Spectrum Energy Efficiency Research Articles

Evolution of Wireless Communication: From 5G to 6G

The evolution of wireless communication from 1G to the forthcoming 6G era marks an extraordinary journey of technological advancement and innovation. This paper offers a comprehensive overview of the imminent 6G revolution, set to redefine the wireless landscape with unparalleled speed, connectivity, and transformative applications. The key drivers behind 6G development, such as the increasing demand for high-speed data, ultra-low latency, and ubiquitous connectivity, are examined. Highlighted potential applications of 6G include immersive augmented reality, smart cities, and advanced healthcare, showcasing its potential to transform numerous aspects of society. In-depth discussions address challenges and opportunities related to spectrum utilization, energy efficiency, security, and privacy. The paper also explores foundational 6G technologies, such as terahertz frequencies and advanced antenna systems, underscoring the necessity for interdisciplinary collaboration among researchers, industry stakeholders, and policymakers. Additionally, it emphasizes the ethical and societal dimensions of 6G, advocating for digital inclusivity and equitable access to ensure the benefits of this technological leap are widely shared, promoting a holistic and responsible approach to its development..

Energy-Efficient Adaptive Bidirectional Transmission Strategy in Simultaneous Wireless Information and Power Transfer (SWIPT)-Enabled Cognitive Relay Network.

Introducing collaborative relay and simultaneous wireless information and power transfer (SWIPT) techniques into a cognitive wireless network, named the SWIPT-enabled cognitive relay network (CRN), is considered a promising approach to deal with insufficiency and the low utilization of spectrum resources, as well as the node's energy-constrained issues in wireless networks. In this paper, to improve the network spectrum efficiency (SE) and energy efficiency (EE) of the SWIPT-enabled CRN, we design an energy-efficient adaptive bidirectional transmission strategy. To be specific, we first select an energy-constrained best relay node with the consideration of signal-to-noise ratio and global channel gain to achieve a better bidirectional relay transmission (BRT). At the same time, we let the energy-constrained best relay node transmit a signal with the SWIPT technique, which can solve the node's energy-constrained issue and improve the network EE. Then, with the selected energy-constrained best relay node, we design a total transmit power threshold (TTPT) determining algorithm to find the TTPT, which lets the total transmission rate of the BRT be equal to the bidirectional direct transmission (BDT). Based on this TTPT, we further design an adaptive bidirectional transmission strategy and let the network achieve adaptive transmission between the BRT and BDT to obtain a higher network SE. Furthermore, to further achieve the energy-efficient transmission of the adaptive bidirectional transmission strategy, we optimize the nodes' power under the requirement of primary users' interference threshold and obtain the analytical expressions of the optimal power. Simulation results show that the transmission rate, the outage probability, and the EE of the designed energy-efficient adaptive bidirectional transmission strategy in the SWIPT-enabled CRN are, respectively, 3.01, 0.07, and 3.10 times that of the non-collaborative transmission, which show the effectiveness of our designed transmission strategy.

Performance of massive MIMO‐NOMA systems with low complexity group SIC receivers and low‐resolution ADCs

AbstractMassive 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.

Simulation of association rule mining based on sensor networks in Chinese language learning recommendation system for college students

Based on the increasing learning needs of students that cannot be met by traditional education in schools, the use of online learning platforms has become a way for many college students to learn Chinese, thereby improving their academic performance and literary literacy. However, it is difficult to guarantee the service quality of online platforms for Chinese learning at present, so this paper proposes an association rule mining algorithm to strengthen Chinese online learning. We using user service quality as a constraint to improve spectrum utilization and energy efficiency, and defining the user state space, borrowing the obtained resource allocation optimization function to reward a small portion of users' communication costs, ultimately obtaining user state space information and one-dimensional state space data. This algorithm performs grouping operations on the data estimated by a large amount of computation, and divides the data into balanced categories to reduce the amount of input data in the network. The performance test results show that this paper has made a great breakthrough in the personalization of Chinese learning, and has outstanding performance in the processing and classification of Big data. There are also some solutions to the problem of too much existing data. In a period of use experience analysis report, we found that the online platform for Chinese learning can give consideration to students' personalized needs and experience.

