Adaptive Beam Tracking and Refinement for 5G Networks

In 5G networks, significant performance gains are expected from directional beamforming/precoding solutions, to efficiently combat path loss, and to boost the spectral efficiency. In the massive MIMO context, maximal beamforming gain along with enhanced multi-users capacity can be achieved through narrow directive beams. However, prominent performance degradation results from beams misalignment, in severe mobility conditions, if narrow beams are used. In order to overcome this problem, to alleviate the risk of call drops in critical mobility context, while maximizing the precoding gains, the study relies on Multi-Resolution Codebook (MRC) with quantized beamwidths: narrow to large beams, tailored to mobility profiles. A flexible and joint beam Tracking and Refinement algorithm is then proposed. It adaptively steers the 5GNB towards appropriate beam selection, with the most suitable resolution. Performances of the algorithm demonstrate the benefit of the algorithm, not only in terms of reduced beam switching rate, but also, in terms of signal to noise ratio.