Channel Allocation Research Articles

Performance of Ad Hoc Wireless Networks under Coexistence Scenario: A Case Study

Owing to the possibility of interference, signal contention, and performance degradation, coexisting IEEE 802.11 (WiFi) and IEEE 802.15.4 (Zigbee, 6LoWPAN) technologies in the 2.4 GHz ISM band presents considerable issues. More packet loss, delay, and decreased throughput for low-power networks can arise from IEEE 802.11's larger bandwidth, higher transmission power, and higher data rates overpowering IEEE 802.15.4's lower power and narrower bandwidth transmissions. This work explores the main facets of their coexistence, emphasising the effects of interference on performance measures including throughput, latency, and energy efficiency and frequency overlap. Dynamic frequency selection and channel allocation algorithms are only a few of the interference reduction techniques that are examined in the analysis. This research offers valuable insights into the more harmonious coexistence of these two wireless technologies in shared surroundings, enabling dependable operation for low-power IoT networks and high-speed Wi-Fi applications. These insights are gained through simulation and experimental validation.

Proximal Policy Optimization for Efficient Channel Allocation with Quality of Service (QoS) in Cognitive Radio Networks

A multi-variable relationship exists in Cognitive Radio Networks (CRNs) where factors such as Energy efficiency, Throughput, Delay and Signal Noise Ratio (SINR) are related. The SINR shows the quality of the signal and is defined as the total power of a specific signal over the total power of an inter signal plus noise. This work proposes an effective energy and delay-efficient channel allocation strategy for CRNs (Cognitive Radio Networks) using Q-Learning and actor-criticism algorithms that maximize rewards. We also propose a Proximal Policy Optimization (PPO) algorithm that uses clipping of surrogate objectives to prevent large policy changes and ensure that the other parameters remain stable over time. We study the tradeoff between rewards, energy efficiency and other parameters and compare the algorithms with respect to the same. Results show that the proposed PPO method, while using optimally increased energy consumption, significantly reduces the delay, improves the thought and reduces the packet loss ratio for efficient channel allocation. This is positive with our findings shown in the results section and by comparing the proposed method with other algorithms to identify improved throughput and channel utilization. As the simulation results indicate that the PPO algorithm has very high throughput and significantly minimizes the delay and packet loss, it is suitable for application in all sorts of services such as video, imaging or M2M. The results are also compared with two of the existing channel allocation schemes and they confirm that the proposed algorithm performs better in terms of throughput discussed in one scheme and channel efficiency in the other.

Overlapping Channel Allocation Method for Wireless Communication Network Based on Discrete Particle Swarm Optimization Algorithm

To address the challenges of interference between network channels and low-frequency resource utilization efficiency, and to improve the overall performance and resource allocation fairness of the network, this study proposes a wireless communication network overlapping channel allocation strategy based on a discrete particle swarm optimization algorithm. First, the overlapping channel allocation problem in wireless networks is abstracted as a linear programming model with specific constraints, with the core objective of minimizing weighted interference between links. To solve this model, a discrete particle swarm optimization algorithm is introduced. In this algorithm, each particle’s position corresponds to a feasible overlapping channel allocation scheme, which is the potential solution to the optimization problem; The velocity of particles reflects the process of transitioning from the current channel configuration to the target channel configuration. Subsequently, based on the characteristics of discrete variables, corresponding operation rules and particle motion equations were defined. In addition, when particle swarm aggregation occurs, an optimal asymptotic mutation operator is introduced, which not only enhances the local search ability of the algorithm but also strengthens its global search ability. During algorithm execution, this mutation operator is applied based on a certain probability of mutation. The experimental results show that when using this strategy for overlapping channel allocation in wireless communication networks, even with an increase in the number of traffic flows, the average network throughput can remain stable, demonstrating a high network carrying capacity. Meanwhile, wireless networks exhibit a higher fairness factor, indicating a more balanced allocation of network resources among different users. In the process of optimal channel allocation, the packet loss rate reaches the lowest level, ensuring the reliability of data transmission.

