OBJECTIVE: Alzheimer's Disease and Related Dementia (ADRD) is growing at alarming rates, putting research and development of diagnostic methods at the forefront of the biomedical research community. Sleep disorder has been proposed as an early sign of Mild Cognitive Impairment (MCI) in Alzheimer's disease. Although several clinical studies have been conducted to assess sleep and association with early MCI, reliable and efficient algorithms to detect MCI in home-based sleep studies are needed in order to address both healthcare costs and patient discomfort in hospital/lab-based sleep studies. METHODS: In this paper, an innovative MCI detection method is proposed using an overnight recording of movements associated with sleep combined with advanced signal processing and artificial intelligence. A new diagnostic parameter is introduced which is extracted from the correlation between high frequency, sleep-related movements and respiratory changes during sleep. The newly defined parameter, Time-Lag (TL), is proposed as a distinguishing criterion that indicates movement stimulation of brainstem respiratory regulation that may modulate hypoxemia risk during sleep and serve as an effective parameter for early detection of MCI in ADRD. By implementing Neural Networks (NN) and Kernel algorithms with choosing TL as the principle component in MCI detection, high sensitivity (86.75% for NN and 65% for Kernel method), specificity (89.25% and 100%), and accuracy (88% and 82.5%) have been achieved.

The aim of this paper is to improve the performance of an energy harvesting (EH) system under millimeter wave (mm-wave) massive MIMO (Multiple Input Multiple Output) structure, as beneficial incorporation in beyond 5G. We consider a power splitting structure for this EH system, which has the best trade-off between transferred energy and data rate. However, it is shown that in such systems the use of Successive Interference Cancellation (SIC) method alone, the minimum probability of error requirements for the systems with more users than Radio Frequency (RF) chains may not be attained. Therefore, new schemes with control strategies over intra-beam interference to achieve an acceptable symbol error rate (SER) for multiple users in the same beam is still a vacancy to be studied in particular for the dense 5G networks. The proposed EH system is using a new Superposition Code (SC) based on Physical Layer Network Coding (PLNC) concept to dramatically improve the throughput and harvested energy for a specific signal to noise ratio (SNR). As the second goal, two beamforming optimization approaches are also suggested. The simulations result indicates that the uniform beamforming performance is acceptable if avoiding the high complexity is desired.

This paper considers the problem of delay-optimal bundling of the input symbols into transmit packets in the entry point of a wireless sensor network such that the link delay is minimized under an arbitrary arrival rate and a given channel error rate. The proposed policy exploits the variable packet length feature of contemporary communications protocols in order to minimize the link delay via packet length regularization. This is performed through concrete characterization of the end-to-end link delay for zero-error tolerance system with first come first serve (FCFS) queuing discipline and automatic repeat request (ARQ) re-transmission mechanism. The derivations are provided for an uncoded system as well as a coded system with a given bit error rate. The proposed packetization policy provides an optimal packetization interval that minimizes the end-to-end delay for a given channel with certain bit error probability. This algorithm can also be used for near-optimal bundling of input symbols for dynamic channel conditions provided that the channel condition varies slowly over time with respect to symbol arrival rate. This algorithm complements the current network-based delay-optimal routing and scheduling algorithms in order to further reduce the end-to-end delivery time. Moreover, the proposed method is employed to solve the problem of energy efficiency maximization under an average delay constraint by recasting it as a convex optimization problem.

Estimation of a binary source using multiple observers, a variant of the so called Chief Executive Officer (CEO) problem, is considered. A low-complexity Distributed Joint Source Channel Coding (D-JSCC) based on the Parallel Concatenated Convolutional Codes (PCCC) is implemented in a cluster of sensors in a distributed fashion. Convergence of the iterative decoder is analyzed by utilizing EXtrinsic Information Transfer (EXIT) chart technique to determine the convergence region in terms of the sensors observation accuracy and channel SNR, where the iterative decoder outperforms the non-iterative one. This leads to design of a bi-modal decoder that adaptively switches between the iterative and non-iterative modes in order to avoid inefficient iterative information exchange without compromising the resulting Bit Error Rate (BER). This adaptive decoding algorithm saves the computational power and decoding time by a factor of about 10 by avoiding unnecessary iterations.

In this paper1, we propose a distributed fair-efficient power allocation game model for two-hop relay networks. The system model consists of N-parallel source-relay-destination links, where each source node sends its packets to its corresponding destination via a preassigned relay node using half-duplex Amplify-and-Forward (AF) relaying method. The previously reported works only account for the efficiency of system throughput, however in this paper we incorporate fairness into this problem and propose a distributed game model that balances the fairness and efficiency objectives.

In conventional two-tiered Wireless Sensor Networks (WSN), sensors in each cluster transmit observed data to a fusion center via an intermediate supernode. This structure is vulnerable to supernode failure. A double supernode system model with a new coding scheme is proposed to monitor a binary data source. A Distributed Joint Source Channel Code (D-JSCC) is proposed for sensors inside a cluster that provides two advantages of low complexity transmitters and scalability to a large number of sensors. In order to setup a robust communication channel from sensors to the data fusion center, Distributed Space-Time Block Coding (D-STBC) is employed at two supernodes prior to relaying that results in additional diversity gain. DeModulate and Forward (DMF) relaying mode is chosen to enable packet reformatting at the supernodes, which is not possible in widely used Amplify and Forward (AF) mode. The optimum power allocation for the two-hop multiple DMF relaying is calculated to minimize the system Bit Error Rate (BER). An upper bound is derived for the system end-to-end BER by analyzing a basic decoder operation over the system model. The simulation results validate this upper bound and also demonstrate considerable improvement in the system BER for the proposed coding scheme.

