Energy harvesting technology allows Internet of Things (IoT) devices to be powered continuously without needing battery charging or replacement. In addressing existing and emerging massive IoT energy supply challenges, this paper presents the design of multi-sourced multiple input and multiple output (MIMO) multiband hybrid wireless RF-perovskite photovoltaic energy harvesting subsystems for IoT application. The research findings evaluate the efficiency and power output of different RF configurations (1 to 16 antennas) within MIMO RF subsystems. A Delon quadruple rectifier in the RF energy harvesting system demonstrates a system-level power conversion efficiency of 51%. The research also explores the I-V and P-V characteristics of the adopted perovskite tandem cell. This results in an impressive array capable of producing 6.4 V and generating a maximum power of 650 mW. For the first time, the combined mathematical modelling of the system architecture is presented. The achieved efficiency of the combined system is 90% (for 8 MIMO) and 98% (for 16 MIMO) at 0 dBm input RF power. This novel study holds great promise for next-generation 5G/6G smart IoT passive electronics. Additionally, it establishes the hybrid RF-perovskite energy harvester as a promising, compact, and eco-friendly solution for efficiently powering IoT devices in smart cities. This work contributes to the development of sustainable, scalable, and smart energy solutions for IoT integration into smart city infrastructures.

The convergence of 5G terrestrial networks with satellite systems offers a revolutionary approach to achieving global, seamless connectivity, particularly for Internet of Things (IoT) applications in urban and rural settings. This paper investigates the implications of this 5G–satellite integrated network architecture, specifically through the application of the two-ray propagation model and the free-space path loss (FSPL) model. By simulating signal characteristics over varying distances, altitudes, and environmental parameters, we explore how factors such as transmitter height, satellite altitude, and frequency impact received power, path loss, channel capacity, and outage probability. The key findings indicate that received power decreases significantly with increasing distance, with notable oscillations in the two-ray model due to interference from ground reflections, particularly evident within the first 100 km. For example, at 50 km, a 300 km satellite altitude yields approximately −115 dBm in received power, while at 1000 km altitude, this power drops to around −136 dBm. Higher frequencies (e.g., 32 GHz) exhibit greater path loss than lower frequencies (e.g., 24 GHz), with a 5 dB difference observed at 1000 km, reinforcing the need for frequency considerations in long-range communication design. In terms of channel capacity, increasing bandwidth enhances achievable data rates but declines with distance due to diminishing received power. At 100 km, a 50 MHz bandwidth supports up to 4500 Mbps, while at 3000 km, capacity drops to around 300 Mbps. The outage probability analysis shows that higher signal-to-noise ratio (SNR) thresholds substantially increase the likelihood of communication failures, especially at distances exceeding 2000 km. For instance, at 3000 km, the outage probability for a 15 dB SNR threshold reaches approximately 25%, compared to less than 5% for a 5 dB threshold. These results underscore the critical trade-offs in designing 5G–satellite IoT networks, balancing bandwidth, frequency, SNR thresholds, and satellite altitudes for optimal performance across diverse IoT applications. The analysis provides valuable insights for enhancing connectivity and reliability in 5G–satellite integrated networks, especially in remote and underserved regions.

The exponential growth of Internet of Things (IoT) devices in smart city environments presents significant challenges, including spectrum congestion, interference, energy inefficiency, and scalability. To address these issues, we propose SmartBeam+, a novel adaptive beamforming algorithm designed to optimize the communication performance in dense IoT networks. SmartBeam+ dynamically adjusts the beamforming weights using real-time Channel State Information (CSI) to maximize the Signal-to-Interference-and-Noise Ratio (SINR), minimize the bit error rate (BER), and suppress interference effectively. Extensive simulations compare SmartBeam+ with conventional algorithms such as Zero-Forcing (ZF), MVDR, and Least Mean Squares (LMS) across key performance metrics: SINR, BER, energy efficiency, reliability, throughput, and latency. Results show that SmartBeam+ consistently performs better, with SINR values ranging from 20 to 13 dB, BER as low as 0.0001, and 30 dB interference suppression. SmartBeam+ maintains 20 bps/W energy efficiency and achieves a reliability of 92%, significantly outperforming other methods, especially under high node density and interference levels. Furthermore, the network lifetime is prolonged up to 90 hours, and the latency is reduced to 5 ms, making SmartBeam+ ideal for time-sensitive applications such as disaster management and urban sensing. These results highlight SmartBeam+ as a scalable, energy-efficient, and robust solution for next-generation IoT networks in smart cities.

