Enhance Spectrum Efficiency Research Articles

Performance analysis of multiple-input multiple-output orthogonal frequency division multiplexing system using arithmetic optimization algorithm

This research aims to optimize the interference mitigation and improve system performance metrics, such as bit error rates, inter-carrier interference (ICI), and inter-symbol interference (ISI), by integrating the Redundant Discrete Wavelet Transform (RDWT) with the Arithmetic Optimization Algorithm (AOA). This will increase the spectral efficiency of MIMOOFDM systems for ultra-high data rate (UHDR) transmission in 5 G networks. The most important contribution of this study is the innovative combination of RDWT and AOA, which effectively addresses the down sampling issues in DWT-OFDM systems and significantly improves both error rates and data rates in high-speed wireless communication. Fifth-generation wireless networks require transmission at ultra-high data rates, which necessitates reducing ISI and ICI. Multiple-input multiple-output orthogonal frequency division multiplexing (MIMOOFDM) is employed to achieve the UHDR. The bandwidth and orthogonality of DWT-OFDM (discrete wavelet transform-based OFDM) are increased; however system performance is degraded due to down sampling. The redundant discrete wavelet transform (RDWT) is proposed for eliminating down sampling complexities. Simulation results demonstrate that RDWT effectively lowers bit error rates, ICI, and ISI by increasing the carrier-to-interference power ratio (CIR). The Arithmetic Optimization Algorithm is used to optimize ICI cancellation weights, further enhancing spectrum efficiency. The proposed method is executed in MATLAB and achieves notable performance gains: up to 82.95 % lower error rates and 39.88 % higher data rates compared to the existing methods. ConclusionThe integration of RDWT with AOA represents a significant advancement in enhancing the spectral efficiency of MIMOOFDM systems for UHDR transmission in 5 G networks. The proposed method not only enhances system performance but also lays a foundation for future developments in high-speed wireless communication by addressing down sampling issues and optimizing interference mitigation.

Energy minimization for IRS-and-UAV-assisted mobile edge computing

Intelligent reconfigurable surface (IRS) is an emerging technology for the enhancement of spectrum and energy efficiency. We propose a novel IRS-and-unmanned aerial vehicle (UAV)-Assisted mobile edge computing (MEC) framework, where a MEC server installing on an UAV to facilitate task calculations by mobile users (MUs), and an IRS modulates channels between MUs and the UAV. Non-orthogonal multiple access (NOMA) is employed for further improving system-wide spectral efficiency. There are needs for joint optimization of multiple parameters, e.g., the task partition parameters and the transmit power of all MUs, the reflection coefficient matrix of the IRS and the movement trajectory of the UAV, and such needs raises the challenge of minimizing the long-term total energy consumption of all MUs while satisfying required transmission rate and task completion delay. We divide optimization tasks into two sub-problems and propose specific solutions respectively, i.e., relevant decisions about the UAV and MUs to be solved by deep reinforcement learning (DRL); and the reflection coefficient matrix of the IRS to be solved by block coordinate descent (BCD). A series of experiments have verified the effectiveness of the proposed communication techniques and optimization algorithms. Simulation results demonstrate that (1) NOMA-IRS technique achieves better energy efficacy compared to the cases where random IRS or no IRS is deployed and the conventional orthogonal multiple access (OMA) technique with IRS. (2) our proposed deep deterministic policy gradient (DDPG)-BCD algorithm outperforms other four benchmark algorithms in solving the complex and dynamic optimization problem.

