132 research outputs found
Angle-of-arrival acquisition and tracking via virtual subarrays in an analog array
© 2019 IEEE. Angle-of-arrival (AoA) estimation is a challenging problem for analog antenna arrays. Typical algorithms use beam scanning and sweeping, which can be time-consuming, and the resolution is limited to the scanning step. In this paper, we propose a virtual-subarray based AoA estimation scheme, which divides an analog array into two virtual subarrays and can obtain a direct AoA estimate from every two temporal measurements. We propose different subarray constructions which lead to different range and accuracy of estimation. We provide detailed beamforming vector designs for these constructions and provide a performance lower bound for the estimator. We also present how to apply the estimator to AoA acquisition and tracking. Simulation results demonstrate that the proposed scheme significantly outperforms existing ones when the signal-to-noise ratio is not very low
AoA-aware Probabilistic Indoor Location Fingerprinting using Channel State Information
With expeditious development of wireless communications, location
fingerprinting (LF) has nurtured considerable indoor location based services
(ILBSs) in the field of Internet of Things (IoT). For most pattern-matching
based LF solutions, previous works either appeal to the simple received signal
strength (RSS), which suffers from dramatic performance degradation due to
sophisticated environmental dynamics, or rely on the fine-grained physical
layer channel state information (CSI), whose intricate structure leads to an
increased computational complexity. Meanwhile, the harsh indoor environment can
also breed similar radio signatures among certain predefined reference points
(RPs), which may be randomly distributed in the area of interest, thus mightily
tampering the location mapping accuracy. To work out these dilemmas, during the
offline site survey, we first adopt autoregressive (AR) modeling entropy of CSI
amplitude as location fingerprint, which shares the structural simplicity of
RSS while reserving the most location-specific statistical channel information.
Moreover, an additional angle of arrival (AoA) fingerprint can be accurately
retrieved from CSI phase through an enhanced subspace based algorithm, which
serves to further eliminate the error-prone RP candidates. In the online phase,
by exploiting both CSI amplitude and phase information, a novel bivariate
kernel regression scheme is proposed to precisely infer the target's location.
Results from extensive indoor experiments validate the superior localization
performance of our proposed system over previous approaches
Terahertz Communications for 6G and Beyond Wireless Networks: Challenges, Key Advancements, and Opportunities
The unprecedented increase in wireless data traffic, predicted to occur
within the next decade, is motivating academia and industries to look beyond
contemporary wireless standards and conceptualize the sixth-generation (6G)
wireless networks. Among various promising solutions, terahertz (THz)
communications (THzCom) is recognized as a highly promising technology for the
6G and beyond era, due to its unique potential to support terabit-per-second
transmission in emerging applications. This article delves into key areas for
developing end-to-end THzCom systems, focusing on physical, link, and network
layers. Specifically, we discuss the areas of THz spectrum management, THz
antennas and beamforming, and the integration of other 6G-enabling technologies
for THzCom. For each area, we identify the challenges imposed by the unique
properties of the THz band. We then present main advancements and outline
perspective research directions in each area to stimulate future research
efforts for realizing THzCom in 6G and beyond wireless networks.Comment: This work has been submitted to the IEEE for possible publication.
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Terahertz-Band Integrated Sensing and Communications: Challenges and Opportunities
The sixth generation (6G) wireless networks aim to achieve ultra-high data
transmission rates, very low latency and enhanced energy-efficiency. To this
end, terahertz (THz) band is one of the key enablers of 6G to meet such
requirements. The THz-band systems are also quickly merging as high-resolution
sensing devices because of their ultra-wide bandwidth and very narrow
beamwidth. As a means to efficiently utilize spectrum and thereby save cost and
power, THz integrated sensing and communications (ISAC) paradigm envisages a
single integrated hardware platform with common signaling mechanism. However,
ISAC at THz-band entails several design challenges such as beam split,
range-dependent bandwidth, near-field beamforming, and distinct channel model.
This article examines the technologies that have the potential to bring forth
ISAC and THz transmission together. In particular, it provides an overview of
antenna and array design, hybrid beamforming, integration with reflecting
surfaces and data-driven techniques such as machine learning. These systems
also provide research opportunities in developing novel methodologies for
channel estimation, near-field beam split, waveform design and beam
misalignment.Comment: 7pages, submitted to IEE
Adaptive and Robust Beam Selection in Millimeter-Wave Massive MIMO Systems
Future 6G wireless communications network will increase the data capacity to unprecedented numbers and thus empower the deployment of new real-time applications. Millimeter-Wave (mmWave) band and Massive MIMO are considered as two of the main pillars of 6G to handle the gigantic influx in data traffic and number of mobile users and IoT devices. The small wavelengths at these frequencies mean that more antenna elements can be placed in the same area. Thereby, high spatial processing gains are achievable that can theoretically compensate for the higher isotropic path loss. The propagation characteristics at mmWave band, create sparse channels in typical scenarios, where only few paths convey significant power. Considering this feature, Hybrid (analog-digital) Beamforming introduces a new signal processing framework which enables energy and cost-efficient implementation of massive MIMO with innovative smart arrays. In this setup, the analog beamalignment via beam selection in link access phase, is the critical performance limiting step. Considering the variable operating condition in mmWave channels, a desirable solution should have the following features: efficiency in training (limited coherence time, delay constraints), adaptivity to channel conditions (large SNR range) and robustness to realized channels (LOS, NLOS, Multipath, non-ideal beam patterns). For the link access task, we present a new energy-detection framework based on variable length channel measurements with (orthogonal) beam codebooks. The proposed beam selection technique denoted as composite M-ary Sequential Competition Test (SCT) solves the beam selection problem when knowledge about the SNR operating point is not available. It adaptively changes the test length when the SNR varies to achieve an essentially constant performance level. In addition, it is robust to non-ideal beam patterns and different types of the realized channel. Compared to the conventional fixed length energy-detection techniques, the SCT can increase the training efficiency up to two times while reducing the delay if the channel condition is good. Having the flexibility to allocate resources for channel measurements through different beams adaptively in time, we improve the SCT to eliminate unpromising beams from the remaining candidate set as soon as possible. In this way, the Sequential Competition and Elimination Test (SCET) significantly further reduces training time by increasing the efficiency. The developed ideas can be applied with different codebook types considered for practical applications. The reliable performance of the beam selection technique is evident through experimental evaluation done using the state-of-the-art test-bed developed at the Vodafone Chair that combines a Universal Software Radio Peripheral (USRP) based platform with mmWave frontends
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