Power Optimization of Unmanned Aerial Vehicle-Assisted Future Wireless Communication Using Hybrid Beamforming Technique in Disaster Management

Several established procedures are required to be followed when responding to disasters. Although most of them have so far been successful, they pose their own set of difficulties. The primary factor is response time, which is crucial for disaster management. The use of unmanned aerial vehicles (UAVs) for wireless communication not only increases response time but also is energy efficient. However, UAVs have limited power capacities, which makes their use in wireless communication challenging. In wireless communication, hybrid beamforming is a technique for optimizing the battery consumption of unmanned aerial vehicles (UAVs). Massive MIMO technology facilitates hybrid beamforming by deploying antenna arrays, efficiently directing transmitted energy to intended receivers, and minimizing power waste. Beamforming relies heavily on accurate Channel State Information (CSI). Advanced channel estimation and tracking techniques optimize power allocation based on real-time channel conditions, thereby minimizing energy consumption and ensuring connection reliability. Hybrid beamforming combines the advantages of digital and analog beamforming to reduce power consumption while maintaining communication performance. This paper proposes a power optimization technique for UAV-based wireless communication using hybrid beamforming to maximize spectral efficiency and increase the battery life of UAVs.


Introduction
Professionals in rehabilitation are finding themselves more and more at the head of emergency response teams as their role in reacting to sudden onset disasters, such as earthquakes or tsunamis, is fast fast-changing scenario.Unmanned Aerial Vehicles (UAVs) have attracted considerable interest and emerged as versatile instruments for a wide range of applications, such as surveillance, monitoring, disaster recovery, and wireless communication.Incorporating unmanned aerial vehicles (UAVs) into wireless communication systems has created new opportunities for extending coverage, improving connectivity, and addressing various challenges in remote and disaster-stricken areas.This paper provides an overview of recent developments and prospective trends in the development of small UAVs, with an emphasis on their function in wireless communication.[1] provide an exhaustive review of small UAVs, emphasizing the most recent technological advancements and their potential applications.They discuss the advances in UAV design, such as miniaturization, lightweight materials, and enhanced propulsion systems, which allow for extended flight durations and greater payload capacities.These developments have made it possible for UAVs to function as base stations, relays, or data collectors in wireless communication systems.[2] demonstrate the use of UAVs in forest fire surveillance and their capacity for autonomous data collection and analysis.They present an unmanned aerial vehicle system outfitted with a variety of sensors, including thermal cameras and gas detectors, that enables real-time monitoring of fire propagation and environmental conditions.This application demonstrates the capability of UAVs to provide timely and accurate data for effective decision-making in critical situations.[3] discuss the application of UAV-assisted communication systems for disaster relief.They propose a network infrastructure based on UAVs that can swiftly deploy communication connections in post-disaster situations where the existing infrastructure may be damaged or nonexistent.The UAVs serve as airborne base stations or relays, providing emergency response teams and affected communities with transient connectivity.[4] investigate the concept of airborne base stations, which provide 2 transient connectivity solutions for events and emergencies.They present a system in which UAVs outfitted with communication equipment can be rapidly deployed to provide wireless coverage in densely populated or disasterstricken areas.These airborne base stations can be repositioned dynamically based on user demand and network conditions, allowing for efficient and adaptable communication services.Wireless communication technologies have also paved the way for unmanned aerial vehicle (UAV)-assisted wireless networks.[5] present a review of 5G millimeter wave communications, emphasizing the role of UAVs in enhancing wireless network coverage and capacity.They discuss the opportunities and challenges of using millimeter wave frequencies for UAV communication connections, including beamforming techniques, interference management, and mobility support.Wang et al. [6] examine the beamforming design of millimeter-wave UAV networks by analyzing the tradeoffs between power consumption and capacity.They propose an optimization framework that takes into account transmit power allocation and beamforming strategy to maximize achievable capacity while minimizing UAV power consumption.The results indicate that meticulous beamforming design can substantially increase network capacity without negatively impacting UAV battery life.

Contribution
UAVs can prioritize which areas need to be evacuated by identifying which buildings may be in danger.The best evacuation routes and the most direct boat rescue routes are visible in the aerial photos acquired by UAVs.

