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Register a new userInferBeam: A Fast Beam Alignment Protocol for Millimeter-wave Networking
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Sai Qian Zhang
Publication date
2018
fields
Informatics Engineering
and research's language is
English
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We introduce fast millimeter-wave base station (BS) and its antenna sector selection for user equipment based on its location. Using a conditional random field inference model with specially designed parameters, which are robust to change of environment, InferBeam allows the use of measurement samples on best beam selection at a small number of locations to infer the rest dynamically. Compared to beam-sweeping based approaches in the literature, InferBeam can drastically reduce the setup cost for beam alignment for a new environment, and also the latency in acquiring a new beam under intermittent blockage. We have evaluated InferBeam using a discrete event simulation. Our results indicate that the system can make best beam selection for 98% of locations in test environments comprising smallsized apartment or office spaces, while sampling fewer than 1% of locations. InferBeam is a complete protocol for best beam inference that can be integrated into millimeter-wave standards for accelerating the much-needed fast and economic beam alignment capability.
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Millimeter wave (mmWave) communication has attracted increasing attention as a promising technology for 5G networks. One of the key architectural features of mmWave is the use of massive antenna arrays at both the transmitter and the receiver sides. Therefore, by employing directional beamforming (BF), both mmWave base stations (MBSs) and mmWave users (MUEs) are capable of supporting multi-beam simultaneous transmissions. However, most researches have only considered a single beam, which means that they do not make full potential of mmWave. In this context, in order to improve the performance of short-range indoor mmWave networks with multiple reflections, we investigate the challenges and potential solutions of downlink multi-user multi-beam transmission, which can be described as a high-dimensional (i.e., beamspace) multi-user multiple-input multiple-output (MU-MIMO) technique, including multi-user BF training, simultaneous users grouping, and multi-user multibeam power allocation. Furthermore, we present the theoretical and numerical results to demonstrate that beamspace MU-MIMO compared with single beam transmission can largely improve the rate performance of mmWave systems.
This article investigates beam alignment for multi-user millimeter wave (mmWave) massive multi-input multi-output system. Unlike the existing works using machine learning (ML), an alignment method with partial beams using ML (AMPBML) is proposed without any prior knowledge such as user location information. The neural network (NN) for the AMPBML is trained offline using simulated environments according to the mmWave channel model and is then deployed online to predict the beam distribution vector using partial beams. Afterwards, the beams for all users are all aligned simultaneously based on the indices of the dominant entries of the obtained beam distribution vector. Simulation results demonstrate that the AMPBML outperforms the existing methods, including the adaptive compressed sensing, hierarchical search, and multi-path decomposition and recovery, in terms of the total training time slots and the spectral efficiency.
Millimeter wave beam alignment (BA) is a challenging problem especially for large number of antennas. Compressed sensing (CS) tools have been exploited due to the sparse nature of such channels. This paper presents a novel deterministic CS approach for BA. Our proposed sensing matrix which has a Kronecker-based structure is sparse, which means it is computationally efficient. We show that our proposed sensing matrix satisfies the restricted isometry property (RIP) condition, which guarantees the reconstruction of the sparse vector. Our approach outperforms existing random beamforming techniques in practical low signal to noise ratio (SNR) scenarios.
This document describes the core concepts of the CCNx architecture and presents a minimum network protocol based on two messages: Interests and Content Objects. It specifies the set of mandatory and optional fields within those messages and describes their behavior and interpretation. This architecture and protocol specification is independent of a specific wire encoding.
Beam training in dynamic millimeter-wave (mm-wave) networks with mobile devices is highly challenging as devices must scan a large angular domain to maintain alignment of their directional antennas under mobility. Device rotation is particularly challenging, as a handheld device may rotate significantly over a very short period of time, causing it to lose the connection to the Access Point (AP) unless the rotation is accompanied by immediate beam realignment. We study how to maintain the link to a mm-wave AP under rotation and without any input from inertial sensors, exploiting the fact that mm-wave devices will typically be multi-band. We present a model that maps Time-of-Flight measurements to rotation and propose a method to infer the rotation speed of the mobile terminal using only measurements from sub-6 GHz WiFi. We also use the same sub-6 GHz WiFi system to reduce the angle error estimate for link establishment, exploiting the spatial geometry of the deployed APs and a statistical model that maps the user positions spatial distribution to an angle error distribution. We leverage these findings to introduce SLASH, a Statistical Location and rotation-Aware beam SearcH algorithm that adaptively narrows the sector search space and accelerates both link establishment and maintenance between mm-wave devices. We evaluate SLASH with experiments conducted indoors with a sub-6 GHz WiFi Time-of-Flight positioning system and a 60-GHz testbed. SLASH can increase the data rate by more than 41% for link establishment and 67% for link maintenance with respect to prior work.
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