Efficient Spectrum Utilization Research Articles

Metamaterial Based Circularly Polarized Parasitic Element Driven DRA for Sub 6 GHz Wireless and 5G Communication Applications

Billions of wireless devices are directly communicating with each other and exchanging data worldwide. The 5G communication provides broad scope to this data exchange with low latency, high data rate, and efficient utilization of the EM spectrum. This paper articulates designing and developing a multiband Dielectric Resonator Antenna (DRA) with circular polarization for sub-6 GHz applications. The designed DRA of alumina occupies the dimension of radius ten and height of twenty mm on the FR4 substrate. The proposed DRA resonates at the quad-band with good impedance bandwidth covering the 5G band under the sub 6 GHz range. The designed antenna provides moderate gain and radiation efficiency of more than ninety-six percent. The prototype measured results show excellent matching with the simulation results of HFSS.

An efficient radio-frequency spectrum utilization technique for cognitive radio networks

Cognitive radio (CR) is a rising technology that unlocks the doors for radio spectrum scarcity problem, which is of great concern nowadays among the researchers at various levels. CR enables to unlicensed users or secondary users (SUs) to access the primary channels when licenced users or primary users (PUs) are not using these channels. Two fundamental access methods namely overlay and underlay are very popular in utilizing the free available channels or the white spaces. Various hybrid access methods have been proposed and recommended by many researchers to further enhance the radio spectrum utilization. Of course hybrid access methods are the better ways to deal this spectrum scarcity problem, but a comprehensive and directed effort is required to optimize the modality of these methods. This work proposes a Markov chain based hybrid access method named as Hybrid Spectrum Utilization Technique (HSUT), which tries to maximize the radio spectrum utilization by enhancing the PU’s detection probability. This work also analyzes the performance of the HSUT and results obtained through OMNeT++ simulator are very encouraging. At last, this work also compares the performance of the HSUT with the overlay, underlay, Hybrid-P1 (Dhurandher et al., 2021), and the Hybrid-P2 (Dhurandher et al., 2021) access methods.

Enhanced photocatalytic splitting of photothermally induced water vapor to evolve hydrogen

Solar energy conversion using photocatalytic water splitting shows promising potential for resolving the current energy crisis. To split vapor into hydrogen in this study, we built a surface carbonized wood/TiO2 (C-wood/TiO2) photothermal-photocatalytic system. Natural wood that had been surface carbonized enabled stable steam production and produced a solid/gas reaction interface with a lower Gibbs free energy, enabling TiO2 to produce hydrogen photocatalytically without the need for a co-catalyst made of a noble metal. The carbonized surface was able to transfer photogenerated electrons quickly from TiO2, while also significantly enhancing solar spectrum absorption. The C-wood/TiO2 system demonstrated efficient solar spectrum utilization in vapor production and hydrogen evolution. The C-wood/TiO2 system's hydrogen evolution rate under AM 1.5G light irradiation was 487.4 μmol·m−2·h−1when using deionized water, and the cycling experiment demonstrated long-term stability. Additionally, this system could evolve hydrogen from seawater (248.1 μmol·m−2·h−1). This study showed the photothermal-photocatalytic biphasic system's strong suit and potential for producing hydrogen.

Wave-Controlled Metasurface-Based Reconfigurable Intelligent Surfaces

Reconfigurable Intelligent Surfaces (RISs) are programmable metasurfaces that can adaptively steer received electromagnetic energy in desired directions by employing controllable phase shifting cells. Among other uses, an RIS can modify the propagation environment in order to provide wireless access to user locations that are not otherwise reachable by a base station. Alternatively, an RIS can steer the waves away from particular locations in space, to eliminate interference and allow for co-existence of the wireless network with other types of fixed wireless services (e.g., radars, unlicensed radio bands, and so on). The novel approach in this work is a wave-controlled architecture that properly accounts for the maximum possible change in the local reflection phase that can be achieved by adjacent RIS elements. It obviates the need for dense wiring and signal paths that would be required for individual control of every RIS element, and thus offers a substantial reduction in the required hardware. We specify this wave-controlled RIS architecture in detail and discuss signal processing and machine learning methods that exploit it in both point-to-point and multi-cell MIMO systems. Such implementations can lead to a dramatic improvement in next-generation wireless, radar, and navigation systems where RIS finds wide applications. They have the potential to improve the efficiency of spectrum utilization and coexistence by orders of magnitude.

