This paper presents the concept, design, channel modeling, beamforming algorithm development, prototype fabrication, and experimental measurement of an electromagnetically reconfigurable fluid antenna system (ER-FAS), in which each FAS array element features electromagnetic (EM) reconfigurability. Unlike most existing FAS works that investigate spatial reconfigurability by adjusting the position and/or orientation of array elements, the proposed ER-FAS enables direct control over the EM characteristics of each element, allowing for dynamic radiation pattern reconfigurability. Specifically, a novel ER-FAS architecture leveraging software-controlled fluidics is proposed, and corresponding wireless channel models are established. Based on this ER-FAS channel model, a low-complexity greedy beamforming algorithm is developed to jointly optimize the analog phase shift and the radiation state of each array element. The accuracy of the ER-FAS channel model and the effectiveness of the beamforming algorithm are validated through (i) full-wave EM simulations and (ii) numerical spectral efficiency evaluations. These results confirm that the proposed ER-FAS significantly enhances spectral efficiency in both near-field and far-field scenarios compared to conventional antenna arrays. To further validate this design, we fabricate prototypes for both the ER-FAS element and array, using Galinstan liquid metal alloy, fluid silver paste, and software-controlled fluidic channels. The simulation results are experimentally validated through prototype measurements conducted in an anechoic chamber. Additionally, several indoor communication experiments using a pair of software-defined radios demonstrate the superior received power and bit error rate performance of the ER-FAS prototype. This paper offers a comprehensive demonstration of a liquid-based ER-FAS array for wireless communication, incorporating a novel electromagnetically reconfigurable design, channel modeling, and beamforming, supported by simulation, hardware implementation, and experimental validation.

Monitoring network traffic to maintain the quality of service (QoS) and to detect network intrusions in a timely and efficient manner is essential. As network traffic is sequential, recurrent neural networks (RNNs) such as long short-term memory (LSTM) are suitable for building network intrusion detection systems. However, in the case of a few dataset examples of the rare attack types, even these networks perform poorly. This paper proposes to use oversampling techniques along with appropriate loss functions to handle class imbalance for the detection of various types of network intrusions. Our deep learning model employs LSTM with fully connected layers to perform multi-class classification of rare network attacks. We enhance the representation of minority classes: i) through the application of the Synthetic Minority Over-sampling Technique (SMOTE), and ii) by employing categorical focal cross-entropy loss to apply a focal factor to down-weight examples of the majority classes and focus more on hard examples of the minority classes. Extensive experiments on KDD99 and CICIDS2017 datasets show promising results in detecting network intrusions (with many rare attack types, e. g., U2R, R2L, Probe, Infiltration, Hearbleed, etc.).

This study introduces a novel, non-invasive approach to monitor hydration status using single-lead electrocardiogram (ECG) signals. MAX86150 internet of medical things (IoMT) module is utilized to collect raw 1-lead ECG data from 65 subjects under fasting and exercise conditions, creating a labeled ECG dataset. To augment the dataset, white Gaussian noise is added to the ECG segments. In addition, synthetic ECG data is generated using STABLE DIFFUSION models trained on the conditioned ECG segments. The augmented data is then fed into various machine learning and deep learning models as part of a baseline evaluation. Two classification tasks are performed: binary classification (hydrated vs. dehydrated) and four-class classification, categorizing hydration level of subjects on a one to four scale. The models report a high accuracy that is up to 98.73% for binary classification, 97.41% for four-class classification in fasting subjects, and 98.32% for sportspeople, showing the potential of using single-lead ECG for hydration monitoring. Additionally, the models decisions are interpreted using LIME-based explainable artificial intelligence (AI) technique, which identifies ECG features such as the RR interval and QRS intervals as relevant biomarkers for dehydration. The study also investigates the use of large language models (LLMs) and large vision models (LVMs) to analyze sequential ECG data for hydration assessment. However, LLMs and LVMs struggle due to the time-series nature of the ECG data and fail to accurately interpret ECG graphs. While LLM and LVM results are not favorable due to hallucinations, this study provides valuable insights into the limitations of these models, paving the way for future research in this area.

The convergence of artificial intelligence (AI) and the Internet of Things (IoT) promotes energy-efficient communication in smart homes. Quality-of-Service (QoS) optimization during video streaming through wireless micro medical devices (WMMDs) in smart healthcare homes is the main purpose of this research. This article contributes in four distinct ways. First, to propose a novel lazy video transmission algorithm (LVTA). Second, a novel video transmission rate control algorithm (VTRCA) is proposed. Third, a novel cloud-based video transmission framework is developed. Fourth, the relationship between buffer size and performance indicators, i.e., peak-to-mean ratio (PMR), energy (i.e., encoding and transmission), and standard deviation, is investigated while comparing LVTA, VTRCA, and baseline approaches. The experimental results demonstrate that the reduction in encoding (32% and 35.4%) and transmission (37% and 39%) energy drains, PMR (5 and 4), and standard deviation (3 and 4 dB) for VTRCA and LVTA, respectively, is greater than that obtained by baseline during video streaming through WMMD.

