The emergence of Internet of Things (IoT) has increased number of connected devices and consequently transmitted traffic over the Internet. In this regard, Long Term Evolution (LTE) is growing its ...utilization in unlicensed spectrum as well, and Licensed Assisted Access (LAA) technology is one of the examples. However, unlicensed spectrum is already occupied by other wireless technologies, such as Wi-Fi. The diverse and dissimilar physical layer and medium access control (MAC) layer configurations of LTE-LAA and Wi-Fi lead to coexistence challenges in the network. Currently, LTE-LAA uses a listen-before-talk (LBT) mechanism, and Wi-Fi uses a carrier sense multiple access with collision avoidance (CSMA/CA) as a channel access mechanism. LBT and CSMA/CA are moderately similar channel access mechanisms. However, there is an efficient coexistence issue when these two technologies coexist. Therefore, this paper proposes a Reinforcement Learning-enabled LBT (ReLBT) mechanism for efficient coexistence of LTE-LAA and Wi-Fi scenarios. Specifically, ReLBT utilizes a channel collision probability as a reward function to optimize its channel access parameters. Simulation results show that the proposed ReLBT mechanism efficiently enhances the coexistence of LTE-LAA and Wi-Fi as compared to the LBT, thus improves fairness performance.
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•Long Term Evolution (LTE) is growing its utilization in unlicensed spectrum as well, and Licensed Assisted Access (LAA) technology is one of the examples.•unlicensed spectrum is already occupied by other wireless technologies, such as Wi-Fi.•The diverse and dissimilar physical layer and medium access control (MAC) layer configurations of LTE-LAA and Wi-Fi lead to coexistence challenges in the network.•Reinforcement Learning is a behaviorist learning technique, which uses experience from the environment to optimize its performance.•This paper proposes a RL-enabled LBT (ReLBT) mechanism for efficient coexistence of LTE-LAA and Wi-Fi scenarios.•Specifically, ReLBT utilizes a channel collision probability as a reward function to optimize its channel access parameters.
WiFi fingerprinting-based indoor positioning system (IPS) has become the most promising solution for indoor localization. However, there are two major drawbacks that hamper its large-scale ...implementation. First, an offline site survey process is required which is extremely time-consuming and labor-intensive. Second, the RSS fingerprint database built offline is vulnerable to environmental dynamics. To address these issues comprehensively, in this paper, we propose WinIPS, a WiFi-based non-intrusive IPS that enables automatic online radio map construction and adaptation, aiming for calibration-free indoor localization. WinIPS can capture data packets transmitted in existing WiFi traffic and extract the RSS and MAC addresses of both WiFi access points (APs) and mobile devices in a non-intrusive manner. APs can be used as online reference points for radio map construction. A novel Gaussian process regression model is proposed to approximate the non-uniform RSS distribution of an indoor environment. Extensive experiments were conducted, which demonstrated that WinIPS outperforms existing solutions in terms of both RSS estimation accuracy and localization accuracy.
Since human bodies are good reflectors of wireless signals, human activities can be recognized by monitoring changes in WiFi signals. However, existing WiFi-based human activity recognition systems ...do not build models that can quantify the correlation between WiFi signal dynamics and human activities. In this paper, we propose a Channel State Information (CSI)-based human Activity Recognition and Monitoring system (CARM). CARM is based on two theoretical models. First, we propose a CSI-speed model that quantifies the relation between CSI dynamics and human movement speeds. Second, we propose a CSI-activity model that quantifies the relation between human movement speeds and human activities. Based on these two models, we implemented the CARM on commercial WiFi devices. Our experimental results show that the CARM achieves recognition accuracy of 96% and is robust to environmental changes.
The Society for Vascular Surgery (SVS) Wound, Ischemia, and foot Infection (WIfI) classification system aims to risk stratify patients with chronic limb-threatening ischemia (CLTI), predicting both ...amputation rates and the need for revascularization. However, real-world use of the system and whether it predicts outcomes accurately after open revascularization and peripheral interventions is unclear. Therefore, we sought to determine the adoption of the WIfI classification system within a contemporary statewide collaborative as well as the impact of patient factor, and WIfI risk assessment on short- and long-term outcomes.
