Improve Spectrum Efficiency Research Articles

Towards contention resolution model to enhance rate of interference during dynamic spectrum utilization in cognitive radio network environment

In cognitive radio networks (CRNs), contention resolution is a fundamental challenge for optimizing dynamic spectrum access among secondary users (SUs) without causing interference to primary users (PUs). This study introduces a contention resolution model within cognitive radio networks. The queuing theory model of M/G/1/K with a First Come First Serve (FCFS) algorithm was employed. This paper proposes a novel contention resolution model using the First-Come-First-Served (FCFS) strategy, framed within a Markov Decision Process (MDP). The model dynamically allocates spectrum to SUs based on their arrival time. It leverages on M/G/1/K queuing model, providing a theoretical foundation for optimizing contention resolution. Through the FCFS algorithm, the model ensures equitable access to the spectrum, prioritizing the first arriving users. As a proof of concept and in order to validate the effectiveness of the model, simulations were performed using MATLAB and Minitab software. MATLAB was employed to simulate the dynamic behavior of the network under varying conditions, providing insights into throughput, delay, and collision metrics while Minitab was used for statistical analysis and validation of the simulation data, ensuring accuracy and reliability of the results. The results obtained from the simulations indicates that the MDP-based FCFS model significantly improves spectrum efficiency, reduces access delays, and minimizes collision rates compared to traditional contention resolution techniques. Another main contribution of this model formulation and evaluation is to help Nigerian Communication Commission (NCC) and other Dynamic Spectrum Usage functionaries in analyzing and optimizing the contention resolution predominant challenge, for the effective utilization of available spectrum resources in dynamic cognitive radio environment.

Performance Analysis of NOMA Systems over Rayleigh Fading Channels in 5G Communication

This paper aims to analyse the performance of Non-vertical Multiple Access (NOMA) systems operating in radio communication systems, especially under Rayleigh fading channels. While NOMA technology improves spectrum efficiency by allowing users to share the same time and frequency resource, Rayleigh fading channels represent multipath propagation effects commonly encountered in real radio environments. In this research, the effects of different user power levels and channel conditions on performance metrics such as system capacity, error rate (BER) and energy efficiency are investigated. Using mathematical modelling and simulation methods, the performance advantages and limitations of NOMA under Rayleigh fading conditions are evaluated. The findings show that NOMA systems offer significant advantages in terms of spectrum efficiency and user fair sharing in Rayleigh fading channels, but require careful design for error rate and energy consumption optimisation. This study provides an important reference for more effective implementation of NOMA technology in radio communication systems.

Energy Optimization and Secure Aware Spectrum Allocation Using Hybrid Meta-Heuristic Algorithms in Cognitive Radio Network

In cognitive radio networks (CRN), spectrum allocation or channel allocation is a major concern for the efficient use of the available spectrum and is explored in detail. To improve spectrum efficiency, great effort must be put into channel allocation. Because ineffective channel assignment often results in interference between SUs and PUs in CRN, channel allocation requires great care. In addition, both SU network data and power transmission powers cannot exceed the predetermined maximum interference threshold for the PU quality-of-service (QoS). With the expected spread of CRN, security concerns are increasing, energy efficiency is a primary issue of major focus in cognitive networks. Therefore, energy-aware channel allocation against jamming attacks is considered the objective of this work. Initially, the optimal channel or spectrum is assigned using the Chaotic Water Strider Algorithm (CWSA). To solve the problem of power optimization, it is necessary to use the Osprey Optimization Algorithm (OOA). MATLAB is used as the platform for implementing the proposed scheme. Several factors are taken into consideration when evaluating the performance of the proposed scheme, including network lifetime, energy efficiency, delay, throughput, packet loss, delivery rate, spectrum allocation, energy consumption, total failure probability, and signal-to-noise ratio (SNR).