This paper investigates the impact of authentication on effective capacity (EC) of an underwater acoustic (UWA) channel. Specifically, the UWA channel is under impersonation attack by a malicious node (Eve) present in the close vicinity of the legitimate node pair (Alice and Bob); Eve tries to inject its malicious data into the system by making Bob believe that she is indeed Alice. To thwart the impersonation attack by Eve, Bob utilizes the distance of the transmit node as the feature/fingerprint to carry out feature-based authentication at the physical layer. Due to authentication at Bob, due to lack of channel knowledge at the transmit node (Alice or Eve), and due to the threshold-based decoding error model, the relevant dynamics of the considered system could be modelled by a Markov chain (MC). Thus, we compute the state-transition probabilities of the MC, and the moment generating function for the service process corresponding to each state. This enables us to derive a closed-form expression of the EC in terms of authentication parameters. Furthermore, we compute the optimal transmission rate (at Alice) through gradient-descent (GD) technique and artificial neural network (ANN) method. Simulation results show that the EC decreases under severe authentication constraints (i.e., more false alarms and more transmissions by Eve). Simulation results also reveal that the (optimal transmission rate) performance of the ANN technique is quite close to that of the GTJ method.

This work presents a novel method to generate secret keys shared between a legitimate node pair (Alice and Bob) to safeguard the communication between them from an unauthorized node (Eve). To this end, we exploit the reciprocal carrier frequency offset (CFO) between the legitimate node pair to extract common randomness out of it to generate shared secret keys. The proposed key generation algorithm involves standard steps: the legitimate nodes exchange binary phase-shift keying (BPSK) signals to perform blind CFO estimation on the received signals, and do equi-probable quantization of the noisy CFO estimates followed by information reconciliation-to distil a shared secret key. Furthermore, guided by the Allan deviation curve, we distinguish between the two frequency-stability regimes-when the randomly time-varying CFO process i) has memory, ii) is memoryless; thereafter, we compute the key generation rate for both regimes. Simulation results show that the key disagreement rate decreases exponentially with increase in the signal to noise ratio of the link between Alice and Bob. Additionally, the decipher probability of Eve decreases as soon as either of the two links observed by the Eve becomes more degraded compared to the link between Alice and Bob.

We detect the deviation of the grid frequency from the nominal value (i.e., 50 Hz), which itself is an indicator of the power imbalance (i.e., mismatch between power generation and load demand). We first pass the noisy estimates of grid frequency through a hypothesis test which decides whether there is no deviation, positive deviation, or negative deviation from the nominal value. The hypothesis testing incurs miss-classification errors-- false alarms (i.e., there is no deviation but we declare a positive/negative deviation), and missed detections (i.e., there is a positive/negative deviation but we declare no deviation). Therefore, to improve further upon the performance of the hypothesis test, we represent the grid frequency's fluctuations over time as a discrete- time hidden Markov model (HMM). We note that the outcomes of the hypothesis test are actually the emitted symbols, which are related to the true states via emission probability matrix. We then estimate the hidden Markov sequence (the true values of the grid frequency) via maximum likelihood method by passing the observed/emitted symbols through the Viterbi decoder. Simulations results show that the mean accuracy of Viterbi algorithm is at least $5$\% greater than that of hypothesis test.

In this paper, we investigate the problem of mitigating interference between so-called antenna domains of a cloud radio access network (C-RAN). In contrast to previous work, we turn to an approach utilizing primarily the optimal assignment of users to central processors in a C-RAN deployment. We formulate this user assignment problem as an integer optimization problem and propose an iterative algorithm for obtaining a solution. Motivated by the lack of optimality guarantees on such solutions, we opt to find lower bounds on the problem and the resulting interference leakage in the network. We thus derive the corresponding Dantzig-Wolfe decomposition, formulate the dual problem, and show that the former offers a tighter bound than the latter. We highlight the fact that the bounds in question consist of linear problems with an exponential number of variables and adapt the column generation method for solving them. In addition to shedding light on the tightness of the bounds in question, our numerical results show significant sum-rate gains over several comparison schemes. Moreover, the proposed scheme delivers similar performance as weighted minimum mean squared-error (MMSE) with a significantly lower complexity (around 10 times less).

This paper studies a system where a set of $N$ relay nodes harvest energy from the signal received from a source to later utilize it when forwarding the source's data to a destination node via distributed beamforming. To this end, we derive (approximate) analytical expressions for the mean SNR at destination node when relays employ: i) time-switching based energy harvesting policy, ii) power- splitting based energy harvesting policy. The obtained results facilitate the study of the interplay between the energy harvesting parameters and the synchronization error, and their combined impact on mean SNR. Simulation results indicate that i) the derived approximate expressions are very accurate even for small $N$ (e.g., $N=15$), ii) time-switching policy by the relays outperforms power-splitting policy by at least $3$ dB.