Optimization of resource allocation in 5G networks: A network slicing approach with hybrid NOMA for enhanced uRLLC and eMBB coexistence

SummaryTraditional Orthogonal Multiple Access (OMA) and spectrum sharing methods struggle to provide the diverse quality of service (QoS) demands for enhanced mobile broadband (eMBB), ultra‐reliable low latency communications (uRLLC), and massive machine type communications (mMTC) leading to suboptimal performance and service quality degradation. Single‐carrier‐non‐orthogonal multiple access (SC‐NOMA) appears to be a more optimized solution. It can serve multiple users simultaneously on the same time‐frequency resources. This approach offers both enhanced spectrum efficiency and meets the QoS requirements for the coexistence of eMBB, uRLLC, and mMTC. However, SC‐NOMA has some drawbacks. Decoding a user's signal involves a complex successive interference cancellation (SIC) process that gets harder with more users causing delays and errors. Additionally, strong user signals can interfere with weaker ones, limiting the number of users per channel. In order to overcome the drawbacks associated with OMA and SC‐NOMA, this paper introduces a new method called user‐paired NOMA (hybrid NOMA). Hybrid NOMA adopts a strategic approach, employing two user pairing techniques: near‐far/far‐near (NF‐FN) and near‐near/far‐far (NN‐FF). NF‐FN pairing prioritizes users with similar signal strengths but different distances from the base station. This minimizes interference for the weaker user during SIC. NN‐FF pairing, on the other hand, groups users with similar signal strengths and proximity. This approach further simplifies SIC and minimizes potential interference altogether. The simulation results demonstrate trade‐offs between eMBB and uRLLC performance. OMA suffers with dedicated resource allocation, while SC‐NOMA balances performance but experiences interference. NN‐FF prioritizes eMBB and offers best latency, while NF‐FN prioritizes uRLLC with high spectral efficiency but suffers from higher latency. Finally, by providing a thorough grasp of how hybrid NOMA resource allocation works to improve the performance of various use cases, this research makes a significant contribution to the field of 5G spectrum optimization.

AI-based resource allocation techniques in D2D communication: Open issues and future directions

The fifth-generation (5G) mobile network enhances network connectivity between many mobile devices by utilizing higher bandwidth with lower communication delay. This massive connectivity generates huge network traffic while burdening the base station (BS). Hence, it is required to offload the BS and enhance the system’s overall performance. Device-to-device (D2D) communication is the prominent solution to offload the network traffic and enhance spectrum efficiency. However, D2D communication significantly disadvantages resource allocation while operating in underlay mode. A prominent solution is to embrace artificial intelligence (AI) in the D2D environment to tackle resource allocation issues. This paper exhaustively surveys all AI-based resource allocation schemes researchers have proposed globally. We found that no exhaustive literature surveys discuss the role of AI in resource allocation in a D2D environment. Thus, this motivated us to prepare a comprehensive survey and propose an AI-based resource allocation taxonomy comprising channel, power, and spectrum allocation. We also discuss the various real-time applications of AI-driven schemes for efficient resource allocation in D2D communication. Further, we presented a case study highlighting AI’s role in efficient resource allocation in D2D communication. Here, we used a novel channel quality indicator (QI) that uses signal-to-noise ratio (SINR) and signal-to-noise-and-distortion Ratio (SNDR) to decide the best D2D users for the resource allocation process. Then, we discussed the open research challenges that point to the future research direction in AI-based efficient resource allocation in D2D communication.

MULTI-TASK crow swarm-intelligent algorithm for enhancing spectrum efficiency and energy conservation in cognitive radio ad-hoc networking

Cognitive Radio Ad-hoc Networking (CRAHN) could unlicensed users access neglected spectrum assets. This research offers an innovative Multi-task Crow Swarm-Intelligent (MCSI) methodology for ensuring the effective usage of spectrum and the preservation of energy in CRAHN. The suggested method uses a crow-inspired swarm's collective cognition to maximize the distribution of spectrum assets, adjust to shifting network circumstances, and consume energy as possible. We covered the important factors that influence spectrum effectiveness, like the population of the swarm, the sensor's threshold measurement, and the optimum amount of iterations, underscoring the significance of finding the right mix of these factors. In comparison to previous algorithms, this work gives a thorough evaluation of MCSI using simulated tests, highlighting its outstanding results with regard to spectrum effectiveness and preserving energy. The results suggest that MCSI combines spectrum use and energy savings, making it a promising CRAHN performance technology.