Using data from a large statewide collaborative, we identified patients with CLTI undergoing open surgical revascularization or peripheral vascular intervention (PVI) between 2016 and 2022. The primary exposure was preoperative clinical WIfI stage. Patients were categorized according to the SVS Lower Extremity Threatened Limb Classification System into clinical WIfI stages 1, 2, 3, or 4. The primary outcomes were 30-day and 1-year amputation and mortality rates. Multivariable logistic regression was performed to estimate the association of WIfI stage on postrevascularization outcomes.
In the cohort of 17,417 patients, 83.4% (n = 14,529) had WIfI stage documented. PVIs were performed on 57.6% of patients, and 42.4% underwent an open surgical revascularization. Of the patients, 49.5% were classified as stage 1, 19.3% stage 2, 12.8% stage 3, and 18.3% of patients met stage 4 criteria. Stage 3 and 4 patients had higher rates of diabetes, congestive heart failure, and renal failure, and were less likely to be current or former smokers. One-half of stage 3 patients underwent open surgical revascularization, whereas stage 1 patients were most likely to have received a PVI (64%). As WIfI stage increased from 1 to 4, 1-year mortality increased from 12% to 21% (P < .001), 30-day amputation rates increased from 5% to 38% (P < .001), and 1-year amputation rates increased from 15% to 55% (P < .001). Finally, patients who did not have WIfI scores classified had significantly higher 30-day and 1-year mortality rates, as well as higher 30-day and 1-year amputation rates.
The SVS WIfI clinical stage is significantly associated with 1-year amputation rates in patients with CLTI after lower extremity revascularization. Because nearly 55% of stage 4 patients require a major amputation within 1 year of intervention, this finding study supports use of the WIfI classification system in clinical decision-making for patients with CLTI.
Of the different branches of indoor localization research, WiFi fingerprinting has drawn significant attention over the past decade. These localization systems function by comparing WiFi received ...signal strength indicator (RSSI) and a pre-established location-specific fingerprint map. However, due to the time-variant wireless signal strength, the RSSI fingerprint map needs to be calibrated periodically, incurring high labor and time costs. In addition, biased RSSI measurements across devices along with transmission power control techniques of WiFi routers further undermine the fidelity of existing fingerprint-based localization systems. To remedy these problems, we propose GradIent FingerprinTing (GIFT) which leverages a more stable RSSI gradient. GIFT first builds a gradient-based fingerprint map (Gmap) by comparing absolute RSSI values at nearby positions, and then runs an online extended particle filter (EPF) to localize the user/device. By incorporating Gmap, GIFT is more adaptive to the time-variant RSSI in indoor environments, thus effectively reducing the overhead of fingerprint map calibration. We implemented GIFT on Android smartphones and tablets, and conducted extensive experiments in a five-story campus building. GIFT is shown to achieve an 80 percentile accuracy of 5.6 m with dynamic WiFi signals.
If you've been searching for a way to get up to speed on IEEE 802.11n and 802.11ac WLAN standards without having to wade through the entire specification, then look no further. This comprehensive ...overview describes the underlying principles, implementation details and key enhancing features of 802.11n and 802.11ac. For many of these features the authors outline the motivation and history behind their adoption into the standard. A detailed discussion of key throughput, robustness, and reliability enhancing features (such as MIMO, multi-user MIMO, 40/80/160 MHz channels, transmit beamforming and packet aggregation) is given, plus clear summaries of issues surrounding legacy interoperability and coexistence. Now updated and significantly revised, this 2nd edition contains new material on 802.11ac throughput, including revised chapters on MAC and interoperability, plus new chapters on 802.11ac PHY and multi-user MIMO. An ideal reference for designers of WLAN equipment, network managers, and researchers in the field of wireless communications.
