International Journal of Communication Systems最新文献

Software-Defined Wireless Sensor Networking (SDWSN) is termed a rising network structure that has become more important to the Internet of Things (IoT). In this network structure, the control planes effectively manage the sensor plane. Due to these kinds of separation, the management of networks became easier and also their efficacies are enhanced in the self-motivated atmosphere. The common issue presented in the sensor atmosphere is minimal life in the network devices inspired by the maximal level of energy consumption rate. The current study provides a system architecture that intends to increase efficiency in an SDWSN by incorporating optimization techniques. A novel hybrid optimization strategy is created by merging the Election-Based Optimization Algorithm (EBOA) with the Ladybird Beetle Optimization Algorithm (LBOA), and the result is known as Hybrid Election-based Ladybird Beetle Optimization (HELBO). This work examines an energy-efficient resource allocation mechanism in SDWSNs that have substantial computing power and memory. These algorithms optimize the bandwidth along with power allocation in the SDWSN to attain a considerable Signal to Interference plus Noise Ratio (SINR) under the individual constraint of quality of service. Finally, empirical findings show that the recommended HELBO method outperforms other present algorithms.

Efficient cluster formation is a significant issue in wireless sensor networks (WSNs) for optimizing energy consumption, enhancing data reliability, and prolonging network lifetime. This paper proposes a dynamic cluster formation scheme modeled as a noncooperative game, where each sensor node acts as a player making strategic decisions on whether to become a cluster head (CH) or remain a member node (MN). In this role selection decision-making phase, nodes evaluate their potential utility function, which incorporates energy consumption, data reliability, and data forwarding efficiency. Nodes dynamically switch roles based on their utility to achieve an optimal network configuration. Simulations conducted using the NS-3 network simulator demonstrate that the proposed game theory-based clustering scheme significantly improves network performance. We analyze key measures like energy consumption, packet delivery ratio (PDR), and data forwarding, and the results shows that the proposed scheme reduces energy consumption by 25%, improves PDR by 15%, and extends the network lifetime by 20%, compared to traditional methods. The dynamic role-switching mechanism prolongs network lifetime by preventing the rapid depletion of node energy, ensuring stable and efficient network operations. It significantly enhances network performance, stability, and longevity.

Recent improvements in wireless sensor networks (WSN) technology enabled more research on energy efficiency. Limited battery and difficulties in renewing the batteries in a critical application require the efficient use of energy for WSN. Besides energy efficiency, providing fast-response systems facilitate real-time applications. Combining machine learning (ML) with the clustering methods that significantly contribute to the energy efficiency of WSN seems to improve the efficiency. In this paper, the low-energy adaptive clustering hierarchy (LEACH) clustering method for WSN is implemented through a supervised learning method, artificial neural networks (ANN), for cluster head (CH) selection. The power of ANN as a superior classifier is thought to contribute much to the field. The details of designing an ANN are given in detail for the first time in WSN field. In addition, a dataset is prepared via MATLAB to be used for classification or other analysis related to a clustered network. The proposed model provides more than 85% accuracy for CH selection, and it is 83.28% faster than LEACH to determine the CHs. This method produces more efficient solutions in large networks in terms of the time for CH selection. The feasibility of ANN is also shown for the issues related to WSN such as CH selection.

This work focuses on analyzing the outage probability (OP) in a downlink multi-user multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) system, specifically employing the maximum-ratio transmission/receive antenna selection structure. In the investigated system, the user close to the base station (BS) acts as a full-duplex (FD) (or half-duplex [HD]) energy harvesting (EH) relay adopting decode-and-forward (DF) protocol for transmission to the far user. With and without a direct link, we derive the exact and asymptotic OP expressions in closed form for Nakagami-$��m�$ fading channels. The analytical results are validated through simulations, which show that the presence of a direct link in the system significantly enhances the performance of the far user. This improvement occurs because the direct link mitigates the performance degradation caused by the self-interference of the near-user. Additionally, the far user exhibits superior performance when the near user operates in the FD mode to the HD mode. Moreover, as the number of transmit antennas of the BS increases, the signal-to-noise ratio gains are more pronounced in FD mode than in HD mode. For both HD and FD modes, the performance is better when the number of antennas at the BS is higher than that of the users.

For the next-generation communication systems, to improve spectral efficiency and increase the data rate, new multiple access techniques have been investigated. Orthogonal multiple access techniques are widely used in traditional communication systems while nonorthogonal multiple access (NOMA), proposed in 5G, has been a promising technology for satisfying the demand for future wireless communication networks. Sparse code multiple access (SCMA) is a code-domain NOMA method that provides diversity gain with signal constellation coding. However, to increase the performance of SCMA, there are only limited works provided in the literature in terms of codebook design and receiver design. In this paper, a new multiple-access model is proposed by applying various diversity techniques for downlink SCMA. The performance of the proposed model is evaluated with both computer simulations and theoretical analysis. Results show that the proposed model provides a 1.6 dB gain in terms of the bit error rate (BER) under the Rayleigh fading channel.

