals within the network.

Performing Hybrid Spectrum Sensing with an Adaptive and Attentive Multi-stacked Deep Learning Network in a Cognitive Radio Network

Performing Hybrid Spectrum Sensing with an Adaptive and Attentive Multi-stacked Deep Learning Network in a Cognitive Radio Network

CitZO‐SVM: Machine Learning‐Based Spectrum Sensing and Channel Allocation for Cognitive Radio Networks

ABSTRACTIn the fifth generation (5G) network, the cognitive radio network (CRN) is emerging as a solution for spectrum utilization by considering minimal spectrum usage. It is used with efficient spectrum sensing information and optimal channel assignment algorithms to optimize secondary user throughput. In this paper, a chaotic iterative zebra optimization‐based support vector machine (CitZO‐SVM) is proposed with the objective of improving the accuracy and reducing the complexity in CRN. To improve the sensing performance, the regularization parameters of SVM are optimized using the CitZO algorithm. The CitZO algorithm is modeled by integrating the chaotic iterative mapping and conventional Zebra optimization algorithm. Then, the channel assignment is employed optimally based on cooperative game theory by considering the utility factor and energy efficiency. Low complexity spectrum sensing and channel assignment algorithm are developed with less computational cost. The effectiveness of the proposed approach is verified through simulation results. The performance of the proposed approach is evaluated with 2.4 and 24 GHz bandwidth. The proposed technique achieves the throughput and the probability of 92.04 and 0.969 for 2.4 GHz frequency. The proposed methodology results in efficient spectrum utilization when compared to the existing channel assignment schemes.

Designing of a Hybrid Communication Framework for Fronthaul Connectivity in 5 G and Future Mobile Networks: A Cost-Effective Solution

ABSTRACT Optical fiber network has surfaced as a viable alternative for offering the fronthaul connectivity between the baseband unit (BBU) and remote radio head (RRH) in cloud radio access network (C-RAN) architecture due to merits like high transmission rates and massive bandwidth availability. However, geographical constraints and high installation costs are major shortcomings of the optical fiber networks and can significantly affect the fronthaul connectivity in C-RAN architecture. Therefore, we are proposing a hybrid optical fronthaul link comprising single-mode fiber (SMF) and multihop free space optical (FSO) links to offer a high-speed and cost-effective solution for fronthaul connectivity in C-RAN network. The proposed link is based on dual-polarized 16-QAM modulation to offer spectral efficiency and employs coherent detection along with digital signal processing to increase link reliability. Moreover, the proposed link also provides a huge data rate of 112 Gb/s and can deliver a transmission distance of 20 km (SMF) + 1.73 km (FSO), while delivering forward error correction (FEC) compatible bit error rate of 10 − 3 , under the impact of diverse channel conditions. Additionally, we have analyzed the capital expenditure (CAPEX) of the proposed link and the CAPEX has been reduced by 32.97% as compared to the conventional network.

Deep reinforcement learning agents for dynamic spectrum access in television whitespace cognitive radio networks

Deep reinforcement learning agents for dynamic spectrum access in television whitespace cognitive radio networks

Multi-Agent Deep Reinforcement Learning-Based Resource Allocation for Cognitive Radio Networks

Multi-Agent Deep Reinforcement Learning-Based Resource Allocation for Cognitive Radio Networks

Physical Layer Security Enhancement in AAV-Assisted Cooperative Jamming for Cognitive Radio Networks: A MAPPO-LSTM Deep Reinforcement Learning Approach

Physical Layer Security Enhancement in AAV-Assisted Cooperative Jamming for Cognitive Radio Networks: A MAPPO-LSTM Deep Reinforcement Learning Approach

Enhanced Signal Energy Detection Technique for Low SNR Spectrum Sensing in Cognitive Radio – Hybrid Approach

