348 research outputs found
Beamforming Design for Joint Localization and Data Transmission in Distributed Antenna System
A distributed antenna system is studied whose goal is to provide data
communication and positioning functionalities to Mobile Stations (MSs). Each MS
receives data from a number of Base Stations (BSs), and uses the received
signal not only to extract the information but also to determine its location.
This is done based on Time of Arrival (TOA) or Time Difference of Arrival
(TDOA) measurements, depending on the assumed synchronization conditions. The
problem of minimizing the overall power expenditure of the BSs under data
throughput and localization accuracy requirements is formulated with respect to
the beamforming vectors used at the BSs. The analysis covers both
frequency-flat and frequency-selective channels, and accounts also for
robustness constraints in the presence of parameter uncertainty. The proposed
algorithmic solutions are based on rank-relaxation and Difference-of-Convex
(DC) programming.Comment: 15 pages, 9 figures, and 1 table, accepted in IEEE Transactions on
Vehicular Technolog
Novel Solution for Multi-connectivity 5G-mmW Positioning
\ua9 2018 IEEE. The forthcoming fifth generation (5G) systems with high beamforming gain antenna units, millimeter-wave (mmWave) frequency bands together with massive Multiple Input Multiple Output (MIMO) techniques are key components for accurate positioning methods. In this paper, we propose the positioning technique that is relying on the sparsity in the MIMO-OFDM channel in time and spatial domains, together with effective beamforming methods. We will study the proposed solution in a multi-connectivity context, which has been considered so far for the purpose of improving the user equipment (UE) communication data rate. We utilize the multi-connectivity for positioning, in order to improve robustness to measurement errors and increase positioning service continuity. In particular, we show that when a UE that has connectivity to more base stations, the total power and delay needed for positioning can be reduced
Power Allocation and Parameter Estimation for Multipath-based 5G Positioning
We consider a single-anchor multiple-input multiple-output (MIMO) orthogonal
frequency-division multiplexing (OFDM) system with imperfectly synchronized
transmitter (Tx) and receiver (Rx) clocks, where the Rx estimates its position
based on the received reference signals. The Tx, having (imperfect) prior
knowledge about the Rx location and the surrounding geometry, transmits the
reference signals based on a set of fixed beams. In this work, we develop
strategies for the power allocation among the beams aiming to minimize the
expected Cram\'er-Rao lower bound (CRLB) for Rx positioning. Additional
constraints on the design are included to ensure that the line-of-sight (LOS)
path is detected with high probability. Furthermore, the effect of clock
asynchronism on the resulting allocation strategies is also studied. We also
propose a gridless compressed sensing-based position estimation algorithm,
which exploits the information on the clock offset provided by
non-line-of-sight paths, and show that it is asymptotically efficient.Comment: 30 pages, 6 figures, submitted to IEEE Transactions on Wireless
Communication
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Power Allocation and Parameter Estimation for Multipath-based 5G Positioning
We consider a single-anchor multiple-input multiple-output orthogonal frequency-division multiplexing system with imperfectly synchronized transmitter (Tx) and receiver (Rx) clocks, where the Rx estimates its position based on the received reference signals. The Tx, having (imperfect) prior knowledge about the Rx location and the surrounding geometry, transmits reference signals based on a set of fixed beams. We develop strategies for the power allocation among the beams aiming to minimize the expected Cram\ue9r-Rao lower bound for Rx positioning. Additional constraints on the design are included to make the optimized power allocation robust to uncertainty on the line-of-sight (LOS) path direction. Furthermore, the effect of clock asynchronism on the proposed allocation strategies is studied. Our evaluation results show that, for non-negligible synchronization error, it is optimal to allocate a large fraction of the available power for the illumination of the non-LOS (NLOS) paths, which help resolve the clock offset. In addition, the complexity reduction achieved by our proposed suboptimal approach incurs only a small performance degradation. We also propose an off-grid compressed sensing-based position estimation algorithm, which exploits the information on the clock offset provided by NLOS paths, and show that it is asymptotically efficient
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