Multiple Transmission Power Levels Research Articles

Data Rate Fingerprinting: A WLAN-Based Indoor Positioning Technique for Passive Localization

Received signal strength (RSS)/channel state information (CSI) fingerprinting techniques have been widely adopted for wireless local area network (WLAN)-based indoor localization. However, in most of the RSS/CSI fingerprinting techniques, an application/software has to be installed on the target for uploading RSS/CSI information to the localization system. As a result, RSS/CSI fingerprinting cannot achieve passive localization (application-free), which is an essential requirement in many existing localization systems, especially for commercial and military purposes. In this paper, we propose data rate (DR) fingerprinting to achieve passive localization. DR fingerprinting is compatible with most off-the-shelf wireless fidelity (Wi-Fi) devices and can be directly implemented in the existing WLAN-based localization system without any extra hardware. In DR fingerprinting, DRs are used to replace the RSS/CSI to form fingerprints, since DR information can be directly obtained by access points (APs). However, the inherent features of DR, including low-resolution and serious fluctuation, significantly impair the performance of DR fingerprinting. For implementing DR fingerprinting, we leverage multiple transmission power levels, propose a time-window mechanism with different fingerprint formulations, design a AP switching strategy, and design a new matching algorithm named dynamic nearest neighbors (DNNs). We conducted extensive experiments in a real-world testbed to study the performance of DR fingerprinting. Specifically, we compared DR fingerprinting with the state-of-the-art RSS/CSI fingerprinting techniques. The experimental results illustrate that DR fingerprinting can achieve the localization accuracy that is comparable to that of the RSS/CSI fingerprinting with the additional benefit of achieving passive localization.

Evaluating the Implications of Varying Bluetooth Low Energy (BLE) Transmission Power Levels on Wireless Indoor Localization Accuracy and Precision.

Bluetooth Low Energy (BLE) based Wireless Indoor Localization System (WILS) with high localization accuracy and high localization precision is a key requirement in enabling the Internet of Things (IoT) in today’s applications. In this paper, we investigated the effect of BLE signal variations on indoor localization caused by the change in BLE transmission power levels. This issue is not often discussed as most of the works on localization algorithms use the highest power levels but has important practical implications for energy efficiency, e.g., if a designer would like to trade-off localization performance and node lifetime. To analyze the impact, we used the established trilateration based localization model with two methods i.e., Centroid Approximation (CA) and Minimum Mean Square Error (MMSE). We observed that trilateration based localization with MMSE method outperforms the CA method. We further investigated the use of two filters i.e., Low Pass Filter (LPF) and Kalman Filter (KF) and evaluated their effects in terms of mitigating the random variations from BLE signal. In comparison to non-filter based approach, we observed a great improvement in localization accuracy and localization precision with a filter-based approach. Furthermore, in comparison to LPF based trilateration localization with CA, the performance of a KF based trilateration localization with MMSE is far better. An average of 1 m improvement in localization accuracy and approximately 50% improvement in localization precision is observed by using KF in trilateration based localization model with the MMSE method. In conclusion, with KF in trilateration based localization model with MMSE method effectively eliminates random variations in BLE RSS with multiple transmission power levels and thus results in a BLE based WILS with high accuracy and high precision.

Leveraging High Order Cumulants for Spectrum Sensing and Power Recognition in Cognitive Radio Networks

Hybrid interweave-underlay spectrum access in cognitive radio networks can explore spectrum opportunities when primary users (PUs) are either active or inactive, which significantly improves spectrum utilization. The practical wireless systems, such as long-term evolution-advanced, usually operate at multiple transmission power levels, leading to a multiple primary transmission power scenario. In such a case, the two fundamental issues in hybrid interweave-underlay spectrum access are to detect the “ON/OFF” status of PUs and to recognize the operating power level of PUs, which are challenging due to non-Gaussian transmitted signals. In this paper, we exploit high-order cumulants (HOCs) to efficiently perform spectrum sensing and power recognition. Specifically, for a given order and time lag, we first propose a single HOC-based spectrum sensing and power recognition scheme with low computational complexity, by leveraging minimum Bayes risk criterion. Moreover, we propose a hybrid multiple HOCs-based spectrum sensing and power recognition scheme with multiple orders and time lags, to further improve the detection performance. Both the proposed schemes can eliminate the adverse impact of the noise power uncertainty. Finally, simulation results are provided to evaluate the proposed schemes.

Device-Free Localization With Multidimensional Wireless Link Information

As an emerging technique with promising application prospects, device-free localization (DFL) could estimate the location of target within the deployment area of wireless networks (WNs) while eliminating the need to equip the target with a wireless device. However, one major disadvantage of this technique is that it needs several wireless links travelling through the deployment area to guarantee good performance. To overcome this problem, a novel multidimensional wireless-link-information-based DFL scheme is proposed. Different from a traditional DFL scheme that scans wireless links sequentially with one frequency and one transmission power level, the proposed scheme makes full use of multiple frequencies and multiple transmission power levels to enrich the link measurement information. Meanwhile, motivated by the fact that the location information of the target is not only sparse but also changes slowly and continuously over time, we present a novel recursive compressive sensing algorithm to reconstruct the location information from undersampled measurements. The experimental results demonstrate the outstanding performance of the proposed scheme.

Service Differentiation Using the Capture Effect in 802.11 Wireless LANs

We investigate the effects of using dual transmission power to achieve quality-of-service (QoS) differentiation in IEEE 802.11 wireless LANs. Specifically, we assume all stations employ the standard IEEE 802.11 distributed coordination function (DCF), with the exception that some stations transmit at a higher power than others. Consequently, in the event of a collision involving frames from both power levels, a high-power frame is more likely to be received correctly as a result of the so-called capture effect (i.e. received at a power sufficiently higher than that of the interference, allowing it to be decoded correctly). This effectively leads to QoS differentiation between two classes, corresponding to the high-power and low-power stations. We develop a Markov model for the IEEE 802.11 DCF in a network with dual transmission power over a Rayleigh-fading channel, and use it to evaluate the resulting performance, in terms of the key metrics of throughput and delay. We explore how the performance of the service classes depends on the proportion of stations in each class and the transmission power ratio, focusing in particular on the bounds achieved in the limit case of 'ideal differentiation', characterised by a perfect capture probability of high-power frames. We find that significant performance differentiation between high-power and low-power stations is achieved even with transmission power ratios that are not very high, leading us to conclude that employing multiple transmission power levels is a viable and efficient approach for service quality differentiation in wireless LANs.

Power levels and packet lengths in random multiple access

Multiple-power-level ALOHA has been proposed to take advantage of the capture phenomenon in order to improve the throughput of a multiple random access system. We study the effect of the use of multiple transmission power levels and of the corresponding packet lengths on the system throughput and energy efficiency. We prove that the single-power-level system in which all transmit at the maximum allowable power level achieves both optimal throughput and energy usage efficiency under a condition on the decodability threshold value.