Unlock insights into mmWave joint sensing and secure communication for healthcare. Download this assignment sample to master simulations, signal processing, data security, and 6G integration—perfect for students seeking practical knowledge and help with assignment writing.
Due to the increased healthcare technology, patient care has improved through monitoring the patients’ status anywhere at any time. Several types of devices that work in contact with the patient’s body are more intrusive and uncomfortable for the patient than the discussed wearable devices. However, new trends in millimeter-wave (mmWave) technology hold potential for a contactless approach, wherein the signs such as heartbeat and breathing can be monitored. Occurring in the 30 GHz to 300 GHz frequency bandwidth, mmWave technology affords long-range sensing solutions with high discriminating power to identify slight physiological motion. Thus, the ideas of this project at the beginning planning stage involved the use of physical hardware like Raspberry Pi and TI mmWave sensors, but due to the restrictions in access to such hardware, the study is conducted using simulations. MATLAB has been chosen as the main software for simulating the sensing and communication capabilities of mmWave technology. It makes it possible to assess all the aspects of the system with regard to data capturing, storage, processing, and secure transmission without any physical equipment. Apart from sensing, the secure communication is also central to the joint sensing and communicating system especially under 6G technologies. Because the information exchanged in healthcare is often sensitive, secure communication maintains data confidentiality, and adherence to the GDPR. This integration is critical and guarantees real-time dependable solutions in the healthcare field. Students seeking help with assignment writing will find this topic essential for understanding modern healthcare monitoring and communication technologies.
Health monitoring systems were disrupted during the COVID-19 pandemic, which pointed to the necessity of enhancing health aware systems. Data obtained from non-contact monitoring are accurate, and the approach can significantly minimize the probability of diseases spreading among individuals. Also, the increased longevity of the global population and the elevated frequency of chronic diseases require effective, immediate, and noncontact methods of health monitoring [1]. Traditionally used contact-based techniques do not fit well within these parameters due to discomfort that one experiences while using them and the consequential accuracy that is likely to be encountered in the long run. On the other hand, most of the mmWave technology has attributes such as no contact, high resolution and ability to perform sensing and communication at the same time hence making it the most suitable candidate to address the challenges discussed. Nevertheless, implementing such a system has multiple technical and legal challenges, among which signal processing, data security, and data protection legislation. Due to the unavailability of the actual physical hardware, this project therefore only employs simulation techniques. MATLAB is a perfect tool to use while studying signal processing and experimenting with mmWave radar or testing secure communication channels [3]. It provides an opportunity to design, develop and evaluate performance of the system in a non-real environment that could be necessary for actual environment conditions. Security in communication has always been considered to play a crucial role in any of the healthcare systems. Coordination of sensing and communicating as a feature of 6G system contributes to the confidentiality of the data while transferring it as well as considering the aspect of privacy. Thus, by implementing these features, the project contributes to the further evolution of systems that meet not only the performance requirement but also the contemporary requirements to the data protection.
The main aim of this project is to design and test mmWave based joint sensing and communication systems in the MATLAB environment.
Objectives:
As indicated in this project, software based simulations have been used hence avoiding the need for development of physical hardware. The system design concentrates on emulating mmWave sensing and secure communication via the extensive MATLAB toolbox. This project involves the implementation and integration of mmWave communication alongside sensing to meet the problem of achieving secure transmission of real-time data. The communication processes are modeled using MATLAB to enable a smooth transfer of the sensed signals. This integration improves the general architecture of the system, according to healthcare needs and 6G specifications.
5 Years | PhD
Key contributions include:
Simulation-Based Sensing: Using simulation for sensing is ideally suited for investigating the potential of mmWave technology for health monitoring without the need for actual subjects and is explained further in the next sections with MATLAB as the tool of choice for this implementation [4]. The project deploys MATLAB to ‘mimic’ the detection of features like pulse and respiration using signal processing and analytical capabilities of the programming environment. Because the study revolves around software simulations, it is easier to come by while not compromising the scientific aspect of the research. Fast Fourier Transform (FFT) and spectral analysis are useful for identifying and analyzing the simulated radar data. FFT helps in conversion of time series data of signals to frequency domain for finding periodicity related to vital signs [6]. Spectral analysis then provided an even higher level of detail by assigning a numerical value representing the power of the signal in different bandwidths, which allows for distinguishing between heartbeat signals, respiration, and noise.