By integrating sensing capability into wireless communication, wireless sensing technology has become a promising contactless and non-line-of-sight sensing paradigm to explore the dynamic ...characteristics of channel state information (CSI) for recognizing human behaviors. In this paper, we develop an effective device-free human gesture recognition (HGR) system based on WiFi wireless sensing technology in which the complementary CSI amplitude and phase of communication link are jointly exploited. To improve the quality of collected CSI, a linear transform-based data processing method is first used to eliminate the phase offset and noise and to reduce the impact of multi-path effects. Then, six different time and frequency domain features are chosen for both amplitude and phase, including the mean, variance, root mean square, interquartile range, energy entropy and power spectral entropy, and a feature selection algorithm to remove irrelevant and redundant features is proposed based on filtering and principal component analysis methods, resulting in the construction of a feature subspace to distinguish different gestures. On this basis, a support vector machine-based stacking algorithm is proposed for gesture classification based on the selected and complementary amplitude and phase features. Lastly, we conduct experiments under a practical scenario with one transmitter and receiver. The results demonstrate that the average accuracy of the proposed HGR system is 98.3% and that the F1-score is over 97%.
With the development of signal processing technology, the ubiquitous Wi-Fi devices open an unprecedented opportunity to solve the challenging human gesture recognition problem by learning motion ...representations from wireless signals. Wi-Fi-based gesture recognition systems, although yield good performance on specific data domains, are still practically difficult to be used without explicit adaptation efforts to new domains. Various pioneering approaches have been proposed to resolve this contradiction but extra training efforts are still necessary for either data collection or model re-training when new data domains appear. To advance cross-domain recognition and achieve fully zero-effort recognition, we propose Widar3.0, a Wi-Fi-based zero-effort cross-domain gesture recognition system. The key insight of Widar3.0 is to derive and extract domain-independent features of human gestures at the lower signal level, which represent unique kinetic characteristics of gestures and are irrespective of domains. On this basis, we develop a one-fits-all general model that requires only one-time training but can adapt to different data domains. Experiments on various domain factors (i.e. environments, locations, and orientations of persons) demonstrate the accuracy of 92.7% for in-domain recognition and 82.6%-92.4% for cross-domain recognition without model re-training, outperforming the state-of-the-art solutions.
High-precision indoor positioning problems have attracted considerable attention recently. The indoor ranging and positioning method based on wireless fidelity (WIFI) round-trip-time has become ...popular. However, resulting from clock drift (CD), there are two problems that are still not completely resolved and need to be addressed. The first problem is ranging error drift, namely the reset of phase distortion errors (PDEs), while the second problem is the communication state change, namely ranging information loss and restoration. To solve these two problems, this study noticed that the reset of PDEs will only change along with the changes in communication states. Thus, a monitoring-based positioning system (MPS) framework is presented based on the theoretical model of CD as a method to account for ranging error drift and communication state change when estimating position with the IEEE 802.11mc protocol. Then, a state monitoring algorithm (SMA) suitable for the MPS framework is proposed to accurately capture the moment of communication state change. Further, a PDE constraint (PDEC) model is proposed based on the SMA to estimate PDEs in different communication states to reduce the impact of CD on positioning results. The experimental results finally show that considerable improvement in positioning accuracy can be obtained using the SMA and PDEC model compared with that obtained using the semiparametric solution algorithm.
To cope with the tremendous growth of data traffic and obtain a given communication service with minimal energy use, traffic offloading and energy efficiency (EE) improving are two important issues ...to address for green cellular networks. The authors investigate downlink WiFi offloading in a heterogeneous network consisting of one long term evolution eNodeB (eNB) and multiple overlaid WiFi access points to maximise the user satisfaction of the whole system. In addition, a designed resource reallocation scheme after offloading is jointly considered to improve the EE of the eNB. In the offloading model, two constraints are considered to guarantee the rate promotion of the offloaded users and less impact on WiFi networks. Moreover, the authors transform the model into a combinatorial optimisation problem and adopt the best response (BR) algorithm based on game-theoretic approach to obtain the optimal offloading user set. Numerical results show that the proposed WiFi-offloading model can significantly improve the aggregate user satisfaction as well as EE of the eNB. Also, the BR algorithm can converge to the optimal solution same as the exhaustive search algorithm through several iterations.
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