System model
The system model of power optimization of an unmanned aerial vehicle (UAV) in future wireless communication employing a hybrid beamforming technique integrates UAVs as aerial base stations or relays to improve wireless connectivity and communication performance.The primary objective is to optimize the power allocation and beamforming strategy of the UAV's communication system to maximize spectral efficiency, prolong the UAV's battery life, and enhance overall system performance.The UAV's communication system uses a hybrid beamforming technique that incorporates analog and digital beamforming techniques.Digital beamforming is performed at the baseband using digital signal processing techniques to fine-tune the beam direction and shape.Analog beamforming is used at the RF (Radio Frequency) front end to redirect beams in different directions.Channel State Information (CSI): To adapt its beamforming strategy, the UAV's communication system acquires accurate and timely Channel State Information (CSI).This information is obtained via a variety of means, including pilot signals transmitted by ground users and channel estimation techniques.The primary objective of power optimization is to allocate transmit power among the UAV's antennas in an efficient manner.By optimizing power allocation, the UAV can increase link reliability, decrease interference, and reduce overall power consumption.This is essential for extending the UAV's flight duration and maintaining its communication capabilities.Figure 1 depicts the massive MIMO UAV-based system model for disaster management employing a hybrid beamforming technique.Antenna beamforming in a MIMO massive MIMO situation is shown in Figure 2.

Massive-MIMO with Beamforming
The exponential growth of wireless data traffic across networks places strain on the current communication system.The MU-MIMO technology has been superseded by the enormous MIMO technology, which is currently in development.Massive MIMO was developed in response to the bandwidth constraint in the wireless communication industry.Massive MIMO provides power efficiency, a wide spectrum, and extremely simple processing by employing a large number of antennas on both the receiver and transmitter.The vast MIMO network employs synchronous TDD technology.To achieve channel hardening, base stations with a large number of antennas (€>>1) are deployed and will communicate simultaneously with single-antenna users on each time/frequency sample.The ratio of base station antennas to user apparatus is always greater than one (€/σ >>1).Each base station operates independently utilizing distinct precoding methods [7].Massive MIMO technology has substantially improved spectral efficiency, and MIMO technology with multiple antennas at both the base station and consumer equipment is a viable solution for enhancing spectral efficiency.The latter satisfies the requirement for high-quality mobile communication services by providing superior coverage while minimizing power consumption, resulting in decreased bandwidth and transmission power [8].When using such short wavelengths, extremely small antennas are required.This reduction in antenna size satisfies the requirements of massive MIMO and contributes to the viability of large-scale antenna array technology [9] and can 1285 (2024) 012025 IOP Publishing doi:10.1088/1755-1315/1285/1/0120254 provide sufficient antenna gain due to which signal attenuations caused by mm waves will be compensated using beamforming capability.

Performance:
MIMO systems are evaluated using several important performance metrics.These include achievable sum rate, signal-to-noise ratio, bit error rate (BER), and energy efficiency.As a way to measure the effectiveness and efficiency of these systems, these measures are crucial.a) Bit Error Rate (BER): The Bit Error Rate (BER) is a statistic that determines how much a communication connection's signal transmission is flawed.BER occurs when bits are altered while being sent, frequently as a result of interference, noise, distortion, or synchronization issues.As an alternative, it shows the percentage of wrong bits received relative to all of the bits transmitted within a given period.In a downlink single-cell big MIMO communication system, the transmission modulation strategy you choose has a significant influence on the BER.In a massive MIMO system employing Zero-Forcing precoding and gray-coded square QAM modulation, the BER for the kth subscriber is calculated as follows: where ߬ = € − K is the level of freedom and k is the number of users, the transmission SNR of the user is given by the formula γk = PT/Kσ 2 where PT is the total transmission power at the BS distributed evenly among all use This is an approximation of the average bit error rate when (ఛାଵ) is omitted because of its negligible effect.The above equation describes the effect of BER on increasing the transmit antennas at the base station.

Achievable sum rate:
In massive MIMO systems, the attainable rate serves as a critical statistic to analyze system performance.This rate represents the greatest feasible data rate that can be produced while operating below the channel capacity, hence it is an important indicator of how well the system is performing.Understanding this parameter is critical for engineers to optimize system design and operation, which will ultimately result in enhanced data transmission and reception.
where B stands for bandwidth and SINR for signal-to-noise plus interference.

Power consumption in massive MIMO
The evaluation of power utilization efficiency in massive MIMO systems needs the combination of two critical parameters: the power that is transmitted and the power that is consumed by the hardware.Power consumption efficiency is a basic problem in massive MIMO systems.To optimize system execution and improve energy efficiency, it is vital to have an accurate calculation of the entire power consumption.This will ultimately lead to improved system design and operation, which may be seen as the following: The inverse of the base station's power efficiency is represented by the symbol ƞ.The term "downlink transmitter power," abbreviated as "Pk," refers to the allotment of resources that are made available to each user, whereas "Pn" refers to the amount of continuous circuit energy that is consumed by each antenna.In addition, Pf is a symbol for the total amount of power that is consumed by the base station, and this amount of power is constant regardless of the number of dispatch antennas.