A Coalition Formation Game-Based Multi-User Grouping Approach in the Jamming Environment

Due to the broadcast nature of wireless communication, it is vulnerable to malicious jamming attacks. Meanwhile, multiple users need to share the limited spectrum resources to enhance the spectrum utilization efficiency because of the scarcity of wireless spectrum resources. Therefore, wireless communication systems need to deal with the malicious jamming attacks and mutual interference among different users. In this paper, a multi-user anti-jamming grouping approach based on a coalition formation game is studied. The proposed approach can maximize users’ transmission rate while avoiding channel jamming through optimization of the coalition formation strategy under the influence of malicious jamming. With the help of the exact potential energy game, we demonstrate that the proposed game model can obtain stable Nash equilibrium solutions. Lastly, the effectiveness of the proposed algorithm is demonstrated through numerical.

Construction of 2D up-conversion calcium copper silicate nanosheet for efficient photocatalytic nitrogen fixation under full spectrum

Using clean and renewable solar energy to convert nitrogen to ammonia is promising, however the low utilization of light and the high cost of catalyst restrain its application. Herein, novel two-dimensional(2D) silicate CaCuSi 4 O 10 nanosheet was prepared by high-temperature solid state method using silica derived from palygorskite clay (Pal) and calcium carbonate derived from egg shells, respectively. The photocatalytic nitrogen fixation was performed using CaCuSi 4 O 10 compound as catalyst. The effect of flux content on the ammonia generation rate was investigated. Results demonstrated that the 2D CaCuSi 4 O 10 nanosheet can chemically adsorb and activate N 2 due to the high surface area along with abundant oxygen vacancies. The ammonia evolution rate of CaCuSi 4 O 10 can reach up to 60.3 μmol·g −1 ·h −1 and 15.6 μmol·g −1 ·h −1 under simulated solar light and near infrared (NIR) light irradiation, respectively. The enhanced photocatalytic nitrogen fixation can be ascribed to the up-conversion capability of CaCuSi 4 O 10 , which converts NIR into visible and UV light improving the utilization efficiency of full solar spectrum. Current study may offer a promising strategy for cost-effective photocatalytic nitrogen fixation under full spectrum. • 2D silicate CaCuSi 4 O 10 nanosheet prepared using mineral and biomass. • CaCuSi 4 O 10 compound owned abundant oxygen vacancies favoring N 2 activation. • Up-conversion capability of CaCuSi 4 O 10 improved solar energy utilization. • CaCuSi 4 O 10 nanosheet demonstrated cost-effective photocatalytic nitrogen fixation.

Study on Fixed and Dynamic Spectrum Access Models for Cellular Networks

Radio frequency spectrum is a scarce resource, hence its effective and efficient utilization for contemporary and future technologies is of paramount importance. This paper mainly focuses on analysing conventional models for spectrum management, such as command and control and market-based models, and their inefficacy to serve the upcoming technological demands globally. Generally outlined observations concerning spectrum mismanagement include lesser availability of unassigned spectrum and under-utilization of spectrum allocated to passive users. Due to the considerable urge for flexible spectrum assignment framework models and policies, new spectrum assignment approaches, such as Dynamic Spectrum Access, are investigated. Moreover, models such as Licensed Shared Access (LSA), which evolved from Europe, and Citizen Bands Radio Service (CBRS) from North America are also evaluated in this paper as potential choices for future spectrum management in Pakistan. In the aforesaid models, spectrum is shared among multiple users as per time, place and dimensions, keeping in view the security and priority of incumbent and licensed users. In addition, the efficiency of spectrum utilization and economic advantages of these models have also been analysed. Lastly, LSA and CBRS are studied comparatively, and spectrum management suggestions are made for effective implementation in Pakistan.

Performance Analysis of Cognitive SWIPT Ergodic Capacity Based on Different Selection Policies

The advent of 5G era and the increase of terminal’s numbers have led to increasing demand for spectrum resources and energy simultaneously. Therefore, this paper studies how to combine the cognitive radio (CR) and the Simultaneous Wireless Information and Power Transfer (SWIPT) technology effectively in a multiuser scenario, in which the spectrum utilization and energy efficiency are both improved. Firstly, the system model is designed of above cognitive SWIPT system, in which the cognitive users (CU) can choose spectrum access policy based on the amount of data transmission of licensed user. And then, according to cognitive users’ channel conditions and forwarding power, a concept of trust value and different relay selection policies is proposed. Subsequently, the according system ergodic capacity is analyzed. Finally, simulation results of system performance are provided to validate the analytical results. It shows that cognitive SWIPT technology can improve the system ergodic capacity and the spectrum access opportunities of cognitive users effectively, thereby improving the efficiency of energy and spectrum utilization.

