International Journal of Communication Systems

Asim Ejaz, Muhammad; Wu, Guowei; Iqbal, Tahir

doi: 10.1002/dac.5916pmid: N/A

The rapid evolution of user equipment (UE) and 5G networks drives significant transformations, bringing technology closer to end‐users. Managing resources in densely crowded areas such as airports, train stations, and bus terminals poses challenges due to diverse user demands. Integrating mobile edge computing (MEC) and network function virtualization (NFV) becomes vital when the service provider's (SP) primary goal is maximizing profitability while maintaining service level agreement (SLA). Considering these challenges, our study addresses an online resource allocation problem in an MEC network where computing resources are limited, and the SP aims to boost profit by securely admitting more UE requests at each time slot. Each UE request arrival rate is unknown, and the requirement is specific resources with minimum cost and delay. The optimization problem objective is achieved by allocating resources to requests at the MEC network in appropriate cloudlets, utilizing abandoned instances, reutilizing idle and soft slice instances to shorten delay and reduce costs, and immediately scaling inappropriate instances, thus minimizing the instantiation of new instances. This paper proposes a deep reinforcement learning (DRL) method for request prediction and resource allocation to mitigate unnecessary resource waste. Simulation results demonstrate that the proposed approach effectively accepts network slice requests to maximize profit by leveraging resource availability, reutilizing instantiated resources, and upholding goodwill and SLA. Through extensive simulations, we show that our proposed DRL‐based approach outperforms other state‐of‐the‐art techniques, namely, MaxSR, DQN, and DDPG, by 76%, 33%, and 23%, respectively.

Pratap Singh, Shekhar; Mohan Pradhan, Pyari

doi: 10.1002/dac.5931pmid: N/A

In this paper, a non‐orthogonal multiple access (NOMA)‐based precoded quadrature spatial modulation (PQSM) technique (NOMA‐PQSM) has been proposed for the downlink scenario. In NOMA‐PQSM, two intended receiving antennas are activated at any time instant. One antenna is activated for the in‐phase component of the transmitted signal, and another one is activated for the quadrature phase component, on the basis of data bits. NOMA‐PQSM provides benefits like improved spatial diversity and spectral efficiency in comparison with spatial modulation. This work uses zero forcing (ZF) precoding over downlink flat fading Rayleigh multiple input multiple output (MIMO) channels, to limit the channel's deteriorating effect on transmitted signal, assuming perfect channel state information (CSI) at the transmitter. A low complexity receiver based on the successive interference cancellation is used. An expression for the upper bound of average bit error probability is derived. Moreover, the expressions for the sum mutual information of users and its lower bound are also derived. The proposed scheme is compared with the preprocessing aided spatial modulation (PSM)‐based counterpart. Monte Carlo simulations reveal that the NOMA‐PQSM scheme outperforms its orthogonal counterpart and the PSM scheme.

Rajavel, Vellaichamy; Ghoshal, Dibyendu

doi: 10.1002/dac.5925pmid: N/A

In today's intricate wireless communication environment, ensuring system quality demands the use of a reliable and versatile antenna system. This research article introduces a polarization reconfigurable antenna integrated with a 4 × 4 Artificial Magnetic Conductor (AMC) surface. The AMC unit cell exhibits a triple‐band reflection phase response at 1.8GHz, 4.5GHz, and 5.5GHz, demonstrating double negative metamaterial behavior. The antenna features two distinct C‐shaped metal strips connected to two PIN diodes, enabling dynamic current distribution adjustment. Consequently, the proposed antenna offers three reconfigurable states, facilitating seamless switching between dual circular polarization (left and right‐hand circular polarization) and linear polarization. With a frequency coverage ranging from 1.29 to 2.52GHz and 3.59 to 6.15GHz, the antenna boasts a maximum axial ratio (AR) bandwidth of 31.96%. Additionally, it achieves a maximum peak gain of 5.5 dB and maintains front‐to‐back ratio (FBR) values exceeding 25 dB, while recording a minimum specific absorption rate (SAR) value of 0.1059 W/kg. The integration of the AMC surface ensures enhanced performance of the antenna. Experimental results from constructed prototypes closely align with simulation outcomes, validating the effectiveness of the proposed antenna. Consequently, this antenna holds significant promise for next‐generation wireless applications.