This article suggests using rate-splitting multiple access (RSMA) and transmit antenna selection (TAS) within a full-duplex relay (FDR) system that operates at high millimeter-wave (MW) frequencies. We perform a mathematical analysis to derive closed-form formulas for the outage probabilities (OPs) and throughputs for two users in the proposed RSMA-MW system with TAS, taking into account the effects of residual self-interference (RSI) at the FDR. Additionally, we consider practical system and channel parameters in our analysis. The numerical results indicate that the OPs in the RSMA-MW system are significantly lower than those in a conventional nonorthogonal multiple access (NOMA)-MW system. Importantly, high MW frequencies have a considerable impact on the OPs and throughputs of the RSMA-MW system. In this context, using multiple antennas at the base station with TAS can greatly enhance the performance of the RSMA-MW system. Furthermore, we thoroughly assess the effects of RSI caused by FDR, the distances between transmitters and receivers, transmit power, common and private rates, power allocation coefficients, and frequencies. Based on the observed behaviors of the RSMA-MW system, several strategies are proposed to reduce OPs and increase throughputs. Finally, Monte Carlo simulations are performed to validate the derived formulas.

Slotted waveguide antenna (SWA) arrays are highly valued for their compact structure, high power handling capability, directivity, and efficiency, making them ideal for advanced radar and communication systems. In broad-wall SWAs, side lobe levels (SLLs) are influenced by slot displacement from the waveguide centerline and slot inclination angles, impacting array performance. This paper introduces a novel design approach for meandered SWA arrays optimized as frequency scanning antennas (FSAs), achieving enhanced gain, scanning coverage, and SLL suppression. The design incorporates an innovative meandering feeding mechanism, significantly improving frequency scanning performance over the Ku band (15.1–16.5 GHz). The array includes 16 radiating slot elements paired with an E-plane horn antenna in elevation plane, narrowing beam width, and optimizing radiation control. Using the discrete Taylor distribution to meet SLL requirements, this approach effectively balances low SLL with high gain. Simulations conducted in CST Microwave Studio confirm the array's performance, showing 70° scanning coverage, over 21 dBi gain, and an SLL reduced to −21 dB. Both simulated and measured results designate that the proposed meandered SWA array architecture is appropriate for next-generation frequency scanning systems, offering a compact, power-efficient, and high-precision solution for demanding applications.

The demand for maintenance-free, battery-less IoT devices, driven by sustainable Industry 4.0 practices, necessitates efficient RF energy harvesting solutions. Addressing the limitations of single-frequency harvesting, a novel dual-band, dual-polarized rectenna operating at 3.6 GHz and 5.8 GHz is proposed. The antenna resonates at 3.6 GHz with linearly polarized (LP) characteristics and 5.8 GHz with circularly polarized (CP) characteristics with impedance bandwidth (IBW) of 2.2% and 5.63%, respectively, achieving a gain of 4.85 dBi and 6.75 dBic, respectively. A rectenna consists of an antenna at the front end and an RF-DC rectifier at the backend. This approach maximizes power conversion efficiency by minimizing impedance mismatches and reflection losses and mitigates interference between the dual bands within the rectifier circuitry. The rectifier achieves peak efficiencies of 65.5% at 3.6 GHz with a 3 dBm input and 44% at 5.8 GHz with a −1 dBm input, both with 2KΩ load. Efficiency exceeds 30% at 3.6 GHz for an input range from −10 to 6.5 dBm and exceeds 20% at 5.8 GHz for an input range from −13 to 3 dBm. These design considerations enable reliable harvesting of ambient RF energy across a range of −20 to 20 dBm, enabling continuous IoT device operation. Our measurements confirm the design's effectiveness in converting low-power RF signals into usable energy, supporting sustainable and autonomous IoT deployments at 3.6 and 5.8 GHz.

This article presents the design and analysis of a dual-port Al₂O₃-based filtering antenna, commonly called a Filtenna. The feeding structure of the proposed Filtenna is engineered to generate two radiation nulls within the cylindrical ceramic. The initial single-port Filtenna design is subsequently modified into a dual-port configuration. A metasurface is introduced above the dual-port radiator to enhance performance, enabling the conversion of linear waves into circularly polarized waves. Both experimental and simulation results show a strong agreement, confirming that the developed radiator operates efficiently within the 2.43–2.65 GHz frequency range with a mutual coupling level less than −25 dB. The final design, incorporating the metasurface, achieves a realized gain of 7.5 dBi, while also exhibiting an axial ratio within the frequency range 2.45–2.55 GHz. Outside of the targeted frequency range, the antenna's gain drops significantly, falling below −15 dB on the lower side and −17 dB on the upper side of the band. This makes the aerial design appropriate for WiMAX application.