The Evolution of Mobile Communication: A Comprehensive Survey on 5G Technology

This comprehensive survey meticulously navigates the evolution of mobile communication, focusing on the revolutionary strides introduced by 5G technology. The study elucidates primary objectives, research methodology, pivotal findings, and implications inherent in the trajectory of mobile communication. It aims for a detailed examination of the technological landscape, outlining features that distinguish 5G from its predecessors. The research methodology involves an exhaustive literature review, culminating in a comprehensive overview of 5G technology. Key findings highlight 5G's transformative capacities, spanning unparalleled data transfer speeds, low-latency communication, and applications like IoT, augmented reality, and autonomous vehicles. The study underscores 5G's far-reaching impact across various sectors. Serving as both a retrospective analysis and a valuable resource, the survey guides researchers, industry professionals, and policymakers through the intricate landscape of 5G technology. Addressing the imperative of accommodating increasing mobile data traffic, the 5G network strives for improved spectrum utilization, energy efficiency, and QoS parameters. Notable innovations include Massive MIMO, promising high spectral efficiency. The focus on efficacy, accuracy, and channel estimation techniques is evident. The article explores specific requirements of 5G wireless communication systems, addressing challenges faced by earlier mobile generations. The advent of 5G emerges as a pivotal solution, aiming for a 1000-fold capacity gain and assuring quality-of-service, higher spectral efficiency, ultra-reliable communication, and facilitating massive machinetype communication. Challenges in traffic load and resource consumption necessitate innovative approaches. The research reviews emerging wireless technologies, including an analysis of Massive MIMO as an efficient solution. The paper promises to be a valuable resource, contributing to the seamless development of state-of-the-art 5G and B5G networks, recognizing the importance of continued research and innovation in the dynamic landscape of mobile communication technologies.

Optimizing Multichannel Path Scheduling in Cognitive Radio Ad Hoc Networks using Differential Evolution

One important area of study in cognitive radio ad hoc networks is multi-channel path scheduling. Cognitive radio networks have trouble communicating and using the spectrum effectively because of weather and dispersion. Optimizing multichannel path scheduling enhances network performance and reliability in a cognitive radio ad hoc net. The Optimizing Multichannel Path Scheduling (OMPS) model methodically tackles various scheduling problems. For the OMPS model, this domain is new. It effectively resolves multichannel path scheduling. The computer method used in the study is called Differential Evolution. During optimization, several factors are carefully considered, including Channel Fade Margin, Cross-Correlation and Coherence Time, Spectral Efficiency, Interference Level, Power Consumption, Retransmission Rate, Access Probability, and Propagation Delay. To increase the scheduling efficiency of the DE algorithm, many steps are meticulously planned: initialization, mutation, crossover, fitness evaluation, selection for iteration evolution, and termination. Latency, Packet Delivery Ratio (PDR), Spectrum Utilization, Interference Level, Energy Efficiency, and Established Path Success Rate are all assessed by the OMPS model. These indicators assess the effectiveness and dependability of the network. OMPS performs better in crucial simulations than the existing model. The demonstration demonstrates decreased latency for real-time applications, greater packet delivery ratios (PDRs), improved spectrum efficiency, channel interference, energy efficiency, and connection formation odds, as well as increased throughput that enhances network resource utilization. To do this, a variety of multichannel path scheduling situations are simulated.