Hybrid Optimization Approach for Handoff Strategy–Based Spectrum Allocation in Cognitive Radio Network

ABSTRACTDevice‐to‐device (D2D) transmission is essential for enhancing the functionality of fifth‐generation (5G) networks. This paper addresses the need for effective power allocation and resource management for D2D users, who operate as secondary users (SUs) alongside primary users (PUs). Ensuring that D2D operations do not disrupt PU interactions is crucial. Traditional bandwidth distribution approaches rely on complete channel state information (CSI) from the base station (BS), leading to uncertainty fin resource allocation. To get rid of these challenges, this paper proposes a novel handoff strategy based on spectrum allocation in cognitive radio networks (CRNs). The data priority–based channel allocation is carried out using proposed hybrid optimization cuttlefish updated dwarf mongoose optimization (CUDMO). It is the combination of both cuttle fish algorithm (CFA) and dwarf mongoose optimization (DMO) algorithms. This optimization considers constraints such as coverage, signal strength, distance, bandwidth and improved strategy like signal‐to‐noise ratio–channel usability (SNR‐CU). Furthermore, an improved fuzzy logic–based proactive handoff mechanism, the fuzzy‐induced modified rules for channel selection (FIMRCS), is introduced. This scheme optimally selects channels, minimizing service interruption during handoff. In the dynamic multichannel selection (DMCS) scheme, parameters like channel rank, channel transmission, and channel usability are considered as the constraints while selecting the channel. They are evaluated against a set of 27 defined rules, ensuring efficient data transmission through the chosen channels. Finally, the performance of proposed CUDMO algorithm is contrasted over state‐of‐the‐art models in terms of various constraints. The CUDMO for Device 450 generated a bandwidth of 1.175 bps, surpassing the lower bandwidth achieved by conventional strategies.

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).

Enhancing energy efficiency in MC-NOMA systems through optimized channel and power allocation

Non-orthogonal multiple access (NOMA) has quickly become popular due to its higher spectral efficiency (SE) and is crucial in extending future networks’ capacity. This paper presents an innovative approach to enhancing energy efficiency (EE) in downlink (DL) multi-carrier (MC) NOMA systems, in which a single base station (BS) supports a group of users across multiple sub-channels. A novel least user sum gain-based user assignment (LUSGUA) algorithm is proposed in this paper, which prioritizes users with the lowest channel gains for optimal sub-carrier allocation. Additionally, a novel power allocation (PA) scheme is developed across sub-carriers to further improve energy efficiency (EE), throughput, and fairness. throughput and fairness. Since the PA optimization problem (OP) is constrained and non-convex, to tackle this problem, a penalty approach (PTA) is proposed, which is then addressed by particle swarm optimization (PSO) with sequential optimization of the inter/intra-level PA. Power is allocated across sub-carriers at the inter-level using PSO, while at the intra-level, power is distributed among users via the Bisection Method (BM). Simulation results demonstrate that the proposed algorithm achieves significant enhancements in EE, with improvements ranging from 13.04% to 48.67% compared to existing resource allocation (RA) schemes techniques, while also improving system throughput, fairness, and outage percentage. These results highlight substantial advancements over traditional methods.

Energy-efficient joint power control and channel allocation for D2D communication underlaying cellular network

The Device-to-device (D2D) communication technology can significantly improve the efficiency of wireless spectrum resources and expand the communication system’s capacity. In order to enhance the energy efficiency (EE) of D2D communication, a two-stage cross-layer optimization approach in which joint power control and channel allocation is proposed. In this work, the optimization objective is to maximize the total EE of D2D users (DUEs) to enable green D2D communications in cellular networks. The proposed resource allocation problems are NP-hard, which is typically difficult to solve directly. We decouple the EE optimization problem of joint power control and channel allocation into two sub-problems: Power Control and Channel Allocation. First, the Improved Grey Wolf Optimization (IGWO) algorithm is proposed to obtain the optimal transmission power of DUEs and Cellular users (CUEs) in the first phase. Then, the Hungarian Algorithm (HA) is utilized to accomplish optimal channel match for DUEs and CUEs in the second phase. Compared to other alternative algorithms, the simulation results show that the suggested scheme effectively improves the system’s EE.