Load based dynamic channel allocation model to enhance the performance of device-to-device communication in WPAN

The modern communication network has advanced to such an extent that it is now possible for devices within a wireless personal area network (WPAN) to communicate among themselves directly. However, the limited shared radio resources of a WPAN lead to numerous issues, such as cross-layer interference and data collisions, which wind up affecting the quality of communication. A load based dynamic channel allocation (LB-DCA) model has been proposed to enhance the performance of device-to-device communication in WPAN. This model uses several control schemes in collaboration with interference estimation and channel load balancing mechanisms to allocate and manage the radio resources efficiently. The objective of this model is to achieve high throughput, low interference and low energy consumption. The control schemes implemented are based on distributed coordination and a cell-splitting approach. These schemes are utilized to estimate the channel usage and number of active nodes in a network. The interference estimation is done by using a new efficiency formula. Further, channel load balancing takes into account the hops and load factor values. The proposed model obtained 98.58% CSI, 95.86% MCC, 96.35% delta-P, 97.96% FMI, 99.83% BMI, 21.52% enhanced spectrum efficiency, 16.38% enhanced scalability, 18.79% enhanced signal quality, 18.64% enhanced power control and 18.89% enhanced energy efficiency.

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.”

Joint optimization of UAV position and user grouping for UAV‐assisted hybrid NOMA systems

This article investigates the use of unmanned aerial vehicles (UAVs) in assisting hybrid non‐orthogonal multiple access (NOMA) systems to enhance spectrum efficiency and communication connectivity. A joint optimization problem is formulated for UAV positioning and user grouping to maximize the sum rate. The formulated problem exhibits non‐convexity, calling for an effective solution. To address this issue, a two‐stage approach is proposed. In the first stage, a particle swarm optimization algorithm is employed to optimize the UAV positions without considering user grouping. With the UAV positions optimized, a game theory‐based approach is utilized in the second stage to optimize user grouping and improve the sum rate of the hybrid NOMA system. Simulation results demonstrate that the proposed two‐stage method achieves solutions close to the global optimum of the original problem. By optimizing the positions of UAVs and user groups, the sum rate can be effectively improved. Additionally, optimizing the deployment of UAVs ensures better fairness in providing communication services to multiple users.

Location Privacy and Spectrum Efficiency Enhancement in Spectrum Sharing Systems

Location Privacy and Spectrum Efficiency Enhancement in Spectrum Sharing Systems

Performance Analysis of Soft-Switching FSO/THz-RF Dual-Hop AF-NOMA Link Based on Cognitive Radio

This paper presents a promising solution to address the scarcity of spectrum resources and enhance spectrum efficiency in the context of cognitive radio (CR)-based soft-switching free-space optical (FSO)/terahertz (THz) radio frequency (RF) dual-hop amplify-and-forward (AF)–non-orthogonal multiple access (ROMANO) links. The impact of maximum tolerable interference power in the primary network, transmit power in the secondary transmitter, and maximum relay transmission power on the link are thoroughly studied. The numerical results ultimately validate the effectiveness of this link in improving performance, and a comparative analysis is conducted with the without-CR scheme, highlighting the distinctive characteristics of the proposed link.

IMPROVED QoE IN 5G BASE STATION USING PSO BASED DSA

In the 5G era, base stations can offer multiple services in various scenarios, enhancing spectrum efficiency by utilizing idle frequency bands for low-demand services while ensuring high-priority services are prioritized. This creates a distributed architecture for spectrum allocation. The proposed dynamic spectrum allocation scheme for base stations, optimizes spectrum rearrangement based on service priority, energy consumption, and renting cost. We employ the Particle Swarm Optimization (PSO) algorithm, inspired by bird flocking or fish schooling behavior, to solve the problem. Moreover, provide detailed information on our approach, demonstrating the effectiveness of PSO in optimizing spectrum allocation. By using PSO, we find the optimal solution considering service priority, energy consumption, and renting cost, supported by mathematical proofs of PSO algorithm performance. MATLAB is used as the simulation tool to carry out these techniques. Simulation experiments validate the approach, showing the stability and efficacy of PSO-based algorithm, which aligns with the theoretical analysis. . Key Words: Spectrum efficiency; 5G; dynamic spectrum allocation; service priority; PSO.