Identification of Primary User (PU) signals in the context of Cognitive Radio (CR) networks essentially calls for an efficient spectrum sensing methodology. Even though the mainstream Energy Detection technique has several severe drawbacks, specifically at low SNR environments that impair effective detection of PU signals, this article presents a novel hybrid methodology consisting of the ESED technique. The integration of wavelet denoising with adaptive thresholding effectively mitigates the shortcomings of traditional ED. Simulation results are presented and clearly show that ESED technique offers high detection accuracy with moderate computational complexity. It is a more favourable approach to resourcelimited CR devices that enhance the reliability of spectrum sensing in low SNR conditions. This new method has outstanding performance. In particular, for probability of detection (Pd) equal to 1 at an SNR of -10 dB, it achieved a detection probability (Pd) of 0.8 at an SNR of -20 dB. High detection probabilities under low SNR circumstances cannot be attained with the use of the traditional method as observed from comparisons. Consequently, this method effectively identifies Primary Users (PUs) and optimizes spectrum utilization, thereby enhancing spectrum management, reducing interference, and ensuring the efficient allocation of resources.

Optimization of Detection Error Rate in Cooperative Spectrum Sensing Using Multi-Objective JAYA Algorithm

In order to make the most of the available spectrum, Cognitive Radio (CR) is used. When a licensed user isn't actively utilizing the spectrum, CR may interact with each other using it. Therefore, CR places a premium on spectrum sensing, the process of observing the presence of the main user. Cooperative spectrum sensing is a method for quickly identifying spectrum gaps in cognitive radio networks (CRNs) by merging the sensing data from different cognitive radio users. Cooperative spectrum sensing's overall performance is largely affected by how well secondary users' and the fusion center's local observations are combined. The accuracy of local observations and the data acquired by the fusion center (FC) are the two main factors that affect the detection performance. That is why system performance is affected by global decision logic and the amount of bits transferred to the fusion center (FC). However, since the sensing data collection control channel has a limited bandwidth, it is essential to quantify the received data on the energy of the signal for every user. We provide a quantized cooperative spectrum sensing method after researching coordinated spectral sensing methods based on energy detection. Through the optimization approach that is based on the Multi-Objective JAYA Algorithm, we are able to create an ideal quantized data fusion scheme that optimizes the detection error probability for a precise value of threshold. Results demonstrate that the proposed framework is valid, with the multi-objective JAYA (MOJAYA) based performance being almost identical to that of the standard soft combining technique while using less bandwidth and overhead.

A Comparative Analysis of F-RAN and C-RAN Architectures for Next-Generation Wireless Networks

By means of optimization, next-generation wireless networks will be able to meet the growing need for lowered latency, fast data rates, and enhanced connection. We investigate two interesting ideas for next-generation radio networks and assess Cloud Radio Access Network (C-RAN) and Flexible Radio Access Network (F-RAN). These designs seek to improve network performance via better use of resources and overcome problems with regular radio access networks. F-RAN guarantees that multiple network components communicate without any problems and lets network management flexibility by letting resources be dispersed according to the demand. On the other hand, C-RAN aggregates cloud-based solutions' administration of radio access resources. This allows networks to save costs and promotes their collaboration. We investigate the benefits and disadvantages of both designs with respect to size, energy economy, interference control, and network speed. We also look at how each solution meets changing needs for high-speed transmission—including those of 5G and beyond. F-RAN is a great choice for large-scale operations as it is better in responding and being flexible in changing surroundings; C-RAN is superior in resource organization throughout the whole network. As this study illustrates, each one of these concepts has benefits and disadvantages. This helps to choose and improve wireless network architectures for future generations of networks.

The effects of emotional self-regulation, ethical evaluations and judgments on customer food waste reduction behaviors in restaurants