Efficient Use of Energy
Energy efficiency is an important metric that determines how successfully a massive MIMO network utilizes power to achieve a particular data rate.Because it is defined as the ratio of the feasible sum rate to the total amount of power that is being consumed, it is a helpful tool that can be used to optimize the design and operation of a system.Monitoring and enhancing this parameter will allow us to improve energy efficiency, limit power consumption, and increase data transfer speeds, which will eventually lead to greater system performance.
Massive MIMO is a mode of operation that uses hundreds of individually programmable antenna components (for example, 256 antennas [10]), often on the base station end of the wireless communication connection, to adaptively change the elevations and azimuth angles at which the signal is propagated [11].In addition, massive MIMO improves system performance by boosting the signal-to-interference-plus-noise ratio (SINR).SINR is an essential statistic that assesses the quality of the received signal about the background clatter and other obstacles.enormous MIMO helps to improve SINR.The following are some of the possible advantages of utilizing this method: The use of spatial multiplexing, interference reduction, improved channel conditions, and higher spectral efficiency are some of the ways that massive MIMO's capacity and connection dependability may be improved.Spatial multiplexing is a method that may be used by base stations to send several data streams to different users at the same time.Massive MIMO makes it possible for base stations to adopt this method.Massive MIMO can expand the number of accessible spatial dimensions since it makes use of a huge number of antennas that are each adjustable on their own.This ultimately leads to an increase in the system's capacity.Beamforming, which allows the signals to be directed towards the targeted users alone and away from the interferers, is one method by which interference may be reduced.This will also raise the signal-to-interference-and-noise ratio (SINR).Enhancing the channel condition, which may be accomplished through the use of spatial multiplexing, is one way to reduce the negative effects of fading channels.In addition, it is well known that the capacity of a network increases with the number of antennas as well.Spectral efficiency is the relationship between the throughput or net data rate and the channel bandwidth.The frequency of a channel's bandwidth is measured in hertz (Hz), whereas the data rate, or throughput, is measured in bits per second (bps).The quantity of data that can be communicated over a specific bandwidth is proportional to a wireless communication system's spectral efficiency, which is measured in bits per second per hertz (bits/sec/Hz).This metric is abbreviated as "bits/sec/Hz."Massive MIMO can achieve great spectrum efficiency because it uses a large number of antennas that are individually controlled.This allows the system to increase the amount of accessible spatial data streams, as well as throughput and multiplexing gain [12].

Energy efficiency:
According to [13], the relationship between transmitted power (pt) and the number of antennas (nt) is inverse as a consequence of coherent combining.As the transmit power decreases significantly as nt increases, the power per antenna must be inversely proportional to nt.Throughput is demonstrated to be directly proportional to the number of transmitting antennas in [13], where the transmitted power is held constant.In addition, [14] specifies the minimum power consumption (in milliwatts).Hybrid precoding is a sophisticated signal processing method that effectively combines digital and analog precoding to reduce the computational complexity of the base station while maintaining high performance levels.

Cost-effectiveness:
Massive MIMO technology stands out for its cost-effectiveness, achieved through the utilization of energy-efficient elements like low-power consumption power amplifiers.Moreover, this technology holds the promise of significantly diminishing the amount of emitted radiation, potentially reducing it by a factor of a thousand [15].Also By employing multiple antennas, the power per transmitted information bit can be decreased, resulting in reduced energy consumption and costs.Massive MIMO technology provides support for a substantially larger number of users than conventional single-antenna systems.

Signal processing:
Massive MIMO technology streamlines signal processing by reducing the effects of interruption, rapid fading, and distortion.Utilizing a large number of antennas enables spatial processing technology, which increases spectrum utilization and data rate [16].In addition, the use of multiple antennas in massive MIMO systems reduces the processing burden at each antenna, and linear processing techniques significantly reduce the complexity of signal processing, resulting in enhanced performance and decreased costs.Massive antenna arrays deployed at base stations offer essential "channel hardening" characteristics.This results in increased predictability of fading channels, causing the vast MIMO channel matrix to converge toward anticipated values, or as the number of antennas tends towards infinity [17].
Let the q single antenna users be supported by p base station antennas sharing the same time-frequency resources.
Let the p×1 transmitted signal vector from p antennas be represented by t, then the q×1 received signal vector s at the user side is given as, ‫ݏ‬ = ( થ ‫ݐ‬ඥφ + n), Where Η belongs to ℂ × , is a channel matrix between transmitter and receiver.In this downlink channel model, at the base station, The channel is predicted to have perfect channel state information, be ergodic, and exhibit Rayleigh fadingThe components of H are viewed as independent, and identically distributed (i.i.d.) Gaussian random functions in the complex space with a mean of zero and a variance of one.t=WZ is the recognized signal at q th user after using the linear precoding.Where Z belongs to ℂ ×ଵ .

