This paper investigates the use of the newly opened, and relatively unexplored, 6 GHz band for office occupancy detection using Wi-Fi sensing. To deliver accurate and efficient occupancy detection, we develop a novel channel model-informed feature engineering method combined with a random forest optimization strategy. Specifically, physically interpretable channel state information (CSI) amplitude-based features, such as the RicianK-factor and channel coherence time, are employed to capture channel variations induced by human presence and movement. A dual sliding window approach is introduced to effectively extract temporally relevant channel parameters, significantly improving computational efficiency and classification accuracy. Experimental validation conducted in a realistic office environment demonstrates that the proposed method achieves an average occupancy classification accuracy of 98.28%, outperforming existing methods while substantially reducing computational complexity. These findings suggest that integrating this Wi-Fi sensing approach into next-generation networks (e.g., IEEE 802.11bf) can enhance real-time responsiveness and reliability in smart building applications such as security and energy management.

In this paper, we present the results of a set of channel measurements conducted within the 6 GHz band used in IEEE 802.11be based wireless local area networks (WLANs). A range of indoor and outdoor client to access point (AP) communication scenarios were considered for both line-of-sight (LOS) and non-LOS (NLOS) channel conditions. We have investigated the path loss, large-scale, and small-scale fading across 256 frequency points between 6.425 and 6.445 GHz. To model the large-scale fading we have utilized the lognormal and gamma distributions, while for the small-scale fading this was the Rayleigh, Rician, and Nakagami-m distributions. The information loss incurred when encoding the empirical distributions with the aforementioned theoretical ones was determined using the resistor-average distance (RAD). It was found that the gamma distribution provided a better fit to the large-scale fading, while the Rician and Nakagami-m distributions observed the lowest RAD values for the small-scale fading. To ascertain the temporal stability of the considered channels, the coherence time was inferred using an analysis of the autocorrelation. Our results indicate that the coherence time for the large-scale fading was typically longer than for small-scale fading.

IEEE 802.11ay is an amendment that enables enhanced throughput for IEEE 802.11 systems operating in the license-exempt 60 GHz band. By specifying advanced physical layer (PHY) features such as channel bonding and aggregation, multiple input multiple output (MIMO) transmission, and non-uniform modulation constellation, IEEE 802.11ay supports a maximum data rate of 100 Gb/s, making it an amendment of great interest for applications as diverse as wireless virtual reality (VR) and backhauling. In this paper, we provide a description and analysis of certain fundamental elements of the single-carrier (SC) PHY defined by IEEE 802.11ay. Among the topics that are discussed are the new frame format, modulation and coding schemes (MCSs), and beamforming training field defined in the amendment. Simulation results of key aspects for the topics covered are presented.

Receiver algorithms which combine belief propagation (BP) with the mean field (MF) approximation are well-suited for inference of both continuous and discrete random variables. In wireless scenarios involving detection of multiple signals, the standard construction of the combined BP-MF framework includes the equalization or multi-user detection functions within the MF subgraph. However, the MF approximation is not particularly effective for multi-signal detection. For this reason, we propose a new factor graph construction for application of the BP-MF framework to problems involving the detection of multiple signals. We also developed a low-complexity variation to the proposed construction in which Gaussian BP is applied to detection and expectation propagation links the discrete BP and Gaussian BP subgraphs. The result is a probabilistic receiver architecture with strong theoretical justification which can be applied to multi-signal detection and, in general, detection in the presence of interference.

This paper presents a new preprocessing stage that allows for the reliable classification of digital amplitude-phase modulated signals in a practical scenario where: 1) the classifier has no knowledge of the timing (symbol transition epochs) of the received signal; 2) the noise added in the channel is non-Gaussian; and 3) the fading experienced by the signal is frequency selective. The proposed preprocessor, which is based on the Gibbs sampling algorithm, is used to acquire timing information and to estimate the channel state and noise distribution parameters blindly, i.e., without knowledge of the received symbol sequence and the modulation scheme used. With the obtained estimates, in a second processing stage, the signal is then classified by using an appropriate (likelihood- or feature-based) classification algorithm. To quantify the performance of the proposed preprocessor, the probability of correct classification obtained by using the preprocessor with different classification algorithms is presented. It is shown that, by using the proposed preprocessor, modulation classification algorithms can perform well compared with clairvoyant classifiers assumed to be symbol synchronous with the received signal and to have perfect knowledge of the channel state and noise distribution.

