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High route loss and line-of-sight requirements are two of the fundamental challenges of millimeter-wave (mm-wave) communications that are mitigated by incorporating sensor technology. Sensing gives the deep reinforcement learning (DRL) agent comprehensive environmental feedback, which helps it better predict channel fluctuations and modify beam patterns accordingly. For multi-user massive multiple-input multiple-output (mMIMO) systems, hybrid precoding requires sophisticated real-time low-complexity power allocation (PA) approaches to achieve near-optimal capacity. This study presents a unique angular-based hybrid precoding (AB-HP) framework that minimizes radio frequency (RF) chain and channel estimation while optimizing energy efficiency (EE) and spectral efficiency (SE). DRL is essential for mm-wave technology to make adaptive and intelligent decision-making possible, which effectively transforms wireless communication systems. DRL optimizes RF chain usage to maintain excellent SE while drastically lowering hardware complexity and energy consumption in an AB-HP architecture by dynamically learning optimal precoding methods using environmental angular information. This article proposes enabling dual optimization of EE and SE while drastically lowering beam training overhead by incorporating maximum reward beam training driven (RBT) in the DRL. The proposed RBT-DRL improves system performance and flexibility by dynamically modifying the number of active RF chains in dynamic network situations. The simulation results show that RBT-DRL-driven beam training guarantees good EE performance for mobile users while increasing SE in mm-wave structures. Even though total power consumption rises by 45%, the SE improves by 39%, increasing from 14 dB to 20 dB, suggesting that this strategy could successfully achieve a balance between performance and EE in upcoming B5G networks. [ABSTRACT FROM AUTHOR]

As the telecommunications landscape braces for the post-5G era, this paper embarks on delineating the foundational pillars and pioneering visions that define the trajectory toward 6G wireless communication systems. Recognizing the insatiable demand for higher data rates, enhanced connectivity, and broader network coverage, we unravel the evolution from the existing 5G infrastructure to the nascent 6G framework, setting the stage for transformative advancements anticipated in the 2030s. Our discourse navigates through the intricate architecture of 6G, highlighting the paradigm shifts toward superconvergence, non-IP-based networking protocols, and information-centric networks, all underpinned by a robust 360-degree cybersecurity and privacy-by-engineering design. Delving into the core of 6G, we articulate a systematic exploration of the key technologies earmarked to revolutionize wireless communication including terahertz (THz) waves, optical wireless technology, and dynamic spectrum management while elucidating the intricate trade-offs necessitated by the integration of such innovations. This paper not only lays out a comprehensive 6G vision accentuated by high security, affordability, and intelligence but also charts the course for addressing the pivotal challenges of spectrum efficiency, energy consumption, and the seamless integration of emerging technologies. In this study, our goal is to enrich the existing discussions and research efforts by providing comprehensive insights into the development of 6G technology, ultimately supporting the creation of a thoroughly connected future world that meets evolving demands. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Cellular vehicle-to-everything (C-V2X) is a promising area for research in wireless communication as it provides an improved performance for safe connectivity of autonomous vehicles over half duplex (HD) Direct Short Range Communication (DSRC) in terms of range, reliability and infrastructure cost. In this work, full duplex (FD) communication for Base Stations (BSs) over shared channels is presented for the first time in C-V2X networks. Three modes, Mode A, B and C have been defined based on autonomous, cellular HD and cellular FD communication. Network model has been built by plotting vehicles, pedestrians and traffic infrastructure according to Cox Process while BSs have been distributed according to 2-D Poisson Point Process (PPP) in a circular region. Mathematical model for success probability of FD communication over BSs in C-V2X networks using nearest neighbor criterion has been developed and same is compared with HD communication. Analytical results have also been compared with system model using Monte Carlo simulations. For the very first time, we have shown that success probability of FD is comparable with that of HD communication in terms of signal to interference noise ratio (SINR) ranging from −40dB to 60dB, with less than 10% degradation in the range from −15dB to 10dB, which is minimal. Other network characteristics like intensities of vehicles, roads and BSs have also shown minimal degradation (less than 10%) in performance of FD as compared to HD for C-V2X network. Effect of imperfect self interference cancellation (SIC) for different values has also been presented to highlight significance of achieving perfect SIC for FD in C-V2X networks. [ABSTRACT FROM AUTHOR]

