Network management, whether for malfunction analysis, failure prediction, performance monitoring and improvement, generally involves large amounts of data from different sources. To effectively integrate and manage these sources, automatically finding semantic matches among their schemas or ontologies is crucial. Existing approaches on database matching mainly fall into two categories. One focuses on the schema-level matching based on schema properties such as field names, data types, constraints and schema structures. Network management databases contain massive tables (e.g., network products, incidents, security alert and logs) from different departments and groups with nonuniform field names and schema characteristics. It is not reliable to match them by those schema properties. The other category is based on the instance-level matching using general string similarity techniques, which are not applicable for the matching of large network management databases. In this article, we develop a matching technique for large NEtwork MAnagement databases (NEMA) deploying instance-level matching for effective data integration and connection. We design matching metrics and scores for both numerical and non-numerical fields and propose algorithms for matching these fields. The effectiveness and efficiency of NEMA are evaluated by conducting experiments based on ground truth field pairs in large network management databases. Our measurement on large databases with 1,458 fields, each of which contains over 10 million records, reveals that NEMA can achieve accuracy of 95%. We further compare with several other existing algorithms, and show that NEMA outperforms them by 7%-15% in numerical matching and achieves the best trade-off for non-numerical matching.

A Multi-User MIMO (MU-MIMO) Access Point (AP) can obtain a capacity gain by simultaneously transmitting to multiple clients. This technique requires Channel State Information (CSI) at the transmitting AP to set antenna gains and phases to enable simultaneous reception through beamforming. The AP must also select both the mode (number of transmit and collective receive antennas) and the user set prior to transmission. While the ideal mode and user selection is a function of CSI, CSI must be estimated with an overhead intensive channel sounding process. We design, implement, and evaluate Pre-sounding User and Mode selection Algorithm (PUMA), a method for mode and user selection prior to channel sounding. We show that even without CSI, PUMA (i) exploits theoretical properties of MU-MIMO system scaling with respect to mode, (ii) characterizes the relative cost of each potential mode, and (iii) estimates per-stream transmission rate and aggregate throughput in each mode for a potential user set, all without CSI. Once PUMA has selected the appropriate mode and user group, the chosen protocol's channel sounding method is used on the intended user subset to carry out the transmission. We show that, on average, PUMA selects the mode and group that achieves an aggregate rate within 3% of the saturation throughput of what would have been achieved by sounding all users (which would require significant additional overhead). Moreover, we show that PUMA obtains 30% higher aggregate throughput compared to the best fixed-mode policy that uses the maximum number of available transmit and receive antennas.

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.

We present a real-time software-defined radio (SDR) platform for prototyping and measuring the performance of broadband UHF radio networks operating over long distances with point-to-multipoint (PTMP) non-line-of-sight (NLOS) networks. Enabled by the Wideband UHF Radio Card (WURC), a custom high-power and frequency-flexible radio transceiver daughter-card for FPGA-based digital basebands, the 802.11 DCF-like MAC and PHY implementation is completely open source. We demonstrate a long-range PTMP NLOS network bonding several of the white space television channels available in Maui, Hawaii. Off-the-shelf client devices can use this network via 802.11a/g links implemented with the same SDR framework. The multi-carrier channel estimates and real-time MAC statistics of connected nodes in UHF and 2.4 GHz are recorded and displayed in real-time, demonstrating an unprecedented amount of flexibility in unlicensed frequency bands, enabling real-time TV-band cognitive networks and small-cell research deployments.

We present the design and experimental evaluation of Simultaneous TRansmission with Orthogonally Blinded Eavesdroppers (STROBE). STROBE is a cross-layer approach that exploits the multi-stream capabilities of existing technologies such as 802.11n and the upcoming 802.11ac standard where multi-antenna APs can construct simultaneous data streams using Zero-Forcing Beamforming (ZFBF). Instead of using this technique for simultaneous data stream generation, STROBE utilizes ZFBF by allowing an AP to use one stream to communicate with an intended user and the remaining streams to orthogonally “blind” (actively interfere with) any potential eavesdropper thereby preventing eavesdroppers from decoding nearby transmissions. Through extensive experimental evaluation, we show that STROBE consistently outperforms Omnidirectional, Single-User Beamforming (SUBF), and directional antenna based transmission methods by keeping the transmitted signal at the intended receiver and shielded from eavesdroppers. In an indoor Wireless LAN environment, STROBE consistently serves an intended user with an SINR 15 dB greater than an eavesdropper.

Multi-user multiple-input multiple-output theory predicts manyfold capacity gains by leveraging many antennas on wireless base stations to serve multiple clients simultaneously through multi-user beamforming (MUBF). However, realizing a base station with a large number antennas is non-trivial, and has yet to be achieved in the real-world. We present the design, realization, and evaluation of Argos, the first reported base station architecture that is capable of serving many terminals simultaneously through MUBF with a large number of antennas (M >> 10). Designed for extreme flexibility and scalability, Argos exploits hierarchical and modular design principles, properly partitions baseband processing, and holistically considers real-time requirements of MUBF. Argos employs a novel, completely distributed, beamforming technique, as well as an internal calibration procedure to enable implicit beamforming with channel estimation cost independent of the number of base station antennas. We report an Argos prototype with 64 antennas and capable of serving 15 clients simultaneously. We experimentally demonstrate that by scaling from 1 to 64 antennas the prototype can achieve up to 6.7 fold capacity gains while using a mere 1/64th of the transmission power.

Multi-User MIMO promises to increase the spectral efficiency of next generation wireless systems and is currently being incorporated in future industry standards. Although a significant amount of research has focused on theoretical capacity analysis, little is known about the performance of such systems in practice. In this paper, we present the design and implementation of the first multi-user beamforming system and experimental framework for wireless LANs. Using extensive measurements in an indoor environment, we evaluate the impact of receiver separation distance, outdated channel information due to mobility and environmental variation, and the potential for increasing spatial reuse. For the measured indoor environment, our results reveal that two receivers achieve close to maximum performance with a minimum separation distance of a quarter of a wavelength. We also show that the required channel information update rate is dependent on environmental variation and user mobility as well as a per-link SNR requirement. Assuming that a link can tolerate an SNR decrease of 3 dB, the required channel update rate is equal to 100 and 10 ms for non-mobile receivers and mobile receivers with a pedestrian speed of 3 mph respectively. Our results also show that spatial reuse can be increased by efficiently eliminating interference at any desired location; however, this may come at the expense of a significant drop in the quality of the served users.