This paper presents a comparative analysis of eigenvector-based signal detection algorithms and a new eigenvector-based modulation classifier for multiple antenna cognitive radio applications. The signal detection algorithms considered in this work are: Feature Template Matching (FTM), Multiple Feature Matching (MFM) and Principal Components (PC)-based detection. A software-defined radio (SDR) platform is used to experimentally evaluate the detection performance of these algorithms. It is observed that MFM, which uses four signal features extracted across space, time and phase dimensions, provides the best performance among the considered algorithms. This paper also presents a new idea on the use of eigenvectors for signal classification in multiple antenna systems. Using computer simulations, it is shown that eigenvectors corresponding to different modulation schemes allow for reliable signal identification even at very low SNR values (on the order of -20 dB), therefore they can be used effectively for signal classification in cognitive radio applications.

In this paper, a new algorithm is proposed for the classification of digital amplitude-phase modulated signals in flat fading channels with time-correlated non-Gaussian noise. The first-order statistics of the additive noise is modeled by a Gaussian mixture distribution and an autoregressive (AR) process is used to model the time-correlation. The proposed classifier involves the use of a whitening filter, necessary to reduce the complexity of the classification process, and maximum-likelihood classification. For the estimation of the whitening filter coefficients, a new blind technique that is based on the use of a robust H∞filter is developed. After whitening the received signal, following a composite hypothesis testing approach, the unknown fading and noise distribution parameters are estimated. Results are presented which show that when the noise process is time-correlated non-Gaussian, the proposed classifier outperforms maximum-likelihood classifiers developed under the assumption that the noise process is either white non-Gaussian or white Gaussian. It is also shown that when the noise process is white Gaussian, the proposed classifier's performance closely approaches that of the maximum-likelihood classifier developed for white Gaussian noise channels.

In this paper, we propose a scheme for the detection of digital amplitude-phase modulated signals in flat fading channels with non-Gaussian noise. The additive noise is modeled by a symmetric alpha-stable distribution, a well-known model of man-made and natural noise that appears in various environments. A major challenge in the design of signal detection schemes is that detectors typically operate without knowledge of the transmitted data, channel state, and noise statistics. The design becomes especially difficult for the considered noise model since conventional signal processing approaches are not applicable. For this reason, we propose a five-stage signal detection scheme that is based on the use of a matched myriad filter. An important contribution of this paper is the development of new algorithms for the estimation of noise distribution parameters in the presence of an unknown signal. Results are presented which show that the proposed detector outperforms a zero-memory non-linearity-based scheme.

In this paper, we propose an algorithm for the classification of digital amplitude-phase modulated signals in flat fading channels with non-Gaussian noise. The additive noise is modeled by a Gaussian mixture distribution, a well-known model of man-made and natural noise that appears in most radio channels. The classifier utilizes a variant of the expectation-maximization algorithm to estimate the channel and noise parameters without the aid of training symbols. With these estimates, the signal is classified using a hybrid likelihood ratio test. Results are presented which show that the proposed classifier's performance approaches that of the ideal classifier with perfect knowledge of the channel state and noise distribution.

This paper presents a new asynchronous modulation classifier for digital amplitude-phase modulated signals in flat-fading channels when the channel state is assumed unknown. In the design of this classifier, we propose new estimators for the unknown amplitude, time offset, and noise power that are blind to the modulation scheme of the received signal. It is shown that the proposed classifier performs well compared to the optimal classifier with perfect channel knowledge for an adequate estimation interval.