Innovative Approaches to Beam Forming Antenna Array Systems with Adaptive Partial Update NLMS Algorithms

Improvements in interference cancellation, energy economy, spectrum efficiency, and system security are among the most pressing needs for today's wireless networks. One effective technique for this is the use of a Beamforming Array Antennas (BAA). However, the complexity of the Beamforming (BF) network, the long convergence time, and the large number of adjustable weight coefficients, all work against full band BAA. The key innovation and contribution of this research was to use Partial Update (PU) instead of full band adaptive algorithms, as no previous attempt had been made to do so. A subset of the array's elements, rather than all of them, will be connected to by PU methods. This allows the system to reduce the number of active antennas across all cells while maintaining high efficiency, and low cost. In this research, a new architectural model was proposed that makes use of PU adaptive algorithms, to reduce the required number of phase shifters (PSs), and hence base station antennas. For the most part, we will be discussing PU Normalized Least Mean Square (PU NLMS) algorithms like M-max NLMS, Periodic-NLMS, and Stochastic-NLMS. Using a Uniform Linear Array (ULA) Antennas in a simulation environment, we find that the, in terms of Mean Square Error (MSE), convergence rate, and steady-state error, it is evident that all PU NLMS algorithms (with the exception of Periodic-NLMS) had performed close, and approximately equivalent performance to the full band NLMS algorithms. In other hands, these PU algorithms maintain the radiation pattern with as little change from the original (distortion-free) as possible and symmetrical of the array, while. fewer elements are needed to produce the same amount of radiation. Reducing the number of required coefficients N to M (M = 5; M: Number of coefficients to be update per iteration) compared to the full update method (N = 8), to obtain the Reduction ratio 38%.

Rate-Compatible, Bandwidth-Efficient, Low-Density Parity-Check (LDPC) Codes for Aeronautical Telemetry.

Low-density parity-check (LDPC) codes form part of the IRIG-106 standard and have been successfully deployed for the Telemetry Group version of shaped-offset quadrature phase shift keying (SOQPSK-TG) modulation. Recently, LDPC code solutions have been proposed and optimized for continuous phase modulations (CPMs), including pulse code modulation/frequency modulation (PCM/FM) and the multi-h CPM developed by the Advanced-Range TeleMetry program (ARTM CPM), the latter of which was shown to perform around one dB from channel capacity. In this paper, we consider the effect of the random puncturing and shortening of these LDPC codes to further improve spectrum efficiency. We perform asymptotic analyses of the ARTM0 code ensembles and present numerical simulation results that affirm the robust decoding performance promised by LDPC codes designed for ARTM CPM.

An Innovative Method for Improving the Performance of UAV‐SM‐NOMA for 6G Networks Over Generalized Fading Channels

ABSTRACTThe emergence of 6G wireless communications has brought numerous challenges and opportunities for meeting the growing demands of enhanced system capacity, higher data rates, lower latency, improved security, and stringent quality of service (QoS) requirements. One promising solution to tackle these challenges involves utilizing unmanned aerial vehicles (UAVs) in conjunction with nonorthogonal multiple access (NOMA) and spatial modulation (SM). However, optimizing the performance of UAV‐based SM‐NOMA systems for both perfect and imperfect channel state information (CSI) over generalized fading channels (α–μ, k–μ and η–μ) while simultaneously considering multiple objectives is a complex task. To address these challenges, this paper proposes integrating the monarch butterfly optimization algorithm (MBOA) into UAV‐SM‐NOMA systems for 6G networks. Inspired by the foraging behavior of monarch butterflies, the MBOA aims to enhance UAV‐based SM‐NOMA systems' performance by optimizing resource allocation, increasing system sum rate, reducing outage probability (OP) and bit error rate (BER), improving spectrum efficiency (SE), and ensuring desirable signal‐to‐interference‐plus‐noise ratio (SINR) levels. Key findings include that the MBOA effectively improves performance metrics across generalized fading channels compared to existing UAV‐assisted NOMA and orthogonal multiple access (OMA) models. Simulation results confirm that the proposed UAV‐assisted SM‐NOMA model with MBOA significantly outperforms conventional approaches, demonstrating superior capacity, reliability, and efficiency in 6G network scenarios.