Data Analysis and Visualization: Data analysis and visualization are crucial to this project as they act as a means of presenting simulated vital sign detection. MATLAB comprises a sound platform for converting raw data into patterns and insights through its enhanced graphical capabilities. A time series plot represents changes in heartbeat, breathing and any pattern or irregularity can be identified instantly. Spectrograms express the frequencies of the signal and can be used within the analysis to separate the physiological signals from the noise [20]. Furthermore, MATLAB offers enriched features that include 3D modelling and surface graphs in order to study the phenomena of several data attributes and gain full understanding of the signal characteristics. Signal conditioning processes for example the noise reduction and low pass filtering are usually applied in imaging to improve on quality of the data before the signal is imaged [8]. Through integrating and incorporating a whole range of data analysis procedures with easily interpretable graphical results, MATLAB confirms the applicability of mmWave simulations in identifying life signs. This streamlined approach gives dependable and usable results that are valuable for real-life non-invasive health monitoring solutions.
Secure Communication Simulations: Privacy is very important in health monitoring systems and this makes security in communicating data significant. This project employs MATLAB in simulating encryption paradigms such as AES and TLS while maintaining data privacy, its correctness, and origin. AES applies different key sizes (128, 192, 256 bits) to vital signal data because longer key implies more secure and slower while TLS uses digital certificates and handshake procedure to protect the communication channel. The tools used in MATLAB designed to mimic a client-server security system analyzes speed of encryption, possible CPU load and latency [5]. These simulations prove that secure communication infrastructure, compliant with GDPR requirements, is feasible and can underpin the development of effective real-time health monitoring systems.
Software-Only Implementation: A software solution relies on MATLAB to perform and verify the requirements in sensing and secure communication using mmWave technology without the use of hardware. This approach greatly reduces costs since there is no costly hardware part of the system that might be designed and implemented inaccurately [22]. The architectural flexibility that MATLAB offers allows the hardware to be modeled in detail and a large number of scenarios or configurations be tested within a short span of time [7]. Its rich repository helps accommodate hectic testing and fine-tuning and encourages robust and flexible growth. Due to the ability to come up with a controlled testing environment this method provides high reliability and repeatability thus making it cheaper and better in providing solutions relating to complicated systems in health monitoring applications.
The current health monitoring systems available involve the use of contact-based methods which are irritating and have low accuracy over some period of time compared to the mmWave technology that has a great potential of performing physiological signals like heartbeat, respiration and others non-invasively [11]. At the same time, obstacles like the absence of physical hardware, signal processing issues, and the problems of secure data transfer and compliance with the requirements of data protection legislation are presented. The goal of this research is to prototype mmWave based health monitoring and secure communication model in MATLAB, which would provide the real-world implementation perspective of such systems in the healthcare industry [12]. However, attaining secure communication together with sensing remains a complex issue in healthcare contexts, as mmWave technology assists with non-invasive monitoring, data protection and adhering to rules, for example, GDPR poses challenges. The project alleviates these challenges by incorporating security communication protocols that include sensing capabilities which are analyzed through MATLAB.
With the rising need to use Healthcare Technology System, Patient Monitoring requires safe, non- invasive methods of measuring vital signs mmWave presents a solution that can measure physiological variables such as pulse rate and respiration rate [9]. However, practical organization of such systems are proven to face technical and legal hurdles like hardware limitations and legal issues in regard to data protection. Using MATLAB simulations, this report will attempt to investigate real-time health monitoring using mmWave while solving the problems of secure data transmission and signal processing that remain crucial for the real-world utilisation of such a technology in healthcare settings.
Get assistance from our PROFESSIONAL ASSIGNMENT WRITERS to receive 100% assured AI-free and high-quality documents on time, ensuring an A+ grade in all subjects.