Resource Allocation for NOMA-Enabled Cognitive Satellite–UAV–Terrestrial Networks With Imperfect CSI

Recently, non-orthogonal multiple access (NOMA)-enabled cognitive satellite-unmanned aerial vehicle (UAV)-terrestrial networks have attracted extensive attention for the advantages of enhancing spectrum efficiency and coping with the exponential growth of access users. In this paper, we propose a joint subchannel assignment and power allocation algorithm for NOMA-enabled cognitive satellite-UAV-terrestrial networks to further enhance the transmission performance, where imperfect channel state information is taken into consideration. Specifically, we formulate a mixed integer non-linear programming resource allocation problem to optimize the sum rate of the secondary network, in which the demands of the interference temperature constraint for primary users, the minimum transmission rate of each secondary user, the maximal transmitter power of the UAV, and the maximal number of secondary users that each subchannel can serve are satisfied. To tackle this tough non-convex problem, we first decouple it into two subproblems, namely, subchannel assignment and power allocation. Next, a heuristic subchannel assignment algorithm and a Taylor series and successive convex approximation-based power allocation algorithm are designed to solve the above subproblems, respectively. By alternately optimizing the sum rate of the secondary network, we finally solve the mixed integer non-linear programming optimization problem. Numerical results reveal the impacts of key parameters on system performance and indicate that our proposed scheme outperforms benchmarks in large-scale networks.

Dynamic spectrum access for Internet-of-Things with hierarchical federated deep reinforcement learning

In Internet-of-Things (IoT), blooming IoT terminals along with growing demand for broadband services, such as video stream and virtual reality (VR) applications, have led the enhancement of spectrum efficiency to the research focus in IoT. The technology of dynamic spectrum access or sharing is commonly utilized to address this challenge. With the development of artificial intelligence (AI), there has been an increasing amount of research on dynamic spectrum access based on machine learning models, such as Q-learning, deep reinforcement learning and federated learning. When using federated learning to secure user privacy in dynamic spectrum access, several key challenges such as the lack of personalized model selection and parameter updates, and the difficulty of meeting the needs of different devices with a single global model are still under-investigated. In this paper, the heterogeneity of IoT users is considered and a hierarchical and personalized federated deep reinforcement learning approach is proposed to fulfill dynamic spectrum access. First, IoT users are trained locally by using deep reinforcement learning to obtain optimal rewards and spectrum access rate. Second, considering that users have their personalized characteristics, a client-edge-cloud federated learning framework is proposed to accelerate the model convergence and reduce communication overhead between cloud servers and clients. Accordingly, user parameters are divided into personalized and basic parameters. Basic parameters are individually uploaded for global model training, meanwhile personalized parameters are retained to guarantee the applicability of the model. Lastly, the proposed framework is used for dynamic spectrum access, thereby improving the efficiency of spectrum access and decrease the bandwidth occupied by exploiting the global optimization capabilities for federated learning. Only basic parameters are uploaded to substantially protect user’s privacy. Simulation results show that the proposed framework can improve user communication performances and the accuracy of spectrum access. The average convergency time for IoT users can be reduced by about 40% compared with common federated-learning-oriented dynamic spectrum access.

Multiple Access for Near-Field Communications: SDMA or LDMA?