This paper investigates the potential transformation to be ushered in by 6G technology in telecommunications, enabling huge data rates, low latencies, high-reliability connectivity, and broadened connections based on its heterogeneous cellular architecture. It is a complex work dealing with an analysis of the evolution toward 6G while using heterogeneous networks (HetNets) that take advantage of various technologies, such as small cells, macro cells, non-terrestrial networks (NTNs), and ultra-dense networks (UDNs). It discusses the architectural progress, such as spectrum efficiency, advanced beamforming, and application of AI and ML toward network optimization for 6G HetNets. The deployment challenge for the 6G HetNets: spectrum allocation, energy efficiency, interoperability, security, with solutions such as cognitive radio networks, energy harvesting, and blockchain for improved security. It also delves into the far-reaching impacts of 6G HetNets in sectors like smart cities, autonomous driving, IoT, telemedicine, and AR/VR experiences. The importance of 6G is further expounded in achieving seamless connectivity, superior mobile broadband, and supporting massive machine-type communications, therefore reshaping our digital interactions. The paper concludes on the significance of collaborative research, policy development, and standardization efforts in 6G deployment complexity management and exploitation of the full potential of 6G toward technological and societal development. It presents a holistic view of 6G's heterogeneous cellular architecture, emphasizing its architectural innovations, challenges, and the promising future it portends for wireless communication systems. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surfaces (RISs) have emerged as a groundbreaking technology, revolutionizing wireless networks with enhanced spectrum and energy efficiency (EE). When integrated with drones, the combination offers ubiquitous deployment services in communication‐constrained areas. However, the limited battery life of drones hampers their performance. To address this, we introduce an innovative energy harvesting (EH), that is, EH‐RIS. EH‐RIS strategically divides passive reflection arrays across geometric space, improving EH and information transformation (IT). Employing a meticulous, exhaustive search algorithm, the resources of the drone‐RIS system are dynamically allocated across time and space to maximize harvested energy while ensuring optimal communication quality. Deep reinforcement learning (DRL) is employed to investigate drone‐RIS performance by intelligently allocating resources for EH and signal reflection. The results demonstrate the effectiveness of the DRL‐based EH‐RIS simultaneous wireless information and power transfer (SWIPT) system, demonstrating enhanced drone‐RIS spectrum‐efficient communication capabilities. Our investigation is summarized in unleashing potential, which shows how DRL and EH‐RIS work together to optimize drone‐RIS for next‐generation wireless networks. [ABSTRACT FROM AUTHOR]

Copyright of Radio Communications Technology is the property of 54th Research Institute of CETC and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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. [ABSTRACT FROM AUTHOR]

A novel reference signal scheme, E DFT-s-OFDM DMRS was proposed for high-mobility scenarios in the Terahertz band. The proposed scheme involved segmenting the reference signal sequence with cyclic prefix and cyclic suffix into a head and tail reference signal sequence. These sequences were then embedded into the tail and head of each OFDM symbol, respectively. The scheme had the advantage of using a complete reference signal sequence formed by the tail of the preceding OFDM symbol and the head of the following OFDM symbol. This allowed the receiver to perform channel estimation in each OFDM symbol interval, improving the accuracy and real-time performance of channel estimation. Furthermore, adjacent OFDM symbols share identical head and tail reference signal sequences, eliminating the traditional cyclic prefix for each OFDM symbol and improving spectral efficiency. Simulation results demonstrate that this scheme provides more precise channel estimation and greater spectral efficiency than DMRS scheme with 5G NR DFT-s-OFDM waveform for high-mobility scenarios in the Terahertz band.

Copyright of Telecommunications Science is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Duzce University Journal of Science & Technology is the property of Duzce University Journal of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