Block Chain Based Automated Multiresolution Channel Allocation of IoT

This research includes the growing popularity of the Internet of Things (IoT) in recent years. Internet of Things (IoT) security and privacy remain a major challenge, mainly due to IoT networks' massive scale and distributed nature. Blockchain-based approaches provide decentralized security and privacy, yet they involve significant energy, delay, and computational overhead unsuitable for most resource-constrained IoT devices. The Internet of Things is a real-time application for high data rates, and blockchain technology is real-time data storage technology. It is an effective way to transfer data from the communication node to the system in the IoT network using blockchain technology, the most recent each IoT node discovers and adjusts for better communication. It is necessary to have the confidence to integrate/maximize the channel's output of the wireless sensor networks' features. The change can be implemented. However, from time to time the channel for the information does not learn to transmit to the protocol. Changing the channel characteristics in these networks like IoTs for achieving and increasing the data transmission and network throughput of wireless sensor networks. Therefore, this research recommends a completely computerized nature-understanding and by-origin procedure designed which may completely automate data transfer communication between multiusers by completely applying channel time spectral and time attributes of wireless network and store the data through blockchain technology then fetch data from any node when you want. For this purpose, designed Protocol channel swapping, data storage from time to time through blockchain technology, and following the shortest path for communication. The planned designed protocol of this WSN is rare as it is understanding and proposed the issue to maximize the WSN intensity established upon the WSN network metrics. This network also permits every node inside that IoT to regularly identify the adjoining network station characteristics so that they will exchange networks which means swapping adjoining channels to realize maximum data transfer.

Foreign Aid, Identities and Interests: Qatar and the UAE in Sudan

ABSTRACT The entanglement of domestic and geopolitical dynamics with the evolution of the aid strategies of the Gulf States is particularly evident in the Horn of Africa (HOA), where the regional competition for exercising influence has affected the modalities of humanitarian and development interventions. A comparison of the foreign aid given by Qatar and the United Arab Emirates (UAE) to Sudan in the context of its political and humanitarian crisis during three different periods (2014-17; 2017-19; and post-2019) allows a better understanding of these dynamics. By adopting a neoclassical realist and constructivist approach, it highlights similarities and differences in the domestic factors at play, including the ideological and security dimension, in both donor and recipient countries, as well as their overlapping with the regional and international spheres, which determine aid strategies in terms of the geographical, channel and sectoral allocation of aid resources.

Retraction Note: Optical wireless communication based mobile edge computing integrated channel allocation using scheduling with machine learning protocols in advanced 5G networks

Retraction Note: Optical wireless communication based mobile edge computing integrated channel allocation using scheduling with machine learning protocols in advanced 5G networks

Green Computation Offloading With DRL in Multi‐Access Edge Computing

ABSTRACTIn multi‐access edge computing (MEC), computational task offloading of mobile terminals (MT) is expected to provide the green applications with the restriction of energy consumption and service latency. Nevertheless, the diverse statuses of a range of edge servers and mobile terminals, along with the fluctuating offloading routes, present a challenge in the realm of computational task offloading. In order to bolster green applications, we present an innovative computational task offloading model as our initial approach. In particular, the nascent model is constrained by energy consumption and service latency considerations: (1) Smart mobile terminals with computational capabilities could serve as carriers; (2) The diverse computational and communication capacities of edge servers have the potential to enhance the offloading process; (3) The unpredictable routing paths of mobile terminals and edge servers could result in varied information transmissions. We then propose an improved deep reinforcement learning (DRL) algorithm named PS‐DDPG with the prioritized experience replay (PER) and the stochastic weight averaging (SWA) mechanisms based on deep deterministic policy gradients (DDPG) to seek an optimal offloading mode, saving energy consumption. Next, we introduce an enhanced deep reinforcement learning (DRL) algorithm named PS‐DDPG, incorporating the prioritized experience replay (PER) and stochastic weight averaging (SWA) techniques rooted in deep deterministic policy gradients (DDPG). This approach aims to identify an efficient offloading strategy, thereby reducing energy consumption. Fortunately, algorithm is proposed for each MT, which is responsible for making decisions regarding task partition, channel allocation, and power transmission control. Our developed approach achieves the ultimate estimation of observed values and enhances memory via write operations. The replay buffer holds data from previous time slots to upgrade both the actor and critic networks, followed by a buffer reset. Comprehensive experiments validate the superior performance, including stability and convergence, of our algorithm when juxtaposed with prior studies.