PurposeThis study aimed to (1) examine the impact of restaurant customers’ emotional self-regulation on ethical evaluations and judgments on their food waste reduction behaviors and (2) explore the influence of ethical judgments and evaluations on food waste reduction behaviors in restaurants.Design/methodology/approachData were collected through a face-to-face survey at 20 restaurants in three major cities in Vietnam, namely Hanoi, Da Nang and Ho Chi Minh City. Responses from 870 restaurant customers were used to analyze the data. Partial least squares structural equation modeling (PLS-SEM) was used to examine the path effects.FindingsBased on the model of Hunt and Vitell (1986), this study incorporates the role assessment of emotional self-regulation, deontological and teleological evaluation into the research model to examine consumers’ ethical judgments and ethical behaviors as food waste reduction behaviors. All hypotheses were accepted with the study’s data. From there, the author makes some implications for state management agencies and businesses in promoting consumers’ food waste reduction behaviors.Practical implicationsSeveral recommendations should be made based on the results of the study. First, there is a need for guidelines for people to regulate emotions to match ethical consumption decisions, such as increasing the amount of study time for subjects on ethics and emotion management, teaching and promoting ethical values and standards as well as discussing ethical perspectives on food waste. Second, governments and businesses need to promote communication about the consequences of food waste on television, radio, newspapers and social networks, should have anti-food waste posters and should even popularize charging for cases of ordering or taking food that is beyond personal needs.Originality/valueFirst, this is believed to be the first study to investigate the role of emotions on ethical consumption decisions in general and food waste reduction in particular. Second, this study is one of the few studies that examine the influence of deontological evaluation, teleological evaluation and ethical judgments on food waste reduction behaviors (reduce, reuse and recycle). Finally, this is one of the few studies that comprehensively examines food waste reduction behaviors, including reduce, reuse and recycle.

On the design of efficient fiber-wireless architectures to enable multigigabit visible light communication

The surge in demand for wireless communication has placed significant strain on existing radio networks, which are struggling to accommodate the rapidly increasing number of mobile and fixed wireless devices. In response to this challenge, industry and academia have collaborated to explore alternative radio access technologies capable of supporting this continuous growth. Visible light communication (VLC) has emerged as a promising alternative due to the hundreds of terahertz of unlicensed bandwidth available for wireless communication. Despite its undisputable potential for ultrahigh-speed communication, the interface between the VLC wireless access and the fiber distribution network is still largely unexplored. Notably, this issue becomes more challenging when dealing with multicolored VLC systems, such as those employing RGB multiplexing. Following this research gap, in this work, we focus our attention on the design of efficient optical/electrical interfaces that can synergistically merge the fiber and VLC sections of an integrated fiber-wireless network. To that end, two fundamental architectural options are proposed and experimentally assessed, resorting to either optical or electrical multiplexing of the VLC constituent colors. From our experimental analysis, similar RGB-VLC data rates of 13–15 Gbit/s could be achieved using both fiber-wireless interface architectures. The choice of the most adequate solution is then fundamentally dependent on a number of network design options and requirements. While electrical multiplexing is deemed to enable more spectrally efficient utilization of the fiber transport network, optical multiplexing provides a simpler infrared-to-visible interface using cost-efficient commercial off-the-shelf equipment.

Three-State Non-Stationary Hidden Markov Model for an Improved Spectrum Inference in Cognitive Radio Networks

Spectrum manufacturers, operators and regulators are faced with the challenge of meeting the astronomical increase in demand by spectrum users due to the limited available radio spectrum already fixed for licensed or Primary Users (PUs). The emergence of Cognitive Radio Network (CRN) allows unlicensed or Secondary Users (SUs) to opportunistically access spectrum holes left unused by the PUs through spectrum sensing, management, sharing and mobility functionalities with the aid of algorithms and protocols. However, CRN suffers prolonged delay with negative impact on spectral efficiency. In order to improve the spectral efficiency, spectrum inference was introduced. Yet, inaccurate spectrum inference by existing mechanisms could not solve spectrum underutilization effectively due to persistent false alarm, interference and missed detection of PUs. Two-state Non-Stationary Hidden Markov Model (NSHMM) focused only on idle and busy states of PUs while previous work on three-state Stationary Hidden Markov Model (SHMM) did not consider the time-varying property of channel states obtainable in real scenarios where the state transition probability of a PU is time-varying. This work has proposed three-state NSHMM for spectrum inference in CRNs by formulating its parameters and modelling PU's dwell time distributions to realize the time-varying property of the stochastic PU behavior apart from the fuzzy state that takes care of noisy effects and undetermined or incomplete observations in the existing mechanisms where only idle and busy states were mostly recognized. The performance of the proposed mechanism was evaluated using Probability of Detection (PD), Prediction Accuracy (PA) and Spectrum Utilization Efficiency (SUE). The results were compared to the performance metrics obtained from spectrum inference of existing 2-state NSHMM and 3-state SHMM. The simulation results obtained revealed that the proposed three-state NSHMM spectrum inference mechanism gave the best performance with the highest PD, PA and SUE which curtailed PU collision because of its least possible chances of incorrect detection of primary users and least false alarm. The outstanding performance of the proposed NSHMM was due to its non-stationarity as well as the fuzzy state incorporated in the development of the mechanism. Therefore, the proposed three-state NSHMM for an improved spectrum inference in CRNs has grossly abated PU collision, false alarm and spectrum underutilization.