This paper presents a performance analysis of an ultra wideband (UWB) communication system based on Multiband Orthogonal Frequency Division Multiplexing (MB-OFDM) in the presence of narrowband interference. Although OFDM-based systems are known to be capable of withstanding some level of narrowband interference, mitigation techniques may have to be used when interference levels are high. Furthermore, as typical UWB systems employ low resolution analog-to-digital converters, the effect of narrowband interference may be exacerbated in the process of analog-to-digital conversion. In order to improve the performance of MB-OFDM UWB systems in the presence of narrowband interference, we consider the use of analog notch filters. This paper proposes an analytical model and formulation that allows for the evaluation of the probability of symbol error of an MB-OFDM UWB system when an analog notch filter is used for narrowband interference mitigation. It is shown that using an analog notch filter can significantly improve the performance of MB-OFDM UWB systems in high-interference scenarios.

This paper presents novel single-source localization techniques for a sensor network that is capable of obtaining both direction and range estimates by considering an optimization approach. Specifically, linear programming and non-linear programming techniques are developed and compared to a classic linear least squares technique. It is shown that the proposed techniques achieve superior results to the least squares approach in most cases.

More than a dozen Wireless @ Virginia Tech faculty are working to address the broad research agenda of cognitive radio and cognitive networks. Our core research team spans the protocol stack from radio and reconfigurable hardware to communications theory to the networking layer. Our work includes new analysis methods and the development of new software architectures and applications, in addition to work on the core concepts and architectures underlying cognitive radios and cognitive networks. This paper describes these contributions and points towards critical future work that remains to fulfill the promise of cognitive radio. We briefly describe the history of work on cognitive radios and networks at Virginia Tech and then discuss our contributions to the core cognitive processing underlying these systems, focusing on our cognitive engine. We also describe developments that support the cognitive engine and advances in radio technology that provide the flexibility desired in a cognitive radio node. We consider securing and verifying cognitive systems and examine the challenges of expanding the cognitive paradigm up the protocol stack to optimize end-to-end network performance. Lastly, we consider the analysis of cognitive systems using game theory and the application of cognitive techniques to problems in dynamic spectrum sharing and control of multiple-input multiple-output radios.

Automatic modulation classification (AMC) is a very important process for any receiver that has no, or limited, knowledge of the modulation scheme of the received signal. In addition to military systems, AMC has been receiving an increasing amount of interest in the context of spectrum sharing cognitive radio systems. In this paper, we present an AMC system based upon the distributed processing of local classification decisions made by multiple radios. These radios consist of two stages: a cyclic spectrum feature-based AMC stage and a decision making (DM) stage that makes a local decision that is sent to a fusion center. This fusion center then makes a global decision based on its own AMC stage and on the local decisions made by the radios. A nonlinear Gauss-Seidel iterative algorithm is used to find the person-by-person optimum decision rules for the fusion center and DMs. It is shown that the proposed distributed approach results in a significant increase in the probability of signal detection and correct classification, at the expense of requiring messages to be transmitted among the fusion center and the radios in the system.

In this paper, a new formulation of the probabilities of detection and false alarm for a class of cyclic-feature detectors known as single-cycle detectors is derived for additive white Gaussian noise channels. As opposed to previously published papers, the formulation derived here is non-asymptotic, being valid for any signal-to-noise ratio value. Additionally, the proposed formulation is general, allowing for the analysis of the singlecycle detection performance for any modulation scheme and any cycle frequency.

In an underground mine, wireless systems based on ultra wideband (UWB) signals have the potential to improve the response time to mine emergencies by providing fast, reliable communications as well as precision position location. Much of the UWB research to date has focused on the in-building and (to a lesser degree) outdoor propagation environments. As a result, little information is available on the nature of UWB propagation in an underground mining environment. This paper presents preliminary path loss and power delay profile measurement results for a typical room-and-pillar underground mine located in southwest Virginia. Transmitters and receivers were separated by distances ranging from 20 m to over 300 m in both line-of- sight and non-line-of-sight configurations. These results indicate that for long corridor environments, similar to indoor hallways, a waveguide effect results in better than free space propagation. Additionally, UWB signals were found to experience less fading over a local area than CW signals. UWB signals also provide the opportunity to aid in position location by achieving very accurate time-of-arrival measurements, or via RF fingerprinting techniques. We show that initial attempts to determine position based on such an approach were also very promising. The results presented here are insufficient in number to make some definitive statements about UWB propagation in underground mines but are only a first step towards characterizing that propagation. Additionally, the initial results are similar to the trends seen in indoor environments thus providing optimism that UWB may be a viable physical layer for wireless systems in underground mines.

