The article provides an overview of signal processing techniques for millimeter wave (mmWave) wireless communication systems, emphasizing the challenges and advancements in MIMO (Multiple-Input Multiple-Output) communication at higher carrier frequencies. Key points include: 1. **Introduction to mmWave**: mmWave systems offer higher bandwidth and are crucial for applications such as 5G cellular systems, wireless local and personal area networks, and vehicular networks. The use of large antenna arrays and radio frequency constraints necessitate new MIMO signal processing techniques. 2. **Channel Models and Propagation**: mmWave channels differ from lower frequency channels due to their shorter wavelengths, leading to unique propagation characteristics such as higher path loss, severe blockage vulnerability, and significant scattering. Statistical models and stochastic geometry are used to evaluate coverage and capacity in mmWave cellular networks. 3. **MIMO Architectures**: Several MIMO architectures are discussed, including analog beamforming, hybrid analog-digital precoding and combining, and low-resolution receivers. Each architecture addresses hardware constraints and channel characteristics, with implications for signal processing algorithms. 4. **Signal Processing Techniques**: - **Analog Beamforming**: Simple and widely used, but limited to single-user and single-stream transmission. - **Hybrid Analog-Digital Precoding and Combining**: Enhances MIMO benefits by dividing the optimization process between analog and digital domains, supporting spatial multiplexing and multi-user MIMO. - **Low Resolution Receivers**: Reduces power consumption by using low-resolution ADCs, but requires different channel estimation and precoding strategies. 5. **Precoding and Combining**: The process is more complex at mmWave due to the need to configure more parameters, the intertwined nature of the channel and analog beamforming, and the increased sparsity and structure in the channel. 6. **Beam Training Protocols**: Closed-loop beam training strategies, such as codebook-based methods, are used to design analog beamformers without explicit channel estimation. The article highlights the critical role of signal processing in mmWave systems, particularly in addressing hardware constraints, channel models, and large arrays, and provides a comprehensive review of the state-of-the-art in mmWave wireless communication systems.The article provides an overview of signal processing techniques for millimeter wave (mmWave) wireless communication systems, emphasizing the challenges and advancements in MIMO (Multiple-Input Multiple-Output) communication at higher carrier frequencies. Key points include: 1. **Introduction to mmWave**: mmWave systems offer higher bandwidth and are crucial for applications such as 5G cellular systems, wireless local and personal area networks, and vehicular networks. The use of large antenna arrays and radio frequency constraints necessitate new MIMO signal processing techniques. 2. **Channel Models and Propagation**: mmWave channels differ from lower frequency channels due to their shorter wavelengths, leading to unique propagation characteristics such as higher path loss, severe blockage vulnerability, and significant scattering. Statistical models and stochastic geometry are used to evaluate coverage and capacity in mmWave cellular networks. 3. **MIMO Architectures**: Several MIMO architectures are discussed, including analog beamforming, hybrid analog-digital precoding and combining, and low-resolution receivers. Each architecture addresses hardware constraints and channel characteristics, with implications for signal processing algorithms. 4. **Signal Processing Techniques**: - **Analog Beamforming**: Simple and widely used, but limited to single-user and single-stream transmission. - **Hybrid Analog-Digital Precoding and Combining**: Enhances MIMO benefits by dividing the optimization process between analog and digital domains, supporting spatial multiplexing and multi-user MIMO. - **Low Resolution Receivers**: Reduces power consumption by using low-resolution ADCs, but requires different channel estimation and precoding strategies. 5. **Precoding and Combining**: The process is more complex at mmWave due to the need to configure more parameters, the intertwined nature of the channel and analog beamforming, and the increased sparsity and structure in the channel. 6. **Beam Training Protocols**: Closed-loop beam training strategies, such as codebook-based methods, are used to design analog beamformers without explicit channel estimation. The article highlights the critical role of signal processing in mmWave systems, particularly in addressing hardware constraints, channel models, and large arrays, and provides a comprehensive review of the state-of-the-art in mmWave wireless communication systems.