Joint Design of Fronthaul and Access Links in Massive MIMO Based CRANs

ABSTRACTIn this paper, we address the challenge of limited channel capacity of wireless fronthaul links in cloud radio access networks (CRANs). A novel architecture is proposed in which the base band unit (BBU) is equipped with a massive multiple‐input multiple‐output (MIMO) antenna array to communicate with a set of remote radio heads (RRHs) that serve multiple user equipments (UEs). Moreover, an optimal signal quantization strategy is also designed at the BBU to accommodate for the limited fronthaul capacity. To this end, a joint optimization of power allocation at the BBU, quantization noise covariance, and RRH beamforming vectors is formulated via maximizing the achievable sum‐rate subject to power constraints at the BBU and RRHs. The resulting highly nonconvex problem is reformulated as a semidefinite relaxation (SDR) problem by using the difference of convex (DC) programming approach. It is also deduced analytically that the solution yielded by this SDR problem is always of rank one. Numerical results confirm the potential of the proposed system in improving the capacity of the CRAN systems with wireless fronthauling. In particular, the proposed design outperforms two proposed benchmark schemes.

Optimal Power Control for Energy Harvesting in Green Cognitive Radio Networks

The integration of cognitive radio and energy has enhanced the utilization efficiency of the spectrum and promoted the application of green energy. To begin with, this paper presents the architecture of green energy-efficient communication and network models. It incorporates the distributed network model and the heterogeneous two-tier network model into the green cognitive radio power control and channel allocation model. The primary focus of this research lies in energy conservation at the physical layer. To mitigate the interference with primary users and address the peak constraint in secondary user power allocation, the article analyzes the system model of the cognitive radio network and subsequently elaborates on the dynamic throughput maximization allocation algorithm. Eventually, through experimental analysis and verification, the distinctiveness and comprehensiveness of the optimal power control for this subject are illustrated.

Power beacon-assisted energy harvesting symbiotic radio networks: Outage performance.

The evolution of next-generation Internet-of-Things (IoT) in recent years exhibits a unique segment that wireless communication paradigms are oriented towards not only improved spectral efficiency transmission but also energy efficiency. This paper addresses these critical issues by proposing a novel communication model, namely power beacon-assisted energy-harvesting symbiotic radio. In particular, the limited energy primary IoT source communicates with its destination by first harvesting energy from a dedicated power beacon and then performing information exchange, while the backscatter device communicates by exploiting the available radio frequency emitted by the primary IoT source. The destination uses successive interference cancellation mechanisms to decode both its received signals. To assess the performance quality of the proposed communication model, we theoretically derive the coexistence outage probability (COP) in terms of highly accurate expressions and upper-bound and lower-bound approximations. Subsequently, we carry out a series of numerical results to verify the developed theory frameworks on the one hand, and on the other hand, analyze the COP performance against the variations of system key parameters (transmit signal-to-noise ratio, the time-splitting coefficient, the energy conversion efficiency factor, the reflection coefficient, and the coexistent decoding threshold). Our numerical results demonstrate that the proposed communication model can potentially work well in practices with reliable communication over 90% (COP is less than 0.1). Additionally, it also demonstrates that optimizing the reflection coefficient at the backscatter device can facilitate achieving minimal COP performance.

