Signal processing techniques for millimeter wave (mmWave) MIMO systems are critical for enabling next-generation wireless communication. mmWave frequencies offer higher bandwidths compared to conventional systems, enabling higher data rates. However, the use of large antenna arrays and power constraints necessitate new signal processing techniques. This article provides an overview of the challenges and opportunities in mmWave communication, with a focus on MIMO systems. The mmWave band is the frontier for commercial wireless communication systems. It offers larger spectral channels, enabling higher data rates. mmWave has applications in wireless local and personal area networks, 5G cellular systems, vehicular networks, and wearables. Signal processing is essential for mmWave systems due to the use of large arrays and power constraints. Techniques like compressed sensing and beamforming are important for mmWave communication. mmWave propagation has unique characteristics due to the small wavelength. Path loss increases with frequency, but directional antennas can mitigate this. mmWave channels have different models compared to lower frequencies, with more sparse and clustered paths. These characteristics influence signal processing algorithms. The use of large arrays in mmWave systems allows for MIMO communication, but hardware constraints and channel characteristics require new signal processing techniques. Hybrid analog-digital precoding and combining are promising approaches, as well as low-resolution ADCs and beam training protocols. These techniques help overcome the challenges of mmWave communication, including blockage and channel variability. The article discusses various signal processing techniques for mmWave MIMO systems, including analog beamforming, hybrid precoding, and low-resolution ADCs. It also highlights the importance of beam training and channel estimation in mmWave systems. The challenges of mmWave communication include blockage, channel variability, and the need for efficient signal processing algorithms. Future research focuses on developing algorithms that can exploit the sparsity and structure of mmWave channels.Signal processing techniques for millimeter wave (mmWave) MIMO systems are critical for enabling next-generation wireless communication. mmWave frequencies offer higher bandwidths compared to conventional systems, enabling higher data rates. However, the use of large antenna arrays and power constraints necessitate new signal processing techniques. This article provides an overview of the challenges and opportunities in mmWave communication, with a focus on MIMO systems. The mmWave band is the frontier for commercial wireless communication systems. It offers larger spectral channels, enabling higher data rates. mmWave has applications in wireless local and personal area networks, 5G cellular systems, vehicular networks, and wearables. Signal processing is essential for mmWave systems due to the use of large arrays and power constraints. Techniques like compressed sensing and beamforming are important for mmWave communication. mmWave propagation has unique characteristics due to the small wavelength. Path loss increases with frequency, but directional antennas can mitigate this. mmWave channels have different models compared to lower frequencies, with more sparse and clustered paths. These characteristics influence signal processing algorithms. The use of large arrays in mmWave systems allows for MIMO communication, but hardware constraints and channel characteristics require new signal processing techniques. Hybrid analog-digital precoding and combining are promising approaches, as well as low-resolution ADCs and beam training protocols. These techniques help overcome the challenges of mmWave communication, including blockage and channel variability. The article discusses various signal processing techniques for mmWave MIMO systems, including analog beamforming, hybrid precoding, and low-resolution ADCs. It also highlights the importance of beam training and channel estimation in mmWave systems. The challenges of mmWave communication include blockage, channel variability, and the need for efficient signal processing algorithms. Future research focuses on developing algorithms that can exploit the sparsity and structure of mmWave channels.