The dissertation is organised in a manner that covers all the aspects of developing and validating a MATLAB simulation based system for mmWave technology in health monitoring and secure communications. Chapter one of the study presents an overview of the proposed project, including information on background to the study, the reason for undertaking the project, the purpose, aim, objectives, scope and expected academic contribution of the project. Chapter 2 is the literature review, which reviews the relevant literature on mmWave technology, non-contact health monitoring, and the current secure communication protocols which established the scientific background of this project. Chapter 3 further explains the simulation process, and how MATLAB was used for signal processing, data analysis, and modeling of secure communications. The Chapter 4 deals with safety and ethics explaining the main problem areas about data protection, patient’s consent, and the compliance with standards including GDPR. In Chapter 5, possible further development of this project is discussed as well as further hardware implementation and use of more complicated machine learning algorithms for data analysis. Last section, Chapter 6 is a summary and discussion of the research the contribution of the project to existing knowledge and its relevance for future research. To that end, this structured approach guarantees that the subject is covered comprehensively while reflecting on both theoretical analysis and application.
Millimeter-wave (mmWave) technology has been seen as an enabler of the synergy between sensing and secure communication specifically in health monitoring systems. Using frequencies of 30GHz to 300GHz, mmWave allows the remote monitoring of parameters such as pulse and breathing with high accuracy. It also provides security in regards to data transmission, also a big issue in healthcare technology. Previous findings shows that mmWave holds promise for eradicating some drawbacks of conventional contact-based techniques but still inefficiency is observed for combining its two different features. This chapter discusses the existing empirical literature, theoretical frameworks, and technology development pertaining to mmWave-based joint sensing and communication systems. It acknowledges these deficiencies as research gaps such as the absence of large-scale simulation studies, and develops a conceptual framework to investigate this topic in order to provide further conceptualisation and specification of the FPT and its technical and regulatory challenges and opportunities.
Joint Sensing-Communication for 5G MmWave CAVs
Figure 1: dynamic frame structure design
According to Zhang et al., 2021, the rapid advancement of artificial intelligence and mobile communication technology has revolutionized the autonomous driving vehicle. These vehicles typically incorporate various sensors, such as optical cameras, LiDAR, ultrasonic sensors, and mmWave radars. However, these sensors' detection capabilities are often compromised by dynamic radio environments and vehicle blockages, thus emphasizing the need for enhanced sensing solutions. Connected Automated Vehicles represent the next big step in autonomous driving technology. CAVs are fitted with in-vehicle sensors and V2X equipment: this renders the CAVs better suited to level 5 autonomous operation. Some research has highlighted the promising reality of using 5G millimeter wave (mmWave) communication technologies to improve the environmental sensing of isolated vehicles [24]. On some of the major issues related to raw sensing data sharing among CAVs this approach provides some solutions in terms of latency and data rate. The leftover problem for the existing autonomous vehicle systems can be a candidate for the Internet of Vehicles (IoV) and 5G mmWave communications. The technology for 5G cellular vehicle-to-everything (C-V2X) provides higher data rate and also ensures good information exchange between the vehicles thus helping in reducing blind zones in sensing [10]. It has increased the accuracy in the integration of information and enhanced vehicle safety as a whole.
Recent spectrum sharing technology based on joint sensing and communication design has been the focus of research. This way, solving low-latency raw sensing data sharing challenges amid enhanced CAV sensing capabilities is expected. Various solutions have been proposed. For instance, there are distributed networking protocols in the form of RadChat, which have shown utmost success in the reduction of radar mutual interference across vehicular networks [29]. The first simple attempts were related to the basic dual-functional aggregation of sensing and communication functionalities. For example, a method using information from radar in assisting beam alignment for 60 GHz mmWave communication was studied via Dedicated Short Range Communication (DSRC). Furthermore, integration of automotive radar at 76.5 GHz with mmWave communication at 65 GHz was investigated for separate antennas to enable radar-aided communication. While these early solutions offered basic sensing and communication capabilities, they only served to primarily solve simple coexistence issues between the two systems. This method incurred a significant amount of overhead in signaling as well as the hardware cost for the implementation and is not quite practical for creating a fully integrated system. Instead, the area has progressed further to more sophisticated solutions based on joint frame structure and waveform design as part of advancing integrated sensing and communication systems in autonomous vehicles.
