Dual Band 1x4 Linear Metamaterial Bowtie Antenna Array for Autonomous Vehicle in Public Safety Band Communications

This research presents the design, simulation, and fabrication of a dual-band 1x4 linear metamaterial bowtie antenna array for applications in 5G wireless systems and public safety band communications. The single-element antenna is a compact dual-band structure with a complementary split-ring resonator (TCSRR) element, and it exhibits resonant frequencies of 3.5 GHz and 4.9 GHz, covering the sub-6GHz 5G spectrum and the public safety band. The antenna aray design is optimized for impedance matching and radiation efficiency. The 1x4 linear antenna array is designed with a quarter-wave transformer feed network and carefully controlled mutual coupling to enhance gain and directionality. Simulated and measured results confirm the performance of the array, with a reflection coefficient within -10 dB from 3.2 GHz to 3.85 GHz and from 4.55 GHz to 5.95 GHz and 3.45 GHz to 3.75 GHz and 4.45 GHz to 5.7 GHz, respectively. The array exhibits a peak realized gain of up to 8.7 dBi and efficient radiation patterns. This work offers promising insights into the development of antenna systems for advanced wireless communication applications and vehicle-to-vehicle communication in public safety band

A dual-band high gain complementary split- ring resonator (CSRR) loaded hexagonal bowtie antenna with enhanced bandwidth for Vehicle- to-Vehicle (V2V) communication applications

A highly reliable and efficient communication system is needed for a vehicle to navigate and drive to the destination without human control (known as an autonomous or self-driving vehicle). In this work, we consider various parameters for the antenna design, ensuring reliable communication amongst vehicles and infrastructure. Specifically, we consider the type of antenna, the method used, operating frequency, substrate type (with thickness and permittivity), size and shape, gain, and bandwidth. An optimal threshold value or range of these parameters is identified. Moreover, a complementary split-ring resonator (CSRR) metamaterial (MTM) based hexagonal bowtie antenna for a high gain V2V communication environment is presented. This antenna covers sub- 6 GHz fifth generation (5G) bands (3.15-3.95 GHz) and Wi-Fi band 2.4GHz. Printing was done on a low-cost FR4 substrate for the radiating patch. Antenna Bandwidth is enhanced using a partial ground plane. The radiating layer is based on hexagonal patches printed on the double side of the substrate, and the CSSR structure is etched from patches to enrich antenna gain and bandwidth. More importantly, the proposed CSRR employed antenna provides gain and bandwidth of 1.6dBi / 6 dBi and 100MHz/ 8000MHz at 2.4GHz /3.5GHz, respectively. A highly known software, CST microwave studio, simulates the proposed antenna. Simulated and measured results make this arrangement a potential candidate for 5G high gain V2V communication

Performance Analysis of Sensing-based Semi-Persistent Scheduling (SB-SPS) MAC Protocol for C-V2X

Sensing-based Semi-Persistent Scheduling (SB-SPS) MAC protocol is proposed as part of the latest cellular vehicle to everything (C-V2X) standard for medium access between vehicles. As C-V2X uses LTE based frame structure, mode 4 of the C-V2X standard uses SB-SPS to allocate resource blocks effectively. C-V2X shows great potential for the future as it brings many improvements such as enhanced range, reliability, and the ability to support and evolve with emerging technologies such as 5G. In this article, the SB-SPS protocol’s performance was analyzed in different scenarios using OMNET++, SUMO, and Veins simulator. Different vehicle speeds and densities were used to observe the effect on packet loss and throughput. It was found that as packet loss decreased, throughput increased when the mobility of vehicles decreased. The effects of changing some important parameters of SB-SPS were also observed. The results showed that while parameters such as increasing the number of subchannels increased the packet delivery ratio (PDR), the change in the probability of resource reselection parameter did not affect the PDR

A Metamaterial-Based Double-Sided Bowtie Antenna for Intelligent Transport System Communications Operating in Public Safety Band

In this paper, a compact design and new structure of bowtie antenna with dual-band characteristics for the 5G and public safety bands in intelligent transport systems (ITS) is presented. The antenna consists of a double-sided bowtie radiating patch with partial ground plane. A triangular complementary split-ring resonator (TCSRR) metamaterial (MTM) structure was etched on the radiating patch, to develop a dual band and a single notch band between 3.85 and 4.65 GHz. The proposed antenna had an overall size of 36 × 36 mm2 and was fabricated using a FR4 substrate with a thickness and dielectric permittivity (εr) of 1.6 mm and 4.3, respectively. CST microwave studio software was used for the design of antenna. The measured frequency results show impedance bandwidths of 3.45–3.85 GHz and 4.65–5.4 GHz, for a voltage standing wave ratio (VSWR) less than 2. The proposed antenna operates at 3.5 GHz and 4.9 GHz, providing bandwidths of 400 MHz and 750 MHz, respectively, which cover the 5G and public safety bands. A prototype was fabricated and measured based upon optimal parameters, and the experimental results showed consistency with the simulation results. The proposed antenna provided a simulated/measured gain of 5.64 dBi/5 dBi and 4 dBi/3.7 dBi at 3.5 GHz and 4.9 GHz, respectively. The enhanced bandwidth and better gain results of the proposed antenna make it an ideal candidate for an ITS operating in the 5G and public safety bands

An Improved Gradient-Based Optimization Algorithm for Solving Complex Optimization Problems

In this paper, an improved gradient-based optimizer (IGBO) is proposed with the target of improving the performance and accuracy of the algorithm for solving complex optimization and engineering problems. The proposed IGBO has the added features of adjusting the best solution by adding inertia weight, fast convergence rate with modified parameters, as well as avoiding the local optima using a novel functional operator (G). These features make it feasible for solving the majority of the nonlinear optimization problems which is quite hard to achieve with the original version of GBO. The effectiveness and scalability of IGBO are evaluated using well-known benchmark functions. Moreover, the performance of the proposed algorithm is statistically analyzed using ANOVA analysis, and Holm–Bonferroni test. In addition, IGBO was assessed by solving well-known real-world problems. The results of benchmark functions show that the IGBO is very competitive, and superior compared to its competitors in finding the optimal solutions with high convergence and coverage. The results of the studied real optimization problems prove the superiority of the proposed algorithm in solving real optimization problems with difficult and indefinite search domains

An Improved Gradient-Based Optimization Algorithm for Solving Complex Optimization Problems

In this paper, an improved gradient-based optimizer (IGBO) is proposed with the target of improving the performance and accuracy of the algorithm for solving complex optimization and engineering problems. The proposed IGBO has the added features of adjusting the best solution by adding inertia weight, fast convergence rate with modified parameters, as well as avoiding the local optima using a novel functional operator (G). These features make it feasible for solving the majority of the nonlinear optimization problems which is quite hard to achieve with the original version of GBO. The effectiveness and scalability of IGBO are evaluated using well-known benchmark functions. Moreover, the performance of the proposed algorithm is statistically analyzed using ANOVA analysis, and Holm–Bonferroni test. In addition, IGBO was assessed by solving well-known real-world problems. The results of benchmark functions show that the IGBO is very competitive, and superior compared to its competitors in finding the optimal solutions with high convergence and coverage. The results of the studied real optimization problems prove the superiority of the proposed algorithm in solving real optimization problems with difficult and indefinite search domains