1,606 research outputs found
On Optimal Multi-user Beam Alignment in Millimeter Wave Wireless Systems
Directional transmission patterns (a.k.a. narrow beams) are the key to
wireless communications in millimeter wave (mmWave) frequency bands which
suffer from high path loss and severe shadowing. In addition, the propagation
channel in mmWave frequencies incorporates only a few number of spatial
clusters requiring a procedure to align the corresponding narrow beams with the
angle of departure (AoD) of the channel clusters. The objective of this
procedure, called beam alignment (BA) is to increase the beamforming gain for
subsequent data communication. Several prior studies consider optimizing BA
procedure to achieve various objectives such as reducing the BA overhead,
increasing throughput, and reducing power consumption. While these studies
mostly provide optimized BA schemes for scenarios with a single active user,
there are often multiple active users in practical networks. Consequently, it
is more efficient in terms of BA overhead and delay to design multi-user BA
schemes which can perform beam management for multiple users collectively. This
paper considers a class of multi-user BA schemes where the base station
performs a one shot scan of the angular domain to simultaneously localize
multiple users. The objective is to minimize the average of expected width of
remaining uncertainty regions (UR) on the AoDs after receiving users'
feedbacks. Fundamental bounds on the optimal performance are analyzed using
information theoretic tools. Furthermore, a beam design optimization problem is
formulated and a practical BA scheme, which provides significant gains compared
to the beam sweeping used in 5G standard is proposed
Intelligent Interactive Beam Training for Millimeter Wave Communications
Millimeter wave communications, equipped with large-scale antenna arrays, are able to provide Gbps data by exploring abundant spectrum resources. However, the use of a large number of antennas along with narrow beams causes a large overhead in obtaining channel state information (CSI) via beam training, especially for fast-changing channels. To reduce beam training overhead, in this paper we develop an interactive learning design paradigm (ILDP) that makes full use of domain knowledge of wireless communications (WCs) and adaptive learning ability of machine learning (ML). Specifically, the ILDP is fulfilled via deep reinforcement learning (DRL), which yields DRL-ILDP, and consists of communication model (CM) module and adaptive learning (AL) module, which work in an interactive manner. Then, we exploit the DRL-ILDP to design efficient beam training algorithms for both multi-user and user-centric cooperative communications. The proposed DRL-ILDP based algorithms enjoy three folds of advantages. Firstly, ILDP takes full advantages of the existing WC models and methods. Secondly, ILDP integrates powerful ML elements, which facilitates extracting interested statistical and probabilistic information from environments. Thirdly, via the interaction between the CM and AL modules, the algorithms are able to collect samples and extract information in real-time and sufficiently adapt to the ever-changing environments. Simulation results demonstrate the effectiveness and superiority of the designed algorithms
Beam-Pattern Assisted Low-Complexity Beam Alignment for Fixed Wireless mmWave xHaul
This paper presents the design of two-stage beam alignment methods employing a hybrid analog-digital antenna array and exploiting the beam pattern in a point-to-point millimeter-wave (mmWave) radio for mmWave massive multiple-input multiple-output systems. We investigate an antenna deactivating approach that generates wider beams at the coarse alignment stage and exploit the theoretical beam pattern at the fine alignment stage. Our numerical results show that the proposed two-stage methods can achieve a better beam alignment than existing exhaustive methods and avail measurements/complexity reductions by tuning key parameters governing the alignment performance
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