In this paper, a decentralized iterative maximum likelihood (ML) direct position determination (DIM-DPD) algorithm is proposed based on the expectation maximization (EM) concept for user equipment (UE) localization in Wi-Fi systems. By innovatively treating the non-line-of-sight (NLoS) angles-of-arrival (AoAs) and observed times-of-arrival (ToAs) as nuisance parameters in the received signal model and parameter estimation procedure, the proposed DIM-DPD demonstrates its adaptability and localization efficiency in dense multipath indoor environments. In the proposed method, the position of the UE is incorporated in the vectors denoting the location differences between the UE and the access point (APs), referred to as the UE-AP location difference vectors. The set of UE-AP location difference vectors allows constructing the related UE-AP variables, serving as the latent variables in the EM iterations. Then, by taking advantage of the alternating projection technique, the nuisance parameters and UE-AP variables in the DIM-DPD algorithm are alternatively updated on separated APs in parallel. Furthermore, instead of traditional grid search, the UE position is updated with an efficient closed-form solution by aggregating the distributed estimated low-dimensional UE-AP variables. Thus, overall, the proposed DIM-DPD approach facilitates decentralized direct localization with implementation feasible processing complexity. The provided numerical simulation results demonstrate that the proposed DIM-DPD algorithm achieves high positioning accuracy, fast convergence, and a good balance between computational complexity and performance.

Enhancing reliability in dense underwater networks with hefty data traffic often raises energy consumption due to constant packet listening and retransmissions caused by packet loss. To address the challenging energy demand in Underwater Acoustic Sensor Networks (UASNs), we propose a Reinforce-ment learning (RL)-based Energy-efficient and Adaptive Multi-modal routing protocol abbreviated as REAM, that integrates Q-learning with multi-modal communication to enhance energy efficiency in underwater networks by adaptively selecting the optimal mode for packet transmission. We compare the performance of two variants of the proposed REAM protocol-REAM-MM, which uses two modems, and REAM-SM, which uses a single modem, against two other state-of-the-art protocols namely QELAR and MARLIN-Q. Our results demonstrate the effective-ness of using multiple modems instead of a single modem in reducing energy consumption and improving reliability. REAM-MM reduces energy consumption per bit by up to 81.2%, 79.6%, and 72.9% under low, medium, and high traffic scenarios, respec-tively, compared to the best-performing alternative, MARLIN-Q. REAM-MM achieves a consistently comparable Packet Delivery Ratio (PDR) to MARLIN-Q and a higher PDR than QELAR and REAM-SM. Additionally, it maintains the lowest energy consumption under all traffic conditions and dense networks.

This paper investigates secure transmission in the uplink of a cell-free massive multiple-input multiple-output (MIMO) system employing three distinct multiple access strategies: rate-splitting multiple access (RSMA), non-orthogonal multiple access (NOMA), and space-division multiple access (SDMA). RSMA, functioning as a unifying paradigm, merges the merits of both NOMA and SDMA, and holds substantial promise for enhancing system secrecy. We derive closed-form expressions for secrecy spectral efficiency (SE) under Rician fading channels and imperfect channel knowledge assumptions. The secrecy SE is subsequently evaluated across a range of system configurations, encompassing varying access point (AP) and user numbers, AP and eavesdropper antenna dimensions, line-of-sight probabilities, successive interference cancellation conditions, and multiple access protocols. Harnessing these expressions, we establish an optimization framework for the users’ power control coefficients and APs’ receiving weights to maximize the sum secrecy SE while ensuring quality-of-service secrecy requirements for users. Additionally, an alternative optimization algorithm is proposed to ascertain a high-quality solution. Comprehensive simulations substantiate our theoretical propositions and evaluate the efficacy of the proposed sum secrecy SE maximization algorithm.