Design of an Improved Model for Spectrum Sharing and Resource Management Using Deep Q-Network, Federated Averaging, and Ant Colony Optimization

Indeed, the ever-evolving wireless communication requires DSS and resource management for effectiveness. The old methods of spectrum allocation have a strategic shortfall since they are static and thus not adapted to real-time network conditions. Besides, more important challenges include issues over privacy and inefficiency of resource utilization. In the light of such problems, this paper proposes a new method by bringing together three deep-learning techniques, which are Deep Q-Networks, Federated Averaging, and Ant Colony Optimisation. Each is chosen because of its various competencies and complementary capabilities. First, DQN is chosen because it has the capability to handle high-dimensional action space. DQN applies deep learning to approximate the Q Value function, which provides the optimal decision of spectrum allocation based on real-time network states, such as spectrum usage and interference levels. With this scheme, up to a 20% gain in network throughput and up to a 15% cut in wasted spectrum compared to traditional methods are achievable. The second component FedAvg tackles the model training collaboration issue with privacy. It enables FedAvg for privacy-preserving resource management, where multiple devices update the global model collaboratively by communicating model updates without exchanging raw data samples. For this approach, the resulting method leads to a further increase in data privacy by reducing data leakage as high as 40%, with an improvement in resource management accuracy of up to 15%. ACO was employed to optimize resource allocation and improve spectrum efficiency. ACO emulates the natural foraging behavior of ants to solve combinatorial optimization problems. Consequently, it has led to up to 25% increase in spectral efficiency and up to 20% improvement in resource allocation balance sets. All these methods put together will provide a wholesome solution that not only optimizes spectrum and resource management but also promises privacy and adaptability. This represents severe improvement in overcoming the weaknesses of the existing approaches and will provide a strong foundation for next-generation wireless networks.

Enhancing Wireless Communication Efficiency Using Dynamic Nonlinear Distortion Adaptive Optimization Analysis

Abstract Modern communication networks require improved signal quality and spectrum efficiency. Modern wireless communication relies heavily on the multiple input multiple output (MIMO) technology to improve spectrum efficiency and achieve faster data rates. However, the performance of MIMO systems is significantly hampered by non-linear distortions in power amplifiers (PA), especially under dynamic operating conditions. Traditional methods to reduce these distortions often lack the adaptability required for effective optimization. The proposed dynamic nonlinear distortion adaptive optimization analysis (DNDAOA) therefore introduces an adaptive approach that dynamically adjusts the pre-distortion signals using real-time feedback mechanisms. DNDAOA effectively reduces non-linear distortions and thus improves the performance of MIMO systems. Simulation results show that DNDAOA significantly increases spectrum efficiency, reduces error rates, and improves signal quality. This method is widely used in areas such as wireless telecommunications, internet of things (IoT), autonomous vehicles, and smart infrastructure, driving advances in connectivity, reliability, and data integrity.

A detailed reinforcement learning framework for resource allocation in non‐orthogonal multiple access enabled‐B5G/6G networks

AbstractThe world of communications technology has recently undergone an extremely significant revolution. This revolution is an immediate consequence of the immersion that the fifth generation B5G and 6G have just brought. The latter responds to the growing need for connectivity and it improves the speeds and qualities of the mobile connection. To improve the energy and spectral efficiency of these types of networks, the non‐orthogonal multiple access (NOMA) technique is seen as the key solution that can accommodate more users and dramatically improve spectrum efficiency. The basic idea of NOMA is to achieve multiple access in the power sector and decode the required signal via continuous interference cancelation. A resource allocation approach is proposed for the B5G/6G‐NOMA network that aims to maximise system throughput, spectrum and energy efficiency and fairness among users while minimising latency. The proposed approach is based on reinforcement learning (RL) with the use of the Q‐Learning algorithm. First, the process of resource allocation as a problem of maximising rewards is formulated. Next, the Q‐Learning algorithm is used to design a resource allocation algorithm based on RL. The results of the simulation confirm that the proposed scheme is feasible and efficient.

Performance Analysis of a Cognitive RIS-NOMA in Wireless Sensor Network

The reconfigurable intelligent surfaces (RIS) represent a transformative technology in wireless communication, offering a novel approach to managing and enhancing radio signal propagation. By dynamically adjusting their electromagnetic properties, RIS can significantly improve the performance and efficiency of 5G and beyond communication systems. In this paper, we study a cognitive RIS-aided non-orthogonal multiple access (NOMA) network that serves multiple users and improves spectrum efficiency. Our analysis assumes a secondary network operates under multi-primary user constraints and interference from the primary source. We derive approximation closed-form formulas for outage probability (OP), and system throughput. To obtain further insights, an asymptotic expression for OP is computed by taking into account two power configurations at the source. Additionally, numerical results show the effects of important factors on performance, confirming the accuracy of the theoretical derivation. According to the simulation results, performance by the system under consideration might be improved considerably by combining a RIS and NOMA, particularly when compared to an orthogonal multiple access scheme.