Kumar, G. Rajiv Suresh; Arul Geetha, G.

doi: 10.1002/dac.5922pmid: N/A

This paper proposes a new approach for optimizing traffic management in multiple access networks (MANETs) by utilizing the stream‐enabled routing (SER) algorithm. The SER algorithm is used to determine which routing path is the most time‐ and resource‐efficient. The proposed approach makes use of multipath routing in a manner that is consistent with the SER method. By combining the states of flows, queues, and links, a graph neural network (GNN)‐based model attempts to break the circular dependencies that are described by these functions. The simulation is setup with joint parameters consisting of residual energy, packet delivery rate (PDR), and end‐to‐end delay. The results of the experiments show that the proposed protocol provides a significant improvement in terms of network efficiency when compared to using some baseline protocols designed for MANETs.

Kumar, Arvind; Kumari, Sangeeta

doi: 10.1002/dac.5929pmid: N/A

Information technology enables the process of spectral sensing and spectral efficiency (SE) with the help of different strategies attracted by researchers in cooperative cognitive radio networks (CCRN). Compared with other wireless technologies, spectral sharing in green metric CCRN (GMCCRN) is an effective strategy. Due to the collaboration between the unlicensed and licensed customers, the spectral sharing between the cooperative customers possesses various challenges. Here, the effectiveness of green CCRN is demonstrated through a variety of useful techniques. The proposed work designed a channel using Markov Gaussian wideband distribution (MGWD), and for communication, dynamic optimal relay‐based protocol (DORP) is used. Also, an effective optimization known as adaptive dynamic group‐based optimization algorithm (ADGCO) is used to examine the false alarm detection and finest spectral sensing. Finally, the effectiveness of GMCCRN is validated in terms of outage probability, spectral efficiency, energy efficiency, and throughput. Furthermore, the results revealed that the proposed method in CCRN reduces power consumption at both the secondary user (SU) and primary user (PU) sides. Also, the method maximized the throughput compared with existing schemes and achieved 0.3 as error prospect and 92.6% as accuracy.

Jayasutha, D.; Hemamalini, V.; Sangeetha, S.; Yeruva, Ajay Reddy

doi: 10.1002/dac.5934pmid: N/A

Wireless body sensor network (WBSN) is essential for monitoring patients' health problems and offers a low‐cost option for various healthcare applications. In this manuscript, a Novel Health Monitoring Approach for WBSNs (DIWGAN‐WBSN) is proposed, which uses Dual Interactive Wasserstein Generative Adversarial Network (DIWGAN) optimized with War Strategy Optimization Algorithm (WSOA). After sensing the aforementioned attribute information, it is the responsibility of WBSN nodes to transfer the sensed data to the sink node. The Volcano Eruption Algorithm (VEA) is applied to select the optimum cluster heads in WBSN. The results from VEA are fed to the target node; it consists of DIWGAN to classify the health records and to portray the patient's health status. Generally, DIWGAN does not adopt any optimization methods for measuring the ideal parameters and guaranteeing accurate health monitoring and risk assessment. So the proposed WSOA is considered to enhance the DIWGAN. The proposed method is activated in MATLAB; its efficacy is estimated under performance metrics, like precision, specificity, accuracy, and energy utilization. The proposed approach attains 23.9%, 21.34%, and 51.09% higher accuracy; 21.45%, 13.94%, and 20.6% higher precision; 31.32%, 29.61%, and 11.03% higher specificity; and 20.9%, 19.87%, and 24.6% lower energy utilization for HD classification using the Cleveland database than the existing methods like back propagation neural network‐based risk detection in WBSN for health monitoring, random forest algorithm–based health monitoring in WBSN, and ensemble deep learning and feature fusion for health monitoring using WBSN methods, respectively.

Mejstrik, Thomas; Berisha, Taulant; Woblistin, Sebastian

doi: 10.1002/dac.5930pmid: N/A

Unmanned aerial vehicle operations are quickly gaining ground due to rapid global market penetration. While on one hand, novel technologies that bridge communication networks to aviation industry are yet to be explored, on the other hand, their development requires highly scalable systems to enable beyond visual line‐of‐sight missions. This requirement imposes a big bottleneck in terms of computation complexity. This paper presents a method for fast computation of multiple diffraction of radio waves over knife‐edge obstacles based on the Deygout technique and some offline computation steps, including a ground profile analysis. We prove that this algorithm is equivalent to the original Deygout algorithm for all non‐line‐of‐sight points, show heuristics confirming that it is mostly applicable in the line‐of‐sight case. The computational and memory complexity of our algorithm is approximately O(N), compared to O(N) for the original Deygout algorithm. Finally we discuss how to apply the approach to the Epstein‐Peterson technique and the Giovanelli technique, and how to use it to compute clutter‐loss.

