International Journal of Communication Systems最新文献

In this research work, the design and analysis of a compact microstrip antenna having a monopole structure with 64.86% of wide bandwidth is presented. The main radiator of the structure is a fractal-based geometry with the shape of a half-Koch snowflake, and the ground plane is partial (defected ground). The design methodology of the antenna is presented using analytical modeling, and the structure prototype is fabricated to verify the performance. Measurement outcomes confirm the −10 dB bandwidth in range of 4–7.84 GHz, thus covering approximately the C-band with omnidirectional radiation patterns. The proposed antenna shows a peak realized gain of 3.4 dBi at 7.2 GHz and has an overall dimension of 0.33λ × 0.49λ × 0.03λ, at 5.8 GHz. The structure is found compact compared to some recently reported designs and the read range is found to be 1.98 cm at 5.8 GHz. The read range and validation of the propagation test indicate the suitability of the compact and wideband planar antenna in various short-range reader applications.

Multiple intelligent reflecting surfaces (IRS) in a wireless system are considered to enhance performance, efficiency, and flexibility in wireless networks. In this paper, we analyze outage probability (OP) for multiple IRS panels-assisted wireless systems over Nakagami-$�m$ fading channels. We focus on selecting the best IRS panel to maintain the quality of service and enhance the user experience. We derive two closed-form OP expressions using the central limit theorem and Laguerre series expansion. Additionally, we develop a novel asymptotic OP expression and obtain a novel diversity order. The diversity order of the considered system model depends on the minimum fading parameter ($�m$) between the transmitter-IRS panel and IRS panel-receiver links and the number of IRS panels. We thoroughly investigate the impact of system parameters and validate our analytical results with simulations. Our findings emphasize that diversity order depends on the minimum fading parameter ($�m$) between the transmitter-IRS panel and IRS panel-receiver links, the number of IRS elements in each panel, and the number of IRS panels.

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.

This work presents a compact two-element multi-input-multi-output (MIMO) antenna for 5G-enabled IoT devices. The antenna operates over a wide frequency range of 24.6 to 31.4 GHz (28-GHz band) and 57.6 to 60.2 GHz (60-GHz band). Each MIMO element consists of an inverted L-shaped slotted radiator with a partial ground plane. The antenna offers a peak gain of 5.45 and 5.56 dBi across two operating bands. The minimum isolation between the two ports is −26.5 dB, reaching a maximum value of over −45 dB. The investigation of MIMO metrics like “envelope correlation coefficient (ECC),” “diversity gain (DG),” “mean effective gain (MEG),” “channel capacity loss (CCL),” and “total active reflection coefficient (TARC)” also show favorable characteristics. The antenna is fabricated on a 10 × 22 × 0.503 mm³ Rogers 5880 substrate. The experimental results are in close agreement with that of the simulation results. The distinguishing features of the proposed antenna such as its compact design, simple geometrical configuration, wide operating bandwidth, low ECC, and high isolation make it a strong candidate for 5G-enabled IoT devices.

Wireless body area network (WBAN) is a potential low-cost technology for privacy-sensitive telemedicine and e-health monitoring and services. However, it faces limited protocol and physical resource support challenges, which can result in packet transfer difficulties. In particular, WBAN requires an emergency-aware technology that ensures a promising quality of service (QoS). One significant issue affecting QoS and energy efficiency in WBAN is congestion. Effective congestion control techniques are essential for achieving proper load balancing. To address these challenges, we propose a congestion severity aware rate control (CSRR) algorithm that enhances packet transmission rate by reducing packet losses and retransmissions. The CSRR algorithm incorporates a fuzzy controller to predict congestion rates based on runtime metrics. To regulate congestion window growth in different algorithm phases, we introduce sequences such as the Fibonacci retracement sequence, knight's move sequence, and the binary logarithm of the $�N^{\mathrm{th}}$ primorial sequence to regulate congestion window growth in the different phases of the proposed algorithm. We mathematically analyze the proposed CSRR algorithm using a Markov model. The simulation results demonstrate the superiority of our algorithm compared to existing approaches. Specifically, our algorithm achieves significant optimizations in terms of throughput (52.92%), packet loss (38.11%), delay (37.23%), and remaining energy (36.86%) when compared to existing algorithms.

Ensuring data integrity in wireless sensor networks (WSNs) is crucial for accurate monitoring, yet missing data due to sensor faults present a significant challenge. This research introduces an innovative approach that integrates advanced data recovery techniques with leading-edge methods to address this issue. The system begins by identifying and isolating fault nodes using a specialized algorithm that analyzes network behavior. By applying fuzzy density-based spatial clustering of applications with noise (FDBSCAN), potential fault nodes are precisely located based on deviations from expected patterns. Subsequently, an intelligent missing data recovery mechanism powered by bidirectional long short-term memory (Bi-LSTM) networks takes action. The Bi-LSTM model is trained on existing sensor data to capture intricate patterns and dependencies, enabling accurate prediction and reconstruction of missing values caused by identified faults. The synergy between Bi-LSTM for missing data recovery and FDBSCAN for fault node detection comprehensively addresses the missing data problem in WSNs. In missing data recovery, it demonstrates low mean absolute deviation (MAD) ranging from 0.021 to 0.13 and mean squared deviation (MSD) ranging from 0.0025 to 0.05 across various missing data ratios. Data reliability remains consistently high at 96% to 98%, even with up to 80% missing data. For fault node detection, the approach achieves precision of 95.7%, recall of 96.3%, F1-score of 96.1%, and accuracy of 97.4%, outperforming existing techniques. The computational cost during training is noted at 5.79 h, presenting a limitation compared to other methods. This research highlights the importance of integrating fault node detection into missing data recovery mechanisms, presenting an innovative solution for the advancement of WSNs.

In recent years, the use of wireless sensor devices in several applications, for example, monitoring in dangerous geographical spaces and the Internet of Things, has dramatically increased. Though sensor nodes (SNs) have limited power, battery replacement is not feasible in most cases. Therefore, energy saving in wireless sensor networks (WSN) is the major concern in the design of effective transmission protocol. Clustering might lower energy usage and increase network lifetime. Routing protocol for WSN represents an engineering area that has gained considerable interest among researchers due to its rapid evolution and development. Among them, the clustering routing protocol corresponds to the most effective technique to manage the energy consumption of each SN. In this manuscript, we focus on the design of a new metaheuristic optimization-based energy-aware clustering with routing protocol for lifetime maximization (MOEACR-LM) method in WSN. The purpose of the MOEACR-LM method is to improve network efficiency via proper selection of cluster heads (CHs) and effective data transmission. Initially, a hunter–prey optimization (HPO) method-based clustering technique is used for cluster construction and the CH selection process. Next, the clouded leopard optimization (CLO) model is used for the route selection process in WSN. The HPO and CLO models derive a fitness function involving multiple parameters for clustering and routing processes. A comprehensive experimental analysis is carried out to demonstrate the enhanced performance of the MOEACR-LM technique. The overall comparison study pointed out the improved energy efficiency results of the MOEACR-LM technique over other existing approaches.

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.