Coexistence of 5G/6G and Digital Terrestrial Television Networks

This paper analyzes practical solutions for implementing 5G/6G and Digital Terrestrial Television (DTT) network coexistence in the UHF band. Two potential scenarios are explored using the case study of a nationwide DTT network: spectrum splitting and spectrum interleaving. Both scenarios are thoroughly examined based on current UHF spectrum usage and the DTT network structure based on a case study of Poland. The analysis includes potential future benefits and limitations of each scenario. The results presented in this paper can guide administrations in implementing decisions made at the International Telecommunication Union—Radiocommunication Sector, World Radiocommunication Conference 2023 (ITU-R WRC23), improving spectrum efficiency and 5G/6G networks development.

Distributed resource optimisation using the Q-learning algorithm, in device-to-device communication: A reinforcement learning paradigm

In the context of wireless systems going forward, particularly in the Beyond 5G (B5G) era, where high data rates and low latency are critical, D2D communication is a pivotal technology that has many advantages. In D2D communication, the allocation of resources plays a critical role in achieving higher throughput while ensuring interference management, improved spectrum and energy efficiency, and system fairness. Conventional resource allocation methodologies encounter challenges in dynamically changing and diverse communication environments. To deal with the dynamic and unpredictable character of channel characteristics, we utilize a distributed iterative resource allocation technique based on the reinforcement learning (RL) approach that empowers the system to learn and adapt to the wireless environment autonomously. In this paper, we formulate a distributed Q learning-based RL method as its real-time learning capabilities, reduced communication overhead, and exploration efficiency make it well-suited for adapting to dynamic D2D environments compared to other RL methods. By applying the Q-learning method, the D2D devices act as learning agents striving to maximize the cumulative rewards. Through interactions with the environment and continuous learning from feedback, these agents adapt to real-time resource allocation decisions over time. Comparing our proposed Q-learning method to the state-of-the-art RL techniques, simulation results show improvements in energy and spectrum efficiency, latency, an increase in Jain's fairness index, and improvements in overall system throughput of about 6 %–8 %. The scalability is found to be 1.69 interpreting that Q-learning exhibits a good scalability as the throughput does not abruptly decrease for an increasing number of devices.

Advancing Green Communications: The Role of Radio Frequency Engineering in Sustainable Infrastructure Design

A thorough examination of the role of radio frequency (RF) engineering is crucial for promoting sustainability in communications infrastructure. This review explores the complex interplay between environmental concerns in communication systems and RF engineering. It examines RF engineering approaches and strategies that support the design, implementation, and preservation of environmentally friendly infrastructure, including the integration of renewable energy sources into RF systems, and the prospects and challenges associated with employing RF technologies for fostering sustainable actions in the communications industry. The major findings revealed the importance of RF engineering as it relates to reducing carbon footprints, lowering energy consumption, and enabling environmental sustainability in communication networks. RF engineering is an essential driver of sustainability in the communications industry, considering that it supports the integration of renewable energy sources, optimization of power usage, and improvement of spectrum efficiency. Therefore, the adoption of eco-friendly practices and utilization of RF technological innovations can potentially support a more sustainable and greener digital ecosystem.