Свойства трафика видеоконференций 5G и его прогнозирование методами искусственного интеллекта

The objectives and prospects of short- and long-term forecasting of data traffic in 5G networks are outlined. An overview of current traffic forecasting methods is presented. A large number of works indicate a variety of ap-proaches to the analysis and forecasting of mobile traffic. These approaches include both traditional statistical methods and deep learning methods, which is an important factor for the development of network technologies and optimization of radio access networks. Forecasting and management plays an important role both at the level of the entire 5G network and at the level of its individual components. With the increase in the volume of big data, traffic forecasting becomes a difficult task due to mobility and different user behavior. In connection with this circumstance, using the example of mobile traffic data from Google Meet, MS Teams and Zoom video conferencing systems, the features of this type of traffic are analyzed and it is concluded that the distribution of time intervals between packet arrivals corresponds to a stable distribution. Based on the results of studying some machine learning methods designed to predict time series, a comparative analysis of the effectiveness of these methods for short-term forecasting of traffic intensity was performed. The following models were used for analysis and prediction: naive seasonal, exponential smoothing, linear regression, ARIMA, Theta and NBEATS. It was found that linear regression provides the best quality indicators for Google Meet traffic, the exponential smoothing method shows the best quality indicators for MS Teams, and the Theta method has the best indicators for Zoom.

Analysis of 6G and B5G waveforms using hybrid MF-ED and ECG-ED spectrum sensing techniques

The rapid evolution of wireless communication has necessitated advanced waveform analysis for beyond-fifth-generation (B5G) and sixth-generation (6G) radio networks, focusing on efficient spectrum utilization. There is a need for greater spectrum allotment in data-intensive applications, and new technologies require faster data rates and reduced latency. This study explores hybrid spectrum sensing techniques, combining matched filter (MF) energy detection (ED) and an equal-gain combining-based energy detection Neyman-Pearson threshold estimation technique (EGC-ED-PTh) to enhance waveform detection accuracy in complex environments. The proposed method offers an enhanced signal-to-noise ratio (SNR) by optimizing the detection performance, particularly in low-SNR environments, thereby improving the signal reliability. The proposed algorithms are evaluated in comparison with traditional SS methods, including ED, MF, and cyclostationary feature detection (CFD). Additionally, characteristics including bit error rate (BER), power spectral density (PSD), probability of detection (pd), and probability of false alarm (pfa) were researched and evaluated for 500 and 1000 samples. The simulation findings show that the projected algorithms perform better than the traditional algorithms with minimum sidelobes of – 3024 and pfa effects and achieve a throughput gain of 5 and 4.7 dB compared with the conventional algorithms.

Wireless mmWave Communication in 5G Network Slicing With Routing Model Based on IoT and Deep Learning Model

ABSTRACTIn fifth‐generation (5G) radio access networks (RANs), network slicing makes it possible to serve large amounts of network traffic while meeting a variety of demanding quality of service (QoS) standards. Higher path loss and sparser multipath components (MPCs) are the primary distinctions, which lead to more notable time‐varying characteristics in mmWave channels. Using statistical models, such as slope‐intercept methods for path loss for delay spread and angular spread, is challenging to characterize the time‐varying properties of mmWave channels. Therefore, adopting mmWave communication systems requires highly accurate channel modeling and prediction. This research proposes a novel technique in wireless mmWave communication 5G network slicing and routing protocol using IoT (Internet of things) and deep learning techniques. An adaptive software‐defined reinforcement recurrent autoencoder model (ASDRRAE) slices the mmWave communication network. A dilated clustering‐based adversarial backpropagation model (DCAB) then performs network routing. The experimental analysis evaluates throughput, packet delivery ratio, latency, training accuracy, and precision. The suggested hybrid model has a 97.21% overall recognition rate, illustrating that the suggested strategy is aptly applicable. A 10‐fold stratified cross‐validation is employed to evaluate the suitability of the proposed method.

Improved quantum inspired evolution algorithm with ResNet50 for spectrum sensing in cognitive radio networks

Improved quantum inspired evolution algorithm with ResNet50 for spectrum sensing in cognitive radio networks