This paper investigates a wireless-powered Internet of Things (IoT) network comprising a hybrid access point (HAP) and two devices. The HAP facilitates downlink wireless energy transfer (WET) for device charging and uplink wireless information transfer (WIT) to collect status updates from the devices. To keep the information fresh, concurrent WET and WIT are allowed, and orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) are adaptively scheduled for WIT. Consequently, we formulate an expected weighted sum age of information (EWSAoI) minimization problem to adaptively schedule the transmission scheme, choosing from WET, OMA, NOMA, and WET+OMA, and to allocate transmit power. To address this, we reformulate the problem as a Markov decision process (MDP) and develop an optimal policy based on instantaneous AoI and remaining battery power to determine scheme selection and transmit power allocation. Extensive results demonstrate the effectiveness of the proposed policy, and the optimal policy has a distinct decision boundary-switching property, providing valuable insights for practical system design.

In this paper, we aim to minimize the age of information (AoI) for a random access internet of things (IoT) network, where AoI is a metric to measure the freshness of information delivery. Since non-orthogonal multiple access (NOMA) can improve network throughput and connectivity, we exploit an AoI-oriented NOMA-based random access scheme, wherein devices simultaneously access wireless channel over multiple power levels with different access probabilities when their AoIs is not smaller than a threshold. We firstly study the comprehensive steady-state analysis of an AoI-independent NOMA-based random access scheme, which is a special case when the threshold is one. The AoI evolution is formulated as a markov chain based on the analyzed transmission success probability, and the probabilities of AoI states and the achieved AoI under generate-at-will are derived. Then, an AoI minimization algorithm is proposed to optimize the power access probabilities. Concerning stochastic-arrival, the steady-state probabilities of devices’ active state, successful transmission, and number of active devices, are derived to analyze the expected AoI. Finally, the steady-state probabilities of AoI states and the achieved AoI of AoI-dependent NOMA-based scheme are obtained. Simulation results validate our analysis, and demonstrate the significant performance improvement in terms of AoI. In specific, the proposed scheme can achieve AoI reduction by 65%, compared with random access without NOMA.

Respiration is one of the most important vital signs indicating physical condition, while the signal detection is challenging due to the complex rhythm and effort in practical scenarios. In this paper, we propose a contactless sensing-aided respiration signal acquisition technique, which can adaptively extract the desired signal under time-varying respiration rhythms within a wide range. To be specific, respiration is perceived by piezoelectric ceramics sensors along with ballistocardiography and other interference in a contactless manner, and the proposed improved empirical wavelet transform (IEWT) performs spectrum division and recognition based on upper envelop and principal component criteria, respectively, to adaptively extract the respiration spectrum for signal reconstruction. For validations, we extracted respiration signals from 8 healthy individuals in lab breathing at specified rhythms from 0.2 Hz to 0.6 Hz as well as 38 in-patients suffering from sleep-disordered-breathing with reference of polysomnogram in practical clinic scenario. The results showed that the detected respiration rhythms perfectly fitted the ones in experimental lab dataset with a correlation coefficient of 0.98, which validated the effectiveness of the respiration spectrum extraction of the proposed IEWT method. Besides, in practical clinical dataset, the proposed IEWT method could yield mean absolute and relative errors of respiration intervals of 0.4 and 0.05 seconds, respectively, achieving significant improvement in comparison with conventional ones. Meanwhile, the performance of IEWT was robust to rhythm variation, individual difference and breathing cycle detection techniques, which demonstrated the feasibility and superiority of the proposed IEWT method for practical respiration monitoring.

Benefiting from non-invasive sensing tech- nologies, heartbeat detection from ballistocardiogram (BCG) signals is of great significance for home-care applications, such as risk prediction of cardiovascular disease (CVD) and sleep staging, etc. In this paper, we propose an effective deep learning model for automatic heartbeat detection from BCG signals based on UNet and bidirectional long short-term memory (Bi-LSTM). The developed deep learning model provides an effective solution to the existing challenges in BCG-aided heartbeat detection, especially for BCG in low signal-to-noise ratio, in which the waveforms in BCG signals are irregular due to measured postures, rhythm and artifact motion. For validations, performance of the proposed detection is evaluated by BCG recordings from 43 subjects with different measured postures and heart rate ranges. The accuracy of the detected heartbeat intervals measured in different postures and signal qualities, in comparison with the R-R interval of ECG, is promising in terms of mean absolute error and mean relative error, respectively, which is superior to the state-of-the-art methods. Numerical results demonstrate that the proposed UNet-BiLSTM model performs robust to noise and perturbations (e.g. respiratory effort and artifact motion) in BCG signals, and provides a reliable solution to long term heart rate monitoring.