Alternating optimized RIS-assisted noma and nonlinear partial differential deep reinforced satellite communication

Satellite communication is crucial for 6 G industrial wireless networks due to its comprehensive communication services, but traditional methods face challenges such as high latency, limited bandwidth, high cost, and integration with terrestrial networks. This highlights the need for innovative communication methods to boost satellite communication efficiency, reliability, and coverage within the context of 6 G industrial applications, which is attempted to address by this research. RISs (Reconfigurable Intelligent Surfaces) are thin sheets with tiny antennas that are programmed to manipulate radio signals. The RIS has the potential to revolutionize industrial wireless communication by providing greater dynamic and goal-oriented control over radio signals. From there, the wireless environment is being transformed into a service. Besides, the NOMA (Non-Orthogonal Multiple Access) mechanism is a highly effective radio access method in advanced wireless communications that optimizes spectrum utilization and offers improved spectrum efficiency, lower latency, more reliability, and increased connectivity. The current industrial communication network faces limitations such as limited capacity, signal interference overload, complex and expensive infrastructure, and latency issues. The fusion of RIS and NOMA is seen as promising strategy to meet the stringent demands of next-generation networks for immense connectivity. It is particularly innovative for the demanding environment of smart factories by enabling efficient resource sharing, dynamic signal control, ultra-reliable, low-latency communication, cost-effective network, overcoming the signal interference and simplifying the network infrastructure. In this article, we propose a novel approach for device-to-device satellite communication in industrial networks. This approach leverages a RIS (Reconfigurable Intelligent Surface) -assisted cooperative network with NOMA (Non-Orthogonal Multiple Access) and NL-PDDRL (Nonlinear Partial Differential Deep Reinforced Learning) termed AONPDDR (Alternating Optimized and Nonlinear Partial Differential Deep Reinforced) communication. This article focuses on important aspects related to industrial communication networks such as power consumption, energy efficiency, data delivery rate, data loss rate, throughput, latency, coverage, false alarm rate, and bit error rate. The Alternating Optimization-based RIS configuration algorithm optimizes reflecting elements in RIS by maximizing channel vectors and providing a sophisticated upper bound on the highest number of reflective elements. Next, The NL-PDDRL algorithm is designed to function effectively despite environmental uncertainties and constraints. The results indicate that the proposed AONPDDR method outperforms previous benchmark methods in system-achievable aspects.

A Microwave Photonic 2 × 2 IBFD-MIMO Communication System with Narrowband Self-Interference Cancellation.

Combined in-band full duplex-multiple input multiple output (IBFD-MIMO) technology can significantly improve spectrum efficiency and data throughput, and has broad application prospects in communications, radar, the Internet of Things (IoT), and other fields. Targeting the self-interference (SI) issue in microwave photonic-based IBFD-MIMO communication systems, a microwave photonic self-interference cancellation (SIC) method applied to the narrowband 2 × 2 IBFD-MIMO communication system was proposed, simulated, and analyzed. An interleaver was used to construct a polarization multiplexing dual optical frequency comb with a frequency shifting effect, generating a dual-channel reference interference signal. The programmable spectrum processor was employed for filtering, attenuation, and phase-shifting operations, ensuring amplitude and phase matching to eliminate the two self-interference (SI) signals. The simulation results show that the single-frequency SIC depth exceeds 45.8 dB, and the narrowband SIC depth under 30 MHz bandwidth exceeds 32.7 dB. After SIC, the desired signal, employing a 4QAM modulation format, can be demodulated with an error vector magnitude (EVM) as low as 4.7%. Additionally, further channel expansion and system performance optimization are prospected.

Dual high-order QAM-modulated mm-wave signal transmission in the Q-band enabled by simple IM/DD architecture and bandpass delta-sigma modulation.

The intensity-modulation (IM)/direct-detection (DD) systems have been proven effective and low-cost due to their simple system architecture. However, the Mach-Zehnder modulator (MZM) of the IM/DD systems only reserves its driving signal intensity. Therefore, the IM/DD systems are generally unable to transmit vector signals and have a restricted spectrum efficiency and channel capacity. Similarly, the radio-over-fiber (RoF) transmission systems based on IM/DD are limited by their simple architecture and generally cannot transmit high-order quadrature amplitude modulation (QAM) signals, which hinders the improvement of their spectrum efficiency. To address the challenges, we propose a novel, to the best of our knowledge, scheme to simultaneously transmit the dual independent high-order QAM-modulated millimeter-wave (mm-wave) signals in the RoF system with a simple IM/DD architecture, enabled by precoding-based optical carrier suppression (OCS) modulation and bandpass delta-sigma modulation (BP-DSM). The dual independent signals can carry different information, which increases channel capacity and improves spectrum efficiency and system flexibility. Based on our proposed scheme, we experimentally demonstrate the dual 512-QAM mm-wave signal transmission in the Q-band (33-50 GHz) under three different scenarios: 1) dual single-carrier (SC) signal transmission, 2) dual orthogonal-frequency-division-multiplexing (OFDM) signal transmission, and 3) hybrid SC and OFDM signal transmission. We achieve high-fidelity transmission of dual 512-QAM vector signals over a 5 km single-mode fiber (SMF) and a 1-m single-input single-output (SISO) wireless link operating in the Q-band, with the bit error rates (BERs) of all three scenarios below the hard decision forward error correction (HD-FEC) threshold of 3.8 × 10-3. To the best of our knowledge, this is the first time dual high-order QAM-modulated mm-wave signal transmission has been achieved in a RoF system with a simple IM/DD architecture.