Joint Secure Transmit Beamforming Designs for Integrated Sensing and Communication Systems
According to Chu et al., 2022, The use of wireless devices has tremendously grown in the recent past and coupled with the need for high data rate services, has presented complicated issues of spectrum scarcity in today’s telecommunication systems. Such a scenario has required subsequent spectrum sharing technologies, especially under the Integrated Sensing and Communication (ISAC). The new technology under this category enables; sharing of radar and communication systems over the same bands; thus, is a new frontier towards solving spectrum congestion issues. ISAC research has evolved along two primary directions: Specific subtopics include Dual-functional Radar-Communication (DFRC) and Radar and Communication Coexistence (RCC). As such, DFRC can concentrate on the implementation of the same signals from a single hardware platform to accomplish both the communication and radar sensing tasks. This approach has advantages such as low power consumption and a smaller platform size, but drawbacks arise from the global optimization of the waveform with bidirectional functionality and from the intricate hardware demands.
Another approach, RCC, is a logical approach that enables separately deployed communication and radar platforms to partially cooperate with each other through independent transmitted signals [14]. This approach has been used in a number of cases, including sharing of spectrum by airborne early warning radars with TDD LTE systems, as well as by battlefield surveillance radars with WLAN. Interference management in the case of RCC systems between non collocated base stations and radar transmitters has been critically discussed and that has created a need to include MIMO or Multi Input Multi Output architecture into both the radar and communication systems which introduced additional spatial dimensions and more flexibility in beam forming. Numerous methods of signal processing have been developed for constructing transmit beamformers of multi-antenna base stations and MIMO radar systems with the use of null space projection approaches [13]. These attempt to regulate interference lightly without negating the peak performance in both the communicational occurrence and the radar act.
Enable Joint Communication and Radar Sensing in Mobile Networks
Figure : Applications and use cases of PMN
According to Zhang et al., 2021, Mobile networks are not just evolving in terms of the communication function that they provide by moving to a more complex type which can be described as mobile networks with the additional function of joint communication and radar/radio sensing. This transformation has led to the development of perceptive mobile networks (PMN), a great leap in the development of wireless technology in integrating communication and sensing functionalities in one system infrastructure. With radio sensing integrated into mobile networks, information can be retrieved about objects and the environment surrounding radio transceivers. This goes beyond the traditional functions of radar in localization and tracking. This represents an important step toward ubiquitous sensing platforms that will support various smart applications while ensuring high-quality communication services. Historically, the relationship between wireless communication and radar sensing was one of parallel development with limited overlap, as these two areas share much commonality in the signal processing algorithms, devices, and system architectures.
Recently, there has been an increased effort to investigate their coexistence and cooperation. Initially, research activities focused on techniques for interference management in systems operating separately, utilizing methods such as beamforming design, cooperative spectrum sharing, and dynamic coexistence [15]. Probably the most significant advancement in this field is the notion of using a single transmitted signal simultaneously for both communication and radar sensing. This notion, of course, challenges the traditional reliance on specially designed waveforms like short pulses and chirps for radar. The feasibility of this approach has already been demonstrated by passive radar systems that successfully use various radio signals for sensing purposes, including TV signals, WiFi signals, or even mobile signals. The development of PMNs is an advanced approach in integrating sensing and communication capabilities [18]. It provides an opportunity for enhancing spectral efficiency, reducing system complexity, and new applications and services in mobile networks.
Waveform Design for Joint Sensing and Communications
According to Mao et al., 2022, Millimeter wave and low THz technology indeed marks a quantum leap in the world of wireless communications, significantly meeting the stringent demands of data traffic on the mobile network through 5G. The latest developments are expected to continue being made in the low terahertz spectrum over 100 GHz to achieve speeds of more than 100 Gbps, therefore making them foundational components for 6G in the future. Such massive communication capacity provided by mmWave and low-THz bands enabled a lot of bandwidth-intensive services, including augmented and virtual reality, high-definition video communication, and wireless backhauling. These technologies have better security features for military use due to the unique "pencil-like" extremely thin beam patterns [30]. In addition to their communication functions, these systems have significant potential for sensing applications, offering ultra-high-resolution estimations of target range and velocity due to their broad operational bandwidth and elevated carrier frequencies. A significant milestone in this field has been the integration of radar sensing and communication applications. The integration becomes more feasible since transceiver hardware architectures for both wireless communication and radar systems converged with the support of advances in digital signal processing [25]. Joint radar sensing and communication (JRC) was considered as the efficient approach where both the systems can operate on the combinations of transceiver system and operational frequency, thus providing better economy and frequency utilization.