Intelligent RAN Automation for 5G and Beyond

5G is becoming more agile and inevitably more complicated for supporting various deployment scenarios and diversified applications. Artificial Intelligence (AI) is becoming one of the most essential driving forces for 5G evolution to handle the complexity and further improve the network spectrum efficiency, energy efficiency and user experience. In this article, we share the latest standardization progress of the industry on AI in 5G. Evolving directions for the intelligent radio access networks (RAN) are analyzed and further enhancements on both AI for RAN and RAN for AI are proposed based on a hierarchical and distributed intelligent RAN architecture. Case studies are presented along with field trial results. An innovative AI-empowered RAN and service-aware cross layer optimization framework is illustrated for the quality of experience (QoE) optimization of cloud virtual reality (VR) with converged communication and computing services provided by RAN. The extension of the solution to support the AI/ML services is also discussed to shed some light on how to evolve network for AI towards the 6G vision.

OPTIMIZATION OF ENERGY EFFICIENCY IN MULTI-CHANNEL CO-OPERATIVE SPECTRUM SENSING IN COGNITIVE RADIO NETWORKS

In this paper, the focus is on energy efficiency in cooperative spectrum sensing in cognitive radio networks by considering multiple channels in a slotted time scenario. The sensing time consists of mini slots. A secondary user uses a mini-slot to sense a channel. Considering multiple channels, throughput, energy consumption, and, finally, energy efficiency are calculated. Additionally, the optimal number of mini-slots are obtained regarding throughput and energy efficiency using the particle swarm optimization algorithm. Simulation results are presented for the algorithms considered in this paper.

Spectrum-Energy-Economy Efficiency Analysis of B5G Wireless Communication Systems With Separated Indoor/Outdoor Scenarios

In this paper, we study the spectrum efficiency (SE), energy efficiency (EE), and economic efficiency (ECE) for a heterogeneous cellular architecture that separates the indoor and outdoor scenarios for beyond 5G (B5G) wireless communication systems. For outdoor scenarios, massive multiple-input-multiple-output (MIMO) technologies and distributed antenna systems (DASs) at sub-6 GHz frequency bands are used for long-distance communications. For indoor scenarios, millimeter-wave (mmWave) and beamforming communication technologies are deployed at wireless indoor access points (IAPs) to provide high-speed short-range services to indoor users. Mathematical expressions for the system capacity, SE, EE, and ECE are derived using a proposed realistic power consumption model. The results shed light on the fact that the proposed network architecture is able to improve SE and EE by more than three times compared to those conventional network architectures. The analysis of system performance in terms of SE, EE, ECE, and their trade-off results in the observation that the proposed network architecture offers a promising solution for future B5G communication systems.

Doubly Selective with Expectation Maximization based Massive-Mimo-Non-Orthogonal Multiple Access systems

The Long-Term Evaluation (LTE) based Fourth Generation (4G) and Fifth Generation (5G) communications require higher data rates to satisfy the requirements of user equipment. However, it is a difficult task for conventional Multiple Input Multiple Output (MIMO) systems to deliver the highest throughput and data rates to end users. Additionally, an increase in data rates led to increased power consumption, which decreased spectrum efficiency and energy efficiency. Therefore, this paper focuses on the implementation of the Doubly Selective with Expectation Maximization (DSEM) channel estimation method for Massive-MIMO based Non-Orthogonal Multiple Access (NOMA) systems. Here, the DSEM scheme divides the channel into expectation and maximization vectors based on antenna diversity. Further, time sample and frequency band base dual channel analysis are performed to maintain the synchronization in the receiver. The simulations conducted using MatlabR2018a show that base station (BS) capacity, user equipment capacity, uplink (UL)-Spectrum Efficiency (SE), and downlink (DL)-SE performance measures are improved in Massive-MIMO-NOMA systems using DSEM channel estimation and shows the superior performance than state of art approaches.

Energy-Efficient Maximization for RIS-Aided MISO Symbiotic Radio Systems

Symbiotic radio (SR) and reconfigurable intelligent surface (RIS) can serve as a complementary technique for each other regarding spectrum efficiency and energy efficiency (EE) improvements. In this paper, we investigate a RIS-aided multiple-input single-output SR system with multiple backscatter devices (BDs). In particular, an EE-based maximization resource allocation problem is formulated by jointly optimizing the passive beamforming vector of the RIS, the active beamforming vector of the primary transmitter (PT), and the reflection coefficient of each BD under multiple constraints, including the minimum rate of each user, the maximum transmit power of the PT, and the minimum harvested energy of each BD. To solve the non-convex problem, a Dinkelbach-based block coordinate descent algorithm is developed to obtain the sub-optimal solution. Numerical results verify that the proposed algorithm achieves two times more than the traditional algorithms in terms of system EE.

