BEAMFORMING TECHNIQUES FOR PATH LOSS MITIGATION IN mmWAVE COMMUNICATION SYSTEMS: A SYSTEMATIC REVIEW OF METHODS, TRENDS, AND CHALLENGES

Abstract

Fifth-generation (5G) and emerging sixth-generation (6G) networks lean heavily on millimeter-wave (mmWave) frequencies for their multi-gigahertz bandwidth, yet the same frequencies pay a steep propagation price: severe path loss, atmospheric absorption, and an acute vulnerability to blockage. Beamforming is the physical-layer answer, and it now spans an unwieldy mix of analog, digital, hybrid, reconfigurable-intelligent-surface (RIS)-assisted, and learning-based designs. This review takes stock of that landscape through 80 peer-reviewed papers published mainly between 2016 and 2026, screened against a PRISMA-informed protocol and synthesized thematically to surface methodological patterns, performance trade-offs, and unresolved gaps. Hybrid analog–digital architectures and deep-learning-assisted precoding dominate the recent literature, accounting for most 2024–2026 work, while RIS-based approaches are the fastest-growing sub-theme. The geographical picture is equally telling: 31 papers come from Nigerian-affiliated researchers, 9 from other African countries, and 40 from the rest of the world—a footprint that complicates the assumption that mmWave research happens elsewhere. The persistent challenges are familiar but stubborn: high pilot overhead, beam-squint in wideband operation, hardware impairments under low-resolution phase shifters, and the brittle generalization of data-driven models on non-stationary channels. The review closes with recommendations for standardized benchmarking, hardware-aware learning, and integrated sensing-communication, the directions most likely to make path-loss-robust mmWave beamforming workable in 6G.