Concurrent Direct Inter-ONU and Upstream Communications in IMDD PONs Incorporating P2MP Flexible Optical Transceivers and Advanced Passive Remote Nodes

Driven by a large number of emerging diversified services, in the 5G and beyond era, concurrent direct inter-ONU and upstream communications inside a PON-based mobile access network are highly desirable to provide dynamic, ultra-dense, and fast ONU-to-ONU (without involving an OLT) and ONU-to-OLT connections. To cost-effectively deliver highly dynamic and low latency direct inter-ONU communications, this paper proposes and experimentally demonstrates novel concurrent direct inter-ONU and upstream communications in an upstream 27 km, >62.47 Gbit/s IMDD PON. For supporting inter-ONU communications between a large number of ONUs, an advanced passive remote node is also proposed. Based on different passive optical components, this remote node can be implemented using two approaches, which can, respectively, reduce the inter-ONU signal power losses by >12.2 dB and >16.6 dB (for 128 ONUs) in comparison with existing inter-ONU communication techniques’ remote nodes. In each ONU and OLT, a single pair of cascaded IFFT/FFT-based point-to-multipoint (P2MP) flexible optical transceivers are employed to simultaneously and dynamically establish multiple ONU-to-ONU and ONU-to-OLT communications according to actual users’ requirements. Experimental results show that the proposed network has excellent robustness against various transmission system impairments, including chromatic dispersion, the Rayleigh and Brillouin backscattering effects, and the channel interference effects. For each ONU, dynamic channel allocation can be made without compromising its overall performance.

Retraction Note: Machine learning in optical networks enhancement based on channel allocation and spectrum analysis for 5G application

Retraction Note: Machine learning in optical networks enhancement based on channel allocation and spectrum analysis for 5G application

Retraction Note: Channel allocation and spectrum use in optical networks using machine learning a 5G application analysis

Retraction Note: Channel allocation and spectrum use in optical networks using machine learning a 5G application analysis

A Weighted AIMD Congestion Control Algorithm for Device-to-Device Communication in 5G Network

ABSTRACT Device-to-Device (D2D) communication offers promising benefits in 5G networks in terms of improved throughput and reduced latency. However, efficient congestion control remains a critical challenge to ensure optimal performance and resource utilization. Existing congestion control mechanisms often struggle with adapting to dynamic network conditions, leading to inefficiencies, such as underutilization of network resources or excessive packet loss. This paper addresses these challenges by proposing a novel Weighted AIMD congestion control algorithm tailored specifically for D2D communication in 5G networks (WACC-D2DC-5GN). Here, the Weighted AIMD Congestion Control Algorithm is proposed for estimating available bandwidth and Hunger Games Search Optimization to optimize the weight parameter of WACC. The proposed algorithm integrates weighted factors that dynamically adjust congestion window sizes based on real-time network feedback. Simulations are run on the NS3-mmWave module, NS3 LTE-A module. The simulation outcomes show that the proposed WACC-D2DC-5GN method attains 27.26%, 20.41%, and 23.26% higher SNR and 16.20%, 26.97%, and 30.34% higher Throughput when analysed with existing techniques like NexGen D-TCP: next generation dynamic TCP congestion control approach (NGD-TCP-CCA), user-centric channel allocation system for 5G higher-speed users by using ML to decrease handover rate (UCC-HSU-MLA), and efficient with reliable hybrid deep learning-allowed method for congestion control in 5G/6G networks (HDL-EMC-CN) respectively.

Closed-form formula for unequal channel allocation to reduce FWM crosstalk in DWDM long-haul networks

Dense wavelength division multiplexing (DWDM) is considered as a mainstream solution for 5G optical transmission networks to confront increasing bandwidth demand. In such a dense multiwavelength system, the four wave mixing (FWM) non-linear effect limits the achievable transmission distance and channel capacity. For this reason, and to the best of our knowledge, a new unequal channel allocation (UCA) has been introduced in this paper based on new closed-form equations to reduce the FWM crosstalk in the DWDM optical long-haul network. The proposed channel allocation has been investigated based on several design parameters that affect FWM nonlinear crosstalk, such as total transmission distance, channel frequency spacing, the number of WDM channels, and the input power per channel. The suggested technique has further been compared with a previous model proposed by other authors to prove the effectiveness of this work. The simulation results have been examined and validated by analysing the optical spectrum analyser and the power of the FWM spectral products. Results demonstrate the reliability and the efficiency of the developed method.