High-level synthesis (HLS) promises to increase the design and verification productivity for digital hardware systems. However, the industry still predominantly uses more time-consuming manual register-transfer level techniques instead of HLS. To accelerate the adoption of HLS, it is vital to explore if it is possible to achieve competitive results with this method. To that end, this paper demonstrates an HLS implementation of the well-known MUSIC algorithm for estimating the direction of arrival of a radio signal. We use as a receiver a four-antenna uniform linear array with one signal source and a resolution of one degree. For the computationally heavy eigenvalue decomposition within MUSIC, we employ the iterative Jacobi algorithm. We target two different Virtex FPGAs for synthesis and obtain results faring well in comparison to the previous literature, with$5.0\ \mu \mathrm{s}$microseconds latency, high accuracy, and low resource consumption. The results show that HLS is suitable for implementing these kinds of algorithms on FPGA.

Terahertz (THz) transmission technologies show great promise in enabling ultra-broadband short-range in the next-generation communications. The incorporation of THz transmission with Aerial Base Stations (ABS) networks offers promising advantages, particularly in establishing favorable ultra-broadband line-of-sight links. In this paper, we provide a performance analysis for a finite THz ABS network serving a given region. We model the spatial distribution of the ABSs as a binomial point process within an overlapped finite circular area. To derive the coverage probability, we consider a probabilistic model that encompasses both line-of-sight and non-line-of-sight propagation scenarios, and a user association policy based on the strongest average received power. The association probabilities and Laplace transform of the interference are also derived. The derived coverage probability is validated through Monte Carlo simulation. We further investigate the impacts of the ABS’s height and the radius of the area on the coverage performance.

In this article, we address the timely topic of cellular bistatic simultaneous localization and mapping (SLAM) with specific focus on end-to-end processing solutions, from raw I/Q samples, via channel parameter estimation to user equipment (UE) and landmark location information in millimeter-wave (mmWave) networks, with minimal prior knowledge. Firstly, we propose a new multipath channel parameter estimation solution that operates directly with beam reference signal received power (BRSRP) measurements, alleviating the need to know the true antenna beampatterns or the underlying beamforming weights. Additionally, the method has built-in robustness against unavoidable antenna sidelobes. Secondly, we propose new snapshot SLAM algorithms that have increased robustness and identifiability compared to prior art, in practical built environments with complex clutter and multi-bounce propagation scenarios, and do not rely on any a priori motion model. The performance of the proposed methods is assessed at the 60GHz mmWave band, via both realistic ray-tracing evaluations as well as true experimental measurements, in an indoor environment. A wide set of offered results demonstrate the improved performance, compared to the relevant prior art, in terms of the channel parameter estimation as well as the end-to-end SLAM performance. Finally, the article provides the measured 60GHz data openly available for the research community, facilitating results reproducibility as well as further algorithm development.

The swarms of unmanned aerial vehicles (UAV) are nowadays finding numerous applications in different fields. While performing their missions, UAVs have to rely on external positioning information to maintain connectivity and communications between units in a swarm. However, some of the critical applications such as rescue missions are performed in locations, where this information is partially or fully not available, e.g., deep woods, mountains, indoors. In this paper, we propose a method for dynamic topology organization and maintenance in UAV swarms. In addition to the baseline functionality, we also design advanced features required for dynamic swarms merging and disjoining, making it suitable for practical applications. Specifically, the proposal is based on the virtual coordinates system allowing for the utilization of conventional geographical routing algorithms. We test the proposed algorithm in different swarm conditions to illustrate that: (i) it is insensitive to distance estimates up to at least 30% allowing for simple estimation techniques, (ii) the accuracy of the topology inference is at least 90% even under impairments caused by mobility and temporal loss of connectivity, and (iii) the impact of the developed merging algorithm for swarms lasts for multiple tens of time steps that correspond to just few seconds in practice. The set of developed algorithms can be utilized to ensure always connected topology in conditions where positioning information is partially or fully unavailable.