Hybrid beamforming in massive MIMO
Figure 3 shows how hybrid beamforming is applied to provide an optimal combination of flexibility, amplitude control, power efficiency, and cost-effectiveness.Utilizing hybrid analog-digital beamforming systems' main objective is to simultaneously modify analog and digital beamformers, increasing possible data rates or simulating the performance of a full-fledged digital precoder.This strategy has grown in prominence over the past five years and is essential for developing enormous MIMO systems that are both economical and energy-efficient.The goal of hybrid systems is to emulate digital beamforming capabilities while minimizing hardware complexity and signal processing demands.A subset of antennas is linked to an RF chain in the setting of partially connected hybrid beamforming, whereas every antenna is connected to every RF chain in fully connected hybrid beamforming.
Whether wholly or partially coupled, hybrid analog-digital precoding and combining designs involve a compromise between complexity and efficacy.The implementation of these strategies increases spectrum efficiency and spectrum efficiency at the expense of a rise in computing complexity.Ultimately, the precise requirements and constraints of the system in question determine whether fully connected or partially connected architectures should be employed [18].Hybrid beamforming is a very intriguing method for large MIMO systems that incorporate digital and analog beamforming.When the number of base station antennas surpasses that of user device antennas, it is anticipated that future communication systems will utilize hybrid beamforming more often.Utilizing enormous MIMO improves channel estimation and spatial resolution.For hybrid beamforming, the base station divides the RF chain into a digital portion and an analog portion.The digital component precedes a limited number of data streams, while the analog component combines the signals from the digital component to generate a beam delivered through the analog beamforming network.This technique maintains a high level of performance despite requiring fewer RF connections.Low-resolution analog-to-digital converters can also be utilized, which further reduces costs and energy consumption.Hybrid beamforming is ideally suited for massive MIMO systems due to its capacity to achieve high spatial resolution and circumvent the hardware limitations of conventional digital beamforming.Broad consensus exists that large MIMO and hybrid precoding techniques will be essential for achieving faster data rates, optimizing spectral efficiency, and reducing energy consumption as the world enters a new phase of broadcasting systems, such as the fifth wave of wireless communication and beyond.It is anticipated that these innovations will play a crucial role in meeting the ever-increasing demand for modern communications operations, making it simpler to serve more consumers and enhancing the quality of service.
The number of data processing paths is (€ × φ 2 ) for an entirely linked design with € transmit antennas and φ RF chains, as depicted in Figure 3, compared to (€ × φ) for a sub-connected architecture [19].However, the beamforming gain of the entirely linked architecture is times greater than that of the sub-connected architecture [20].Using a hybrid analog-digital beamforming approach that is entirely connected, each antenna can receive the entire beamforming gain [19].The use of multiple RF chains permits the construction of beam patterns with enhanced flatness over the service area as a result of hybrid analog-digital precoding and combining techniques, as well as fewer overlaps between beams, similar to those generated by a fully digital architecture.A fully connected hybrid beamformer utilizes all Angles of Departure (AoD) to accomplish the mapping via a system of phases.In massive MMO grouping and selection of antennae according to the channel behavior and calculation of SNR with best optimal solutions has been discussed [21][22][23].

Result and discussions
The process of choosing the best antenna group to get the highest possible data rate is shown in the flowchart Figure 4.This might lead to markedly increased data rates throughout the whole cell zone, including greater peak data rates.Achievable data rates in the world of mobile communication systems are continually constrained by the quantity of available received signal power or, more generally, the ratio of received signal power to noise power.This restriction is particularly noticeable in situations when noise is the main cause of radio connection quality loss (referred to as a noise-limited scenario).Any increase in data rates within a given bandwidth must, at the very least, be accompanied by an equal but opposite rise in signal intensity.5's utilization of various antenna pairs, ranging from 4x4 to 32x32, is that the attainable rate increases as we increase the pair of antennas while maintaining the same SNR value.

Conclusion
The power optimization of future wireless communication, which will make use of hybrid beamforming and will be assisted by UAVs, can bring about a considerable amount of change in wireless networks.This change has a great lot of potential.It combines the advantages of employing UAVs as mobile aerial base stations with the adaptive signal direction that is provided by hybrid beamforming in one convenient package.This technology opens up new doors for a wide range of businesses, such as those in the field of long-distance connectivity, emergency response, and public safety.The coverage, capacity, flexibility, and energy efficiency are all improved as a result.
It will be required, as research and development activities progress, to overcome hurdles relating to implementation and regulation to fully release the latent potential of this forward-thinking communication paradigm.This will be necessary to fully realize the paradigm's full potential.

3 Figure 1 .
Figure 1.UAV-assisted wireless communication network in disaster management