Spatial division multiple access (SDMA) is essential to improve the spectrum efficiency for multi-user multiple-input multiple-output (MIMO) communications. The classical SDMA for massive MIMO with hybrid precoding heavily relies on the angular orthogonality in the far field to distinguish multiple users at different angles, which fails to fully exploit spatial resources in the distance domain. With the dramatically increasing number of antennas, the extremely large-scale antenna array (ELAA) introduces additional resolution in the distance domain in the near field. In this paper, we propose the concept of location division multiple access (LDMA) to provide a new possibility to enhance spectrum efficiency compared with classical SDMA. The key idea is to exploit extra spatial resources in the distance domain to serve different users at different locations (determined by angles and distances) in the near field. Specifically, the asymptotic orthogonality of near-field beam focusing vectors in the distance domain is proved, which reveals that near-field beam focusing is able to focus signals on specific locations with limited leakage energy at other locations. This special property could be leveraged in hybrid precoding to mitigate inter-user interferences for spectrum efficiency enhancement. Moreover, we provide the spherical-domain codebook design method for LDMA communications with the uniform planar array, which provides the sampling method in the distance domain. Additionally, performance analysis of LDMA is provided to reveal that the asymptotic optimal spectrum efficiency could be achieved with the increasing number of antennas. Finally, simulation results verify the superiority of the proposed LDMA over SDMA in different scenarios.

NOMA-Based Radio Resource Allocation in Vehicular Networks

The increasing prevalence of intelligent transportation systems necessitates more frequent communication between vehicles and the external environment. Due to the large number of user connections and frequent information exchange in this scenario, higher requirements for both delay and reliability are also imposed and they are crucial to improving spectrum efficiency. In an effort to find a solution to this problem, we advocate a proposal where multiple V2V share the spectrum with each V2I, and we introduce NOMA to enhance spectrum efficiency, mitigate resource collision issues among users, and eliminate the delay overhead resulting from retransmission. In this paper, we propose a three-stage scheme. In addition, we compare the effectiveness of two different OFDM strategies in V2X systems. Simulation results demonstrate that using NOMA can effectively improve the system capacity.

A REVIEW OF NEW IMPROVEMENTS IN RESOURCE ALLOCATION PROBLEM OPTIMIZATION IN 5G USING NON-ORTHOGONAL MULTIPLE ACCESS

Because of the arising requirements of emerging networks (Fifth generation and beyond) such as supporting diverse Quality of Services (QoS), low latency, and high spectral efficiency; the previous and traditional generations of communication systems are becoming inappropriate. Furthermore, due to the huge connectivity and ever-growing demands of diverse services and high data rate applications, more effective radio access techniques are required for the purpose of a full-scale implementation of the fifth generation (5G) and beyond wireless systems. Therefore; the researchers are looking for new mechanisms to accomplish these demands, and one of the key techniques been proposed is NOMA due to its capability of spectrum efficiency enhancement. In NOMA-Based systems, the information signals of various users are superimposed at the transmitter side, by utilizing the differences of channel gain to work for different users simultaneously. In this study, recent papers on resource allocation problem based on Power-Doman NOMA (PD-NOMA) in 5G networks were reviewed and the goal (objective function), optimization methods used, and obtained results of each analyzed paper are investigated. In addition, the discussed resource allocation problems were classified into: optimal rate problems and power/energy-efficient problems and the proposed solutions in each of it are analyzed. Finally, this study highlights some of the present and future challenges in this field.

Robust Downlink Precoding for LEO Satellite Systems With Per-Antenna Power Constraints

Downlink precoding has been widely accepted as a crucial technique in low-earth-orbit (LEO) satellite communications due to its advantages in improving the space division multiplexing capability and enhancing spectrum efficiency. Because of the strong spatial directivity in the satellite channels, the downlink precoder can be efficiently designed based on the angle of departure (AoD)-based channel state information (CSI). However, acquiring perfect AoD-based CSI is challenging due to the angle estimation error and attitude jitter of the LEO satellites. In light of this, this paper investigates the precoder design for the LEO satellite downlinks with imperfect AoD-based CSI. For robust design, we treat the angle deviations of each user as random variables, which are supposed to exhibit a particular distribution, then integrate them within a certain range to achieve the robustness of the proposed downlink precoder against the angle mismatch. Instead of using the sum-power constraint on the transmit antennas, we adopt a more realistic per-antenna power constraint (PAPC). Accordingly, we formulate the problem to maximize the ergodic sum rate of the system. By leveraging the corresponding Lagrangian formulation and identifying the optimal precoder structure as the solution to a generalized eigenvalue problem, an iterative algorithm for the robust precoder design is derived. To reduce the computational complexity, a low-complexity version of the proposed algorithm is implemented based on deep learning techniques to meet the high demand for real-time applications in LEO satellite systems. Simulation results verify the effectiveness of the proposed precoding approach.