High route loss and line-of-sight requirements are two of the fundamental challenges of millimeter-wave (mm-wave) communications that are mitigated by incorporating sensor technology. Sensing gives the deep reinforcement learning (DRL) agent comprehensive environmental feedback, which helps it better predict channel fluctuations and modify beam patterns accordingly. For multi-user massive multiple-input multiple-output (mMIMO) systems, hybrid precoding requires sophisticated real-time low-complexity power allocation (PA) approaches to achieve near-optimal capacity. This study presents a unique angular-based hybrid precoding (AB-HP) framework that minimizes radio frequency (RF) chain and channel estimation while optimizing energy efficiency (EE) and spectral efficiency (SE). DRL is essential for mm-wave technology to make adaptive and intelligent decision-making possible, which effectively transforms wireless communication systems. DRL optimizes RF chain usage to maintain excellent SE while drastically lowering hardware complexity and energy consumption in an AB-HP architecture by dynamically learning optimal precoding methods using environmental angular information. This article proposes enabling dual optimization of EE and SE while drastically lowering beam training overhead by incorporating maximum reward beam training driven (RBT) in the DRL. The proposed RBT-DRL improves system performance and flexibility by dynamically modifying the number of active RF chains in dynamic network situations. The simulation results show that RBT-DRL-driven beam training guarantees good EE performance for mobile users while increasing SE in mm-wave structures. Even though total power consumption rises by 45%, the SE improves by 39%, increasing from 14 dB to 20 dB, suggesting that this strategy could successfully achieve a balance between performance and EE in upcoming B5G networks.

Summary: The solution of resource allocation is based on NOMA and OMA structures that are suboptimal to satisfy the demanding QoS and higher data rate requirements compared to EE requirements in 5G cellular networks. In this work, the resource allocation problem in the hybrid MC‐NOMA system is solved. The system achieves the trade‐off between spectral and energy efficiency (EE) with the lowest rate of user requirements. The system's resource allocation, including power allocation, is considered the crucial factor and is identified as a single‐objective problem. The suggested work focuses on user pairing and allocation on paired users following processing. The user grouping is designed to connect near‐ and far‐users with high C3 to increase the fairness of NOMA. As a result, this work considers multiple goals, such as C3 maximization, spectrum efficiency, and energy efficiency. This paper aims to present a novel hybrid optimization model with the combination of coati and bald eagle optimization algorithms, to solve the defined optimization problem. The experimental results show that the suggested system outperforms the conventional algorithms by evaluating different performance measures such as channel correlation, spectrum efficiency, energy efficiency, and power allocation. [ABSTRACT FROM AUTHOR]

Abstract Upcoming Earth Exploration Satellite Services (EESS) missions, especially to monitor Brazilian diversified biomes, will require progressively higher data rates for downlink transmissions, besides the ability to share its frequency spectrum with cellular base stations. Both impact issues on spectral efficiency (in bps/Hz) and coexistence in frequency, time, location, etc. This paper proposes a technique suitable for LEO Earth Observation Satellites (EOS) by combining two strategies. We initially present the Cognitive Radio (CR) spectrum awareness and exploitation approaches to propose techniques for improving their uses. Next, we detail the Adaptive MODulation and CODing (MODCOD) techniques (ACM) based on DVB-S2X systems to increase RF power and spectral efficiencies. Finally, we evaluate our solution by monitoring the Signal to Interference plus Noise Ratio (SINR) and combining CR/MODCOD techniques. Two case studies are presented that demonstrate the proposed approach on Brazilian satellites developed by the National Institute for Space Research (INPE). A real in-situ characterization of the interfering scenarios was performed during the passes of the two EESS satellites that proves the effectiveness of spectral efficiency and coexistence.

Abstract In the context of 6G and 5G communication networks, particularly in disaster-stricken areas with a surging demand for connectivity and the rapid evolution of the internet of everything (IoE), the imperative for augmenting spectrum efficiency in unmanned aerial vehicles (UAVs) communication-enabled wireless networks becomes ever more pronounced. This work embarks on the mission of significantly enhancing spectrum efficiency by capitalizing on redundancies, such as correlations inherent in data sources like co-located video sensors. While the intuitive potential of exploiting redundancies for spectrum efficiency enhancement is apparent, our approach is systematically structured. We introduce a linear programming framework designed to meticulously allocate bandwidth to individual links based on their respective demands, while judiciously adhering to spatial spectrum reuse constraints. Subsequently, we employ this linear program to empirically quantify the improvements in spectrum efficiency engendered by source correlations. Furthermore, we elucidate various strategies for harnessing these correlations to maximize spectrum efficiency gains, while navigating the trade-off terrain between computational complexity and precision. Our findings resoundingly underscore the trans-formative power of identifying the precise set of source correlations, resulting in spectrum efficiency enhancements of up to two orders of magnitude. In terms of network performance, the judicious exploitation of source correlations grants admission to nearly 100% of delay-intolerant traffic, alongside substantial reductions in mean delay for delay-tolerant traffic.”

