Millimeter (mmWave) beamforming is an integral component of fifth-generation (5G) and beyond radio commu-nications. 5G beamforming involves the initial beam selection procedure using a codebook with multiple radio beam directions. Conventional codebook-based alignment schemes involve exhaustive sweeping over the predefined beam directions, the number of which increases significantly with large numbers of antennas resulting in undesirable latency and communications signal overhead. In this paper, we propose a novel algebraic-based codebook using Gröbner basis polynomial solvers to reduce the signal overhead during beam alignment. We also analyze the complexity-performance tradeoff between the proposed algebraic-based codebook and the exhaustive-based beam alignment across different monomial thresholds, multiple antenna configurations and radio contextual location information. Our results show that the proposed approach reduces the beam-search overhead at an average complexity reduction ratio of 73.95% with a performance tradeoff error of 32.25%.

We propose a new power amplifier (PA) behavioral modeling approach, to characterize and compensate for the signal quality degrading effects induced by a PA with a machine learning (ML) aided piece-wise (PW) modeling approach. Instead of using a single pruned Volterra model, we use multiple small-size pruned Volterra models by classifying the input data into different classes. For that purpose, an ML classifier model is trained by extracting some crucial features from both the input signal statistics and the PA operating point. The simulation results indicate that our approach contributes to an improved performance/complexity trade-off than a single generalized memory polynomial (GMP) model in terms of PA behavior modeling and linearization.

Unmanned aerial vehicles (UAVs) are the emerging vital components of millimeter wave (mmWave) wireless systems. Accurate beam alignment is essential for efficient beam based mmWave communications of UAVs with base stations (BSs). Conventional beam sweeping approaches often have large overhead due to the high mobility and autonomous operation of UAVs. Learning-based approaches greatly reduce the overhead by leveraging UAV data, like position to identify optimal beam directions. In this paper, we propose a deep Q-Network(DQN)-based framework for uplink UAV-BS beam alignment where the UAV hovers around 5G new radio (NR) BS coverage area, with varying channel conditions. The proposed learning framework uses the location information and maximize the beamforming gain upon every communication request from UAV inside the multi-location environment. We compare the proposed framework against multi-armed bandit (MAB)-based and exhaustive approaches, respectively and then analyse its training performance over different coverage area requirements, antenna configurations and channel conditions. Our results show that the proposed framework converge faster than the MAB-based approach and comparable to traditional exhaustive approach in an online manner under real-time conditions. Moreover, this approach can be further enhanced to predict the optimal beams for unvisited UAV locations inside the coverage using correlation from neighbouring grid locations.

Unmanned aerial vehicles (UAVs) are the vital components of sixth generation (6G) millimeter wave (mmWave) wireless networks. Fast and reliable beam alignment is essential for efficient beam-based mmWave communications between UAVs and the base stations (BSs). Learning-based approaches may greatly reduce the overhead by leveraging UAV data, such as position, to identify the optimal beam directions. In this paper, we propose a deep reinforcement learning (DRL)-based framework for UAV-BS beam alignment using the hierarchical deep Q-Network (hDQN) in a mmWave radio setting. We consider uplink communications where the UAV hovers around 5G new radio (NR) BS coverage area, with three dimensional (3D) beams under diverse channel conditions. A BS serves with learnt beam-pairs in an uplink manner upon every communication request from UAV inside the multi-location environment. Compared to our prior DQN-based method, the proposed hDQN framework uses the location information and the fixed spatial arrangement of the antenna elements to reduce the beam search complexity and maximize the data rates efficiently. The results show that our proposed hDQN-based framework converges faster than the DQN-based approach with an average overall training reduction of 43% and, is generic to multi-location environments across different uniform planar array (UPA) configurations and diverse channel conditions.

Unmanned aerial vehicles (UAVs) are the emerging vital components of millimeter wave (mmWave) wireless systems. Accurate beam alignment is essential for efficient beam based mmWave communications of UAVs with base stations (BSs). Conventional beam sweeping approaches often have large overhead due to the high mobility and autonomous operation of UAVs. Learning-based approaches greatly reduce the overhead by leveraging UAV data, like position to identify optimal beam directions. In this paper, we propose a reinforcement learning (RL)-based framework for UAV-BS beam alignment using deep Q-Network (DQN) in a mmWave setting. We consider uplink communications where the UAV hovers around 5G new radio (NR) BS coverage area, with varying channel conditions. The proposed learning framework uses the location information to maximize data rate through the optimal beam-pairs efficiently, upon every communication request from UAV inside the multi-location environment. We compare our proposed framework against Multi-Armed Bandit (MAB) learning-based approach and the traditional exhaustive approach, respectively and also analyse the training performance of DQN-based beam alignment over different coverage area requirements and channel conditions. Our results show that the proposed DQN-based beam alignment converge faster and generic for different environmental conditions. The framework can also learn optimal beam alignment comparable to the exhaustive approach in an online manner under real-time conditions.

We study the performance of a prototype of a millimeter-wave transceiver with radio frequency (RF) beamforming capabilities. More specifically, the focus is on the architecture and software implementation of a smart-RF that allows self-beam-alignment. Simulation studies shows the expected performance in terms of probability of misalignment and coverage using different beam search strategies as well as realistic antenna beam patterns and phase-shifters quantization constraints.