Mobile edge computing (MEC) harvests the computation capability at the network edge to perform the computation intensive tasks for diverse IoT applications. Meanwhile, the unmanned aerial vehicle (UAV) has a great potential to flexibly enlarge the coverage, and enhance the network performance. Accordingly, it has been a promising paradigm to use the UAV to provide the edge computing service for massive IoT devices. This paper studies the path planning problem of a UAV-assisted edge computing network, where an UAV is deployed with an edge server to execute the computing tasks offloaded from multiple devices. We consider the mobility of devices, where a GaussMarkov random movement model is adopted. By taking the energy consumed for the dynamic flying and executing the tasks at the UAV into account, we formulate a path planning problem that aims to maximize the amount of offloaded data bits by the devices while minimizing the energy consumption of the UAV. To deal with the dynamic change of the complex environment, we apply the deep reinforcement learning (DRL) method to develop an online path planning algorithm based on double deep Q-learning network (DDQN). Extensive simulation results validate the effectiveness of the proposed DRL-based path planning algorithm in terms of the convergence speed and the system reward.

Spectrally efficient frequency division multiplexing (SEFDM) has been recognised as a promising multi‐carrier technology since it can offer a higher spectral efficiency than orthogonal frequency division multiplexing (OFDM). In this study, the authors provide a framework for SEFDM transceiver design and investigate the techniques of channel equalisation and data detection of SEFDM over frequency selective fading scenarios. Specifically, they first demonstrate through mathematical analysis that SEFDM with the proposed frequency‐domain equalisation scheme can benefit from the frequency diversity, and thus, performs more robust to the frequency selective fading environment than OFDM. To effectively mitigate inter‐carrier interference raised by the non‐orthogonality between sub‐carriers of SEFDM, they then propose a low‐computational data detection scheme based on the Viterbi principle and maximum a posteriori criterion. The proposed detector can provide an indistinguishable performance from the maximum likelihood detection using sphere decoding while entailing a much lower computation. Numerical results validate the superiority of SEFDM to OFDM over frequency selective fading channels.

Spectrally efficient frequency division multiplexing enables higher spectral efficiency at the cost of suffering from inter‐carrier interference (ICI), which leads to either a severe error floor or a heavy computational burden at the receiver. In this study, the authors propose a low‐complexity demodulation technique, in which the detected symbols are used as feedback to effectively mitigate ICI. To restrain the error propagation caused by non‐ideal feedback, the authors enumerate subsequent unknown symbols as preconditions, and then perform the demodulation based on minimum distance criterion, in order to enable the optimal solution in each step of feedback. As a consequence, the computations necessitated to data demodulation can be significantly reduced as a quadratic order of symbol‐size. Furthermore, the authors mathematically characterise the demodulation performance by deriving the signal‐to‐interference‐plus‐noise ratio as a function of bandwidth compression factor. It is shown that the demodulation scheme can effectively reduce the error floor raised by ICI while preserving a low complexity. Simulation results demonstrate that the proposed demodulator outperforms the conventional ones, and achieves indistinguishable performance as that of orthogonal frequency‐division multiplexing while increasing up to 25% spectrum efficiency.

This paper considers a wireless-energy-transfer (WET)-enabled massive multiple-input-multiple-output (MIMO) system based on superimposed pilot (SP). With the aid of SP, the uplink (UL) channel estimation and wireless information transmission (WIT) that powered by the downlink (DL) WET can be operated simultaneously, and thus provide the potential for improving the UL achievable rate. The impact that SP has on the performance of such a WET-enabled massive MIMO system is mathematically characterized, and the UL achievable throughput is maximized by optimizing the variables, including the SP power-allocation factor and the time-allocation factor between the duration of WET and WIT. Numerical results validate the effectiveness of the proposed scheme.

