Massive explorations in 5G and 5G communication technologies have envisioned the need for large-scale wireless networks that are potentially capable of handling high data traffic without compromising on the link reliability. This specific requirement demands architectural developments in wireless networks to support the data-hungry mobile communication systems. One of the critical aspects of large-scale wireless networks is the efficiency of the resources in the wireless system. The 5G and 5G technologies are engineered to work seamlessly in heterogeneous platforms unlike 2G, 3G, and 4G wireless networks. This heterogeneous nature includes the mobile devices, signal processing techniques, associated spectrum allocation, and most importantly the network model.

To meet the needs of this heterogeneity in 5G and 5G wireless systems, researchers constantly focus on the implementation of novel and robust signal processing algorithms. In addition, efficiency and performance enhancement can be achieved by adopting machine learning techniques that are supposed to empower the signal processing algorithms. The signal processing algorithms that use machine learning techniques are anticipated to improve the channel capacity, link reliability, device compatibility, and thereby enhance the user flexibility in adopting the technology.

This Special Issue is targeted to consolidate the theory and prospective implementation of machine learning algorithms in future wireless systems. Furthermore, machine learning techniques play a pivotal role in antenna modeling for large-scale wireless systems. We welcome original research and review articles.

Potential topics include but are not limited to the following:

• Machine learning empowered signal processing algorithms
• Machine learning-based optimization of resource allocation
• Front haul, backhaul, and cross layer-based optimization techniques
• Sensing techniques using machine learning and signal processing
• Location and context-aware communication algorithms using machine learning in signal processing
• Machine learning-based solutions for network coverage
• Modulation, acquisition, and synchronization algorithms for large-scale wireless networks
• Machine learning-based antenna modeling in massive Multiple-Input Multiple-Output (MIMO) systems
• Energy auditing using machine learning and efficiency enhancement techniques
• Testbed and hardware implementation of future wireless systems