In this paper, we investigate the secure transmission in the downlink of a cell-free massive multiple-input multiple-output (mMIMO) system that relies on rate-splitting multiple access (RSMA). We specifically evaluate the impact of hardware impairments (HWIs) originating from non-ideal access points (APs), user equipments (UEs), and Eavesdroppers (Eves) on the system’s secrecy performance. The investigation encompasses scenarios with both colluding and non-colluding Eves orchestrating pilot spoofing attacks against a designated UE, subsequently intercepting transmissions from both common and private streams. By taking into account a spatially correlated Ricean fading channel model and imperfect channel state information, we derive closed-form expressions for both legitimate and secrecy rates. The secrecy performance is scrutinized across different system configurations, including varying HWI levels, power splitting ratios, AP/Eve transmission powers, spatial correlations, line-of-sight components, and the presence of colluding versus non-colluding Eves. To enhance the secrecy rate for the compromised UE, we propose a secure power control strategy for adjusting the downlink transmission powers of the common and private streams. A sequential convex approximation-based algorithm is introduced to iteratively address this non-convex problem. Through comprehensive simulations, we validate our theoretical propositions and extract pivotal insights for system design.

The rapid growth of chronic diseases and medical conditions (e.g. obesity, depression, diabetes, respiratory and musculoskeletal diseases) in many OECD countries has become one of the most significant wellbeing problems, which also poses pressure to the sustainability of healthcare and economies. Thus, it is important to promote early diagnosis, intervention, and healthier lifestyles. One partial solution to the problem is extending long-term health monitoring from hospitals to natural living environments. It has been shown in laboratory settings and practical trials that sensor data, such as camera images, radio samples, acoustics signals, infrared etc., can be used for accurately modelling activity patterns that are related to different medical conditions. However, due to the rising concern related to private data leaks and, consequently, stricter personal data regulations, the growth of pervasive residential sensing for healthcare applications has been slow. To mitigate public concern and meet the regulatory requirements, our national multi-partner SPHERE-DNA project aims to combine pervasive sensing tech-nology with secured and privacy-preserving distributed privacy frameworks for healthcare applications. The project leverages local differential privacy federated learning (LDP-FL) to achieve resilience against active and passive attacks, as well as edge computing to avoid transmitting sensitive data over networks. Combinations of sensor data modalities and security architectures are explored by a machine learning architecture for finding the most viable technology combinations, relying on metrics that allow balancing between computational cost and accuracy for a desired level of privacy. We also consider realistic edge computing platforms and develop hardware acceleration and approximate computing techniques to facilitate the adoption of LDP-FL and privacy preserving signal processing to lightweight edge processors. A proof-of-concept (PoC) multimodal sensing system will be developed and a novel multimodal dataset will be collected during the project to verify the concept.

Radio frequency fingerprinting (RFF), a technique based on specific transmitter hardware impairments, has emerged as an effective solution for wireless device identification. In this paper, we present a flexible deep CNN-LSTM for RF feature extraction capable of handling inputs with varying lengths. We construct a channel-independent spectrogram by exploiting the amplitude and phase information of the received RF signals, ensuring the extractor’s resilience to channel variations. To evaluate the performance of the proposed approach, we utilize the open-source LoRa dataset consisting of 60 commercial off-the-shelf LoRa devices and a USRP N210 software-defined radio platform. The experimental results show that classifiers perform better when trained with RF templates generated from amplitude-phase spectrogram than amplitude-only spectrogram. This is due to the additional information present in the amplitude-phase channel-independent spectrogram.

A symbolic feature that integrates the space, air and ground network components for service in challenging and remote areas is being envisaged with continuity and high mobility of the 6G mobile system. Simultaneously providing sensing and connectivity over the radio signal becomes essential to support the management of low-space air crafts in the mobile system with limited spectrum resources. In this paper, we investigate the optimal joint radar and communications beamforming scheme with the presence of the clutter to support the low-space airborne vehicles, e.g. unmanned aerial vehicles or drones that are essential components of Non-Terrestrial Networks. The proposed scheme achieves the optimal signal-to-clutter-plus-noise ratio of the sensing function while maintaining the performance of the predefined communications. The novel application of approximations and rank-reduction algorithms in this work maximizes the joint radar and communications performance, for a system model similar to the one that is solved with a local optimum solution in a previous work. The numeric simulation results show that our approach maintains low complexity while guaranteeing the global optimum beamforming solution.