In this paper, we undertake an examination of the complicated challenges associated with bandwidth allocation and power control in multi- unmanned aerial vehicles (UAVs) network. The focal point of our investigation is the formulation and subsequent proposition of a novel algorithm aimed at optimizing bandwidth utilization and energy efficiency across multiple UAVs. The intricate joint optimization problem is cast as a nontrivial nonlinear programming (NLP) challenge; thus, to efficiently address it, we split it into two subproblems. In the first sub-problem, UAVs bandwidth resources are being optimized while considering that they are operating at their maximum transmit powers. In the second sub-problem and after optimizing bandwidth allocations, UAVs transmit powers are optimized based on their inter-links speed. Sequential quadratic programing (SQP) based on coloring graph representation is proposed to address the first sub-problem, while $\mathcal {M}$ - matrix theory is employed to address the second one. Rigorous numerical simulations are conducted to prove the effectiveness of the proposed scheme against other benchmarks in maximizing both data rate and energy efficiency of the proposed multi-UAV network.

In order to solve the spectrum efficiency optimization problem of device-to-device (D2D) communication in many-to-one reusing mode, the channel allocation and power control problem in the frequency-selective channel environment is investigated. A resource allocation algorithm based on hierarchical optimization is proposed. The highly coupled resource allocation problem is decoupled by introducing the layering idea. Firstly, in the outer layer, a power control algorithm based on discrete power selection and two fast channel allocation algorithms based on priority setting are proposed. By using discrete power control for cellular users, the problem of multiple local optimal solutions is solved, which makes the power control algorithm of the whole system converge. Meanwhile, the proposed channel allocation algorithm reduces the complexity by setting priorities for D2D users, and at the same time realizes the suppression of cross-layer interference. In the inner layer, a resource allocation algorithm based on a non-cooperative game is proposed. By modeling D2D users share the same channel as a non-cooperative game model and introducing the cost function in term of same-layer interference, it achieves the suppression of same-layer interference while improving the spectrum efficiency. And the Nash equilibrium solution of the game is obtained by solving the KKT condition. Simulation experiments show that the proposed algorithm not only improves the spectrum efficiency of the system compared with the existing algorithms, but also proves the superiority of the algorithm in ultra-dense D2D communication scenarios.

Energy and spectral efficiency are attracting attention as “emerging technology” that exploits the opportunities of sensing, communications and artificial intelligence. This paper proposes the features of these state of art techniques a new energy and spectrum efficient technique for 5G and beyond networks. By using Interference aware (IA) beam selection in beam-space MIMO, the large number of users are supported with a smaller number of radio-frequency (RF) chains at the same time and frequency resource. It has been observed from the proposed scheme that the achievable sum-rate of mm-wave channel model is also improved. From the simulation results it is found that Interference aware (IA) beam selection is used in beam-space to minimize the total obligatory RF-chains in mm-wave massive MIMO communication systems without noticeable loss in performance.

The performance of the phase shift keying (PSK) modulation technique over additive white Gaussian noise (AWGN) and multipath propagation channels generally becomes worse for higher-order modes. Therefore, a new modulation technique should be provided in order to have a system that is capable of transmitting data with higher efficiency while maintaining better performance at the same time. This paper presents the development of a fractal wavelet packet transform incorporated within the M-ary PSK system, namely M-ary PSK orthogonal wavelet division multiplexing (OWDM), which is proposed to obtain high performance of modulation in terms of spectrum efficiency and bandwidth resources intended for wireless communication systems. To demonstrate performance improvement over a Rayleigh frequency selective fading channel and in the presence of AWGN noise, the proposed system was evaluated and compared to the basic modulation system and M-ary PSK employing orthogonal frequency division multiplexing (OFDM). The performance results show that M-ary PSK OWDM had better performance in comparison with M-ary PSK OFDM and the conventional system. By utilizing 16 subcarriers, QPSK OWDM achieved bit error rate performance improvement from 1.5 × 10-3 to 1 × 10-4 for Eb/N0 of 20 dB with efficient bandwidth.