Effective and efficient crowd spectrum detection with active reconfigurable intelligent surface

The reconfigurable intelligent surface (RIS) assisted crowd spectrum detection (CSD) has emerged as a promising approach for achieving higher enhancements in both spectrum efficiency (SE) and energy efficiency (EE). Nevertheless, due to the double fading effect in reflecting links, the detection performance gains obtained through current passive RIS are negligible, especially in typical communication scenarios. As a result, the performance enhancement in passive RIS-assisted CSD is ineffective. To address the issue, this paper incorporates the advanced active RIS into CSD to assist the spectrum requestor (RU) in achieving notable and effective improvement in detection performance gains. Through amplifying the amplitude of reflected signals, active RIS can further enhance the received signal power at the RU while consuming more energy than the passive RIS. Therefore, for enabling both effective and efficient CSD with the assistance of active RIS, this paper investigates the detection efficiency (DE) maximization problem by jointly optimizing the RU’s sampling number and reward budget that is paid to compensate active RIS controllers for amplifying reflected signals. In a word, the key point of this paper is to evaluate whether the active RIS-assisted CSD can outperform the passive RIS-assisted CSD in both effective and efficient detection performance gains by joint parameter optimization. Quadratic transform (QT) and particle swarm optimization (PSO) methods are leveraged to solve the DE maximization problem. Finally, extensive simulation results demonstrate that, when taking the no-RIS mechanism as the benchmark, the proposed active RIS-assisted CSD can outperform the conventional passive-RIS mechanism in terms of significantly improved detection performance gains and a higher DE in most simulation scenarios.

Active RIS Assisted Spectrum Sharing: Able to Achieve Energy-Efficient Notable Detection Performance Gains

Efficient radio resource utilization is crucially important for future wireless communication systems. The advanced reconfigurable intelligent surface (RIS) has recently been incorporated into the spectrum sharing mechanism, allowing for both spectrum efficiency (SE) and energy efficiency (EE). Spectrum sensing is the cornerstone of spectrum sharing. Nevertheless, due to the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">doubling fading</i> effect, the path loss experienced from the reflection link is thousands of times that from the direct link. Consequently, the detection performance gains obtained through passive RIS are negligible, especially in typical communication scenarios where the direct link is not weak. To address this issue, this paper proposes an active RIS assisted spectrum sensing mechanism that can notably improve the detection performance gains. Multiple active RISs are introduced and the closed-form expressions of the detection performance metrics are derived in this paper. More importantly, due to the extra energy consumption in amplifying reflected signals, it's necessary to investigate the EE maximization problem for the active RIS assisted mechanism by jointly optimizing the detection parameter and the reflecting precoding. Alternating optimization (AO) algorithm and quadratic transform (QT) method are utilized to decouple the multidimensional <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fractional programming</i> (FP) problem. Then, the optimal detection parameter is obtained with a closed-form solution based on the convex optimization theory. The phase shift and amplification coefficient of each active element are optimized through the sequential rotation (SR) method and an improved differential evolutionary (DE) algorithm, respectively. Simulation results show that the proposed active-RIS mechanism could outperform the conventional passive-RIS mechanism in achieving notably enhanced detection performance gains and higher EE by optimizing the reflecting precoding.

