Direction-of-arrival (DoA) estimation is essential in many industrial internet of things (IIoT) applications where IoT users may require a high-resolution estimation algorithm for features limited to positioning and device localization. The conventional DoA estimation is challenging as the number of antenna arrays increases leading to an increase in design complexity and cost in RF circuitry. As a result, the conventional DoA estimation algorithms may not be suited for IIoT applications. In this work, a joint DOA and carrier frequency offset (CFO) schemes is introduced for typical uplink IIoT systems by equipping the receiver with a leaky-wave antenna (LWA). By considering that the LWA can simultaneously steer its beam direction towards the active user. As a result, each user is estimated independently. The joint algorithm is developed based on the high-resolution MUSIC scheme, which utilizes the LWA properties to compute the DoA spectrum. Furthermore, we develop an iterative CFO estimation approach as the desired solution to a multi-dimensional search. Subsequently, we further formulate the Cramer–Rao lower bounds (CRLB) to serve as a performance benchmark for the accuracy of the proposed scheme. The SDR platform is provided to validate the proposed algorithm in real-time. Finally, the simulation results and the SDR experiments justify that the proposed scheme offers more robustness than conventional counterparts with linear array antenna, which achieves a root mean square error (RMSE) approach to the derived CLRB, especially at a medium signal-to-noise ratio (SNR) range which suited for IIoT applications.

The Internet of Things (IoT) and wireless sensor networks (WSNs) face an energy shortage challenge that could be overcome by the novel wireless power transfer (WPT) technology. The combination of WSNs and WPT is known as wireless rechargeable sensor networks (WRSNs), with the charging efficiency and charging scheduling being the primary concerns. Therefore, this paper proposes a probabilistic on-demand charging scheduling for integrated sensing and communication (ISAC)-assisted WRSNs with multiple mobile charging vehicles (MCVs) that addresses three parts. First, it considers the four attributes with their probability distributions to balance the charging load on each MCV. The attributes are residual energy of charging node, distance from MCV to charging node, degree of charging node, and charging node betweenness centrality. Second, it considers the efficient charging factor strategy to partially charge network nodes. Finally, it employs the ISAC concept to efficiently utilize the wireless resources to reduce the traveling cost of each MCV and to avoid the charging conflicts between them. The simulation results show that the proposed protocol outperforms cutting-edge protocols in terms of energy usage efficiency, charging delay, charging coverage, survival rate, travel distance, queue length, and service time.

Narrowband Internet of Things (NB-IoT) is an emerging IoT cellular-based wireless technology that builds based on the existing LTE standard to extend its network coverage suited for Low Power Wide Area Network (LPWAN) connectivity. In the NB-IoT system, LTE turbo codes and Cyclic Redundancy Check (CRC) codes are adopted as the main channel coding technique for Narrowband Physical Uplink Shared Channel (NPUSCH). However, turbo codes require iterative decoding algorithm at the expense of high computation complexity of decoding hence is crucial to achieving the LPWAN requirement of NB-IoT systems. Polar codes are best suited in this regard due to its capacity approaching and complexity reduction in short block length code. In this paper, we proposed an efficient polar coding technique using the Belief Propagation (BP) decoding algorithm for uplink data transmission on the NPUSCH channel. Furthermore, the BP algorithm is incorporated with the CRC stoppage criterion to decrease the number of decoding iteration and reduce computational complexity. In this scheme, single-tone numerology of NPUSCH using 3.75 kHz and 15 kHz subcarrier spacing is adopted. Then, the encoded data is generated with different NPUSCH resources. The theoretical formulation and simulation demonstrate that the proposed scheme provides better error rate performance over the adopted LTE turbo codes and other polar decoding algorithms while reducing the computational complexity.

