Beam Alignment Algorithm Research Articles

Fast Beam Alignment via Pure Exploration in Multi-Armed Bandits

The beam alignment (BA) problem consists in accurately aligning the transmitter and receiver beams to establish a reliable communication link in wireless communication systems. Existing BA methods search the entire beam space to identify the optimal transmit-receive beam pair. This incurs a significant latency when the number of antennas is large. In this work, we develop a bandit-based fast BA algorithm to reduce BA latency for millimeter-wave (mmWave) communications. Our algorithm is named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Two-Phase Heteroscedastic Track-and-Stop</i> (2PHT&S). We first formulate the BA problem as a pure exploration problem in multi-armed bandits in which the objective is to minimize the required number of time steps given a certain fixed confidence level. By taking advantage of the correlation structure among beams that the information from nearby beams is similar and the heteroscedastic property that the variance of the reward of an arm (beam) is related to its mean, the proposed algorithm groups all beams into several beam sets such that the optimal beam set is first selected and the optimal beam is identified in this set after that. Theoretical analysis and simulation results on synthetic and semi-practical channel data demonstrate the clear superiority of the proposed algorithm vis-à-vis other baseline competitors.

Beam alignments based on the spectrum decomposition of orbital angular momentums for acoustic-vortex communications

Given the enhanced channel capacity of wave chirality, acoustic communications based on the orbital angular momentum (OAM) of acoustic-vortex (AV) beams are of significant interest for underwater data transmissions. However, the stringent beam alignment is required for the coaxial arrangement of transceiver arrays to ensure the accuracy and reliability of OAM decoding. To avoid the required multiple measurements of the traditional orthogonality based algorithm, the beam alignment algorithm based on the OAM spectrum decomposition is proposed for AV communications by using simplified ring-arrays. Numerical studies of the single-OAM and OAM-multiplexed AV beams show that the error of the OAM spectrum increases with the translation distance and the deflection angle of the transceiver arrays. To achieve an ideal arrangement, two methods of the single-array translation alignment and the dual-array deflection alignment are developed based on the least standard deviation of the OAM spectrum (SD-OAM). By decreasing the SD-OAM towards zero using transceiver arrays of 16 transmitters and 16 receivers, accurate beam alignments are accomplished by multiple adjustments in three dimensions. The proposed method is also demonstrated by experimental measurements of the OAM dispersion and the SD-OAM for misaligned beams. The results demonstrate the feasibility of the rapid beam alignment based on the OAM spectrum decomposition by using simplified transceiver ring-arrays, and suggest more application potentials for acoustic communications.

Fast Beam Alignment for Millimeter Wave Time-Varying Channels Using Sparse Codes

In this paper, a novel beam alignment algorithm based on the sparse graph coding theory is proposed for millimeter wave (mmWave) time-varying channels. Firstly, a pilot design method is introduced to transform the mmWave time-varying beam alignment into a sparse-graph design and detection problem. Inspired by Low-Density-Parity-Check (LDPC) codes and fountain codes, a multi-stage sparse coding method is proposed for the design of the measurement matrix and the theoretical bound of the probability of success is derived to guide the design of the sparse-graph. A beam alignment algorithm is subsequently proposed to detect the beam index and estimate the carrier frequency offset (CFO). Then, the Carmeŕ-Rao Lower Bound (CRLB) is derived. Simulation results demonstrate that the proposed beam alignment algorithm achieves significant performance improvements over the conventional counterparts in both the noiseless and noise cases.

Millimeter-Wave Communications: Recent Developments and Challenges of Hardware and Beam Management Algorithms

In millimeter-wave (mmWave) communication systems, beam management has been recognized as an essential technique to ensure robust communication links. Recently, as an interest in developing a faster and more accurate beam management algorithm was triggered, various approaches have been devised and theoretically proved. However, most researchers have great difficulty in experimentally demonstrating realistic performance, since implementing the entire system in the mmWave band is another difficult challenge. In this article, we provide a well balanced understanding of those two essential technologies for mmWave communications: hardware technologies for prototyping and signal processing techniques for beam management. This article investigates the recent developments and future challenges to contribute to this active research area. Various commercial state-of-the-art mmWave system hardware components and beam management algorithms are comprehensively classified and compared. Meanwhile, the article provides a demonstration study based on the location-aided beam alignment algorithm by using an mmWave system prototype. Furthermore, we discuss open challenges and some future research directions.

Fast mmwave Beam Alignment via Correlated Bandit Learning

Beam alignment (BA) is to ensure the transmitter and receiver beams are accurately aligned to establish a reliable communication link in millimeter-wave (mmwave) systems. Existing BA methods search the entire beam space to identify the optimal transmit-receive beam pair, which incurs significant BA latency on the order of seconds in the worst case. In this paper, we develop a learning algorithm to reduce BA latency, namely Hierarchical Beam Alignment (HBA) algorithm. We first formulate the BA problem as a stochastic multi-armed bandit problem with the objective to maximize the cumulative received signal strength within a certain period. The proposed algorithm takes advantage of the correlation structure among beams such that the information from nearby beams is extracted to identify the optimal beam, instead of searching the entire beam space. Furthermore, the prior knowledge on the channel fluctuation is incorporated in the proposed algorithm to further accelerate the BA process. Theoretical analysis indicates that the proposed algorithm is asymptotically optimal. Extensive simulation results demonstrate that the proposed algorithm can identify the optimal beam with a high probability and reduce the BA latency from hundreds of milliseconds to a few milliseconds in the multipath channel, as compared to the existing BA method in IEEE 802.11ad.