The deployment of JRC systems has been explored through two fundamental philosophies: co-existence and co-design. In the schema of the coexistence strategy, both radar and the communication subsystem are considered as interferences. A direct way to counteract interference is to use a TDD operation where target sensing and data transfer are performed in different phases. This approach appears to have performance issues when both functions have to work at the same time in effect posing the likelihood of interference with performance. The challenge of coordination and operation has been met by a number of innovations including duplexing of precoder and decoder for purposes of improving the signal to interference plus noise density. They show that much progress has been made in overcoming technological barriers to the development of successful JRC systems. While analyzing radar signals used in such systems the following is noteworthy, the beam widths of these radar signals are fairly narrow which has turned out to improve on sensing performance especially due to multipath clutter impacts [19]. This coupling of communication and sensing appears as a big milestone in growth of wireless technology with more efficient and capable systems for future uses.
However, in the perspective of CAVs adopting 5G mmWave, the following section will clarify the core theories and models in the framework of the joint sensing and secure communication system. Design of such an integrated system with both communication and sensing has been inspired with an anti-theory base including but not limited to communication theory, sensing model, interference management and security theorems and theories. These models form the basis to design practical systems capable of performing in real-time environments including autonomous driving and vehicular networks.
Communication Theories for Sensing and Communication Integration
Another of the fundamental theories, which serve in designing the ISCS are the communication theories supported by Shannon’s Information Theory. This theory provides a foundation of data on which it is possible to develop more detailed information concerning the possibilities and potential of the communication channels and the possible load. The SNR principle and channel capacity play significant roles when it comes to system design particularly in areas regarding high data rate transmission in demanding terrain such as the CAV operating conditions. The application of the principles enables the exchange of data from one vehicle to another reducing the latency and bandwidth issues accompanying 5G mmWave communications [17]. This communication is important since many cases of an autonomous vehicle application require the exchange of near-instantaneous information without errors in various aspects such as the safety and functional aspects of a vehicle.
Integrated Sensing and Communication (ISAC) Models
Both the communication and sensing problems have appeared tied with the newly developed notion of an ISAC as its main concept. The systems based on ISAC will use this frequency for radar as well as for communication, eliminating the problems of limited spectrums and improving its effectiveness. Within the ISAC framework, two key streams are Dual-functional Radar Communication or DFRC and Radar and Communication Coexistence or RCC. This is particularly attractive to DFRC because signals can be transmitted using the radar and communication sector through the same ‘chip set’ which saves power and minimises the hardware requirements. Nevertheless, this method does have its drawbacks, of which are finding waveforms that can be optimal for both functions at the same time [16]. However, REC allows the independent separation of both radar and communication systems, usually utilizing different signals for each system, which seems to be a more realistic approach in practical applications using the existing radar and communication systems and continuously improving the corresponding signals’ interference management with the help of advanced signal processing techniques.
Waveform Design for Joint Sensing and Communication Systems
Part of the integrated systems involves developing a waveform for a joint radar communication (JRC) system. Unlike most conventional radar systems that signal waveforms include short pulse or chirp these waveforms are not very effective in communication especially in high bandwidth environments. JRC eliminates this by use of one waveform that has the capability of performing both radar and communication tasks in the most efficient way possible given the limited available space in the frequency spectrum. This joint capability is possible with the help of technologies such as Orthogonal Frequency Division Multiplexing which forms a part of the current wireless communication system. OFDM based waveforms possess a great amount of capability to reconfigure the bandwidth which synthesizes its compatibility for higher data rates with radar’s stringent requirement for accurate target identification. However, the conflict of two functions is the problem while implementing JRC As stated by Cao et al., 2021. Adaptive beamforming and null space projection are employed to address this interference to make both radar sensing and communication functions functional without compromising the system capabilities.
Secure Communication Models
Security is another important aspect of integrated sensing and communication systems used in the context of autonomous vehicles where communication must be used as the means of coordination and ensure safety. The incorporation of secure beamforming models is important for enabling exclusive communication between autonomous vehicles without compromising their content to an unauthorized third party. In an effort to do that, the beamforming in such systems is generally organized to direct the signals to the intended receiver while denying reception from unwanted receivers. This technique is very useful in vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) environments where data such as position of the car and the speed is sensitive in regards to private information and potential attackers interfering with the network. Another approach is known as Physical Layer Security (PLS) which utilizes properties of the wireless environment, making it very hard for the attacker to eavesdrop or demodulate the radiated information [26]. These techniques are especially useful where high security is required; as seen in defence or infrastructural security projects.