UAV-Assisted Opportunistic Beamforming in Internet of Things Networks

Opportunistic beamforming (OBF) is a promising multiple-input-multiple-output (MIMO) downlink precoding technique, where multiuser diversity gain is achieved to deal with the performance loss caused by low-complexity channel estimation. Due to high performance and low-complexity channel estimation, OBF is appropriate for internet of things (IoT) networks.However, the conventional OBF cannot achieve satisfactory performance in post-disaster communication, and the low signal-to-noise ratio (SNR) makes it hard to establish communication between the base station (BS) and users. Therefore, we propose an unmanned aerial vehicle (UAV)-assisted OBF IoT network (UON) to satisfy the quality of service (QoS) requirements of users. Then, an optimization problem is formulated to maximize the weighted energy and spectrum efficiencies, while the optimization problem is non-convex and hard to solve. To obtain the solution, we divide the optimization problem into three suboptimal issues, and then a joint iterative algorithm is applied. According to numerical results, the proposed scheme achieves higher system performances compared with the conventional OBF scheme in both Rayleigh and Rician channels. Moreover, we discuss the objective functions with different weights of spectrum efficiency (SE) and energy efficiency (EE) to adapt the QoS requirements of different scenarios. It is indicated that the proposed scheme can achieve high performance with low computational complexity, regardless of the weighted energy and spectrum efficiency.

Intelligent Resource Allocation for V2V Communication with Spectrum-Energy Efficiency Maximization.

Aiming to address the limitations of traditional resource allocation algorithms in the Internet of Vehicles (IoV), whereby they cannot meet the stringent demands for ultra-low latency and high reliability in vehicle-to-vehicle (V2V) communication, this paper proposes a wireless resource allocation algorithm for V2V communication based on the multi-agent deep Q-network (MDQN). The system model utilizes 5G network slicing technology as its fundamental feature and maximizes the weighted spectrum-energy efficiency (SEE) while satisfying reliability and latency constraints. In this approach, each V2V link is treated as an agent, and the state space, action, and reward function of MDQN are specifically designed. Through centralized training, the neural network parameters of MDQN are determined, and the optimal resource allocation strategy is achieved through distributed execution. Simulation results demonstrate the effectiveness of the proposed scheme in significantly improving the SEE of the network while maintaining a certain success rate for V2V link load transmission.

Green integrated cooperative spectrum sensing for cognitive satellite terrestrial networks

AbstractIn this paper, a two‐way relay‐aided cognitive satellite terrestrial network (TR‐CSTN) model is proposed, where primary users are located at the edge of the base station. In the TR‐CSTN, one of satellite terminal users (STUs) is selected by the fusion center as the TR to forward information between two edge primary users with power of the TR. Meanwhile, these edge primary users share the licensed frequency band with the selected TR to send information to the satellite. Then, given the limited spectrum utilization and energy efficiency (EE) of the communication system, the cooperative spectrum sensing is employed to realize green communication. Specifically, the fusion center threshold, energy detection threshold, sensing duration and number of STUs are jointly optimized to enhance EE. Furthermore, considering that the node's energy shortage results in a short network lifetime, absolute EE gets improved. In detail, a power allocation scheme named normalized power aided Lévy flight trajectory‐based whale optimization algorithm (NP‐LWOA) is provided, which fulfills effective energy compensation among STUs to prolong the network lifetime notably. Finally, numerical results confirm the theoretical analysis and show the effectiveness of the TR‐CSTN and the NP‐LWOA in efficiently achieving the concept of green communication compared with other methods.

Energy-Efficient Resource Allocation Algorithm for CR-WSN-Based Smart Irrigation System under Realistic Scenarios

Cognitive radio wireless sensor networks (CR-WSNs) are a type of WSNs that use cognitive radio technology to enhance the spectrum utilization and energy efficiency. This paper proposes an energy-efficient resource allocation algorithm (EERAA) to prolong the lifetime of a WSN-based smart irrigation system under realistic scenarios. In the proposed algorithm, power allocation and subcarrier assignment are performed consecutively. Considering the impact of the intercarrier interference (ICI) caused by timing offset, the problem of maximizing network-averaged capacity is formulated considering power and interference constraints in realistic scenarios. The obtained results reveal that the proposed algorithm attempts to maximize the averaged capacity of the CR-WSN subject to the total power constraint and tolerable interference. Numerically, the proposed algorithm can reduce the network energy consumption by up to 30%, compared with conventional approaches, while maintaining a high level of system performance in terms of secondary users’ (SUs) averaged capacity.

