Cooperative MIMO field measurements for military UHF band in low-rise urban environment

Hammons, A.R.; Hampton, Jerry R.; Merheb, N.M.; Cruz, Marcos Antônio

doi:10.1109/sam.2008.4606838

Cited by 9 publications

(7 citation statements)

References 6 publications

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“…Virtual MIMO systems have been introduced [30] to improve data-rate in a wide-area MIMO system by allowing multiple users to cooperate. Propagation modeling efforts in virtual MIMO includes inter-base-station cooperation measurements of capacity in [31], comparison of MIMO and single-input-single-output (SISO) links in [32], outdoor-toindoor cellular scenario in [33] and antenna selection for multiuser (MU)-MIMO based distributed antenna systems in [34]. However, propagation modeling in [34] is executed for WLAN application based on ray-tracing and therefore, is applicationspecific and location-specific.…”

Section: B Related Workmentioning

confidence: 99%

Virtual MIMO Wireless Sensor Networks: Propagation Measurements and Fusion Performance

Dey

Rossi

Butt

et al. 2019

IEEE Trans. Antennas Propagat.

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In this paper, we investigate the practical implications of employing virtual multiple-input-multiple output (MIMO) systems for prototyping future-generation wireless sensor networks, especially in the light of recently proposed distributed detection based decision fusion rules. In order to do that, an indoor-to-outdoor measurement campaign has been conducted recently for investigating the propagation characteristics of an 8 ⇥ 8 virtual multiple-input-multiple-output (MIMO) system. The campaign is conducted with transmit antennas representing the sensors deployed in different indoor environments and receive antennas mounted on an outside tower representing the decision fusion center. Channel measurements are reported when a 20 MHz wide signal is transmitted at 2.53 GHz. Measurements are collected for different spatial combinations of the transmit antennas. After analyzing the collected data, performance of different decision fusion rules are compared and tested over the measured channel. The results show that the fusion rules perform differently over different sets of measured channels. The results obtained here are important for maximizing performance and enabling air-interface design of next-generation wireless sensor networks.

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Section: B Related Workmentioning

confidence: 99%

Virtual MIMO Wireless Sensor Networks: Propagation Measurements and Fusion Performance

Dey

Rossi

Butt

et al. 2019

IEEE Trans. Antennas Propagat.

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“…Other works exhaustively characterize MIMO transmissions in the UHF band [10,15,19,22,25]. However, they focus on outdoor, Single-user MIMO transmissions and thus focus on point to point transmissions with a single transmitter/receiver pair, each equipped with multiple antennas.…”

Section: Uhf Band Characterizationmentioning

confidence: 99%

The case for UHF-band MU-MIMO

Anand

Guerra

Knightly

2014

Proceedings of the 20th Annual International Conference on Mobile Computing and Networking

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While the UHF band exhibits superior propagation characteristics compared to other frequency bands used for broadband communications, limited spectral availability in time and space necessitates high spectral efficiency techniques such as Multi-user MIMO (MU-MIMO). In this paper we design and implement the first open MU-MIMO Software-Defined Radio (SDR) platform that operates on an order of magnitude frequency range, from 300 MHz to 5.8 GHz.We perform a comprehensive set of over-the-air experiments to evaluate the potential of UHF-band MU-MIMO in comparison to 2.4 and 5.8 GHz WiFi bands encompassing a range of operating environments. We evaluate MU-MIMO performance in both outdoor, indoor, line-of-sight (LOS), and non-line-of-sight (NLOS) environments, and demonstrate that while the temporal correlation of the measured UHF environment is increased, it does not come at the cost of increased spatial correlation as measured by the Demmel condition number, thus proving highly attractive for MU-MIMO. This evaluation demonstrates the effectiveness of MU-MIMO transmission techniques in UHF bands for high spectral efficiency and low-overhead wireless access.

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“…By transmitting or receiving from geographically separated nodes, distributed transmission/reception can increase spatial diversity, resulting in improved throughput and reliability. Distributed transmission/reception is also of much interest for civil and military communications where a squad of radios can serve as distributed transmission/reception nodes in battle fields [4], [5]. We focus on distributed reception techniques in this paper.…”

Section: Introductionmentioning

confidence: 99%

Receive spatial coding for distributed diversity

Love

Choi

Bidigare

2013

2013 Asilomar Conference on Signals, Systems and Computers

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We consider a distributed reception scenario where the transmitter transmits a signal and multiple geographically separated receive nodes forward the processed version (using only a small number of bits) of their received signal to the fusion center. The fusion center tries to decode the transmitted signal only based on the forwarded bits from the distributed nodes. We show that there is a strong connection between distributed reception and channel coding and develop the unified framework of coded diversity techniques for distributed reception. To enable efficient encoding at the distributed nodes, we focus on linear block codes that achieve the Griesmer bound with equality to have the maximum diversity gain.

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Cooperative MIMO field measurements for military UHF band in low-rise urban environment

Cited by 9 publications

References 6 publications

Virtual MIMO Wireless Sensor Networks: Propagation Measurements and Fusion Performance

Virtual MIMO Wireless Sensor Networks: Propagation Measurements and Fusion Performance

The case for UHF-band MU-MIMO

Receive spatial coding for distributed diversity

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