Kumar Baudh, Rishabh; Sahu, Sonal; Singh Parihar, Manoj; Kumar V., Dinesh

doi: 10.1002/dac.5933pmid: N/A

A compact, flexible, low‐profile end‐fire broadband wearable antenna operating in Ku‐band /X‐band is proposed in this manuscript for defense and satellite communications (Satcom) applications. The main objective of this work is cross‐polarization reduction by the defected ground structure (DGS), which offers a wider bandwidth. Due to its flexibility and ability to absolutely conform to the curved‐shaped human body, denim fabric is used as a substrate, whereas copper tape is used as a conductor, which allows for the integration of the antenna into garments and makes it appropriate for a wide range of wearable applications in various bands. The prototype has been developed with a size of 20×20×0.5 mm3 for experimental validation. The measured results from a fabricated prototype are well matched with the simulated ones of the proposed design, which indicate a wide bandwidth of 57.35% (7.76–14 GHz) appropriate for use in applications such as defense operating from 8 to 12 GHz, satellite TV (11.7–12.2 GHz), Ku‐band downlink (10.95–11.7 GHz), Ku‐band uplink (11.7–14.5 GHz), and a high gain of 5.1 dBi. The specific absorption rate (SAR) is much below the permissible limit of 1.6 W/kg, with better radiation characteristics. Thus, the proposed antenna is more compact, and it clearly achieves a smaller footprint, larger impedance bandwidths, and a low SAR with potential prospect for Satcom and defense purposes.

Bakshi, Shalley; Sharma, Surbhi; Khanna, Rajesh

doi: 10.1002/dac.5935pmid: N/A

Relay selection plays a crucial role in enhancing the performance of wireless networks particularly in the context of cognitive radio (CR) systems with energy harvesters. In this paper, we propose a novel approach, namely, CGAPSO Shapley, for the best relay selection while simultaneously optimizing the parameters of signal‐to‐interference‐plus‐noise ratio (SINR), throughput, and outage probability. The CGAPSO Shapley algorithm combines the Shapley value, a cooperative game theory concept, with cellular genetic algorithm particle swarm optimization (CGAPSO) to achieve effective and efficient optimization of relay selection. The CGAPSO framework provides a hybrid structure that integrates cellular genetic algorithm (CGA) and particle swarm optimization (PSO), enabling simultaneous evolution of the population and particles within cells. The incorporation of the Shapley value and the hybrid CGAPSO framework enables effective exploration of the solution space and provides decision‐makers with comprehensive insights for relay selection. By utilizing the Shapley value, we assign weights to the relay nodes based on their contributions to the overall optimization objectives, considering their CR capabilities and energy harvesting capabilities. Some benchmark test functions are used to compare the hybrid algorithm with both the standard CGAPSO, Particle swarm optimization gravitational search algorithm (PSOGSA) and PSO algorithms in evolving best solution. The results show the hybrid algorithm possesses a better capability to escape from local optimums with faster convergence than the standard algorithms. The novel CGAPSO Shapley approach achieves an outage probability of 0.323324, marking a significant improvement of 60% over the outage probability achieved with conventional approach.

Menon U, Vivek; Selvaprabhu, Poongundran

doi: 10.1002/dac.5923pmid: N/A

In the world of emerging wireless networks, interference poses a significant challenge to reliable wireless communication. Additionally, these networks are prone to path loss and blockages, which can be addressed by utilizing the advanced technology of multihop communication with instantaneous relay (IR). However, scenarios involving IR‐assisted networks are considered instances of multihop communications that face potential obstacles caused by interference. As a result, multiple interference management approaches exist to tackle this interference issue, among which aligned interference neutralization (AIN) is a state‐of‐the‐art technology that seamlessly unifies two established interference management strategies: interference alignment (IA) and interference neutralization (IN). Therefore, this paper presents a novel tristaged AIN scheme to mitigate interference in a multicellular multiple‐input multiple‐output (MIMO) interference multiple access channel (IMAC). In the proposed scheme, the initial stage‐1 involves transmitting message signals from individual transmitters or users to the IR and the receiving base stations (BSs). In stage‐2, the IR neutralizes half of the interference signals by performing IN. Finally, in stage‐3, IA is carried out at the receiver BS terminals, aligning the remaining interference signals equally within the available dimensions. Based on this proposed approach, we determined that for an IR‐aided multicellular MIMO IMAC, the achievable degree of freedom (DoF) is 2N. The proposed approach's robustness and effectiveness have been analyzed through extensive simulations, and these simulation results indicated that the proposed approach outperforms other benchmark interference management techniques in terms of DoF and sum rate, thereby improving user performance.