This paper designs the vehicle tracking and resource allocation in the terahertz (THz) vehicle-to-infrastructure communications (V2I) networks, where roadside units (RSUs) equipped with leaky-wave antennas help to estimate the driving states of multiple vehicles and optimize the transmit power and bandwidth per vehicle after receiving the vehicles’ feedback. Different from the conventional phased arrays, the leaky-wave antenna has the potential of improving the sensing accuracy with lower system overhead thanks to its unique spatial-spectral coupling feature. The generalized mobile scenario is studied in which vehicles drive at time-varying speeds. A novel unscented Kalman filter (UKF) based solution is proposed to track the vehicles without requirement of addressing the Doppler effect. Based on the estimated states of multiple vehicles, a low-complexity resource allocation method is developed to maximize the sum rate under user fairness concern. Simulation results confirm that the proposed tracking solution can evaluate the propagation angle, vehicle’s states and inter-vehicle distance accurately, and the tailored resource allocation method strikes a delicate balance between the sum rate and user fairness in the multi-vehicle V2I scenario.

In this paper, we design the vehicle tracking in the terahertz (THz) vehicle-to-infrastructure (V2I) networks, where roadside units (RSUs) equipped with leaky-wave antennas help to estimate the driving states of multiple vehicles after receiving the vehicles’ feedback. Different from the conventional phased arrays, the leaky-wave antenna has the potential of improving the sensing accuracy with lower system overhead thanks to its unique spatial-spectral coupling feature. The generalized mobile scenario is studied in which vehicles drive at time-varying speeds. A novel unscented Kalman filter (UKF) based solution is proposed to track the vehicles without requirement of addressing the Doppler effect. Simulation results confirm that the proposed tracking solution can evaluate the propagation angle, vehicle’s states and inter-vehicle distance accurately.

Remote Human Activity Recognition (HAR) in a private residential area has a beneficial influence on the elderly population's life, since this group of people require regular monitoring of health conditions. This paper addresses the problem of continuous detection of daily human activities using mm-wave Doppler radar. Unlike most previous research, this work records the data in terms of continuous series of activities rather than individual activities. These series of activities are similar to real-life activity patterns. The Dynamic Time Warping (DTW) algorithm is used for the detection of human activities in the recorded time series of data and compared to other time-series classification methods. DTW requires less amount of labelled data. The input for DTW was provided using three strategies, and the obtained results were compared against each other. The first approach uses the pixel-level data of frames (named UnSup-PLevel). In the other two strategies, a Convolutional Variational Autoencoder (CVAE) is used to extract Un-Supervised Encoded features (UnSup-EnLevel) and Supervised Encoded features (Sup-EnLevel) from the series of Doppler frames. Results demonstrates the superiority of the Sup-EnLevel features over UnSup-EnLevel and UnSup-PLevel strategies. However, the performance of the UnSup-PLevel strategy worked surprisingly well without using annotations.

This paper studies a drone relay assisted cooperative wireless communication system. Specifically, a drone is used as the relay node to establish communication between the base station and an aerial mobile terminal under realistic channel models with the consideration of line-of-sight probability. The total transmission time is divided into smaller time slots and in each time slot the relay uses decode-and-forward protocol to forward the received information to the mobile terminal. Then, an optimization problem is formulated where the objective is to maximize the sum rate over the whole transmission time. The formulated problem is non-convex. However, we show that for several cases the global optimal solution can be achieved. Moreover, we develop a low-complexity algorithm to find suboptimal solutions for the other cases.

Dual functional radar communication (DFRC) is a promising approach that provides a viable solution for the problem of spectrum sharing between communication and radar applications. This paper studies a DFRC system with multiple communication users (CUs) and one radar target. The goal is to devise beamforming vectors at the DFRC transmitter in such a way that the radar received signal to noise ratio (SNR) is maximized while the minimum data rate requirements of the individual CUs are satisfied. Even though the formulated optimization problem is non-convex, it is shown that it can be solved optimally through semi-definite relaxation (SDR). Also, it is observed that there is no need to transmit dedicated probing signal for the radar detection.