It is known that a major practical implementation challenge of ultra-wideband (UWB) receivers is the design of the coarse acquisition stage. Due to the fine time resolution of UWB signals, the acquisition stage has to acquire a large number of low-energy multipath components, with no or little knowledge of the state of the channel. In addition, the complexity further increases with the presence of narrowband interference due to the proposed spectral overlay. Our goal in this paper is to evaluate the affects of the lack ofa prioriknowledge of the channel state and the presence of narrowband interference during acquisition. Maximum-likelihood and maximuma posterioriprocedures for estimation in the presence of narrowband interference are formulated, and two different interference mitigation techniques are evaluated. In particular, this paper considers UWB communication systems that use spectral encoding as both the multiple access scheme and the interference suppression technique. The qualitative results are, however, believed to be valid for any UWB system implementation. It is shown that the acquisition performance strongly depends on the amount ofa prioriknowledge of the channel state at the receiver, and on whether or not interference suppression is employed.

It is known that a major practical implementation challenge of ultra-wideband (UWB) receivers is the design of the coarse acquisition stage. Due to the fine time resolution of UWB signals, the acquisition stage has to acquire a large number of low-energy multipath components, with no or little knowledge of the state of the channel. In addition, the complexity further increases with the presence of narrow-band interference (NBI) due to the proposed spectral overlay. Our goal in this paper is to evaluate the affects of the lack ofa prioriknowledge of the channel state and the presence of NBI during acquisition. Maximum likelihood and maximuma posterioriprocedures for estimation in the presence of NBI are formulated, and two different interference mitigation techniques are evaluated. In particular, this paper considers UWB communication systems that use spectral encoding as both the multiple access scheme and the interference suppression technique. The qualitative results are, however, believed to be valid for any UWB system implementation. It is shown that the acquisition performance strongly depends on the amount ofa prioriknowledge of the channel state at the receiver, and on whether or not interference suppression is employed.

The proposed spectrum-overlay scenario for ultra-wideband systems brings new technical challenges that must be considered during the system design. In this scenario, the receiver has to perform functions, such as multipath combining and channel estimation, on a large number of low-energy multipath components in the possible presence of strong in-band interference. This paper proposes an analytical formulation that allows for the analysis of the channel estimation in the presence of narrowband interference. It is shown that the affect of narrowband interference is exacerbated by channel estimation errors, and that the number of training bits used for channel estimation is strongly dependent on whether or not interference suppression is employed. It is also shown that sources of degradation, such as imperfect channel estimation and the presence of narrowband interference, impose a practical limit on the number of multipath components that should be combined.

This paper considers a particular implementation of an ultra-wideband communication system that uses spectral encoding as both the multiple-access scheme and the interference-suppression technique. The main advantage of this technique is that the transmitted signal spectrum can be conveniently shaped to suppress narrowband interference and to not cause noticeable interference to overlaid systems. An extensive analysis of a possible implementation of this system by using surface acoustic-wave models is presented, and general expressions for the system performance are obtained. Numerical results show that a significant improvement in the system performance is obtained when the proposed interference-suppression method is used.

This work considers the performance of a DS/CDMA system in the presence of narrow-band interference and imperfect channel estimation. A receiver based on MMSE channel estimation and maximum-likelihood data detection is assumed. An adaptive MMSE receiver, based on the LMS algorithm, is also explored. The performance of the DS/CDMA system is compared with that of a recently proposed UWB spectral-encoded system. The formulation and numerical results presented allow for a better understanding of the spectral overlay effects for UWB communication systems.