Distributed Safe Multi-Agent Reinforcement Learning: Joint Design of THz-Enabled UAV Trajectory and Channel Allocation

Distributed Safe Multi-Agent Reinforcement Learning: Joint Design of THz-Enabled UAV Trajectory and Channel Allocation

Energy evaluation for a dynamic and reconfigurable TWDM‐PON on a novel network traffic adaptable pattern

SummaryEven with existing methodologies, dynamic resource allocation, service inconsistency, operational costs, and excess energy/power consumption in optical access networks remain the key challenges for the next‐generation passive optical network (NG‐PON) design and development. This paper introduces a novel method for analyzing energy/power consumption in time and wavelength division multiplexed (TWDM) passive optical networks (PONs) with a ring‐based topology. We propose subscriber‐based channel allocation models (CAMs) for on‐demand services, allocate bandwidth dynamically, and evaluate their downstream power consumption. To determine the necessary optical devices for the various CAMs, we investigate subscriber take‐up rates by optimizing the utilization of optical components based on active subscribers. Implementing a novel Poisson distribution‐based network traffic model addresses energy‐related concerns and explores optimization techniques to minimize power and improve energy efficiency. With a take‐up rate of 64 active subscribers, simulation results show significant power consumption reductions ranging from 62.2 to 67.7%, achieved by the proposed subscriber‐based architecture models with varying subscriber counts, when compared with the conventional model. The model with the most subscribers (2048 subscribers) saves the most energy by reducing power consumption to 292 W from the conventional model's (2048 subscribers) 440 W. Furthermore, the model with the most subscribers saves 33.4% of energy, outperforming other subscriber‐based architecture models without affecting QoS even during peak hours of the day. These numerical results show that TWDM‐PONs with the most subscribers exhibit improved energy efficiency than the conventional and other proposed subscriber‐based architecture models.

Evaluation of Handoff Drop Call Rate in GSM Network in Delta State with Dynamic Cut off Channel Allocation Scheme

This study aims at evaluating Handoff drop call rate in GSM Network using dynamic Cut off Channel Allocation Scheme by evaluating different Mobile Network Operators, MNOs, and comparing with Nigerian Communication Commission (NCC)’s Key Performance Indicator (KPI). A hybrid handoff algorithm was developed to reduce drop calls, analyze the performance of the hybridized handoff algorithm; and compare the simulated results of the hybridized algorithm with that of the existing Mobile network operators, MNOs. The current handoff failure and drop call rates of the best mobile communication network in Nigeria was evaluated and was discovered that the existing Hand off failure rate HFR per cell was 5% and the drop call rate DCR was 10%. These were above the NCC maximum target of 2%. These defects account for the unacceptable drop call rates experienced and thus negatively impact on the quality of service QoS. An optimal solution was provided by developing an enhanced and highly sensitive handoff algorithm from two different algorithms - the Entire Channel Allocation, and the Fixed Channel Allocation - while putting the Modified Reserved Channel Scheme with Guard Channel, and the Fuzzy handoff initiation method into consideration. A software known as Netbeans 7.2 was interfaced with Octave 5.1.0 and used to simulate the developed algorithm. The programme trial was made to run repeatedly for 1200s. Our simulation results indicated HFR of 3% as against 5% from that of the existing network, and DCR of 6% as against the 10% from the network. In further comparing the existing DCR with that obtained from our simulation, an average of 40% dropped calls reduction was realized. This implied that the algorithm reduced drop calls thereby enhancing connectivity and mobility management through the minimization of handoff failures.

Channel Allocation in mobile multimedia network using artificial neural networks

Capital asset control systems may enhance network performance, reduce congestion, and optimise resource allocation with the use of artificial intelligence. Using machine learning models like decision trees and neural networks allows for more informed and adaptive admission control options. These models can precisely predict how the network's quality of service (QoS) will be affected by accepting a new call. Exploring AI-enhanced CAC schemes requires a thorough analysis of various machine learning methods and their potential applications to real-time network management. To ensure that AI-based CAC implementations are actually possible in resource-limited mobile situations, it is crucial to consider their computational complexity and resource requirements. Accuracy and computational efficiency are two competing goals, and this study examines both. Finding a happy medium that can meet the stringent requirements of mobile multimedia networks is the driving force behind this research. Also covered in this study is the possibility of mixing AI-powered CAC with cutting-edge tech like 5G networks and cloud computing. Collectively, these technology and artificial intelligence (AI) have to open up new opportunities for situationally-conscious, dynamic admission manipulate. As a result, mobile multimedia networks might be a lot more efficient and adaptable. An exploration of CAC schemes is provided in this article, which draws attention to the potential game-changing impact of AI-based methods for mobile multimedia network optimisation. The increasing demand for multimedia services is creating new challenges, yet these challenges may be amenable to artificial intelligence integration into CAC systems. More adaptive and intelligent network management solutions would be possible as a result.