Microbial fuel cell (MFC) technology is anticipated to be a practical alternative to the activated sludge technique for treating domestic and industrial effluents. The relevant literature mainly focuses on developing the systems and materials for maximum power output, whereas understanding the fundamental electrochemical characteristics is inadequate. This experimental study uses a double-chamber MFC having graphite electrodes and an anion-exchange membrane to investigate the electrochemical process limitations and the potential of bioelectricity generation and dairy effluent treatment. The results revealed an 81% reduction in the chemical oxygen demand (COD) in 10 days of cell operation, with an initial COD loading of 4520 mg/L. The third day recorded the highest open circuit voltage of 396 mV, and the maximum power density of 36.39 mW/m² was achieved at a current density of 0.30 A/m². The electrochemical impedance spectroscopy analysis disclosed that the activation polarization of the aerated cathode was the primary factor causing the cell’s resistance, followed by the ohmic and anodic activation overpotentials.

The electrochemical treatment of canal water was investigated in a batch-wise system in the presence of stainless steel 316-grade electrodes. Three effective process parameters, including current density, reaction time, and electrode spacing, were evaluated in the range of 0.25–2.5 mA/cm², 1–10 min, and 0.5–2.5 cm, respectively. Operational variables of electrochemical disinfection are optimized in response surface methodology (RSM) using Box–Behnken design. Before electrochemical disinfection, a pretreatment process of coagulants mixing for turbidity removal was conducted. Results revealed that a 10 ppm dosage of Ferric chloride (FeCl₃.6H₂O) and alum (Al₂(SO₄)₃·16H₂O) at neutral pH is appropriate. Furthermore, the RSM analysis shows that interelectrode spacing is the most prominent factor affecting the disinfection performance, and increasing electrode spacing inversely affects the disinfection efficiency. Results revealed that 1.52 mA/cm² current density, 6.35 min reaction time, and 1.13 cm of electrode spacing are the optimum conditions, resulting in a statistically 98.08% disinfection of the total coliform. The energy required for electrochemically disinfection of water at optimum conditions was 0.256 kWh/m³.

Massive surge in mobile data traffic, high power consumption, and limited spectrum availability are the major challenges of existing cellular networks. Industries and academia have conducted many studies and proposed solutions to meet the exploding user demand and minimize the network operators’ capital and operational expenditures. The cloud radio access network (CRAN) system is a paradigm shift for cellular networks due to its central processing, which is also considered a potential candidate for 5G and beyond fifth generation (5G) networks. This paper presents a comprehensive overview of the state-of-the-art CRAN architecture and critically analyzes its performance metrics. Moreover, it provides the first holistic insight into the recent advances in CRAN architecture; integrating CRAN architecture with state-of-the-art technologies such as massive multiple-input multiple output (MIMO) systems by equipping the transmitter and the receiver with multiple antennas, intelligent reflecting surfaces, unmanned aerial vehicles, integrated sensing and communication, rate splitting method, and energy harvesting. Further, the related research challenges are also discussed, proposing future directions.

Massive data traffic over capacity-limited fronthaul link of a cloud radio access network (CRAN) poses a severe challenge in modern wireless systems. We propose a novel CRAN architecture to address this challenge that leverages a large array of antennas or massive MIMO at the baseband unit (BBU) to communicate with a set of distributed remote radio heads (RRHs) and subsequently serve multiple user equipment (UEs) over the access link. Transmission of data from massive MIMO at BBU takes place over a millimeter wave (mmWave) channel to exploit large bandwidth. The proposed architecture leverages a substantial array gain of massive MIMO in conjunction with a large idle mmWave frequency spectrum that can deal with a wide range of throughput requirements. In addition, a data compression strategy is designed by finding an optimal quantization noise covariance matrix across all the RRHs to handle the fronthaul traffic load. To this end, an optimization problem to maximize the sum rate of the UEs is thus formulated subject to constraints on fronthaul capacity, data compression, and total transmit powers at the BBU and RRHs. To solve this non-convex and intractable problem, a path following an iterative algorithm based on the difference of convex (DC) programming is used. The performance of the proposed architecture is evaluated in comparison with nontrivial benchmark schemes/architectures. Simulation results reveal that the proposed architecture can significantly improve the throughput of a CRAN by overcoming the fronthaul bottleneck by using massive MIMO over the mmWave channel.

Cloud Radio Access Networks (CRAN) face a critical challenge due to the limited capacity of fronthaul links overwhelmed by massive data transmissions. This paper proposes a novel CRAN design that effectively tackles this challenge. Our approach combines three key elements: (1) Massive MIMO at the baseband unit to leverage large array gain and interference suppression; (2) a novel simultaneous transmitting and reflecting (STAR) reconfigurable intelligent surface (RIS) that can both transmit and reflect signals concurrently, improving fronthaul capacity through energy splitting technique by enabling communication with remote radio heads serving multiple user equipments; and (3) a data compression technique by optimizing the quantization noise covariance matrix across remote radio heads, significantly reducing the fronthaul traffic load. We formulate a problem to maximize the overall network sum-rate by jointly optimizing transmit power, fronthaul capacity, beamforming vectors at RRHs, data compression, and STAR-RIS transmission-reflection coefficients. To address the nonconvexity of the resulting joint optimization problem, successive convexification along with alternating optimization technique are used to develop an iterative algorithm. Simulations demonstrate that our STAR-RIS-aided CRAN design surpasses conventional reflecting-only RIS aided CRAN by providing full-space coverage and thus offering more degrees-of-freedom compared to traditional RIS.

This paper presents a novel system design that considerably improves the entrapment of terrestrial ultraviolet (UV) irradiance in a customized honeycomb structure to produce hydrogen at a standard rate of 7.57 slpm for places with a UV index > 11. Thermolysis of high salinity water is done by employing a solid oxide electrolyzer cell (SOEC), which comprises three customized, novel active optical subsystems to filter, track, and concentrate terrestrial UV solar irradiance by Fresnel lenses. The output of systems is fed to a desalinator, a photovoltaic system to produce electrical energy, and a steam generator with modified surface morphology to generate the required superheated steam for the SOEC. A simulation in COMSOL Multiphysics ver. 5.6 has shown that a customized honeycomb structure, when incorporated on the copper–nickel surface of a steam generator, improves its absorptance coefficient up to 93.43% (48.98%—flat case). This results in generating the required superheated steam of 650 °C with a designed active optical system comprising nine Fresnel lenses (7 m²) that offer the concentration of 36 suns on the honeycomb structure of the steam generator as input. The required 1.27 kW of electrical power is obtained by concentrating the photovoltaic system using In_0.33Ga_0.67N/Si/InN solar cells. This production of hydrogen is sustainable and cost effective, as the estimated cost over 5 years by the proposed system is 0.51 USD/kg, compared to the commercially available system, which costs 3.18 USD/kg.