In this preliminary work, we study the problem of distributed authentication in wireless networks. Specifically, we consider a system where multiple Bob (sensor) nodes listen to a channel and report their correlated measurements to a Fusion Center (FC) which makes the ultimate authentication decision. For the feature-based authentication at the FC, channel impulse response has been utilized as the device fingerprint. Additionally, the correlated measurements by the Bob nodes allow us to invoke Compressed sensing to significantly reduce the reporting overhead to the FC. Numerical results show that: i) the detection performance of the FC is superior to that of a single Bob-node, ii) compressed sensing leads to at least 20% overhead reduction on the reporting channel at the expense of a small (<;1 dB) SNR margin to achieve the same detection performance.

This paper proposes to exploit the so-called {\it reciprocity parameters} (modelling non-reciprocal communication hardware) to use them as decision metric for binary hypothesis testing based authentication framework at a receiver node Bob. Specifically, Bob first learns the reciprocity parameters of the legitimate sender Alice via initial training. Then, during the test phase, Bob first obtains a measurement of reciprocity parameters of channel occupier (Alice, or, the intruder Eve). Then, with ground truth and current measurement both in hand, Bob carries out the hypothesis testing to automatically accept (reject) the packets sent by Alice (Eve). For the proposed scheme, we provide its success rate (the detection probability of Eve), and its performance comparison with other schemes.

We study here the problem of Antenna Domain Formation (ADF) in cloud RAN systems, whereby multiple remote radio-heads (RRHs) are each to be assigned to a set of antenna domains (ADs), such that the total interference between the ADs is minimized. We formulate the corresponding optimization problem, by introducing the concept of interference coupling coefficients among pairs of radio-heads. We then propose a low-overhead algorithm that allows the problem to be solved in a distributed fashion, among the aggregation nodes (ANs), and establish basic convergence results. Moreover, we also propose a simple relaxation to the problem, thus enabling us to characterize its maximum performance. We follow a layered coordination structure: after the ADs are formed, radio-heads are clustered to perform coordinated beamforming using the well known Weighted-MMSE algorithm. Finally, our simulations show that using the proposed ADF mechanism would significantly increase the sum-rate of the system (with respect to random assignment of radio-heads).

Provision of high data rates with always-on connectivity to high mobility users is one of the motivations for design of fifth generation (5G) systems. High system capacity can be achieved by coordination between large number of antennas, which is done using the cloud radio access network (CRAN) design in 5G systems. In terms of baseband processing, allocation of appropriate resources to the users is necessary to achieve high system capacity, for which the state of the art uses the users' channel state information (CSI); however, they do not take into account the associated overhead, which poses a major bottleneck for the effective system performance. In contrast to this approach, this paper proposes the use of machine learning for allocating resources to high mobility users using only their position estimates. Specifically, the `random forest' algorithm, a supervised machine learning technique, is used to design a learning-based resource allocation scheme by exploiting the relationships between the system parameters and the users' position estimates. In this way, the overhead for CSI acquisition is avoided by using the position estimates instead, with better spectrum utilization. While the initial numerical investigations, with minimum number of users in the system, show that the proposed learning-based scheme achieves 86% of the efficiency achieved by the perfect CSI-based scheme, if the effect of overhead is factored in, the proposed scheme performs better than the CSI-based approach. In a realistic scenario, with multiple users in the system, the significant increase in overhead for the CSI-based scheme leads to a performance gain of 100%, or more, by using the proposed scheme, and thus proving the proposed scheme to be more efficient in terms of system performance.

Token-passing wireless network protocols (TPWNP) (e.g., EchoRing), designed for hard real-time systems, typically need to provide ultra-low-latency coupled with ultrahigh-reliability guarantees. In this paper, we initiate a study to investigate the feasibility of distributed relay beamforming (DRBF) in a TP-WNP with the aim to enhance its reliability (and latency) performance even further. Specifically, we consider employing N i) amplify-and-forward (AF), ii) decode-and-forward (DF) relays in a wireless network running a TP-WNP. The relays operate in FDD mode, and do distributed transmit beamforming to realize low-latency, highly-reliable communication between each of the M source-destination pairs in the TP-WNP (a.k.a TDMA) system. The enablers/pre-requisites for the proposed DRBF-TDMA system are frequency, phase and timing synchronization among the relay nodes. To this end, we propose a novel distributed method for frequency synchronization among the AF/DF relay nodes operating in FDD mode. Furthermore, for oscillators with drift, we derive a rule of thumb which provides us the maximum relaying delay Tdelayup to which the proposed frequency synchronization method is effective. For phase and timing synchronization, we employ standard techniques from the literature. Our simulation results verify the analytical results, i.e., by means of proposed DRBF using N AF (DF) relays, upto a factor of N (N2) gains in received SNR can be achieved, at each of the M destination nodes in the proposed system.