This paper concerns with the uplink transmission of a massive MIMO system under high mobility environments. By modeling the fading channel as complex exponential basis expansion model, this paper presents a superimposed training (ST)-aided channel estimation scheme, and mathematically characterizes the impact that ST has on the performance of such a very large MIMO system when the number of antennas M grows to infinity. Compared with the existing methods, the proposed scheme requires no additional time-slot reserved for pilots, and thus provide the potential for improving the transmission efficiency. Both theoretical and numerical results validate the effectiveness of our scheme.

This study presents a ‘subblock tracking’ based equalisation technique for orthogonal frequency division multiplexing (OFDM) based device‐to‐device communications over high mobility environment. This technique is to use a rectangular‐shaped receive window to partition the OFDM block into subblocks rendering the channel response of each subblock to be time‐invariant, thereby allowing one to equalise the channel frequency response on each subcarrier of the subblocks simply by a single tap. The equalised signals are then combined to give the final output, where the combination weights are designed to minimise detection errors. The authors mathematically characterise the detection performance by deriving the signal‐to‐interference ratio as a function of channel‐time‐variation and the numbers of subblocks. To further enhance the proposed design without imposing computational consumption, a subblock tracking scheme with partially overlapped partition is presented and theoretically analysed. Simulation results demonstrate the advantages this method over the conventional schemes in high mobility environment.

ABSTRACT An approach of superimposed training (ST)‐aided time‐varying (TV) channel estimation for multiple‐input multiple‐output orthogonal frequency division multiplexing systems is presented. By modeling the TV channel with the truncated discrete basis expansion model, a two‐step approach is adopted to estimate the TV channel. In addition, the mean square error (MSE) of the proposed channel estimation is analyzed, and its closed‐form expression is derived, which is a function of the data‐to‐ST power ratio. Using the developed channel MSE, we case the problem of ST power‐allocation by minimizing the lower bound on the average channel capacity. To enhance the performance of channel estimation, a low‐complexity decision feedback mechanism is introduced to iteratively mitigate the unknown data interference. Numerical results verify the performances of the proposed approach. Copyright © 2012 John Wiley & Sons, Ltd.

How to improve the short-term fairness and increase the aggregated throughput simultaneously is a major problem in WLANs, yet has not been solved satisfactorily in previous work, especially in the situation that network loads vary significantly. To improve the performance in saturation state, we propose a novel access mechanism that sets an optimal Contention Window (CW) for all nodes in the network. By introducing a new parameter of CW Index (CWI), we present a linear CW adjustment rule based on the active node number and then develop an estimation algorithm of node number with three thresholds. Compared to previous algorithms, the new algorithm predicts the future node number based on the current network status, thus minimizes the fluctuating effect of idle slot intervals observed at each process. Moreover, it can identify the dense degree of idle slot intervals and track the changes of the accurate node number quickly. By selecting the optimal CWI adaptively, the maximum aggregated throughput keeps basically a constant despite the variation of the number of nodes. Meanwhile, by eliminating multiple transmission attempts of the same node, the short-term fairness is improved significantly. To evaluate the mechanism, we derive closed-form expressions for the network performance. The simulation results demonstrate the validity and good scalability of the proposed access mechanism.

Channel estimation for multiple-input multiple-output/orthogonal frequency-division multiplexing (MIMO/ OFDM) systems in linearly time-varying (LTV) wireless channels using superimposed training (ST) is considered. The LTV channel is modeled by truncated discrete Fourier bases. Based on this model, a two-step approach is adopted to estimate the LTV channel over multiple OFDM symbols. We also present a performance analysis of the channel estimation and derive a closed-form expression for the channel estimation variances. It is shown that the estimation variances, unlike that of the conventional ST-based schemes, approach to a fixed lowerbound as the training length increases, which is directly proportional to information-pilot power ratios. To further enhance the channel estimation performance with a limited pilot power, an interference cancellation procedure is introduced to iteratively mitigate the information sequence interference to channel estimation. Simulation results show that the proposed algorithm outperforms frequency-division multiplexed trainings schemes.