Realization of Index Modulation with Intelligent Spatiotemporal Metasurfaces

Advanced wireless communication with high spectrum efficiency and energy efficiency has always fascinated humanity, especially with the explosive increase of global mobile data services. Index modulation (IM) has recently been found to be a promising technique due to the transmission of additional data bits via the indices of the available transmit entities. However, the practical implementation of IM remains a great challenge associated with complicated radio components. Herein, IM with intelligent spatiotemporal metasurfaces is experimentally demonstrated. The spatiotemporal metasurfaces provide a natural and versatile platform to achieve IM in a green and lightweight manner. The whole system is driven by a built‐in inverse‐design agent that automates spatiotemporal metasurfaces to cater to diverse application demands. In doing so, how to mitigate the inherent nonuniqueness issue and how to setup the input target from practical scenes are concretely discussed. In the microwave experiment, the spatiotemporal metasurfaces are fabricated and demonstrate the feasibility by harvesting two harmonic waves as communication channels. An intelligent electromagnetic platform that can manipulate electromagnetic waves in multidimensions is provided, meriting other numerous intelligent meta‐devices that avoid overburdening data analysis networks in smart cities of the future.

Research on Beam Allocation of Millimeter Wave Massive MIMO based on Matching Game

As one of the most promising critical technologies for realizing 5G networks, mmWave communication technology has received more and more attention and research. At the same time, due to the integration and symbiosis of mmWave communication, massive MIMO, and ultra-dense network, recent organizations have also jointly proposed the rational introduction of these three technologies in mobile communication systems to improve spectrum efficiency and energy efficiency in 5G. This paper focuses on a single-cell multi-user mmWave massive MIMO system. To optimize the total data rate, we propose a beam allocation algorithm based on a one-to-one matching game under the condition that the number of RF chains is less than the number of users. The simulation results show that the proposed algorithm obtains almost the same performance as the exhaustive method on the premise that the algorithm complexity is lower than the thorough method.

Enhanced capsule generative adversarial network for spectrum and energy efficiency of cooperative spectrum prediction framework in cognitive radio network

AbstractCognitive radio network (CRN) is an intellective technology that frequently monitors the unused licensed spectrum in a particular frequency band. When more count of secondary users enters inside the channel may results in collisions. So, the secondary user may not sense the state of inactive and busy state of the primary user resulting reduces the spectrum efficiency (SE) and energy efficiency (EE) of CRN. To overcome these issues, an enhanced capsule generative adversarial network (ECGAN) for spectrum and energy efficiency of cooperative spectrum prediction (CSP) framework in CRN is proposed to improve the channel occupancy. Initially, CRN system is designed to improve the spectrum efficiency and energy efficiency. Then, channel state of primary user is predicted by using the coupled hidden Markov model (CHMM) and ECGAN predictors. Hence, the correlation among the secondary user predict the next channel status, like AND, OR, majority rule fusion methods. The prediction error, spectrum efficiency, energy efficiency based these methods are compared with CHMM and ECGAN predictors as the process of channel occupancy based on. Also, the effect of busy and idle state prediction errors on SE is detected. The proposed method is simulated in MATLAB. Then, the performance of the proposed method shows higher energy efficiency 13.86%, 24.84%, 34.75%, compared with existing methods, such as spectrum and energy efficiency of CSP in CRN (SEE‐CSP‐CRN‐HMM), (SEE‐CSP‐CRN‐MLP), differential evolution based machine learning scheme for secure cooperative spectrum sensing system (SEE‐CSP‐CRN‐EML), cooperative spectrum sensing for primary user detection in CRN utilizing support vector machine (SEE‐CSP‐CRN‐SVM), respectively.