This paper investigates the effect of channel estimation error on performance of physical layer network coding, in which the relay maps the superimposed noisy modulated data received from the two end terminals, to network coded combination of the source packets. Knowledge of channel coefficients is required for network code selection at the relaying terminal. We consider physical layer network coding with optimum code design and evaluate network throughput in presence of channel estimation error.

In this paper, problem of throughput optimization in relay networks where all users transmit their packets on a multiple-access channel is studied by introducing a new Markovian game theoretical solution. Despite the previously reported works, in the proposed model, simultaneously transmitted packets on a multiple-access channel are not always discarded. In this article, the possibility of capturing one of these packets is considered. Both cases of cooperative and non-cooperative stochastic game solutions are investigated and compared. The main objective is to maximize the system throughput with minimum transmission delay and power consumption cost. Effect of different packet error rates due to possible collision occurrence is considered in the game definition that improves system performance further. Performance of the proposed non-cooperative game model approaches the cooperative case, however the non-cooperative game model adds less signaling load to the system, therefore it is more likely to be used in practical applications.

The CEO problem of estimating a single binary data source using multiple observations is considered. A closed form equation for maximum achievable information capacity of the system is derived for different system parameters including number of sensors, observation accuracy and channel quality. A new criterion for sensor clustering is provided based on the minimum number of sensors required to achieve arbitrarily low estimation error using different coding rates. A distributed joint source-channel coding (D-JSCC) scheme is proposed as an implementation example. The proposed coding scheme is based on the parallel concatenated convolutional codes (PCCC) that achieves high BER performance utilizing the number of sensors determined by the derived criterion.

This paper presents a framework of hybrid models based on multilateral radio localization estimators that use the received signal strength (RSS), the time of arrival (TOA), the time difference of arrival (TDOA) and the angle of arrival (AOA) measurements of signals traveling from a mobile station (MS) to a set of base stations (BSs) in broadband wireless systems. The accuracy of hybrid localization is analyzed based on the Cramer-Rao lower bound, the Taylor-series approximation and the multi-step least square estimator. Explicit formulas that provide the link among RSS, TOA, TDOA or AOA methods is analytically explained. Numerical simulations show that a simple and efficient hybrid estimation scheme combining RSS, TOA, TDOA and AOA data achieves higher positioning precision than that of the RSS-only, the TOA-only, the TDOA-only or AOA-only methods.

Sparsely distributed clusters of wireless sensors with applications ranging from state-wide wireless bridge monitoring to animal herds tracking have gained recent attention due to their special characteristics and challenging resource allocation problem. A novel optimal power allocation paradigm based on game theory is proposed to minimize the inter and intra-cluster interference. Cost functions are assigned to the transmission power, estimate of the aggregate interference generated by the cluster on the overall network, and the signal to interference and noise ratio (SINR) at the node level. It is observed that the proposed algorithm demonstrates acceptable convergence properties when used after a channel allocation scheme and is more power efficient if used in parallel with a channel allocation method. Simulation results are presented to support the proposed analysis.

In this article, a new method for performance evaluation of turbo-like codes is presented. This is based on estimating the probability density function (pdf) of the bit log-likelihood-ratio (LLR) using higher order statistics. We do not restrict ourselves to any specific model for thepdfand try to estimate it directly using a cumulant matching method. Numerical results show a close agreement between the proposed method and simulations. The complexity of this method is similar to the Monte-Carlo simulation with the advantage of providing similar accuracy using significantly fewer samples.

In this paper, a new method for performance evaluation of turbo-like codes is presented. This is based on estimating the probability density function of the bit log-likelihood-ratio using higher order statistics. We do not restrict ourselves to any specific model for the pdf and try to estimate it directly using a cumulant matching method. Numerical results show a close agreement between the proposed method and the simulations. The complexity of this method is similar to the Monte-Carlo simulation with the advantage of providing similar accuracy using significantly fewer samples.

This work studies certain properties of the probability density function (pdf) of the bit log-likelihood ratio (LLR) for binary linear block codes over a memoryless channel with discrete input and discrete or continuous output. We prove that under a set of mild conditions, the pdf of the bit LLR of a specific bit position is independent of the transmitted codeword. It is also shown that the pdf of a given bit LLR when the corresponding bit takes the values of zero and one are symmetric with respect to each other (reflection of one another with respect to the vertical axis). For the case of channels with binary input, a sufficient condition for two bit positions to have the same pdf is presented.

An analytical method for approximate performance evaluation of binary linear block codes using an additive white Gaussian noise channel model with binary phase-shift keying modulation is presented. We focus on the probability density function of the bit log-likelihood ratio (LLR), which is expressed in terms of the Gram-Charlier series expansion. This expansion requires knowledge of the statistical moments of the bit LLR. We introduce an analytical method for calculating these moments. This is based on some recursive calculations involving certain weight enumerating functions of the code. It is proved that the approximation can be as accurate as desired, if we use enough terms in the Gram-Charlier series expansion. Numerical results are provided for some examples, which demonstrate close agreement with simulation results.