The study and exploration of massive multiple-input multiple-output (MMIMO) and millimeter-wave wireless access technology has been spurred by a shortage of bandwidth in the wireless communication sector. Massive MIMO, which combines antennas at the transmitter and receiver, is a key enabler technology for next-generation networks to enable exceptional spectrum and energy efficiency with simple processing techniques. For massive MIMOs, the lower band microwave or millimeter-wave band and the antenna are impeccably combined with RF transceivers. As a result, the 5G wireless communication antenna differs from traditional antennas in many ways. A new concept of the MIMO tri-band hexagonal antenna array is being introduced for next-generation cellular networks. With a total scaling dimension of 150 × 75 mm², the structure consists of multiple hexagonal fractal antenna components at different corners of the patch. The radiating patch resonates at 2.55–2.75, 3.45–3.7, and 5.65–6.05 GHz (FR1 band) for better return loss (S11) of more than 15 dB in all three operating bands. The coplanar waveguide (CPW) feeding technique and defective ground structure in the ground plane have been employed for effective impedance matching. The deviation of the main lobe of the radiation pattern is achieved using a two-element microstrip Taylor antenna array with series feeding, which also boosts the antenna array’s bandwidth and minimizes sidelobe. The proposed antenna is designed, simulated, and tested in far-field radiating conditions and generates tri-band S-parameters with sufficient separation and high-quality double-polarized radiation. The fabrication and testing of MIMO antennas were completed, where the measurement results matched the simulation results. In addition, the 5G smartphone antenna system requires a new, lightweight phased microwave antenna (μ-wave) with wide bandwidth and a fire extender. Because of its decent performance and compact architectures, the proposed smartphone antenna array architecture is a better entrant for upcoming 5G cellular implementations.

In this paper, a compact bandpass filter with improved band stop and band pass characteristics for wireless applications is built with four internal conductive poles in a single resonating cavity, which adds novel quad-resonating modes to the realization of band pass filter. This paper covers the design and testing of the S-band combline coaxial cavity filter which is beneficial in efficient filtering functions in wireless communication system design. The metallic cavity high Q coaxial resonators have the advantages of narrowband, low loss, better selectivity and high potential for power handling, as compared to microstrip filter in the application to determine the quality factor of motor oils. Furthermore, the tuning of coupling screws in the combline filter allows in frequency and bandwidth adjustments. An impedance bandwidth of 500 MHz (fractional bandwidth of 12.8%) has been achieved with an insertion loss of less than 2.5 dB and return loss of 18 dB at the resonant frequency. Four-pole resonating cavity filters have been developed with the center frequency of 4.5 GHz. Insert loss at 0 dB and estimated bandwidth at 850 MHz and a quality factor of 4.3 for the band pass frequencies between 4 and 8 GHz is seen in the simulated result.

Wearable communication technologies necessitate antenna designs that harmonize ergonomic compatibility, reliable performance, and minimal interaction with human tissues. However, high specific absorption rate (SAR) levels, limited radiation efficiency, and challenges in integration with flexible materials have significantly constrained widespread deployment. To address these limitations, this manuscript introduces a novel wearable cavity-backed substrate-integrated waveguide (SIW) antenna augmented with artificial magnetic conductor (AMC) structures. The proposed architecture is meticulously engineered using diverse textile substrates, including cotton, jeans, and jute, to synergistically integrate SIW and AMC technologies, mitigating body-induced performance degradation while ensuring safety and high radiation efficiency. The proposed design demonstrates significant performance enhancements, achieving SAR reductions to 0.672 W/kg on the spine and 0.341 W/kg on the forelimb for the cotton substrate. Furthermore, the AMC-backed implementation attains ultra-low reflection coefficients, as low as −26.56 dB, alongside a gain improvement of up to 1.37 dB, culminating in a total gain of 7.09 dBi. The impedance bandwidth exceeds the ISM band specifications, spanning 150 MHz (2.3–2.45 GHz). The design maintains remarkable resilience and operational stability under varying conditions, including dynamic bending and proximity to human body models. By substantially suppressing back radiation, enhancing directional gain, and preserving impedance matching, the AMC integration optimally adapts the antenna to body-centric communication scenarios. This study uniquely investigates the dielectric and mechanical properties of textile substrates within the AMC-SIW configuration, emphasizing their practicality for wearable applications. This research sets a precedent for wearable antenna innovation, achieving an unprecedented balance of flexibility, safety, and electromagnetic performance while establishing a foundation for next-generation wearable systems.

A method provides for the design of Fast Haar wavelet with the supervised neural network for diagnosis of diabetes mellitus with nearer accuracy with in short time application non-invasively is proposed. Our proposed work consists of two parts. First, analysis bank of haar wavelet is modified by using polyphase structure. Second, supervised back propagation neural network in the diagnosis of diabetes mellitus with the output of fast haar wavelet as its input is formulated. Finally, a fast haar wavelet based neural network is designed and it satisfies alias free and gives perfect nearer blood glucose value non-invasively which correlates the value of 99.69% of clinical diagnosis. Number of arithmetic calculations (convolution and addition) is reduced in fast haar wavelet transform with neural network. This makes the less computational complexity and less power consumption by reducing in time. Wavelet based supervised back propagation network is used for diagnosing blood glucose which extract from the blood flow doppler signals non-invasively.