Interference Management Models
Interference management becomes the key for the proper functioning of sensing and communication systems jointly. The overall interference of multiple sources that Radar and communication experience the overlapping of signals of both radar and communication results in significant reduction in the performance of the two systems. The type of interference management is called Interference Alignment (IA) which aligns the interference across multiple communication links while attempting to optimize each link. This technique is more effective in high dimensional signal space such as the Multiple Input Multiple Output (MIMO) technology where different antennae space is used to handle interferences in an efficient manner [27]. Another prominent approach is known as Cooperative Interference Management in which different vehicular or network nodes coordinate and exchange data with regards to interference sources and adapt their transmitter settings.
Despite the tremendous progress in integrated sensing and communications (ISAC) technologies, there are yet a number of critical gaps that are yet to be addressed in the current literature. First, considerable work has been undertaken on the interference management between radar and communication systems, but only limited studies have addressed how such systems can dynamically adapt to rapidly changing environments. However, few existing solutions are applicable to a real time mobile setting where there are multiple targets moving across time and space and the channel conditions vary [23]. Secondly, there are no comprehensive studies in the literature regarding the practical implementation challenges of joint sensing and communication systems in commercial networks. Although theoretical frameworks have been developed, hardware limitations, system calibration requirements, and real world performance degradation factors are little understood in commercial deployments.
Finally, present research mainly focuses on system optimization and systems from an individual’s perspective, while attention is paid more on the network wide implementation. Understanding how multiple ISAC systems can properly operate together across a large-scale network environment, such as in an urban environment, with dense deployments, is not clearly addressed [21]. Additionally, there is little security work in integrated systems. Physical layer security has to some extent been addressed and there exist comprehensive security frameworks addressing sensing and communication vulnerability in the integrated systems. It is particularly to be reckoned with in applications which deal with sensitive data or critical infrastructures. Finally, research gaps in resource allocation optimization between sensing and communication functions for real time scenarios remain significant. However, much of the existing work in current solutions suggests static or semi static resource allocation methods and does not provide dynamic or adaptive resource management strategies that can adapt to changing quality of service requirements and changing environmental context [28]. In the domain of emerging applications requiring simultaneous high performance sensing and communication capabilities, this gap is particularly significant.
2.6 Conclusion
This chapter discussed various literature reviews on the mmWave technology focusing on the use of joint sensing and secure communication systems. The review also described the future promise of mmWave technology in overcoming the shortcomings of the conventional wearable health monitoring system through non-contact and high definition characteristics. Quantitative analysis showed how mmWave can identify important signals including heartbeat and respiration rate and offer essential information on the healthcare field in real time. Furthermore, theoretical models also highlighted the applicability of other methods like FFT and spectral analysis that forms the core of many sensing operations and improves their credibility. A study also showed how it is possible to use techniques such as AES and TLS to protect every communication to ensure security, privacy, and conform with the regulation of GDPR. However, gaps were recognized and they included relatively lacking simulation investigations and integration of sensing and secure communication elements into a single networked system. In addition, more could still be realized with machine learning solutions in enhancing signal analysis and enhancing system performance. To fulfil these gaps, it has suggested a conceptual framework that involves sensing, secure communication, and machine learning under an experimental MATLAB simulated environment. This framework should call for a solution to technical problems but without compromising privacy laws. The findings derived from this review will therefore help in the subsequent methodological and experimental parts of the study.
Reference List
Journals
Task 1: Essay Get free samples written by our Top-Notch subject experts for taking Assignment Help Online...View and Download
1. Introduction :The Role of Nursing in Stroke Care and Rehabilitation Take a look at this well-organized and thoroughly...View and Download
1. Introduction Get expert guidance with Online Assignment Help, ensuring top-quality academic support for sustainable business...View and Download
Introduction This assessment is totally based on the Economics of International Trade which critically analyzes the effect of...View and Download
Introduction: Starting A Business And Fundamentals...View and Download
1. Introduction Get free samples written by our Top-Notch subject experts for taking online Assignment...View and Download
Copyright 2025 @ Rapid Assignment Help Services