PHY layer authentication of a wireless sender has gained much interest recently. In this paper, we consider the famous Alice, Bob and Eve model and investigate (for the first time) the feasibility of using time-varying clock offsets for sender-node-authentication at Bob. Specifically, we exploit the fact (and de-facto problem) that clock offset between every node pair is unique; moreover, the two clock offsets between any two node pairs drift independently and randomly over time. Therefore, an explicit mechanism is needed to track the time-varying clock offsets. To this end, we model oscillator drift as brownian motion frequency and phase drift, and present a novel framework which is based on interplay between a hypothesis testing device and a bank of two Kaiman filters; one KF (KFh0) tracks Alice's clock while other KF (KFh1) tracks Eve's clock. Building on aforementioned framework, we then propose a novel sender-node-authentication method (so-called MHF method) by means of which Bob can automatically accept (reject) a received packet if it is sent by Alice (Eve). Finally, simulation results are presented which corroborate the efficiency of the proposed method.

We describe a fully-wireless prototype of distributed transmit beamforming on a software-defined radio platform. Distributed beamforming is a cooperative transmission technique that can achieve orders of magnitude increases in range or energy efficiency of wireless communication systems. However, this technique requires precise synchronization of the radio frequency signal from each transmitter. The significance of our prototype is in demonstrating that this requirement can be satisfied using digital signal processing methods on commodity hardware with low-quality oscillators. Our synchronization approach scales to large numbers of transmitters: each transmitter runs independent algorithms based on periodically transmitted feedback packets from the receiver. A key simplification is the decoupling of the algorithms for frequency locking and beamsteering at each transmitter, even though both processes use the same feedback packets. Frequency locking employs an Extended Kalman filter to track the local oscillator offset between a transmitter and the receiver, using frequency offset measurements based on the feedback packet it waveform, while the phase adjustments for beamsteering are determined using a one-bit feedback algorithm based on the feedback packet it payload. Our prototype demonstrates that distributed transmit beamforming can be incorporated into wireless networks without requiring hardware innovations, and provides open-source building blocks for future research and development.

We present an implementation of distributed transmit beamforming using software-defined radios. The transmit nodes synchronize in carrier frequency using a pilot signal sent by a master node, and employ feedback from the receiver to adjust their carrier phases so as to add up coherently at the receiver. Our implementation advances the state of the art for all-wireless distributed beamforming in two important ways. The first is the implementation of extended Kalman filters for frequency synchronization at the slave nodes, which is shown to be effective despite the high local oscillator (LO) offsets typical of software-defined radios, and the low duty cycle of the pilot transmitted by the master node. The second advance is the implementation of the well-known one bit feedback scheme for phase adjustment using digital feedback from the receiver. We present experimental results that show the efficacy of our implementation for frequency synchronization and beamforming.

We describe the key ideas behind our implementation of distributed beamforming on a GNU-radio based software-defined radio platform. Distributed beamforming is a cooperative transmission scheme whereby a number of nodes in a wireless network organize themselves into a virtual antenna array and focus their transmission in the direction of the intended receiver, potentially achieving orders of magnitude improvements in energy efficiency. This technique has been extensively studied over the past decade and its practical feasibility has been demonstrated in multiple experimental prototypes. Our contributions in the work reported in this paper are three-fold: (a) the first ever all-wireless implementation of distributed beamforming without any secondary wired channels for clock distribution or channel feedback, (b) a novel digital baseband approach to synchronization of high frequency RF signals that requires no hardware modifications, and (c) an implementation of distributed beamforming on a standard, open platform that allows easy reuse and extension. We describe the design of our system in detail, present some initial results and discuss future directions for this work.

We present a fully wireless implementation of distributed transmit beamforming using software-defined radios. Distributed beamforming is a cooperative transmission scheme whereby a number of nodes in a wireless network organize themselves into a virtual antenna array and focus their transmission in the direction of the intended receiver, potentially achieving order of magnitude improvements in energy efficiency. The main technical challenge in realizing these gains is in precisely synchronizing the radio frequency signals of the cooperating nodes. This idea has been studied extensively over the past decade, and several techniques and architectures for its practical implementation have been developed. In this work, we demonstrate our recent implementation of distributed beamforming on a standard, open-source, software-defined radio platform, where the low quality oscillators make synchronization particularly challenging. Our demonstration will consist of three cooperating transmitters sending signals that add up constructively at the receiver. Low-rate feedback packets broadcast from the receiver are employed for frequency and phase synchronization at each transmitter in completely distributed fashion.