Delay Optimization in Mobile Edge Computing: Cognitive UAV-Assisted eMBB and mMTC Services

Mobile edge computing (MEC) in cognitive radio networks is an optimistic technique for improving the computational capability and spectrum utilization efficiency. In this study, we developed an MEC system assisted by a cognitive unmanned aerial vehicle (CUAV), where a CUAV equipped with an MEC server can serve as a relay node and computing node. In such networks, a non-orthogonal multiple access scheme is considered to serve enhanced mobile broadband communication (eMBB) and massive machine-type communication (mMTC) users, in which the transmission delay for both users is derived. To optimize the delay in this system, we formulated an optimization problem aimed at minimizing the processing delay of eMBB and mMTC users by jointly optimizing the transmit power of the users’ information, considering the constraints of the transmit power of the secondary network. The numerical results demonstrate that the proposed Rosen’s gradient projection algorithm can considerably minimize the processing delay for a CUAV with a fixed position compared with a CUAV with a predetermined trajectory.

RanNet: Learning Residual-Attention Structure in CNNs for Automatic Modulation Classification

With the rapid emergence of advanced technologies for wireless communications, automatic modulation classification (AMC) has been deployed in the physical layer to blindly identify the modulation fashion of an incoming signal at the receiver and consequently improve the efficiency of spectrum utilization and management. Although recent works on AMC have adopted deep learning with convolutional neural networks (CNNs) to deal with large-confusing signal data, they have shown to be vulnerable to channel deterioration by primitive architectures. In this letter, we design a high-performance CNN architecture, namely Residual-attention Convolutional Network (RanNet), that mainly involves multiple advanced processing blocks to learn intrinsic features of combined waveform data (including in-phase, quadrature, amplitude, and phase components). Each block incorporates attention connection and skip connection in a sophisticated-designed structure to strengthen relevant features and weaken irrelevant features while preventing the network from vanishing gradient. Simulation results on the RadioML 2018.01A dataset show that RanNet is robust to different channel impairments and outperforms state-of-the-art deep networks in terms of accuracy while having a reasonable complexity.

Multiuser Scheduling in Centralized Cognitive Radio Networks: A Multi-Armed Bandit Approach

In wireless communication networks, the network provider serves certain licensed primary users who pay for a dedicated use of the frequency channels. However, not all the channels are occupied by the primary users at all times. For efficient spectrum utilization, in centralized cognitive radio networks (CRNs), a cognitive base station (CBS) dynamically identifies the spectrum holes and allocates the frequency channels to the on-demand unlicensed secondary users known as cognitive radios (CRs). Although existing literature has developed various dynamic spectrum access mechanisms for CBS, there is still a dearth of studies due to the wide range of assumptions made in the solutions. Most of the existing works study the CBS scheduling problem scheme by adopting optimization-based methods and rely on the prior knowledge of the network parameters such as primary users’ activity. Moreover, the impact of channel switching costs on the network throughput has not been well studied. In this paper, we aim to maximize the CRNs total throughput, and we formulate the CBS scheduling problem as a non-stochastic (i.e., adversarial) combinatorial multi-armed bandit problem with semi-bandit feedback and arm switching costs. We propose two novel online learning algorithms for CBS scheduling with and without channel switching costs, where their regret performances are proved sublinear order-optimal in time as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T^{1/2}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T^{2/3}$ </tex-math></inline-formula> , respectively, offering throughput-optimal scheduling for CRNs. Experiments on the synthetic and real-world spectrum measurement data complement and validate our theoretical findings.

MIMO-OFDM Modulation Classification Using Three-Dimensional Convolutional Network

Automatic modulation classification (AMC) plays a vital role in cognitive radio to improve spectrum utilization efficiency, however, most of the existing works have focused on single-carrier communications in single-input single-output systems. In this paper, we propose an efficient AMC method for multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) communication systems with the assumption of unknown frequency-selective fading channels and signal-to-noise ratio. At the receiver, the complex envelope samples of a burst signal acquired by multiple antennas are decomposed into in-phase and quadrature samples, which are then structured into a high-dimensional data array. To learn the modulation patterns from received signals, we develop a deep network, namely three-dimensional MIMO-OFDM convolutional neural network (MONet). With cuboidal convolution filters, the proposed MONet allows the network to capture underlying features as intra- and inter-antenna correlations at multi-scale signal representations. Relying on simulations, MONet achieves the classification accuracy of over 95% at 0 dB SNR under various channel impairments and shows the robustness with different MIMO antenna configurations.