Performance Analysis and Optimal Cooperative Cluster Size for Randomly Distributed Small Cells Under CAN Protocol

The ease of imposing multicell coordination mechanisms to enhancing system spectrum efficiency (SE) and performance estimate is one of the key advantages of cloud/centralized control area networks (CAN). Enormous number of cooperative cells should theoretically result in a higher SE, but they may also cause considerable delays due to extra channel state information (CSI) feedback and joint processing computing needs at the cloud data centre, resulting in performance deficit. I partition the network into numerous clusters of cooperating tiny cells and create a throughput optimization problem to study the impact of delays on throughput gains. As a function of cluster size, I figure various delay factors and the network's sum-rate, treating cluster size as the fundamental optimization variable. For both linear and planar network installations, I treat both base station and user geometric locations as random variables in my study. On the basis of the homogeneous Poisson point processing (PPP)model, the output SINR (signal-to-interference- plus-noise ratio) and ergodic sum-rate are calculated. The sum-rate optimization problem is formulated and solved in terms of cluster size. The suggested analytical framework may be used to precisely evaluate the performance of practical cloud-based small cell networks via clustered cooperation, according to simulated study.

NOMA-Based Overlay Cognitive Satellite-UAV-Terrestrial Networks with Multiple Primary Users

Satellite communication is becoming more and more significant in present and future communication systems for its unique advantages of strong survivability and seamless coverage, which can offset the defects of terrestrial communication such as being restricted by terrain and little coverage. Moreover, unmanned aerial vehicle (UAV)-assisted communication is considered as a promising development direction due to its flexibility and expansibility in the integrated satellite-terrestrial networks (ISTN). In addition, to overcome the spectrum shortage and low spectrum utilization problems, cognitive radio and nonorthogonal multiple access (NOMA) has been widely utilized to enhance spectrum efficiency, which is considered as the key technologies of the next generation communication. In this regard, our paper investigates the performance of cognitive satellite-UAV-terrestrial networks with NOMA scheme and multiple primary users. Specifically, the exact expressions of outage probability (OP) and ergodic capacity for both primary network and secondary network are derived. To gain further views, the asymptotic expression of OP and diversity orders for the two networks are provided. Finally, through numerical simulations, the correctness of the theoretical derivations is verified and the influences of critical variables on system indexes are also analyzed.

An enhanced cross‐layer solution for IFC applications using HTS systems at Q/V‐band

The Q/V band communication systems open new avenues for next generation of high-throughput satellite (HTS) systems. More specifically, they are attractive due to their high bandwidth for user and gateway links. Having a wide bandwidth at Q/V band makes it permissible to offload the links between the satellite and its earth station hubs (gateway) from the Ka band to the Q/V band. This enables additional bandwidth available for HTS feeder links and user links of specific market verticals, which in turn results in reduction of cost per bit for in-flight connectivity applications to provide WiFi capability on the aircraft. This study provides a deep overview of network architecture at the ground gateway infrastructure with respect to the network management system (NMS) to optimize network traffic utilization. We have looked at a gateway constellation platform which optimizes the actual cost-efficient solution for deploying routing user traffic among the networks. In addition, the study discusses the elements involved in the NMS which interfaces with the gateway platform. The overall NMS platform design and capabilities are discussed as they provision the network service parameters throughout the entire operation. The proposed solution integrates the physical antenna, antenna control unit and the power supply into one bundle on the aircraft. This will result in approximately 10% throughput increase compared to conventional methods that do not deploy N+P gateway architecture. The proposed method has enhanced spectrum efficiency along with the NMS unit that has been discussed in detail. Finally, a wideband and multibeam antenna is introduced as an essential part of the solution followed up by simulation results.