Multi-Armed Bandit Beam Alignment and Tracking for Mobile Millimeter Wave Communications

We propose a novel beam alignment and tracking algorithm for time-varying millimeter wave channels with a dynamic channel support. Millimeter wave beam alignment is challenging due to the expected large number of antennas. A multi-armed bandit training beam selection policy is used to balance exploration of the set of feasible beams. We track the channel using a synthesis of sparse Bayesian learning and Kalman filtering and smoothing. Results show our algorithm has a more rapid rate of initial beam alignment compared to other beam selection policies and, for dynamic channel support, long-term beamforming gain commensurate to omni-directional training.

Fast beam alignment algorithm for multi‐user mmWave communications

The training overhead in pursuing pencil beams is commonly heavy for millimetre-wave (mmWave) communications, particularly in multi-user (MU) scenarios with conventional sequential-training methods. To reduce the training overhead, a parallel beam alignment algorithm is proposed for the MU mmWave system with a hybrid structure. The proposed algorithm jointly exploits parallel beam alignment among different user-subsets and hierarchical beam training for each user-subset. Consequently, the training overhead is significantly reduced to for the mmWave systems with transmit antennas. Numerical results verify that the proposed algorithm outperforms traditional alternatives in MU scenarios.

Interference-Aware Adaptive Beam Alignment for Hyper-Dense IEEE 802.11ax Internet-of-Things Networks.

The increasing use of Internet of Things (IoT) devices in specific areas results in an interference among them and the quality of communications can be severely degraded. To deal with this interference issue, the IEEE 802.11ax standard has been established in hyper-dense wireless networking systems. The 802.11ax adopts a new candidate technology that is called multiple network allocation vector in order to mitigate the interference problem. In this paper, we point out a potential problem in multiple network allocation vector which can cause delays to communication among IoT devices in hyper-dense wireless networks. Furthermore, this paper introduces an adaptive beam alignment algorithm for interference resolution, and analyzes the potential delays of communications among IoT devices under interference conditions. Finally, we simulate our proposed algorithm in densely deployed environments and show that the interference can be mitigated and the IEEE 802.11ax-based IoT devices can utilize air interface more fairly compared to conventional IEEE 802.11 distributed coordination function.

Swift-Link: A Compressive Beam Alignment Algorithm for Practical mmWave Radios

Next generation wireless networks will exploit the large amount of spectrum available at millimeter wave (mmWave) frequencies. Design of mmWave systems, however, is challenging due to strict power, cost and hardware constraints at higher bandwidths. To achieve a good SNR for communication, mmWave systems use large antenna arrays. Beamforming with highly directional beams is one way to use the antennas. As the channel changes over time, the beams that maximize the SNR have to be estimated quickly to reduce the training overhead. Prior work has exploited the observation that mmWave channels are sparse to perform compressed sensing (CS) based beam alignment with few channel measurements. Most of the existing CS-based algorithms, however, assume perfect synchronization and fail in the presence of carrier frequency offset (CFO). This paper presents Swift-Link, a fast beam alignment algorithm that is robust against the offset. Swift-Link includes a novel randomized beam training sequence that minimizes the beam alignment errors due to CFO and a low-complexity algorithm that corrects these errors. Even with strict hardware constraints, our algorithm uses fewer channel measurements than comparable CS algorithms and has analytical guarantees. Swift-Link requires a small output dynamic range at the analog-to-digital converter compared to beam-scanning techniques.

Adaptive Millimeter Wave Beam Alignment for Dual-Polarized MIMO Systems

Fifth-generation wireless systems are expected to employ multiple-antenna communication at millimeter wave (mmWave) frequencies using small cells within heterogeneous cellular networks. The high path loss of mmWave and the physical obstructions make communication challenging. To compensate for the severe path loss, mmWave systems may employ a beam alignment algorithm that facilitates highly directional transmission by aligning the beam direction of multiple antenna arrays. This paper discusses a mmWave system employing dual-polarized antennas. First, we propose a practical soft-decision beam alignment (soft-alignment) algorithm that exploits orthogonal polarizations. By sounding the orthogonal polarizations in parallel, the equality criterion of the Welch bound for training sequences is relaxed. Second, the analog beamforming system is adapted to the directional characteristics of the mmWave link, assuming a high Ricean $K$ -factor and poor scattering environment. A soft-alignment algorithm enables the mmWave system to align a large number of narrow beams to the channel subspace in an attempt to effectively scan the mmWave channel. Third, we propose a method to efficiently adapt the number of channel sounding observations to the specific channel environment based on an approximate probability of beam misalignment. Simulation results show that the proposed soft-alignment algorithm with adaptive sounding time effectively scans the channel subspace of a mobile user by exploiting polarization diversity.