Wireless Communication with Artificial Intelligence: Emerging Trends and Applications

Wireless communication has seen significant advancements, with emerging technologies such as wireless sensor networks, IoT, cloud computing, mm-Wave, Massive MIMO, CR, VLC, wireless optical communication, signal processing, and channel modeling. AI-based applications, machine learning techniques, and deep learning are key research areas for reliable wireless communication.

Format: Hardback
Length: 352 pages
Publication date: 16 September 2022
Publisher: Taylor & Francis Ltd

Wireless communication has seen significant advancements in recent years, driven by the increasing demand for high-speed data transmission and the proliferation of wireless devices. However, designing reliable wireless communication systems remains a challenging task due to various design challenges. In this reference text, we will discuss the latest advances in wireless communication, including wireless sensor networks, Internet of Things (IoT), cloud computing, millimeter-wave (mm-Wave), massive MIMO, cognitive radios (CR), visible light communication (VLC), wireless optical communication, signal processing, and channel modeling.

Wireless sensor networks are a rapidly growing area of research that involves the deployment of large numbers of sensors to monitor and collect data in various environments. These networks have numerous applications, such as environmental monitoring, smart cities, healthcare, and agriculture. However, designing reliable wireless sensor networks is challenging due to the limited power and bandwidth of the sensors, the mobility of the nodes, and the harsh environmental conditions.

Internet of Things (IoT) is another emerging technology that has the potential to revolutionize the way we live and work. IoT refers to the network of physical devices, such as sensors, cameras, and smart appliances, that are connected to the internet and can communicate with each other. IoT has numerous applications, including smart homes, smart cities, healthcare, and transportation. However, designing reliable wireless communication systems for IoT is challenging due to the large number of devices, the diverse nature of the applications, and the stringent security requirements.

Cloud computing is another emerging technology that has the potential to transform the way we store, process, and analyze data. Cloud computing involves the use of remote servers to store and process data instead of local storage devices. Cloud computing has numerous applications, such as data storage, data analysis, and artificial intelligence. However, designing reliable wireless communication systems for cloud computing is challenging due to the high bandwidth requirements, the mobility of the users, and the unpredictable nature of the internet.

Millimeter-wave (mm-Wave) is a new wireless technology that operates in the high-frequency range, ranging from 30 GHz to 300 GHz. mm-Wave has the potential to provide high-speed data transmission with low latency and high bandwidth. However, designing reliable wireless communication systems for mm-Wave is challenging due to the high attenuation of the signal in the atmosphere, the limited availability of spectrum, and the high cost of the equipment.

Massive MIMO is a technology that uses multiple antennas to transmit and receive data simultaneously. Massive MIMO has the potential to provide high-speed data transmission with low latency and high bandwidth. However, designing reliable wireless communication systems for massive MIMO is challenging due to the complexity of the signal processing, the high cost of the equipment, and the limited availability of spectrum.

Cognitive radios (CR) are a technology that allows wireless devices to adapt their communication behavior to the environment. CR has the potential to improve the efficiency of wireless communication systems by reducing interference and improving spectrum utilization. However, designing reliable wireless communication systems for CR is challenging due to the complexity of the signal processing, the uncertainty of the environment, and the limited availability of spectrum.

Visible light communication (VLC) is a technology that uses visible light to transmit data. VLC has the potential to provide high-speed data transmission with low latency and high bandwidth. However, designing reliable wireless communication systems for VLC is challenging due to the limited availability of spectrum, the sensitivity of the light to interference, and the high cost of the equipment.

Wireless optical communication is a technology that uses optical signals to transmit data. Wireless optical communication has the potential to provide high-speed data transmission with low latency and high bandwidth. However, designing reliable wireless communication systems for wireless optical communication is challenging due to the complexity of the signal processing, the limited availability of spectrum, and the high cost of the equipment.

Signal processing is a critical component of wireless communication systems. Signal processing involves the manipulation of signals to improve their quality, reduce interference, and enhance communication performance. Signal processing techniques include modulation, coding, and detection. However, designing reliable wireless communication systems for signal processing is challenging due to the complexity of the signal processing, the uncertainty of the environment, and the limited availability of spectrum.

Channel modeling is a technique that is used to predict the behavior of wireless channels. Channel modeling involves the use of mathematical models to describe the characteristics of the wireless channel, such as the channel gain, channel delay, and channel noise. Channel modeling is critical for designing reliable wireless communication systems, as it allows engineers to predict the performance of the system in different environments.

Artificial intelligence-based applications in wireless communication are a rapidly growing area of research. Artificial intelligence-based applications include machine learning techniques and challenges in wireless sensor networks, and deep learning for channel and bandwidth estimation during optical wireless communication. Machine learning techniques are used to improve the performance of wireless communication systems by learning from data and adapting to the environment. Machine learning techniques include supervised learning, unsupervised learning, and reinforcement learning.

Wireless sensor networks are a rapidly growing area of research that involves the deployment of large numbers of sensors to monitor and collect data in various environments. Wireless sensor networks have numerous applications, such as environmental monitoring, smart cities, healthcare, and agriculture. However, designing reliable wireless sensor networks is challenging due to the limited power and bandwidth of the sensors, the mobility of the nodes, and the harsh environmental conditions.

Machine learning techniques are used to improve the performance of wireless sensor networks by learning from data and adapting to the environment. Machine learning techniques include supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves training a machine to predict the output of a system based on input data. Unsupervised learning involves training a machine to find patterns in data without any prior knowledge. Reinforcement learning involves training a machine to make decisions based on feedback from the environment.

Deep learning is a type of machine learning that involves the use of neural networks to model complex systems. Deep learning is used to improve the performance of wireless communication systems by learning from data and adapting to the environment. Deep learning involves training a neural network to recognize patterns in data and make decisions based on those patterns.

Deep learning is used to improve the performance of wireless communication systems by learning from data and adapting to the environment. Deep learning involves training a neural network to recognize patterns in data and make decisions based on those patterns.

In conclusion, wireless communication has seen significant advancements in recent years, driven by the increasing demand for high-speed data transmission and the proliferation of wireless devices. However, designing reliable wireless communication systems remains a challenging task due to various design challenges. In this reference text, we have discussed the latest advances in wireless communication, including wireless sensor networks, Internet of Things (IoT), cloud computing, millimeter-wave (mm-Wave), massive MIMO, cognitive radios (CR), visible light communication (VLC), wireless optical communication, signal processing, and channel modeling. We have also discussed the challenges and opportunities in designing reliable wireless communication systems for these emerging technologies. We believe that this reference text will be useful for senior undergraduate, graduate students, and professionals in the fields of electrical engineering, and electronics and communication engineering.

Dimension: 234 x 156 (mm)
ISBN-13: 9781032137124