Recent works have achieved considerable success in improving the concurrency of backscatter network. However, they do not optimize the balance between throughput and spectrum occupancy, both of which serve as pivotal parameters in concurrent transmissions. Moreover, these works also introduce complex components on tag thereby increasing both power consumption and deployment costs. In this article, we propose Spray, a tag-lightweight system to achieve high throughput and narrow-band occupancy with low power. The key idea is to incorporate an agile channel allocating and scheduling mechanism into the backscatter network. This approach allows for efficient spectrum utilization and concurrency without the need for energy-intensive components. To optimize throughput in the presence of the challenge of harmonic interference, we introduce a novel algorithm that determines the channels with an optimal combination of central frequencies and bandwidths. Additionally, we propose a fair scheduling strategy to ensure equitable transmission opportunities for all tags. We prototype the Spray tag using commercial off-the-shelf components and implement the excitation and receiver with software-defined radio platform. Our evaluation shows that the system supports 30 parallel tags transmitting in the bandwidth of 600 kHz and the throughput can reach more than 280 kbps. [ABSTRACT FROM AUTHOR]

This paper introduces the novel IRS-based Optimal Relay Selection (ORS-IRS) method, aimed at analyzing the performance of wireless communication systems with an emphasis on symmetry. The ORS-IRS approach presents an innovative communication algorithm that seamlessly integrates Intelligent Reflecting Surfaces (IRS) with relay selection techniques. Through adaptive adjustments of reflection coefficients, IRS elements efficiently manipulate incoming signals, fostering symmetry in signal strength enhancement and latency reduction for improved signal delivery to the intended destination. This symmetrical optimization in channel capacity and transmission power ensures reliable data transmission with low latency, achieved through the seamless integration of IRS and relay selection techniques. In contrast, the Cell-Free Massive MIMO (CF-M-MIMO), with its decentralized architecture, excels in serving a larger user base and attaining remarkable capacity gains, showcasing a different dimension of symmetry. The Decode-and-Forward (DF) relaying approach demonstrates its potential in enhancing signal reliability across extended distances, contributing to the overall symmetry of the comparative analysis. This comprehensive evaluation provides valuable insights into selecting appropriate transmission strategies, particularly for applications that demand high capacity and reliability in the design of modern wireless communication systems with a symmetrical focus. [ABSTRACT FROM AUTHOR]

In the context of 6G and 5G communication networks, particularly in disaster-stricken areas with a surging demand for connectivity and the rapid evolution of the internet of everything (IoE), the imperative for augmenting spectrum efficiency in unmanned aerial vehicles (UAVs) communication-enabled wireless networks becomes ever more pronounced. This work embarks on the mission of significantly enhancing spectrum efficiency by capitalizing on redundancies, such as correlations inherent in data sources like co-located video sensors. While the intuitive potential of exploiting redundancies for spectrum efficiency enhancement is apparent, our approach is systematically structured. We introduce a linear programming framework designed to meticulously allocate bandwidth to individual links based on their respective demands, while judiciously adhering to spatial spectrum reuse constraints. Subsequently, we employ this linear program to empirically quantify the improvements in spectrum efficiency engendered by source correlations. Furthermore, we elucidate various strategies for harnessing these correlations to maximize spectrum efficiency gains, while navigating the trade-off terrain between computational complexity and precision. Our findings resoundingly underscore the trans-formative power of identifying the precise set of source correlations, resulting in spectrum efficiency enhancements of up to two orders of magnitude. In terms of network performance, the judicious exploitation of source correlations grants admission to nearly 100% of delay-intolerant traffic, alongside substantial reductions in mean delay for delay-tolerant traffic." [ABSTRACT FROM AUTHOR]

In order to optimize the energy efficiency and spectral efficiency of decoded-and-forwarded full-duplex relay network in imperfect channel state information, this paper proposed an energy efficiency and spectral efficiency balancing strategy based on this network model. It could replace the non-convex multi-objective optimization problem to a convex single-objective optimization problem by constructing a compromise optimization function of energy efficiency and spectral efficiency. It used derivation method and Lagrange multiplier method to solve the optimal relay transmission power under different compromise factors. The simulation results show that changing the compromise factor can change the energy efficiency and spectral effectiveness of the system and obtain the performance tradeoff of energy efficiency and spectrum effectiveness. [ABSTRACT FROM AUTHOR]

