DoA Estimation Using Reconfigurable Antennas in MillimiterWave Frequency 5G Systems To achieve low latency and high throughputs, future 5G systems will have to utilize complex antenna systems able to provide beamforming and directionofarrival (DoA) estimation capabilities. Most of the concepts available in the literature rely on analog or digital beamforming, which is well developed and can be used both at a base station and in a user terminal. However, in applications, in which energyefficiency or cost is one of the key concerns, the use of traditional beamforming systems is limited. To overcome this limitation, reconfigurable antennas can be used as they rely on a simple but effective concept, in which a transceiver is connected to a single input port and beamforming capabilities are enabled by a number of passive elements electronically switched via transceiveru0027s digital inputoutput ports. In the article, we show how simple singleinput mmwave reconfigurable antenna can be used to provide not only beamforming but also DoA capabilities relying on received signal strength measurements. Therefore, the proposed approach can be applied in future 5G millimeterwave nodes or gateways, in which lowcost and powerefficiency are important.

Deeplearning Based Adaptive Beam Management Technique for Mobile Highspeed 5G mmWave Networks 5G core frequency band, mmWave, is about ten times wider than the existing commercial frequency band, enabling various services to be created. However, due to the characteristics of the mmWave frequency, it has various limitations. In the mobile environment, the misaligned beam problem, in which the SNR is degraded because the beam align between the sender and the receiver does not match, is one of the biggest problems to be solved. In this paper, we propose a adaptive beam management scheme based on deeplearning to solve misaligned beam problem. In the proposed scheme, 5G basestation (gNB) learns the mobility information, SNR, and current beam information of the associated user equipment (UE) by the deeplearning agent, and predicts whether the beam is aligned or not. From the prediction result, gNB and UE perform beam handsoff in advance before loss of connectivity.