In the last decade, the field of wireless optical communication has gathered immense interest due to its adoption in growing bandwidth-hungry underwater applications. The expensive and non-standardized on-field research measurements call for a reliable simulation tool that allows researchers to realistically design and assess the performance of Underwater Optical Communication (UOC) systems before conducting actual underwater experiments. In this paper, we present a UOC module as an extension to the network simulator ns-3. The module can study the impact of different water conditions on underwater optical networks from the physical layer to the network layer. The proposed UOC module realizes physical layer models of the UOC channels where the added noise and interference effects are modeled as Additive White Gaussian Noise (AWGN). Results show the capability of our module to facilitate large underwater optical network design and optimization. Since ns-3 is an open-source software, the module has the flexibility and reusability to be further developed by the worldwide research community.

This paper focuses on the spatial-spectral terahertz (THz) networks, where transmitters equipped with leaky-wave antennas send information to their receivers at the THz frequency bands. As a directional and nearly planar antenna, the leaky-wave antenna allows for information transmissions with narrow beams and high antenna gains. The conventional large antenna arrays are confronted with challenging issues such as scaling limits and path discovery in the THz frequencies. Therefore, this work exploits the potential of leaky-wave antennas in the dense THz networks, to establish low-complexity THz links. By addressing the propagation angle-frequency coupling effects, the transmission rate is analyzed. The results show that the leaky-wave antenna is efficient for achieving the high-speed transmission rate. The co-channel interference management is unnecessary when the THz transmitters with large subchannel bandwidths are not extremely dense. A simple subchannel allocation solution is proposed, which enhances the transmission rate compared with the same number of subchannels with the equal allocation of the frequency band. After subchannel allocation, a low-complexity power allocation method is proposed to improve the energy efficiency.

Integrating sensing and communications is becoming a rising trend in the architecture design of the foreseeable mobile communications system, which could be driven by multifold applications and scarce spectrum resources. Regarding the demand for the economic surveillance solution in the secondary airports, the inborn imaging function in the 5G networks could be a promising candidate. This paper investigates the feasibility and capability of using 5G uplink and downlink reference signals for imaging purposes. An ambiguity function-based signal processing method is proposed in this paper to elaborate the imaging functionality in the 5G networks. The 5G signal-based imaging idea is validated with a realistic ray-tracing channel model generated from a simulated 3D airport model. Our method empowers the imaging functionality of the wireless communications system solely without the aid of external signal resources. Different from the conventional synthetic-aperture radar processing, our methods are adjusted for unevenly allocated reference signal symbols, which causes mirror images problem. The mirror images are quantified in the simulation result, and the mitigation strategies such as lower flight speed and narrower beam are proposed to resolve the problem.

This paper proposes a cyclic prefix (CP) free spectral efficiency frequency division multiplexing (SEFDM) wireless signal transmission and reception method based on alignment signal (AS). The method needs the cooperation of both the transmission and reception sides. At the transmitter, time-domain AS is designed to prevent inter-symbol interference (ISI) caused by multipath propagation in the received signal. At the receiver, the channel circularity convolution providing processing is used to enable high accurate frequency domain one-tap equaliser. The extensive computer simulation results under the 5G new radio (5G-NR) channel model, TDL-D, show that the AS aided SEFDM has similar bit error rate performance as CP-SEFDM without energy and latency cost on CP. The AS-SEFDM capability to mitigate the ISI and to enable the one-tap equaliser in SEFDM systems makes it a promising technique for future wireless communication systems.

Cyclic-prefix orthogonal frequency-division multiplexing (CP-OFDM) is a widely adopted modulation scheme for broadband wireless communication systems. The use of CP makes the scheme robust and facilitates simple and effective channel equalization since it makes the frequency-selective multipath fading to appear as flat-fading at subcarrier level. However, CP causes overhead in the spectrum efficiency and power efficiency. Various schemes have been proposed in the literature to avoid the use of CP or other forms of guard intervals (GIs) between OFDM symbols, but none of those has been fully satisfying. One recent proposal is based on adding a so-called alignment signal to GI-less OFDM signal in such a way that (i) intersymbol interference is avoided and (ii) the multipath channel effect appears as the cyclic convolution between the effective channel impulse response and each OFDM symbol. While assuming channel knowledge on the transmitter side, this approach allows basic OFDM signal processing to be used on the receiver side for channel estimation, equalization, and synchronization. In this work, we provide enhancements to the alignment signal structure and generation, which provide clear improvement in the link performance and reduce the complexity of transmitter processing. Also, new insights about the characteristics of alignment signal based CP-free OFDM waveforms are provided.