Unmanned aerial vehicle-assisted wideband cognitive radio network based on DDQN-SAC

Cognitive radio (CR) systems have emerged as effective tools for improving spectrum efficiency and meeting the growing demands of communication. This study focuses on a flexible CR system based on opportunistic spectrum access technology, which enables secondary networks to efficiently utilize unoccupied spectrum resources for information transmission by actively sensing the spectrum utilization of primary networks. Specifically, we introduce unmanned aerial vehicles (UAV) technology into the CR system to further enhance its flexibility and adaptability, which enables the transmission efficiency of low-altitude UAV networks. In this CR system, UAVs are employed for more flexible spectrum management. The objective of this research is to maximize the average achievable rate of SUs by jointly optimizing the trajectories of secondary UAV, the trajectories of primary UAV, the beamforming of secondary UAV, subchannel allocation and sensing time. To achieve this goal, we employ deep reinforcement learning (DRL) algorithms to optimize these variables. Compared to traditional optimization algorithms, DRL algorithms not only have lower computational complexity but also achieve faster convergence. To address the mixed-action space problem, we propose a Dueling DQN-Soft Actor Critic algorithm. Simulation results demonstrate that the proposed approach in this paper significantly enhances the performance of the CR system compared to traditional baseline schemes. This is manifested in higher spectrum efficiency and data transmission rates, while minimizing interference with the primary network. This innovative research combines drone technology and DRL algorithms, bringing new opportunities and challenges to the future development of cognitive communication systems.

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.

Joint optimization of sampling point and sensing threshold for spectrum sensing

AbstractWith the continuous evolution and in‐depth integration between wireless communication and emerging technology such as internet of things (IoT), artificial intelligence (AI) etc., wireless terminals are growing exponentially, thus bringing great challenges to available spectrum resources. The contradiction between unlimited frequency needs and limited spectrum resources has become a bottleneck restricting the development of wireless communication technology. As an efficient way to improve spectrum efficiency, cognitive radio (CR) continues to be the focus of wireless communication within decades. To conduct CR, the main procedure is the discovery of available spectral holes by periodically monitoring the target authorized band, namely spectrum sensing (SS). Energy detector (ED) is widely accepted for SS due to its low complexity and high convenience. The essence of traditional ED based SS schemes consist in the adaptive variation of sensing threshold/sampling point with environmental signal‐to‐noise ratio (SNR) at the receiver of CR terminal, namely adaptive sensing threshold/sampling point based SS. However, the performance of both adaptive sensing threshold and adaptive sampling point based SS schemes are always at the expense of computation complexity due to the excessive sampling point. In addition, these two schemes are both about the optimization issue of a single variable under constraints. Actually, both detection probability and false alarm probability of ED are a two‐dimensional function of sensing threshold and sampling point for a given SNR. The optimal solution of sensing performance can not be obtained by optimizing sensing threshold or sampling point alone. Motivated by these, the joint optimization of sampling point and sensing threshold is considered for SS in this paper, where sampling point and sensing threshold are jointly adaptive with the variation of environmental SNR. In addition, Q‐learning is considered in this paper to obtain the sub‐optimal solution due to the non‐convexity of the considered optimization problem. Finally, the simulation experiments are made and the results validate the effectiveness of the proposed scheme.

Optimizing spectrum efficiency in 6G multi-UAV networks through source correlation exploitation

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.”