Deep contextual bandits for fast neighbor-aided initial access in mmWave cell-free networks

Abstract Access points (APs) in millimeter-wave (mmWave) user-centric (UC) networks will have sleep mode functionality. Initial access (IA) is a challenging problem in UC networks due to the coherent serving of the users. In this letter, a novel deep contextual bandit (DCB) learning-based instantaneous beam selection method is proposed as a complementary tool to current IA schemes. In the proposed approach, the DCB model at an AP uses beam selection information from the neighboring active APs as the input to solve the beam search problem of the host AP. The proposed fast beam selection scheme enables APs to be in energy-saving modes while maintaining the ability to serve users without any delay when restored. Simulations are carried out with realistic channel models generated using a ray-tracing tool. The results show that the proposed system with the 5G IA scheme can respond to dynamic throughput demands with negligible latency compared to the 5G IA scheme without the proposed scheme

Deep contextual bandits for fast initial access in mmWave based user-centric ultra-dense networks

Abstract Millimeter wave (mmWave) based multiple-input multiple-output (MIMO) capable user-centric (UC) ultra-dense (UD) networks are suggested to facilitate high throughput requirements of future networks. Due to the high blockage susceptibility of mmWave, the connections may drop frequently. Hence efficient and fast beam management in initial access (IA) is essential. Current cellular systems use beam sweeping based IA mechanisms. UC UD concept requires all of its access points (APs) to perform IA. This leads to a shortage of orthogonal radio resources. Nonorthogonal resource allocation causes interference which leads to a higher misdetection probability. In this paper, we propose a novel deep contextual bandit (DCB) based approach to perform fast and efficient IA in mmWave based UC UD networks. The DCB model uses one reference signal from the user to predict the IA beam. The reduced use of reference signals improves beam discovery delay and relaxes the requirement for radio resources. Ray-tracing and stochastic channel model-based simulations show that the suggested system outperforms its beam sweeping counterpart in terms of probability of beam misdetection and beam discovery delay in mmWave based UC UD networks