Farm detection using low resolution satellite images is an important topic in digital agriculture. However, it has not received enough attention compared to high-resolution images. Although high resolution images are more efficient for detection of land cover components, the analysis of low-resolution images are yet important due to the low-resolution repositories of the past satellite images used for timeseries analysis, free availability and economic concerns. The current paper addresses the problem of farm detection using low resolution satellite images. In digital agriculture, farm detection has significant role for key applications such as crop yield monitoring. Two main categories of object detection strategies are studied and compared in this paper; First, a two-step semi-supervised methodology is developed using traditional manual feature extraction and modelling techniques; the developed methodology uses the Normalized Difference Moisture Index (NDMI), Grey Level Co-occurrence Matrix (GLCM), 2-D Discrete Cosine Transform (DCT) and morphological features and Support Vector Machine (SVM) for classifier modelling. In the second strategy, high-level features learnt from the massive filter banks of deep Convolutional Neural Networks (CNNs) are utilised. Transfer learning strategies are employed for pretrained Visual Geometry Group Network (VGG-16) networks. Results show the superiority of the high-level features for classification of farm regions.

In this paper, we propose an ultra-low-resolution infrared (IR) images based activity recognition method which is suitable for monitoring in elderly care-house and modern smart home. The focus is on the analysis of sequences of IR frames, including single subject doing daily activities. The pixels are considered as independent variables because of the lacking of spatial dependencies between pixels in the ultra-low resolution image. Therefore, our analysis is based on the temporal variation of the pixels in vectorised sequences of several IR frames, which results in a high dimensional feature space and an "n<; <; p" problem. Two different sparse analysis strategies are used and compared: Sparse Discriminant Analysis (SDA) and Sparse Principal Component Analysis (SPCA). The extracted sparse features are tested with four widely used classifiers: Support Vector Machines (SVM), Random Forests (RF), K-Nearest Neighbours (KNN) and Logistic Regression (LR). To prove the availability of the sparse features, we also compare the classification results of the noisy data based sparse features and non-sparse based features respectively. The comparison shows the superiority of sparse methods in terms of noise tolerance and accuracy.

In this paper, we propose a time precoding scheme for cancelling inter-carrier interference in non-orthogonal frequency division multiplexing for the first time. Achieving high spectral efficiencies is a recurring and key challenge in wireless communications systems and researchers generally use high order and advanced modulation formats to approach this problem, in particular, non-orthogonal modulation formats are a topic of particular interest. Fast orthogonal frequency division multiplexing (F-OFDM) doubles the throughput of conventional OFDM by violating orthogonality of the quadrature carrier, causing interference in the real and imaginary domains. Here, we propose a precoding scheme that enables self-interference cancellation without the need of the interference level calculation. The proposed scheme is implemented in the context of a narrowband internet-of-things (NB-IoT) system and verified on a software define radio (SDR) testbed with realistic AWGN and multiple path channels from channel emulator for concept proving. By comparing with the standard OFDM transmission, the time precoded F-OFDM outperforms around 3dB by BER with same signal-to-ratio (SNR) level.

In spectrally efficient frequency division multiplexing (SEFDM), the separation between subcarriers is reduced below the Nyquist criteria, enhancing bandwidth utilisation in comparison to orthogonal frequency division multiplexing (OFDM). This leads to self-induced inter-carrier interference (ICI) in the SEFDM signal, which requires more sophisticated detectors to retrieve the transmitted data. In previous work, iterative detectors (IDs) have been used to recover the SEFDM signal after processing a certain number of iterations, however, the sequential iterative process increases the processing time with the number of iterations, leading to throughput reduction. In this work, ID pipelining is designed and implemented in software defined radio (SDR) to reduce the overall system detection latency and improve the throughput. Thus, symbols are allocated into parallel IDs that have no waiting time as they are received. Our experimental findings show that throughput will improve linearly with the number of the paralleled ID elements, however, hardware complexity also increases linearly with the number of ID elements.