Terahertz (THz) communications and simultaneous wireless information and power transfer (SWIPT) hold the potential to energize battery-less Internet-of-Things (IoT) devices while enabling multi-gigabit data transmission. However, severe path loss, blockages, and rectifier nonlinearity significantly hinder both throughput and harvested energy. Additionally, high-power THz beams pose safety concerns by potentially exceeding specific absorption rate (SAR) limits. We propose a sensing-adaptive power-focusing (APF) framework in which a reconfigurable intelligent surface (RIS) embeds low-rate THz sensors. Real-time backscatter measurements construct a spatial map used for the joint optimisation of (i) RIS phase configurations, (ii) multi-tone SWIPT waveforms, and (iii) nonlinear power-splitting ratios. A weighted MMSE inner loop maximizes the data rate, while an outer alternating optimisation applies semidefinite relaxation to enforce passive-element constraints and SAR compliance. Full-stack simulations at 0.3 THz with 20 GHz bandwidth and up to 256 RIS elements show that APF (i) improves the rate–energy Pareto frontier by 30–75% over recent adaptive baselines; (ii) achieves a 150% gain in harvested energy and a 440 Mbps peak per-user rate; (iii) reduces energy-efficiency variance by half while maintaining a Jain fairness index of 0.999;; and (iv) caps SAR at 1.6 W/kg, which is 20% below the IEEE C95.1 safety threshold. The algorithm converges in seven iterations and executes within <3 ms on a Cortex-A78 processor, ensuring compliance with real-time 6G control budgets. The proposed architecture supports sustainable THz-powered networks for smart factories, digital-twin logistics, wire-free extended reality (XR), and low-maintenance structural health monitors, combining high-capacity communication, safe wireless power transfer, and carbon-aware operation for future 6G cyber–physical systems.

The deployment of fifth-generation (5G) and beyond-5G wireless communication systems necessitates advanced transceiver architectures to support high data rates, spectrum efficiency, and energy-efficient designs. This paper presents a highly adaptive reconfigurable receiver front-end (HARRF) designed for 5G and satellite applications, integrating a switchable low noise amplifier (LNA) and a single pole double throw (SPDT) switch. The HARRF architecture supports both X-band (8–12 GHz) and K/Ka-band (23–28 GHz) operations, enabling seamless adaptation between radar, satellite communication, and millimeter-wave (mmWave) 5G applications. The proposed receiver front-end employs a 0.15 μ$\mathrm{m}$ pseudomorphic high electron mobility transistor (pHEMT) process, optimised through a three-stage cascaded LNA topology. A switched-tuned matching network is utilised to achieve reconfigurability between X-band and K/Ka-band. Performance evaluations indicate that the X-band LNA achieves a gain of 23–27 dB with a noise figure below 7 dB, whereas the K/Ka-band LNA provides 23–27 dB gain with a noise figure ranging from 2.3–2.6 dB. The SPDT switch exhibits low insertion loss and high isolation, ensuring minimal signal degradation across operational bands. Network analysis and scattering parameter extractions were conducted using advanced design system (ADS) simulations, demonstrating superior return loss, power efficiency, and impedance matching. Comparative analysis with state-of-the-art designs shows that the proposed HARRF outperforms existing solutions in terms of reconfigurability, stability, and wideband operation. The results validate the feasibility of the proposed reconfigurable RF front-end in enabling efficient spectrum utilisation and energy-efficient transceiver systems for next-generation communication networks.

The challenge of optimally allocating transmit power among multiple beams remains a central problem in multi-beam beamforming for massive MIMO networks, especially as user density and interference become increasingly dynamic in next-generation wireless deployments. Conventional normalization techniques, such as Global and Per-Beam Normalization, are unable to adapt to varying channel conditions and user demands, often resulting in inefficient power distribution and limited system performance. To address this, we propose Power Redistribution Normalization (PRN), a novel approach that dynamically reallocates transmit power across multiple beams in real time, optimizing both desired signal peaks and interference nulls. The PRN framework is integrated with diverse beamforming algorithms—including Least Mean Square (LMS), Minimum Variance Distortionless Response (MVDR), Recursive Least Squares (RLS), Zero Forcing (ZF), and Sample Matrix Inversion (SMI)—and is evaluated under realistic antenna configurations, user densities, and interference scenarios. Results demonstrate that PRN-MVDR achieves the highest spectral efficiency (~25 bps/Hz), SINR (~8.7 dB), and energy efficiency (~25 bps/Hz/W) compared to existing normalization methods. Moreover, PRN-LMS provides comparable performance with significantly lower computational complexity (~0.4 ms latency), making it attractive for real-time and hardware-constrained applications. The proposed PRN method is thus well-suited for dense urban 5G/6G networks and large-scale IoT deployments, where robust power allocation and interference management are critical to system capacity and reliability.