Narrowband Internet of Things (NB-IoT) systems were specified by 3GPP in release 13 as a low power wide area network (LPWAN) technology to operate with a very narrow bandwidth of 180 kHz only. Due to fragile radio signal operating conditions (where a signal is weaker than noise), NB-IoT channel status becomes highly complex. Therefore, an effective and low complexity channel estimation will perform a significant role in the receiver operation. The linear minimum mean square error (LMMSE) scheme is very effective in estimating the channel but introduces massive complexity because of having complex matrix inversion. In this paper, we first derive the analytical model of the signal for long-term evolution (LTE)-based NB-IoT downlink systems and propose a reduced complexity LMMSE channel estimation for the downlink NB-IoT systems by applying singular value decomposition (SVD) technique along with partitioning the whole channel matrix into small submatrices. Furthermore, we apply the overlap banded technique to optimize the performance of the proposed channel estimator. As a result of exploiting several submatrices instead of a larger channel matrix, the operational complexity is significantly optimized. Lastly, we propose a polyphase filter structure for implementing the interpolation procedure instead of the conventional interpolation method to further optimize the performance and complexity of the proposed channel estimator further. The performance of the proposed technique has been justified by the mean square error (MSE), bit error rate (BER), and instantaneous throughput for the related signal-to-noise ratio (SNR). The system complexity is verified by the number of complex multiplications used. Simulation evaluations indicate that with the sacrifice of negligible performance, the proposed modified LMMSE technique along with the proposed interpolation possesses a good balance between the performance and the system complexity that could help the proposed techniques to be applied successfully in the low complexity NB-IoT systems.

This paper addresses the main crucial aspects of physical (PHY) layer channel coding in uplink NB-IoT systems. In uplink NB-IoT systems, various channel coding algorithms are deployed due to the nature of the adopted Long-Term Evolution (LTE) channel coding which presents a great challenge at the expense of high decoding complexity, power consumption, error floor phenomena, while experiencing performance degradation for short block lengths. For this reason, such a design considerably increases the overall system complexity, which is difficult to implement. Therefore, the existing LTE turbo codes are not recommended in NB-IoT systems and, hence, new channel coding algorithms need to be employed for LPWA specifications. First, LTE-based turbo decoding and frequency-domain turbo equalization algorithms are proposed, modifying the simplified maximum a posteriori probability (MAP) decoder and minimum mean square error (MMSE) Turbo equalization algorithms were appended to different Narrowband Physical Uplink Shared Channel (NPUSCH) subcarriers for interference cancellation. These proposed methods aim to minimize the complexity of realizing the traditional MAP turbo decoder and MMSE estimators in the newly NB-IoT PHY layer features. We compare the system performance in terms of block error rate (BLER) and computational complexity.

In this paper, a diagnostic tool or procedure based on Bayesian compressive sensing (BCS) is proposed for identification of failed element(s) which manifest in millimeter-wave planar antenna arrays. With adequate a priori knowledge of the reference antenna array radiation pattern, a diagnostic problem of faulty elements was formulated. Sparse recovery algorithms, including total variation (TV), mixed ${\ell }_1/{\ell }_2$ norm, and minimization of the ${\ell }_1$ , are readily available in the literature, and were used to diagnose the array under test (AUT) from measurement points, consequently providing faster and better diagnostic schemes than the traditional mechanisms, such as the back propagation algorithm, matrix method algorithm, etc. However, these approaches exhibit some drawbacks in terms of effectiveness and reliability in noisy data, and a large number of measurement data points. To overcome these problems, a methodology based on BCS was adapted in this paper. From far-field radiation pattern samples, planar array diagnosis was formulated as a sparse signal recovery problem where BCS was applied to recover the locations of the faults using relevance vector machine (RVM). The resulted BCS approach was validated through simulations and experiments to provide suitable guidelines for users, as well as insight into the features and potential of the proposed procedure. A Ka-band $\left(28.9\mathrm{GHz}\right)$ $10\times 10$ rectangular microstrip patch antenna array that emulates failure with zero excitation was designed for far-field measurements in an anechoic chamber. Both simulated and measured far-field samples were used to test the proposed approach. The proposed technique is demonstrated to detect diagnostic problems with fewer measurements provided the prior knowledge of the array radiation pattern is known, and the number of faults is relatively smaller than the array size. The effectiveness and reliability of the technique is verified experimentally and via simulation. In addition to a faster diagnosis and better reconstruction accuracy, the BCS-based technique shows more robustness to additive noisy data compared to other compressive sensing methods. The proposed procedure can be applied to next-generation transceivers, aerospace systems, radar systems, and other communication systems.