NC-OFDM Satellite Communication Based on Compressed Spectrum Sensing.

With the fast development of giant LEO constellations, the effective spectrum utilization has been regarded as one of the key orientations for satellite communications. This paper focuses on improving the spectrum utilization efficiency of satellite communications by proposing a non-continuous orthogonal frequency division multiplexing (NC-OFDM) method. Based on the models of NC-OFDM system, we first propose a sub-carrier allocation method by using spectrum sensing to efficiently perceive and utilize the spectrum holes in the satellite communication system. Then, a hybrid genetic particle swarm optimization method is adopted to allocate the channel resources effectively. Finally, simulation results verify that the proposed algorithm can significantly improve the spectrum efficiency of satellite communications.

FMCPR: Flexible Multiparameter-Based Channel Prediction and Ranking for CR-Enabled Massive IoT

The cognitive radio-enabled <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$massive$ </tex-math></inline-formula> Internet of Things (CR- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> IoT) is envisioned to shape the future of densely connected IoT devices in the sixth-generation networks to support the hyperconnected society. In conventional CR networks, secondary users (SUs) sense the whole block of spectrum to find idle channels, which is an energy-consuming, delay-inducing, and processing-intensive task. With the large scale of resource-constrained heterogeneous devices in CR- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> IoT, the sensing process becomes a major hurdle for CR- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> IoT devices to achieve efficient utilization of the limited device and network resources. Thus, a novel multiparameter-based flexible scheme is proposed for idle channel prediction and channel ranking, which considers priorities as well as heterogeneity of users. The scheme uses a probabilistic approach and employs multiple parameters simultaneously to evaluate the suitability of a channel before selecting it for transmission. In addition, valid channel obsolescence, a major problem inherent with channel prediction and ranking, is countered by the proposed scheme. The scheme is evaluated under the impact of variable primary and SUs’ arrivals and under multiple channel failures rates and variable sensing and frame time duration. The proposed scheme is also compared with its own modified version that disregards channel failures, and with the random channel selection approach followed by IEEE 802.22. The overall evaluation is conducted under realistic spectrum sensing. Simulation results show that for different parameter values, the proposed scheme improves the collision probability by 11%–55%, reduces sensing time and energy by 60% and 65%, respectively, and enhances throughput by 4%–70%, and spectrum utilization efficiency by 11%–40%.

Opportunistic non-contiguous OFDMA scheduling framework for future B5G/6G cellular networks

In this paper, we consider the problem of resource allocation within Beyond 5G (B5G) and the envisioned 6G wireless networks with Cognitive Radio (CR) capability. CR technology allows opportunistic and dynamic access to the unused licensed spectrum bands. Unlike the conventional orthogonal frequency division multiple access (OFDMA) schemes that impose spectrum contiguity constraints in assigning the OFDMA resource blocks (RBs), the Dis-contiguous OFDMA (D-OFDMA) approach allows for flexible time–frequency RB scheduling, in which each contending CR user can access adjacent and/or non-adjacent RBs. This can provide a more efficient and flexible spectrum utilization compared to conventional OFDMA schemes. Specifically, we formulate the spectrum assignment and access problem as an optimization problem that attempts to effectively utilize the time–frequency spectrum holes of the D-OFDMA RBs. The main goal is to improve the total network throughput by maximizing the achieved sum-rate of all contending CR users subject to achieving a set of interference and performance requirement constraints. The formulated problem is classified as a binary-linear program (BLP), which is, in general, NP-hard. Thus, we develop a sub-optimal polynomial-time solution based on a relaxing-and-rounding method. Simulation results reveal that the proposed D-OFDMA RB scheduling protocol significantly improves the network throughput while maintaining fairness compared to conventional contiguous-based OFDMA protocols. Specifically, the proposed protocol achieves up to 90% and 150% performance improvements over the contiguous frequency-OFDMA and contiguous time-OFDMA, respectively.

Communication Audio and Video Coding and Decoding Methods in Wireless Cooperative Communication Network

Wireless communication is ubiquitous in people’s daily life. As people’s requirements for wireless communication continue to increase, how to improve system performance has become a core issue that communication researchers pay attention to. Wireless cooperative communication is considered to be a key technology in the future communication system because it can provide better communication quality, higher system capacity, wider communication coverage, richer services, and high spectrum utilization efficiency. The proposed network coding can effectively eliminate the transmission bottleneck problem faced by wireless cooperative communication. The development of audio and video communication coding technology has increased the demand for audio and video communication services. The goal of audio and video coding is shifting from traditional storage applications to network transmission. Therefore, the audio and video coding technology for network transmission is being affected by academia and industry. The purpose of this paper is to study the communication audio and video codec methods in wireless cooperative communication networks and to understand the development status of audio and video codec by reading a large number of related documents. On the basis of the problem, relevant experiments are designed to conduct in-depth research. Experimental results show that this scheme can simplify the implementation of scalable coding, improve the coding efficiency of the enhancement layer, and improve the quality of the reconstructed video when the audio and video streams are truncated.