Physical activity classification is an important tool for various applications such as activity of daily living (ADL) recognition and fall detection. Additionally, the non-contact nature of radar systems provides minimally invasive sensing platform. Doppler-based radar has been used for activity classification in the past. However, most of these studies considered supervised classification which requires labeled training data sets. In this paper, we propose a novel procedure of using micro Doppler radar for unsupervised classification with Hidden Markov Models (HMM). A low-complexity time alignment method for capturing activity is developed and an Elbow test has been adopted for model selection. Test results confirm the efficacy of the selected feature set and the proposed methodology. The results prove the proposed system can deliver a very good performance in ADL recognition tasks.

Device free activity recognition and monitoring has become a promising research area with increasing public interest in pattern of life monitoring and chronic health conditions. This paper proposes a novel framework for in-home Wi-Fi signal-based activity recognition in e-healthcare applications using passive micro-Doppler (m-D) signature classification. The framework includes signal modeling, Doppler extraction and m-D classification. A data collection campaign was designed to verify the framework where six m-D signatures corresponding to typical daily activities are sucessfully detected and classified using our software defined radio (SDR) demo system. Analysis of the data focussed on potential discriminative characteristics, such as maximum Doppler frequency and time duration of activity. Finally, a sparsity induced classifier is applied for adaptting the method in healthcare application scenarios and the results are compared with those from the well-known Support Vector Machine (SVM) method.

Physical activity envelope provides invaluable information in numerous pervasive health applications. Physical activity is traditionally gleaned using a range of wearable inertial sensors and/or video technology. This paper introduces a novel opportunistic and non-intrusive monitoring system which can quantify activity levels based on analysis of ambient WiFi signal scatter. A real-time signal processing framework is developed, and the proposed system is implemented in software defined radio platform. Experimental results corroborate the efficacy of the proposed system in long term ADL monitoring in residential healthcare applications.

Non-contact breathing monitoring systems are very attractive for a range of e-Healthcare applications. This paper proposes a passive radar based system for measuring human breathing rate. A novel signal processing method is introduced to extract breathing rate based on micro Doppler derived from cross ambiguity function (CAF). The passive radar system is built within a software defined radio (SDR) platform. The proposed system uses opportunistically energy harvesting transmitter as an illumination signal. Passive detection is compared and verified using the ground truth from clinical chest belt respiration detector. Two experiments have been conducted to show the feasibility of passive detection system in the line of sight and also through-wall conditions. We conclude that a low frequency narrow band signal with non-contact passive detection can offer a realistic alternative to UWB based radars for future e-Healthcare passive sensing applications.

This paper describes two Doppler only indoor passive Wi-Fi tracking methods based on high Doppler resolution passive radar. Two filters are investigated in this paper, the extended Kalman filter and the sequential importance resampling (SIR) particle filter. Experimental results for these two tracking filters are presented using results from software defined passive Wi-Fi radar using a standard 802.11 access point as an illuminator. The experimental results show that the SIR particle filter performs well using Wi-Fi signals for indoor tracking with a high degree of accuracy. Proposals for simplifying the SIR particle and application to multiple target tracking are also discussed.

In this paper, the performance of discrete Fourier transform (DFT) based channel estimation (CE) for orthogonal frequency division multiplexing (OFDM) systems with transmit antenna diversity is analyzed. Conventional DFT-based CE suffers from the dispersive distortion of an estimated channel impulse response (CIR) due to the existence of virtual carriers (VCs). We analyze the mean squared error (MSE) performance and provide a concise expression which reveals the leakage effect due to VCs. Further, analytical expression for bit error rate (BER) with the effect of VCs is also derived. Simulation results illustrate the accuracy of the theoretical analysis.