The performance of the phase shift keying (PSK) modulation technique over additive white Gaussian noise (AWGN) and multipath propagation channels generally becomes worse for higher-order modes. Therefore, a new modulation technique should be provided in order to have a system that is capable of transmitting data with higher efficiency while maintaining better performance at the same time. This paper presents the development of a fractal wavelet packet transform incorporated within the M-ary PSK system, namely M-ary PSK orthogonal wavelet division multiplexing (OWDM), which is proposed to obtain high performance of modulation in terms of spectrum efficiency and bandwidth resources intended for wireless communication systems. To demonstrate performance improvement over a Rayleigh frequency selective fading channel and in the presence of AWGN noise, the proposed system was evaluated and compared to the basic modulation system and M-ary PSK employing orthogonal frequency division multiplexing (OFDM). The performance results show that M-ary PSK OWDM had better performance in comparison with M-ary PSK OFDM and the conventional system. By utilizing 16 subcarriers, QPSK OWDM achieved bit error rate performance improvement from 1.5 × 10-3 to 1 × 10-4 for Eb/N0 of 20 dB with efficient bandwidth. [ABSTRACT FROM AUTHOR]

Enhanced mobile broadband is an essential target in fifth-generation (5G) networks with higher demands among service consumers. It offers very high-speed Internet connections for several geographical areas: urban, suburban, and rural. Many mobile network operators (MNOs) continuously monitor the quality of service in terms of multiple services to guarantee high network performance. To the best of our knowledge, no extensive studies and analyses are conducted on mobile broadband (MBB) services that cover various implementation scenarios and several performance metrics. This study comprehensively analyzes the existing MBB performance in an urban area: Cyberjaya City, Malaysia. The measurement data were collected through drive tests from various MNOs supporting 3G and 4G technologies: Maxis, Celcom, Digi, U Mobile, and Unifi. Several performance metrics, such as signal quality, throughput (downlink and uplink), ping, and handover, were measured during the drive tests. The data measurements were conducted in two scenarios: outdoor and indoor environments. Measurement results of the outdoor drive test demonstrate that the maximum average throughput with downlink and uplink data rates is 14.3 and 7.1 Mbps, respectively, whereas the minimum average ping and loss are 36.5 ms and 0.14, respectively, for all MNOs. However, the in-building measurements achieve an acceptable overall average data rate of 2 Mbps. This paper provides several suggestions and recommendations for MBB providers to improve their performance networks and quality of experience to meet customers’ satisfaction. Several limitations not considered in this study and can point to possible future work are presented.

Unlicensed Long-Term Evolution (U-LTE) is one of the few scientific fields that allow illegitimate access to the 5GHz band to boost data transmission throughput. However, the overall client-perceived Quality-of-Service (QoS) was not improved. This has an impact on the scheduling process since scheduling allows clients to be assigned channels or subframes based on their QoS requirements. To solve this issue, a Desirable U-LTE-Wi-Fi with QoS-aware Based Resource Allocation (D-U-LTE-Wi-Fi-QBRA) has been designed. It allocates the necessary resources and ensures the QoS of the Primary User (PU) and Secondary User (SU) in 3GPP-LTE-Cognitive Radio Network (CRN). But, from the Primary Network’s (PN) perspective, it does not prevent the interference generated by SUs. Similarly, from the Secondary Networks (SNs) perspective, there was a need to allocate PNs to SUs to minimize the total interference. This article dwells on how the coexistence of Wi-Fi and LTE networks using Cognitive Radio (CR) is enhanced by introducing the Flower Pollination optimization Algorithm (FPA). The main aim of this framework is to enable SUs with a high QoS using reasonable cost and data rate according to the interruption limits of all accessible networks with inactive systems. First, an optimization dilemma is devised to reduce the interruption experienced by the authorized clients and the price that SUs must spend to utilize the PNs. Then, this dilemma is rectified using the FPA. This improved the cost and SE for the co-occurrence of Wi-Fi and LTE networks using CR. Further, the modeling outcomes reveal that the proposed model achieved better Spectrum Efficiency (SE) than the usual models.