A Safe Cognitive Radio Spectrum Handoff Method Using Coordinating Cognitive User

A new cognitive user emulation attack (CUEA) in a cognitive radio network (CRN), which could be utilized by intruders during spectrum handoff is introduced. The need for more efficient spectrum utilization in our increasingly digitalized society is becoming more important. This has given raise to the variety of new security threats. A safe handoff mechanism that could successfully counter such an attack by introducing a coordinating cognitive user that computes the level of trust of each cognitive user based on its behavioral characteristics is proposed. Malicious users could be effectively identified by the coordinating cognitive user by looking up the trust values. The activity of the proposed mechanism is validated using MATLAB simulation. The simulation executed describes the utilization of the proposed mechanism by correctly identifying the probability of false detection, detection rate, incorrect detection shown as it decreases the data transmission time or increases the transmission rates of primary user's signals.

Optical frequency comb assisted denoising for multiple access and capacity enhancement of covert wireless communication.

An optical frequency comb (OFC)-assisted covert wireless communication system with multiple access and enhanced capacity is proposed and experimentally demonstrated. In the scheme, signals in multiple channels are spread and mixed together to use a single transmitter and then received by individual receivers according to multiple access channels. The mixed signal is highly contaminated by noise to achieve high concealment in both the time and frequency domains, and then effectively recovered as different channels using the OFC assisted analog deep denoising technique. In experiments, mixed signals of 16 access channels with a signal-to-noise ratio (SNR) from -18 to -5 dB are accommodated, showing high covertness and 16× capacity enhancement (16×10 Mbit/s). Mutual interference among different channels is also analyzed and greatly eliminated by phases optimization in the spectral-spreading process. This scheme can greatly improve the time and spectrum utilization efficiency, which will be of great significance for enabling multiple access, large capacity, and high security for wireless communications.

Modulation Scheme Classification in Cognitive Radio Networks Using the Long Short Term Memory (LSTM) of Deep Learning

Cognitive Radio (CR) system has been adopted for efficient utilization of radio frequency spectrum. The classification of signal modulation schemes is one of the main characteristics of the CR for appropriate demodulation of sensed signals. However, conventional Modulation Classification (MC) techniques require extensive extraction of signal features, which is not often guaranteed. Thus, Deep Learning (DL) has been seen as a promising solution to this drawback in MC. This paper proposes a DL-based MC technique using the Long Short-Term Memory (LSTM) network architecture. The proposed LSTM model was trained on M-ary Phase Shift Keying (MPSK) and M-ary Quadrature Amplitude Modulation (MQAM) signal types. The LSTM directly learns the features of a given modulation scheme of a signal sample during training. The signal samples were generated via computer simulation for Signal-to-Noise Ratios (SNRs) from -10 dB to 20 dB with an interval of 5 dB over flat fading channel and Additive white Gaussian noise (AWGN). Simulation results show that the proposed LSTM model achieves an average classification accuracy of 95% at SNRs above 0 dB.

Adaptive Rate NOMA for Cellular IoT Networks

Internet-of-Things (IoT) technology is envisioned to enable a variety of real-time applications by interconnecting billions of sensors/devices. These IoT devices rely on low-power wide-area wireless connectivity for transmitting, mostly fixed- but small-size, status updates of the random processes observed by them. Owing to their ubiquity, cellular networks are seen as a natural candidate for providing reliable wireless connectivity to IoT devices. Given the massive number of IoT devices, enabling non-orthogonal multiple access (NOMA) for the mobile users and IoT devices is appealing in terms of the efficient utilization of spectrum compared to the orthogonal multiple access (OMA). For instance, the uplink NOMA can also be configured such that the mobile users adapt their transmission rates depending upon the channel conditions while the IoT devices transmit at a fixed rate. For this setting, we analyze the ergodic capacity of the mobile users and the mean local delay of IoT devices using stochastic geometry. Our analysis demonstrates that the aforementioned NOMA configuration provides better ergodic capacity for mobile users compared to OMA when delay constraint of IoT devices is strict. We also show that NOMA supports a larger packet size at IoT devices than OMA under the same delay constraint.