The simplified universal time-domain windowed-orthogonal frequency-division multiplexing (Simplified UTW-OFDM) has been proposed to improve spectral efficiency. This study proposed a novel receiving method using an optimal inter-carrier interference (ICI) cancellation technique to improve communication quality during the application of the simplified UTW-OFDM to 5G. The simplified UTW-OFDM suppresses out-of-band emission (OOBE) through the application of very long time-domain windowing to the conventional OFDM with a cyclic prefix (CP-OFDM) symbol. However, in exchange for the OOBE suppression performance, a large ICI is generated due to the symbol distortion caused by the application of the time-domain window, resulting in the degradation of the reception quality. Therefore, in this study, we proposed a new method for calculating the log-likelihood ratio that takes into account the effect of the time-domain window. Further, an ICI canceller that included a process for suppressing the noise enhancement effect of the time-domain window was proposed. The effectiveness of the proposed method was evaluated through computer simulations and experiments using software-defined radio. The experimental evaluation showed that the proposed ICI canceller could suppress the OOBE by 23.5 dB compared to CP-OFDM under the condition that BLER of 0.1 is achieved even when 64QAM is applied.

In the licensed shared access (LSA) spectrum-sharing scheme, the protection of the incumbent leads to degradation in the licensee's spectrum and energy efficiency. In this paper, the optimization of these two performance metrics for an LSA vertical sharing between an airport incumbent and a mobile network licensee during the period when the LSA spectrum is not available, (i.e, the incumbent is active) is examined. Considering a restriction zone of a predefined radius, a power allocation scheme for joint optimization of the energy and spectrum efficiency during the period of the incumbent's occupation of the LSA band is formulated. Specifically, for the joint optimization of the spectrum and energy efficiency, the weighted sum approach to solve the ensuing multi-objective optimization problem is adopted. Furthermore, various critical operational parameters in conjunction with the two performance metrics were investigated. The results obtained show that, with the proper selection of cell radius, eNodeB transmit power and user number per cell, the proposed system can simultaneously achieve the trade-off objective of improved spectrum and energy efficiency. [ABSTRACT FROM AUTHOR]

It is important to design a spectrum-efficient and power-efficient wireless communication system, which is the main design target in cellular and wireless communication engineering. Basically, to make it, the desired system must have a low peak-to-average power ratio (PAPR), low-level of out-of-band (OOB) power emission and can provide a high throughput. In this paper, we propose a high-throughput cellular communication system, namely Discrete Fourier Transform-Spread Windowing and Restructuring Orthogonal Frequency Division Multiplexing (DFT-Spread WR-OFDM), which has considerably low PAPR and low OOB power emission for upcoming next-generation beyond 5G (B5G) and 6G cellular communication systems. High PAPR is still one of the challenging issues for the MIMO-OFDM system. The proposed system is an effective arrangement of DFT-Spreading and windowing techniques to reduce PAPR and OOB power emission, respectively. Multiuser diversity is obtained using localized subcarrier mapping. We take advantages of spatial multiplexing to enhance the data throughput significantly. Additionally, frequency-domain equalization (FDE)-minimum mean squared error (MMSE) is used to combat the inter-symbol interference (ISI) effect. Simulation results verify that the suggested system considerably lowers PAPR and OOB power emission compared to the conventional systems. Furthermore, the proposed scheme shows better bit error rate (BER) performance over the MIMO Rayleigh fading channel environment. Simulation results also confirmed that the channel capacity of our suggested system is better than the conventional multi-carrier systems.

The Internet of Things (IoT) has become increasingly popular, and its communication requirements have grown beyond what traditional ground networks can handle. The space–air–ground (SAG) integrated network has been proposed as a potential solution, where unmanned aerial vehicles (UAVs) collect data from IoT devices and transmit them to satellites. However, the limited energy of UAVs is one of the key factors restricting communication performance, so it is necessary to consider communication energy efficiency (EE). In addition, the improvement of EE will bring about the decline of spectral efficiency (SE). Therefore we consider the tradeoff between EE and SE. Considering a SAG-IoT network, the focus of the paper is to optimize sub-channel selection, power control, and UAV position deployment to maximize EE and SE of the network, which is a multi-objective optimization (MOO) problem. On this premise, we try to improve the data throughput between IoT devices and UAVs. To solve this MOO problem, we use the ϵ -constraint to convert it into a single-objective optimization problem. We then employ various optimization algorithms such as the Dinkelbach algorithm, successive convex approximation algorithm, Lagrange dual algorithm, and block coordinate descent algorithm to solve the mixed integer non-convex problem. Simulation results show that the proposed algorithm converges to at least one sub-optimal solution. [ABSTRACT FROM AUTHOR]